diff --git a/.devcontainer/14.04.25.=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py b/.devcontainer/14.04.25.=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py new file mode 100644 index 0000000..2fa9785 --- /dev/null +++ b/.devcontainer/14.04.25.=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py @@ -0,0 +1,3931 @@ +{ + "schemaVersion": "0.3", + "description": "Example SSM document for L.I.F.E algorithm", + "mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + ] +} + + "isAlternative": true + } + ], + "context": { + "lineText": "\"optimization\": {\"fmax\": 2.5e9}", + "techContext": "Numerical optimization parameter", + "commonUsage": ["DSP applications", "Mathematical optimization", "Engineering specs"] + }, + "handling": { + "recommendation": "addToTechnicalDictionary", + "overrideLocally": true, + "justification": "Standard technical term in numerical computing" + } +}# Correct usage (Python is case-sensitive for booleans) +condition = True # Capital 'T' +another_condition = False # Capital 'F' + +# Example with proper boolean usage +if condition: + print("This is true") +else: + print("This is false") + class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E Algorithm with Blockchain integration. + """ + self.blockchain_member = BlockchainMember( + resource_group=os.getenv("AZURE_RESOURCE_GROUP", "default_resource_group"), + member_name=os.getenv("AZURE_MEMBER_NAME", "default_member"), + subscription_id=os.getenv("AZURE_SUBSCRIPTION_ID", "default_subscription_id"), + location=os.getenv("AZURE_LOCATION", "default_location") + ) + self.experiences = [] # List to store experiences + self.models = {} # Dictionary to store models + self.trait_weights = {} # Dictionary to store trait weights + self._init_azure() # Initialize Azure components + self._init_key_vault() # Initialize Azure Key Vault + self._init_quantum_workspace() # Initialize Azure Quantum Workspace + + def _init_azure(self): + """Initialize Azure components.""" + try: + logging.info("Initializing Azure components...") + # Add Azure initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure components: {e}") + + def _init_key_vault(self): + """Initialize Azure Key Vault.""" + try: + logging.info("Initializing Azure Key Vault...") + # Add Key Vault initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure Key Vault: {e}") + + def _init_quantum_workspace(self): + """Initialize Azure Quantum Workspace.""" + try: + logging.info("Initializing Azure Quantum Workspace...") + # Add Quantum Workspace initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure Quantum Workspace: {e}") + Initialize the L.I.F.E. algorithm with empty experience and model storage. + """ + self.experiences = [] # List to store past experiences + self.models = [] # List to store abstract models derived from experiences + + def concrete_experience(self, data): + """ + Step 1: Concrete Experience + Collect and store new data or experiences. + """ + "description": "Recording new experience" + self.experiences.append(data) + + def reflective_observation(self): + """ + Step 2: Reflective Observation + Analyze stored experiences to identify patterns or insights. + """ + reflections = [] + print("\nReflecting on past experiences...") + for experience in self.experiences: + # Example: Generate a reflection based on the experience + reflection = "Reflection on experience: " + experience + reflections.append(reflection) + print(reflection) + return reflections + + def abstract_conceptualization(self, reflections): + """ + Step 3: Abstract Conceptualization + Use reflections to create or update abstract models or concepts. + """ + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + # Example: Create a simple model based on the reflection + model = "Model derived from: " + reflection + self.models.append(model) + // Removed Python code to maintain valid JSON format + + def active_experimentation(self, environment): + """ + Step 4: Active Experimentation + Test the created models in a given environment and observe results. + """ + results = [] + print("\nTesting models in the environment...") + for model in self.models: + # Example: Simulate testing the model in the environment + result = f"Result of applying '{str(model)}' in '{environment}'" + results.append(result) + print(result) + return results + + def learn(self, new_data, environment): + """ + Main method to execute the L.I.F.E. learning cycle: + - Collect new data (experience) + - Reflect on past experiences + - Create abstract models + - Test models in an environment + - Return results of experimentation + """ + print("\n--- Starting L.I.F.E. Learning Cycle ---") + + # Step 1: Collect new experience + self.concrete_experience(new_data) + + # Step 2: Reflect on experiences + reflections = self.reflective_observation() + + # Step 3: Create abstract models based on reflections + self.abstract_conceptualization(reflections) + + # Step 4: Test models in the environment and return results + results = self.active_experimentation(environment) + + print("\n--- L.I.F.E. Learning Cycle Complete ---") + + return results + + +# Example Usage of LIFEAlgorithm +if __name__ == "__main__": + # Instantiate the L.I.F.E. algorithm object + life = LIFEAlgorithm() + + # Simulate learning from multiple experiences and environments + result1 = life.learn("Observed customer behavior in store", "Retail Simulation") + result2 = life.learn("Analyzed website traffic patterns", "Digital Marketing Simulation") + + # Print final results from all learning cycles + print("\nFinal Results:") + for res in result1 + result2: + print(res) +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Observed customer behavior in store + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Analyzed website traffic patterns + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store +Reflection on experience: Analyzed website traffic patterns + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store +Created model: Model derived from: Reflection on experience: Analyzed website traffic patterns + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +Final Results: +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' +import numpy as np +import logging + +class AdaptiveLearningEEG: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and learning models. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (e.g., cognitive strengths, preferences) + self.models = [] # Models created from neuroplasticity-inspired learning + self.learning_rate = 0.1 # Initial learning rate, adaptable based on performance + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data to detect neuroplasticity markers. + """ + print("Analyzing EEG data for neuroplasticity markers...") + # Example: Extract delta wave activity as a marker of plasticity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + + # Simulate trait adaptation based on EEG patterns + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 # Increase learning rate + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 # Decrease learning rate + + # Cap the learning rate to a safe range to prevent runaway updates. + # Here we cap the value between 0.01 and 1.0 (adjust these limits as needed). + self.learning_rate = max(min(self.learning_rate, 1.0), 0.01) + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}, Learning Rate: {self.learning_rate}") + + def adapt_learning_model(self, experience): + """ + Step 3: Adapt the learning model based on neuroplasticity and user traits. + """ + print("Adapting learning model...") + + # Example: Update model weights based on user traits and experience + model = { + 'experience': experience, + 'trait_adaptation': f"Model optimized for focus: {self.user_traits['focus']}", + 'learning_rate': self.learning_rate + } + + self.models.append(model) + + def test_adapted_model(self, environment): + """ + Step 4: Test the adapted model in a given environment. + """ + print("Testing model in environment...") + + results = [] + + for model in self.models: + # Simulate testing the model + result = f"Tested {model['trait_adaptation']} in {environment} with learning rate {model['learning_rate']}" + results.append(result) + print(result) + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adapt the learning model + - Test the model in an environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adapt the learning model based on experience and traits + self.adapt_learning_model(experience) + + # Step 4: Test the adapted model in a simulated environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of AdaptiveLearningEEG +if __name__ == "__main__": + # Instantiate the adaptive learning system + system = AdaptiveLearningEEG() + + # Simulate EEG signals (e.g., delta wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3, 'beta': 0.1} + eeg_signal_2 = {'delta': 0.4, 'alpha': 0.4, 'beta': 0.2} + + # Simulate experiences and environments + experience_1 = "Learning a new language" + experience_2 = "Practicing motor skills" + + environment_1 = "Language Learning App" + environment_2 = "Motor Skills Training Simulator" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) + import numpy as np +import random + +class NeuroplasticLearningSystem: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and neural network. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (focus, relaxation, etc.) + self.network = self.initialize_network() # Neural network structure + self.experiences = [] # Past experiences + self.learning_rate = 0.1 # Adaptive learning rate + + def initialize_network(self): + """ + Initialize a small neural network with minimal neurons. + """ + return { + "input_layer": 10, + "hidden_layers": [5], # Start with one small hidden layer + "output_layer": 2 + } + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data for neuroplasticity markers. + """ + print("Analyzing EEG data...") + + # Example: Extract delta and alpha wave activity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + alpha_wave_activity = np.mean([signal['alpha'] for signal in self.eeg_data]) + + # Update user traits based on EEG analysis + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 + + if alpha_wave_activity > 0.4: + self.user_traits['relaxation'] = 'high' + + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}") + print(f"Alpha Wave Activity: {alpha_wave_activity}, Relaxation: {self.user_traits.get('relaxation', 'low')}") + + def neuroplastic_expansion(self): + """ + Step 3: Expand or prune the neural network dynamically. + """ + print("Adjusting neural network structure...") + + # Example: Add neurons to hidden layers based on focus level + if self.user_traits['focus'] == 'high': + self.network["hidden_layers"][-1] += random.randint(1, 3) # Add neurons + print(f"Expanded hidden layer to {self.network['hidden_layers'][-1]} neurons.") + + # Prune dormant neurons (simulate pruning) + elif self.user_traits['focus'] == 'low' and len(self.network["hidden_layers"]) > 1: + pruned_neurons = random.randint(1, 2) + self.network["hidden_layers"][-1] -= pruned_neurons + print(f"Pruned {pruned_neurons} neurons from hidden layer.") + + def consolidate_experience(self, experience): + """ + Step 4: Consolidate new experience into the system. + """ + print("Consolidating experience...") + + # Store experience and stabilize learning + self.experiences.append(experience) + + def test_model(self, environment): + """ + Step 5: Test the model in a simulated environment. + """ + print("Testing model in environment...") + + results = [] + + for _ in range(3): # Simulate multiple tests + result = { + "environment": environment, + "performance": random.uniform(0.7, 1.0) * len(self.network["hidden_layers"]), + "neurons": sum(self.network["hidden_layers"]) + } + results.append(result) + print(f"Test Result: {result}") + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adjust neural network structure (expansion/pruning) + - Consolidate new experience + - Test the model in a dynamic environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adjust neural network structure dynamically + self.neuroplastic_expansion() + + # Step 4: Consolidate new experience into memory + self.consolidate_experience(experience) + + # Step 5: Test the model in a dynamic environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of NeuroplasticLearningSystem +if __name__ == "__main__": + system = NeuroplasticLearningSystem() + + # Simulate EEG signals (e.g., delta and alpha wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3} + eeg_signal_2 = {'delta': 4, 'alpha': 0.5} + + # Simulate experiences and environments + experience_1 = "Learning motor skills" + experience_2 = "Improving memory retention" + + environment_1 = "Motor Training Simulator" + environment_2 = "Memory Game Environment" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 8 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': ..., 'neurons': ...} +--- Adaptive Learning Cycle Complete --- +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 7 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.2, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 8.1, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.5, 'neurons': 17} +--- Adaptive Learning Cycle Complete --- + +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.4, Focus: low +Alpha Wave Activity: 0.5, Relaxation: high +Adjusting neural network structure... +Pruned 2 neurons from hidden layer. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.6, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 6.3, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.9, 'neurons': 15} +--- Adaptive Learning Cycle Complete ---confirm +import asyncio +from concurrent.futures import ThreadPoolExecutor +import numpy as np +from azureml.core import Model +import mne + +class RealTimeLIFE: + def __init__(self): + self.data_stream = asyncio.Queue() + self.model = None + self.executor = ThreadPoolExecutor() + self._init_azure_components() + + def _init_azure_components(self): + """Initialize Azure ML components with connection pooling""" + self.workspace = Workspace.from_config() + self.model = Model(self.workspace, name="phi-3-28k-instruct") + self.preprocessing_pipeline = self._create_preprocessing_pipeline() + + async def real_time_learning_cycle(self): + """Continuous optimization loop with adaptive rate control""" + while True: + try: + # Process 10ms EEG data windows + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallel execution of critical path + with self.executor: + learn_task = asyncio.create_task( + self.update_learning_model(processed_data) + ) + infer_task = asyncio.create_task( + self.real_time_inference(processed_data) + ) + + _, predictions = await asyncio.gather(learn_task, infer_task) + + # Adaptive rate control + await self.adjust_processing_rate(predictions) + + except Exception as e: + self._handle_error(e) + await asyncio.sleep(0.1) # Backoff period + + async def process_eeg_window(self, raw_data): + """Real-time EEG processing pipeline""" + # Convert to MNE RawArray + info = mne.create_info(ch_names=['EEG'], sfreq=256, ch_types=['eeg']) + raw = mne.io.RawArray(raw_data, info) + + # Apply preprocessing pipeline + return self.preprocessing_pipeline.transform(raw) + + async def update_learning_model(self, processed_data): + """Incremental model update with Azure ML integration""" + try: + # Online learning with partial_fit + self.model.partial_fit(processed_data) + + # Azure model versioning + if self.update_counter % 100 == 0: + self.model.version = f"1.0.{self.update_counter}" + self.model.register(self.workspace) + + except Exception as e: + self._handle_model_update_error(e) + + async def real_time_inference(self, processed_data): + """Low-latency predictions with Azure acceleration""" + return self.model.deploy( + processed_data, + deployment_target="azureml-kubernetes", + replica_count=2 # For failover + ) + + def _create_preprocessing_pipeline(self): + """MNE-based preprocessing with Azure-optimized params""" + return mne.pipeline.make_pipeline( + mne.filter.create_filter( + data=None, + sfreq=256, + l_freq=1, + h_freq=40 + ), + mne.preprocessing.ICA(n_components=15) + ) + + async def adjust_processing_rate(self, predictions): + """Adaptive rate control based on system load""" + current_load = self._calculate_system_load() + target_latency = 50 # milliseconds + + if current_load > 0.8: + self.processing_rate = max( + 0.9 * self.processing_rate, + target_latency * 0.8 + ) + else: + self.processing_rate = min( + 1.1 * self.processing_rate, + target_latency * 1.2 + ) + + async def stream_eeg_data(self, device_source): + """Real-time EEG data acquisition and buffering""" + async for data_chunk in device_source: + await self.data_stream.put(data_chunk) + if self.data_stream.qsize() > 1000: + await self.data_stream.join() # Backpressure + + def _handle_error(self, error): + """Azure-aware error handling with retry logic""" + if "Azure" in str(error): + self._reinitialize_azure_connection() + # Implement other error handling strategies + +# Example Usage +async def main(): + rt_life = RealTimeLIFE() + await asyncio.gather( + rt_life.real_time_learning_cycle(), + rt_life.stream_eeg_data(eeg_device_source) + ) + +if __name__ == "__main__": + asyncio.run(main()) +from concurrent.futures import ProcessPoolExecutor + +async def real_time_learning_cycle(self): + with ProcessPoolExecutor(max_workers=4) as executor: + while True: + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallelize CPU-bound tasks + loop = asyncio.get_running_loop() + learn_task = loop.run_in_executor( + executor, self.model.partial_fit, processed_data + ) + infer_task = loop.run_in_executor( + executor, self.model.predict, processed_data + ) + + await asyncio.gather(learn_task, infer_task) +def process_eeg_window(self, raw_data): + # Use float32 instead of float64 + data = np.array(raw_data, dtype=np.float32) + + # In-place operations to reduce memory allocation + return mne.filter.filter_data( + data, + sfreq=256, + l_freq=1, + h_freq=40, + verbose=False, + copy=False + ) +class PIDController: + def __init__(self, Kp=0.8, Ki=0.2, Kd=0.1): + self.Kp, self.Ki, self.Kd = Kp, Ki, Kd + self.integral = 0 + self.last_error = 0 + + def update(self, error, dt): + self.integral += error * dt + derivative = (error - self.last_error) / dt + output = self.Kp*error + self.Ki*self.integral + self.Kd*derivative + self.last_error = error + return output + +# In learning cycle: +pid = PIDController() +current_latency = measure_processing_time() +rate_adjustment = pid.update(target_latency - current_latency, 0.01) +self.processing_rate *= (1 + rate_adjustment) +# Quantize model weights to FP16 +quantized_model = torch.quantization.quantize_dynamic( + original_model, {torch.nn.Linear}, dtype=torch.float16 +) + +# Prune less important weights +pruning.l1_unstructured(quantized_model, 'weight', amount=0.2) + +# Implement ONNX runtime for inference +session = ort.InferenceSession("life_model.onnx") +input_name = session.get_inputs()[0].name +def stream_eeg_data(self): + # Use shared memory buffers + shm = SharedMemory(name='eeg_buffer') + + while True: + # Batch process 50ms windows + window = np.ndarray((256,), dtype=np.float32, buffer=shm.buf) + preprocessed = self.preprocessing_pipeline(window) + + # Zero-copy queue insertion + self.data_stream.put_nowait(preprocessed) + + # Backpressure management + if self.data_stream.qsize() > 1000: + self.data_stream.get() # Drop oldest sample +from prometheus_client import Gauge + +# Metrics trackers +LATENCY = Gauge('life_latency', 'Processing latency (ms)') +THROUGHPUT = Gauge('life_throughput', 'Samples processed/sec') + +# In learning cycle: +start_time = time.perf_counter() +# ... processing ... +LATENCY.set((time.perf_counter() - start_time)*1000) +THROUGHPUT.inc() +import torch +import onnxruntime as ort +from torch import nn, quantization +from torch.utils.data import DataLoader +from torch.ao.pruning import prune +from neural_compressor import quantization as inc_quant + +# 1. Enhanced Quantization with Intel Neural Compressor +def quantize_model(model, calibration_loader): + config = inc_quant.PostTrainingQuantConfig( + approach='static', + calibration_sampling_size=[500] + ) + q_model = inc_quant.fit( + model=model, + conf=config, + calib_dataloader=calibration_loader, + eval_func=accuracy_eval + ) + return q_model + +# 2. Structured Pruning with Iterative Magnitude Pruning +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Iterative pruning with fine-tuning + for _ in range(3): + prune.global_unstructured( + parameters_to_prune, + pruning_method=prune.L1Unstructured, + amount=amount/3 + ) + # Remove pruned parameters permanently + for module, _ in parameters_to_prune: + prune.remove(module, 'weight') + + # Short fine-tuning cycle (add your training loop here) + # model.train() + # train(model, epochs=1) + + return model + +# 3. ONNX Runtime Optimization with Execution Providers +def create_optimized_onnx_session(model_path): + session_options = ort.SessionOptions() + session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + return ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ], + sess_options=session_options + ) + +# 4. Full Optimization Pipeline +def optimize_model(original_model, calibration_data): + # Step 1: Prune first for better quantization results + pruned_model = prune_model(original_model) + + # Step 2: Quantize with Intel Neural Compressor + calibration_loader = DataLoader(calibration_data, batch_size=32) + quantized_model = quantize_model(pruned_model, calibration_loader) + + # Step 3: Export to ONNX with optimization + dummy_input = torch.randn(1, 3, 224, 224) + torch.onnx.export( + quantized_model, + dummy_input, + "life_model.onnx", + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + + # Step 4: Create optimized inference session + return create_optimized_onnx_session("life_model.onnx") + +# Usage example +session = optimize_model(original_model, calibration_dataset) +import torch +from torch import nn, optim +from torch.cuda import amp +from torch.ao.quantization import QuantStub, DeQuantStub, prepare_qat, convert +from torch.ao.pruning import prune, remove +from torch.nn.utils import prune as prune_utils + +class LIFETheoryModel(nn.Module): + def __init__(self): + super().__init__() + self.quant = QuantStub() + self.dequant = DeQuantStub() + self.fc1 = nn.Linear(256, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, 10) + + def forward(self, x): + x = self.quant(x) + x = torch.relu(self.fc1(x)) + x = torch.relu(self.fc2(x)) + x = self.fc3(x) + return self.dequant(x) + +def train_model(model, train_loader, epochs=10): + scaler = amp.GradScaler() + criterion = nn.CrossEntropyLoss() + optimizer = optim.AdamW(model.parameters(), lr=0.001) + + model.train() + for epoch in range(epochs): + for data, target in train_loader: + data, target = data.cuda(), target.cuda() + optimizer.zero_grad() + + with amp.autocast(): + output = model(data) + loss = criterion(output, target) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Global magnitude pruning + prune_utils.global_unstructured( + parameters_to_prune, + pruning_method=prune_utils.L1Unstructured, + amount=amount + ) + + # Remove pruning reparameterization + for module, _ in parameters_to_prune: + remove(module, 'weight') + + return model + +def quantize_model(model): + model.qconfig = torch.ao.quantization.get_default_qat_qconfig('fbgemm') + model = prepare_qat(model) + return model + +# Full optimization pipeline +def optimize_life_model(): + # Initialize model + model = LIFETheoryModel().cuda() + + # 1. Mixed Precision Training + train_loader = ... # Your DataLoader + train_model(model, train_loader) + + # 2. Pruning + # 2. Pruning + model = prune_model(model, amount=0.3) + + # 3. Prepare for Quantization-Aware Training (QAT) + model = quantize_model(model) + + # 4. Fine-tune with QAT and Mixed Precision + train_model(model, train_loader, epochs=5) # Short fine-tuning + + # 5. Convert to quantized model + model = model.cpu() + quantized_model = convert(model) + + return quantized_model + +# Usage +optimized_model = optimize_life_model() +import numpy as np +from functools import lru_cache +from multiprocessing import Pool + +class OptimizedLIFE: + def __init__(self): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.user_traits = {} + self.learning_rate = 0.1 + self._precompute_normalization() + + def _precompute_normalization(self): + self.trait_baseline = np.array([10, 10, 10]) # Openness, Resilience, EI baseline + + @lru_cache(maxsize=128) + def calculate_traits(self, traits): + return np.sum(traits) / np.linalg.norm(self.trait_baseline) + + def concrete_experience(self, eeg_signal, experience): + print(f"Recording new experience: {experience}") + self.eeg_data.append(eeg_signal) + self.experiences.append(experience) + self.process_eeg_data(eeg_signal) + + def reflective_observation(self): + reflections = [] + print("\nReflecting on past experiences...") + for experience, eeg_signal in zip(self.experiences, self.eeg_data): + delta_wave_activity = eeg_signal.get('delta', 0) + reflection = { + "experience": experience, + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {experience} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + print(reflection['insight']) + return reflections + + def abstract_conceptualization(self, reflections): + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + print(f"Created model: {model}") + + def active_experimentation(self, environment): + results = [] + print("\nTesting models in the environment...") + for model in self.models: + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + print(f"Test result: {result}") + return results + + def learn(self, eeg_signal, experience, environment): + print("\n--- Starting L.I.F.E Learning Cycle ---") + self.concrete_experience(eeg_signal, experience) + reflections = self.reflective_observation() + self.abstract_conceptualization(reflections) + results = self.active_experimentation(environment) + print("--- L.I.F.E Learning Cycle Complete ---\n") + return { + "eeg_signal": eeg_signal, + "experience": experience, + "environment": environment, + "performance_score": np.mean([r['performance_score'] for r in results]) + } + + def process_eeg_data(self, eeg_signal): + return eeg_signal.get('delta', 0) + + def run_optimized_pipeline(self, users): + with Pool() as p: + results = p.map(self.process_user, users) + return self._analyze_results(results) + + def process_user(self, user_data): + return self.learn(user_data['eeg_signal'], user_data['experience'], user_data['environment']) + + def _analyze_results(self, results): + return results + +def neuroadaptive_filter(raw_data: Dict, adaptability: float) -> Dict: + """ + Filters EEG signals based on adaptability. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + +# Example usage +if __name__ == "__main__": + life_system = OptimizedLIFE() + users = [ + { + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Improving memory retention", + 'environment': "Memory Game Environment" + } + ] + optimized_results = life_system.run_optimized_pipeline(users) + print("Optimized Results:", optimized_results) +import numpy as np +import asyncio +from datetime import datetime +from azure.storage.blob import BlobServiceClient +from azure.ai.ml import MLClient +import json +from azure.identity import DefaultAzureCredential + +class OptimizedLIFE: + def __init__(self, azure_config=None): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.learning_rate = 0.1 + self.azure_config = azure_config + self._init_components() + + def _init_components(self): + """Initialize Azure components and preprocessing""" + self.trait_baseline = np.array([10, 10, 10]) + if self.azure_config: + self._init_azure_connection() + self._create_ml_client() + + def _init_azure_connection(self): + """Connect to Azure Blob Storage""" + self.blob_client = BlobServiceClient.from_connection_string( + self.azure_config['connection_string'] + ) + self.container_client = self.blob_client.get_container_client( + self.azure_config['container_name'] + ) + + def _create_ml_client(self): + """Initialize Azure Machine Learning client""" + credential = DefaultAzureCredential() + self.ml_client = MLClient( + credential=credential, + subscription_id=self.azure_config['subscription_id'], + resource_group_name=self.azure_config['resource_group'], + workspace_name=self.azure_config['workspace_name'] + ) + + async def process_experience(self, eeg_signal, experience): + """Async experience processing pipeline""" + try: + processed_data = await self._process_eeg(eeg_signal) + self._store_azure_data(processed_data, "eeg-data") + return processed_data + except Exception as e: + self._handle_error(e) + return None + + async def _process_eeg(self, raw_signal): + """Enhanced EEG processing with real-time filtering""" + return { + 'timestamp': datetime.now().isoformat(), + 'delta': raw_signal.get('delta', 0) * 1.2, # Example processing + 'alpha': raw_signal.get('alpha', 0) * 0.8, + 'processed': True + } + + def _store_azure_data(self, data, data_type): + """Store processed data in Azure Blob Storage""" + if self.azure_config: + blob_name = f"{data_type}/{datetime.now().isoformat()}.json" + self.container_client.upload_blob( + name=blob_name, + data=str(data), + overwrite=True + ) + + async def full_learning_cycle(self, user_data): + """Complete async learning cycle""" + result = await self.process_experience( + user_data['eeg_signal'], + user_data['experience'] + ) + + if result: + reflection = self.create_reflection(result, user_data['experience']) + model = self.generate_model(reflection) + test_result = self.test_model(model, user_data['environment']) + return self._compile_results(user_data, test_result) + return None + + def create_reflection(self, processed_data, experience): + """Enhanced reflection with cognitive load analysis""" + reflection = { + 'experience': experience, + 'delta_activity': processed_data['delta'], + 'cognitive_load': self._calculate_cognitive_load(processed_data), + 'timestamp': processed_data['timestamp'] + } + self._log_azure_metric('reflection', reflection) + return reflection + + def _calculate_cognitive_load(self, data): + """Calculate cognitive load score""" + return (data['delta'] * 0.6) + (data['alpha'] * 0.4) + + def generate_model(self, reflection): + """Model generation with version control""" + model = { + 'version': f"1.0.{len(self.models)}", + 'parameters': { + 'learning_rate': self.learning_rate, + 'delta_weight': reflection['delta_activity'] + }, + 'environment': None + } + self._register_azure_model(model) + return model + + def _register_azure_model(self, model): + """Register model in Azure ML registry""" + if self.azure_config: + self.ml_client.models.create_or_update( + name="LIFE_model", + version=model['version'], + description=f"LIFE model v{model['version']}" + ) + + def test_model(self, model, environment): + """Enhanced model testing with performance metrics""" + performance = self._calculate_performance(model, environment) + self._log_azure_metric('performance', performance) + return performance + + def _calculate_performance(self, model, environment): + """Calculate performance score with environment factors""" + base_score = model['parameters']['delta_weight'] * 100 + env_factor = 1.2 if "Simulator" in environment else 1.0 + return round(base_score * env_factor, 2) + + def _compile_results(self, user_data, performance): + """Compile final results package""" + return { + 'user_id': user_data.get('id', 'anonymous'), + 'experience': user_data['experience'], + 'environment': user_data['environment'], + 'performance': performance, + 'timestamp': datetime.now().isoformat() + } + + def _log_azure_metric(self, metric_type, data): + """Log metrics to Azure ML studio""" + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name=f"LIFE_{metric_type}", + value=data, + description=f"LIFE {metric_type} metric" + ) + + def _handle_error(self, error): + """Enhanced error handling with Azure logging""" + print(f"Error occurred: {str(error)}") + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name="LIFE_errors", + value=1, + description="Runtime error occurred" + ) + +# Example usage with Azure configuration +azure_config = { + 'connection_string': "", + 'container_name': "life-data", + 'subscription_id': "", + 'resource_group': "life-resources", + 'workspace_name': "life-theory-ml" +} + +async def main(): + life_system = OptimizedLIFE(azure_config=azure_config) + users = [ + { + 'id': "user_001", + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'id': "user_002", + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Memory Training", + 'environment': "Cognitive Lab Environment" + } + ] + + results = [] + for user in users: + result = await life_system.full_learning_cycle(user) + if result: + results.append(result) + + print("Final Results:") + for res in results: + print(f"User {res['user_id']} achieved {res['performance']}%") + +if __name__ == "__main__": + asyncio.run(main()) +pip install azure-ai-ml azure-storage-blob azure-identity +graph TD +A[Experiential Learning] --> B(Continuous Feedback Loop) +B --> C{Real-Time Data Processing} +C --> D[Azure Stream Analytics] +C --> E[Azure Event Hubs] +D --> F[Performance Insights] +E --> F +F --> G[Personalized Learning Paths] +G --> H[Scenario-Based Training] +EEG Sensors → Azure IoT Hub → Stream Analytics → +│ +├─▶ Azure Synapse (Neuroplasticity Metrics) +├─▶ Power BI (Real-Time Dashboards) +# Existing code... +# Train and deploy model function +def train_and_deploy_model(dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + ws = Workspace.from_config() + experiment = Experiment(ws, "eeg_classification") + + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + run = experiment.submit(automl_config) + run.wait_for_completion(show_output=True) + + best_model, fitted_model = run.get_output() + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + print(f"Model deployed successfully. Scoring URI: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Example usage +scoring_uri = train_and_deploy_model("", "aks-cluster") + +# GDPR-compliant EEG preprocessing +def eeg_preprocessing(eeg_signal): + """GDPR-compliant EEG processing""" + try: + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Stream from IoT Hub +def stream_from_iot_hub(): + """ + Stream EEG data from Azure IoT Hub and preprocess it. + """ + try: + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = eeg_preprocessing(eeg_signal) + if processed_signal: + print(f"Processed EEG signal: {processed_signal}") + + try: + with client: + client.receive_batch(on_event_batch, starting_position="-1") # Receive from the beginning + print("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error: {e}") + +# Example usage of streaming +stream_from iot_hub() + +# Data flow diagram for reference: +# EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment +# │ │ +# └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + +# Example usage of streaming +stream_from iot_hub()text +EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment + │ │ + └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + """GDPR-compliant EEG processing""" + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + + # Stream from IoT Hub + client = EventHubConsumerClient.from_connection_string("") + partition_handler = lambda: client.receive_batch(on_event=eeg_preprocessing) + client.run(partition_handler) + s// Unity C# Script +public class VREnvironmentController : MonoBehaviour { + void UpdateEnvironment(float focus, float stress) { + if (focus > 0.7f && stress < 0.3f) { + IncreaseTaskComplexity(0.2f); + } else { + ActivateRelaxationProtocol(); + } + } +} +// Unity C# Script +public class VRController : MonoBehaviour { + void Update() { + float focus = Input.GetAxis("Focus"); + float stress = Input.GetAxis("Stress"); + VREnvironmentController.Instance.UpdateEnvironment(focus, stress); + } +}# Azure ML Model Deployment +import json +from azureml.core import Workspace, Model +from azureml.core.model import InferenceConfig +from azureml.core.webservice import AciWebservice, Webservice +from azureml.core.model import Model + +# Load the workspace +ws = Workspace.from_config() + +# Load the model +model = Model(ws, "your-model-name") + +# Define the inference configuration +inference_config = InferenceConfig( + entry_script="score.py", + environment=Environment.from_conda_specification( + name="myenv", + file_path="environment.yml" + ) +) + +# Define the ACI configuration +aci_config = AciWebservice.deploy_configuration( + cpu_cores=1, + memory_gb=1, + auth_enabled=True, + enable_app_insights=True +)from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)) + +run = experiment.submit(automl_config) +best_model = run.get_output() +best_model.deploy(aks_cluster, autoscale_enabled=True) +from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)Data Anonymization SHA-256 hashing of user IDs + signal timestamp offsetting +Right to Erasure Automated data purge workflows via Azure Logic Apps +Encryption AES-256 for data at rest (Blob Storage) + TLS 1.3 for data in transit +Access Control RBAC with Just-In-Time access via Azure AD PIM +Data Retention RBAC with Just-In-Time access via Azure AD PIM +import json +import requests + +# Define the API endpoint URL +url = "https://your-api-endpoint.com/api/v1/your-endpoint" + +# Define the payload +payload = { + "user_id": "12345", + "signal_timestamp": "2022-01-01T00:00:00Z" +} + +# Send a POST request to the API endpoint +response = requests.post(url, json=payload) +# Check the response status codebash +# Deploy to AKS with autoscaling +az aks create --resource-group life-rg --name life-cluster \ + --node-count 3 --enable-cluster-autoscaler \ + --min-count 1 --max-count 10 + +# Configure HPA +kubectl autoscale deployment life-model --cpu-percent=80 --min=1 --max=10 +az aks get-credentials --resource-group life-rg --name life-cluster +kubectl apply -f deployment.yaml +kubectl apply -f service.yaml +kubectl get pods +kubectl get services +kubectl Decrease +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -wKnowledge Retention Pre/post assessments + EEG focus metrics +40% +Engagement VR interaction logs + heart rate variability +35% +Retention EEG signal stability + task completion rates +30% +# Data Storage Azure Blob Storage with GDPR compliance +25% +# cSpell:ignore Azure +# cSpell:ignore Azure + +# cSpell:ignore Azure +#Metric Measurement Tool Target Improvement +Task Completion Speed VR simulation timestamps + error rates +25% +Stress Reduction EEG beta wave analysis + self-reports -30% +# Focus Improvement EEG alpha wave analysis + task performance +40% +# Learning Retention EEG theta wave analysis + task performance +35%Component Success Criteria Measurement Frequency +Real-Time Processing <100ms latency for EEG→VR feedback Continuous +Model Accuracy >90% stress classification accuracy Weekly +GDPR Compliance 0 critical audit findings Quarterly +User Engagement >70% session completion rate Bi-Weekly +# Data Security 0 data breaches Monthly +# Data Retention 0 data retention violations Monthly +"schemaVersion": "0.3", +"description": "Example SSM document for L.I.F.E algorithm", +"mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + // Removed Python imports to maintain valid ssm-json format + + // Removed Python imports to maintain valid ssm-json format + // Azure ML imports + // from azureml.core import Workspace, Model, Experiment + // from azureml.pipeline.core import Pipeline, PipelineData, Schedule, ScheduleRecurrence + // from azureml.pipeline.steps import PythonScriptStep + // from azureml.train.automl import AutoMLConfig + // from azureml.core.webservice import AksWebservice + // from azureml.core.compute import AksCompute + + // Removed Python imports to maintain valid ssm-json format + + "neurokit2": "EEG processing library", + "azureQuantum": { + "workspace": "QuantumWorkspace", + "optimization": ["Problem", "ProblemType"] + } + + // Removed Python imports to maintain valid ssm-json format + + // Removed Python code to maintain valid JSON format. + """ + Calculate self-development score based on learning, individuality, and experience. + """ + // Define or import BlockchainMember in a separate Python file and reference it here. + "blockchainMember": { + "resource_group": "default_resource_group", + "member_name": "default_member", + "subscription_id": "default_subscription_id", + "location": "default_location" + } + + "blockchainMember": { + "resource_group": "default_resource_group", + "member_name": "default_member", + "subscription_id": "default_subscription_id", + "location": "default_location" + } + ) + blockchain_member = self.blockchain_member # Ensure blockchain_member is initialized + print(f"BlockchainMember initialized: {blockchain_member.resource_group}, {blockchain_member.member_name}") + + def calculate_self_development_score(learning, individual, experience): + try: + # Example logic for calculating self-development score + score = (learning * 0.5) + (individual * 0.3) + (experience * 0.2) + logging.info(f"Calculated self-development score: {score}") + return score + except Exception as e: + logging.error(f"Error calculating self-development score: {e}") + return None + + # Example usage + learning = 0.8 + individual = 0.7 + experience = 0.9 + + def calculate_self_development_score(learning, individual, experience): + """ + Calculate self-development score based on learning, individuality, and experience. + """ + try: + score = (learning * 0.5) + (individual * 0.3) + (experience * 0.2) + logging.info(f"Calculated self-development score: {score}") + return score + except Exception as e: + logging.error(f"Error calculating self-development score: {e}") + return None + + if __name__ == "__main__": + calculate_self_development_score(learning, individual, experience) + + class BlockchainMember: + """Represents a blockchain member with necessary attributes.""" + def __init__(self, resource_group, member_name, subscription_id=None, location=None): + self.resource_group = resource_group + self.member_name = member_name + self.subscription_id = subscription_id + self.location = location + + class LIFEAlgorithm: + """Implements L.I.F.E Learning Cycle with Azure integration""" + + def __init__(self): + """ + Initialize the L.I.F.E Algorithm with Blockchain integration. + """ + """ + # Define the BlockchainMember class before using it + class BlockchainMember: + def __init__(self, resource_group, member_name, subscription_id=None, location=None): + self.resource_group = resource_group + self.member_name = member_name + self.subscription_id = subscription_id + self.location = location + + # Initialize the BlockchainMember instance + self.blockchain_member = BlockchainMember( + resource_group=os.getenv("AZURE_RESOURCE_GROUP"), + member_name=os.getenv("AZURE_MEMBER_NAME", "default_member") # Use environment variable or default value + ) + self.experiences = [] # Raw code inputs + self.models = { # Trained code analysis models + "complexity": None, + "quality": None + } + self.trait_weights = { # Individualized trait importance + "functions": 0.8, + "comments": 0.6 + } + self._init_azure() + self._init_key_vault() + self._init_quantum_workspace() + + def _init_azure(self): + """Azure ML Workspace Connection""" + try: + self.workspace = Workspace.from_config() + self.model_registry = Model(self.workspace) + logging.info("Azure Workspace and Model Registry initialized successfully.") + except Exception as e: + logging.error(f"Azure connection failed: {str(e)}") + self.workspace = None + + def _init_key_vault(self): + """ + Initialize Azure Key Vault for secure credential management. + """ + try: + logging.info("Initializing Azure Key Vault...") + credential = DefaultAzureCredential() + self.secret_client = SecretClient(vault_url="https://life-vault.vault.azure.net/", credential=credential) + self.api_key = self.secret_client.get_secret("EEG-API-KEY").value + logging.info("Azure Key Vault initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Key Vault: {e}") + self.api_key = None + + def _init_quantum_workspace(self): + """ + Initialize Azure Quantum Workspace for optimization tasks. + """ + try: + logging.info("Initializing Azure Quantum Workspace...") + self.quantum_workspace = QuantumWorkspace( + subscription_id="", + resource_group="", + name="", + location="" + ) + logging.info("Azure Quantum Workspace initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Quantum Workspace: {e}") + self.quantum_workspace = None + + def configure_percept_device(self, device_ip): + """ + Configure Azure Percept DK for real-time EEG processing. + """ + try: + logging.info("Configuring Azure Percept DK...") + self.device = VisionDevice(device_ip) + self.device.enable_module("EEGProcessor") + logging.info("Azure Percept DK configured successfully.") + except Exception as e: + logging.error(f"Error configuring Azure Percept DK: {e}") + + def process_biometrics(self): + """ + Real-time EEG stream processing using Azure Percept DK. + """ + try: + logging.info("Starting real-time EEG stream processing...") + while True: + eeg_data = self.device.get_eeg_data() + stress_level = self.analyze_stress(eeg_data.alpha, eeg_data.beta) + + # Update Synapse Analytics or dashboard + self.update_dashboard(stress_level) + except Exception as e: + logging.error(f"Error during real-time EEG processing: {e}") + + def analyze_stress(self, alpha, beta): + """ + Analyze stress level based on EEG alpha and beta waves. + """ + try: + stress_level = beta / (alpha + 1e-9) # Avoid division by zero + logging.info(f"Calculated stress level: {stress_level}") + return stress_level + except Exception as e: + logging.error(f"Error analyzing stress level: {e}") + return None + + def update_dashboard(self, stress_level): + """ + Update Synapse Analytics or a dashboard with the stress level. + """ + try: + logging.info(f"Updating dashboard with stress level: {stress_level}") + # Placeholder for actual dashboard update logic + except Exception as e: + logging.error(f"Error updating dashboard: {e}") + + def concrete_experience(self, code: str): + """Stage 1: Capture new code experience""" + self.experiences.append(code) + logging.info(f"Added new code experience: {code[:50]}...") + + def reflective_observation(self): + """Stage 2: Analyze code patterns""" + traits, experiences = [], [] + + for code in self.experiences: + try: + tree = ast.parse(code) + # Extract advanced traits + current_traits = { + "func_count": sum(1 for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef)), + "docstring_presence": any(isinstance(n, ast.Expr) + for n in tree.body[:1]), + "import_complexity": len([n for n in ast.walk(tree) + if isinstance(n, ast.Import)]) + } + traits.append(current_traits) + + # Extract experiences + experiences.extend([ + n.value.s for n in ast.walk(tree) + if isinstance(n, ast.Expr) and isinstance(n.value, ast.Str) + ]) + + except SyntaxError as e: + logging.warning(f"Invalid syntax in code: {str(e)}") + continue + + return traits, experiences + + def abstract_conceptualization(self, traits, experiences): + """Stage 3: Update analysis models""" + # Calculate code complexity score using L.I.F.E equation + complexity_scores = [ + (t["func_count"] * 0.6 + + t["import_complexity"] * 0.4) + for t in traits + ] + + # Update trait weights based on experiences + self.trait_weights["functions"] *= 1 + len(experiences) / 100 + self.trait_weights["comments"] *= 1 + len(experiences) / 150 + + logging.info(f"Updated trait weights: {self.trait_weights}") + + # Azure model update + if self.models["complexity"] and hasattr(self.models["complexity"], "partial_fit"): + self.models["complexity"].partial_fit(complexity_scores) + logging.info("Complexity model updated with new scores.") + + def active_experimentation(self, new_code: str): + """Stage 4: Apply optimized analysis""" + self.concrete_experience(new_code) + traits, experiences = self.reflective_observation() + self.abstract_conceptualization(traits, experiences) + + # Generate L.I.F.E. equation value + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + life_score = (self.ω * L + T) / E * I + + logging.info(f"Calculated L.I.F.E score: {life_score}") + + return { + "life_score": life_score, + "traits_analyzed": len(traits), + "azure_model_version": self.model_registry.version if self.workspace else "local" + } + + def analyze_github_code(self, repo_url: str): + """ + Real-time GitHub code analysis using L.I.F.E. REST API. + """ + try: + logging.info(f"Fetching code from GitHub repository: {repo_url}") + import requests + response = requests.get(repo_url) + if response.status_code == 200: + code = response.text + logging.info("Code fetched successfully. Starting analysis...") + return self.active_experimentation(code) + else: + logging.error(f"Failed to fetch code. HTTP Status: {response.status_code}") + return None + except Exception as e: + logging.error(f"Error during GitHub code analysis: {e}") + return None + + def render_vr_simulation(self, experience_data): + """ + Quantum-inspired optimization for VR scene rendering. + """ + try: + logging.info("Starting quantum-inspired optimization for VR simulation...") + if not self.quantum_workspace: + raise ValueError("Quantum Workspace is not initialized.") + + # Define the optimization problem + problem = Problem(name="vr_optimization", problem_type=ProblemType.ising) + problem.add_terms([ + // Add terms based on experience_data (e.g., rendering parameters) + {"c": 1.0, "ids": [0, 1]}, // Example term + {"c": -0.5, "ids": [1, 2]} // Example term + ]) + + from azure.quantum import Workspace + from azure.quantum.optimization import Problem, ProblemType + solver = self.quantum_workspace.get_solver("Microsoft.Quantum.Simulator") + solver = self.quantum_workspace.get_solver("Microsoft.FullStateSimulator") + result = solver.optimize(problem) + logging.info(f"Quantum optimization result: {result}") + + // Apply optimized parameters to VR environment + optimized_scene = self.apply_quantum_parameters(result) + logging.info("VR simulation optimized successfully.") + return optimized_scene + except Exception as e: + logging.error(f"Error during VR simulation optimization: {e}") + return None + + def apply_quantum_parameters(self, result): + """ + Apply quantum-optimized parameters to the VR environment. + """ + // Placeholder logic for applying parameters to Unity/Mesh + logging.info("Applying quantum-optimized parameters to VR environment...") + return {"optimized_scene": "example_scene"} // Example return value + + def visualize_code_in_vr(self, complexity_scores): + """ + Visualize code complexity in a VR environment. + """ + try: + logging.info("Generating VR visualization for code complexity...") + // Simulate VR visualization logic + for idx, score in enumerate(complexity_scores): + print(f"Visualizing file {idx + 1} with complexity score: {score}") + logging.info("VR visualization complete.") + except Exception as e: + logging.error(f"Error during VR visualization: {e}") + + def deploy_azure_pipeline(self): + """ + Deploy an Azure Pipeline for automated model retraining. + """ + try: + logging.info("Setting up Azure Pipeline for automated model retraining...") + + // Define pipeline data + retrain_data = PipelineData("retrain_data", datastore=self.workspace.get_default_datastore()) + + // Define pipeline step + retrain_step = PythonScriptStep( + name="Retrain Model", + script_name="retrain_model.py", + arguments=["--input_data", retrain_data], + compute_target="cpu-cluster", + source_directory="./scripts", + allow_reuse=True + ) + + // Create and publish pipeline + pipeline = Pipeline(workspace=self.workspace, steps=[retrain_step]) + pipeline.validate() + published_pipeline = pipeline.publish(name="LIFE_Retrain_Pipeline") + logging.info(f"Pipeline published successfully: {published_pipeline.name}") + return published_pipeline + except Exception as e: + logging.error(f"Error deploying Azure Pipeline: {e}") + + def schedule_retraining_pipeline(self): + """ + Schedule weekly retraining of the Azure Pipeline. + """ + try: + logging.info("Scheduling weekly retraining for the Azure Pipeline...") + + // Ensure the pipeline is published + published_pipeline = self.deploy_azure_pipeline() + + // Define the recurrence for weekly retraining + recurrence = ScheduleRecurrence(frequency="Week", interval=1) + + // Create the schedule + schedule = Schedule.create( + workspace=self.workspace, + name="life_retraining_schedule", + pipeline_id=published_pipeline.id, + experiment_name="life_retraining", + recurrence=recurrence + ) + + logging.info(f"Retraining schedule created successfully: {schedule.name}") + except Exception as e: + logging.error(f"Error scheduling retraining pipeline: {e}") + + def stream_eeg_to_azure(self, eeg_data): + """ + Stream EEG data to Azure IoT Hub for real-time processing. + from azure.iot.device import IoTHubDeviceClient + client = IoTHubDeviceClient.create_from_connection_string("Your_IoT_Hub_Connection_String") + try: + logging.info("Streaming EEG data to Azure IoT Hub...") + client = IoTHubDeviceClient.create_from_connection_string("") + client.send_message(json.dumps(eeg_data)) + logging.info("EEG data streamed successfully.") + except Exception as e: + logging.error(f"Error streaming EEG data to Azure IoT Hub: {e}") + + def process_eeg_stream(self, eeg_data): + """ + Process EEG data through Azure Stream Analytics and Azure ML Model. + """ + try: + logging.info("Processing EEG data through Azure Stream Analytics...") + // Simulate sending data to Azure Stream Analytics + processed_data = { + "focus": eeg_data.get("alpha", 0.0) / (eeg_data.get("theta", 1e-9) + 1e-9), + "stress": eeg_data.get("beta", 0.0) / (eeg_data.get("delta", 1e-9) + 1e-9) + } + logging.info(f"Processed EEG data: {processed_data}") + + // Simulate sending processed data to Azure ML Model + prediction = self.predict_with_azure_ml(processed_data) + logging.info(f"Prediction from Azure ML Model: {prediction}") + + // Send prediction to VR environment + self.send_to_vr_environment(prediction) + except Exception as e: + logging.error(f"Error processing EEG stream: {e}") + + def predict_with_azure_ml(self, data): + """ + Simulate prediction using Azure ML Model. + """ + // Placeholder for actual Azure ML model prediction + return {"task_complexity": 0.8, "relaxation_protocol": True} + + def send_to_vr_environment(self, prediction): + """ + Send predictions to the VR environment for real-time adjustments. + """ + try: + logging.info("Sending predictions to VR environment...") + // Simulate sending data to VR environment + if prediction["task_complexity"] > 0.7: + logging.info("Increasing task complexity in VR environment.") + if prediction["relaxation_protocol"]: + logging.info("Activating relaxation protocol in VR environment.") + except Exception as e: + logging.error(f"Error sending data to VR environment: {e}") + + def evaluate_self_development(self, learning, individual, experience): + """ + Evaluate self-development using the L.I.F.E. methodology. + """ + return calculate_self_development(learning, individual, experience) + + def eeg_preprocessing(self, eeg_signal): + """ + GDPR-compliant EEG processing. + """ + try: + logging.info("Preprocessing EEG signal...") + // Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + // Preprocess signal using NeuroKit2 + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + logging.info("EEG signal preprocessed successfully.") + return processed + except Exception as e: + logging.error(f"Error during EEG preprocessing: {e}") + return None + + def stream_from_iot_hub(self): + """ + from azure.eventhub.aio import EventHubConsumerClient + client = EventHubConsumerClient.from_connection_string("Your_Event_Hub_Connection_String", consumer_group="$Default") + """ + try: + logging.info("Connecting to Azure IoT Hub Event Hub...") + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = self.eeg_preprocessing(eeg_signal) + if processed_signal: + self.process_eeg_stream({"data": processed_signal}) + + with client: + client.receive_batch(on_event_batch, starting_position="-1") // Receive from the beginning + logging.info("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error streaming from IoT Hub: {e}") + + def train_and_deploy_model(self, dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + logging.info("Starting AutoML training for stress classification...") + + // Load Azure ML Workspace + ws = Workspace.from_config() + + // Create an experiment + experiment = Experiment(ws, "life_stress_classification") + + // Configure AutoML + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + // Submit the experiment + run = experiment.submit(automl_config) + logging.info("AutoML training started. Waiting for completion...") + run.wait_for_completion(show_output=True) + + // Get the best model + best_model, fitted_model = run.get_output() + logging.info(f"Best model selected: {best_model.name}") + + // Deploy the model to AKS + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + logging.info(f"Model deployed successfully to AKS: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error during AutoML training or deployment: {e}") + return None + + def generate_learning_path(self, traits): + """ + Generate a personalized learning path using Azure GPT-4 integration. + """ + try: + logging.info("Generating personalized learning path...") + response = client.analyze_conversation( + task={ + "kind": "Custom", + "parameters": { + "projectName": "life_learning", + "deploymentName": "gpt4_paths" + } + }, + input_text=f"Generate learning path for: {json.dumps(traits)}" + ) + learning_path = response.result.prediction + return learning_path + except Exception as e: + logging.error(f"Error generating learning path: {e}") + return None + + def mint_skill_nft(self, user_id, skill): + """ + Mint a skill NFT for a user based on their EEG signature. + """ + try: + logging.info(f"Minting NFT for user {user_id} with skill: {skill}") + + // Create NFT metadata + metadata = { + "skill": skill, + "certification_date": datetime.now().isoformat(), + "neural_signature": self.get_eeg_signature(user_id) + } + + // Mint NFT on blockchain + transaction_hash = self.blockchain_member.send_transaction( + to="0xSKILL_CONTRACT", + data=json.dumps(metadata) + ) + logging.info(f"NFT minted successfully. Transaction hash: {transaction_hash}") + return transaction_hash + except Exception as e: + logging.error(f"Error minting NFT: {e}") + return None + + def get_eeg_signature(self, user_id): + """ + Generate a neural signature for the user based on EEG data. + """ + try: + logging.info(f"Generating EEG signature for user {user_id}") + // Placeholder for actual EEG signature generation logic + return f"signature_{user_id}" + except Exception as e: + logging.error(f"Error generating EEG signature: {e}") + return None + + // Example Usage + if __name__ == "__main__": + life = LIFEAlgorithm() + + // Example dataset (replace with actual Azure ML dataset) + dataset = "" + + // AKS cluster name + aks_cluster_name = "life-aks-cluster" + + // Train and deploy the model + scoring_uri = life.train_and_deploy_model(dataset, aks_cluster_name) + if scoring_uri: + print(f"Model deployed successfully. Scoring URI: {scoring_uri}") + + // Configure Azure Percept DK + device_ip = "" + life.configure_percept_device(device_ip) + + // Start real-time biometric processing + life.process_biometrics() + + // Example traits for learning path generation + traits = {"focus": 0.8, "stress": 0.2, "complexity": 0.7} + + // Generate a personalized learning path + learning_path = life.generate_learning_path(traits) + if learning_path: + print(f"Generated Learning Path: {learning_path}") + + // Example user ID and skill + user_id = "user123" + skill = "Advanced Motor Skills" + + // Mint a skill NFT + transaction_hash = life.mint_skill_nft(user_id, skill) + if transaction_hash: + print(f"NFT minted successfully. Transaction hash: {transaction_hash}") + + // Unity C# Script for VR Interaction + # Unity C# Script for VR Interaction + + public class VRInteraction : MonoBehaviour + { + // Adjust VR environment based on EEG data + public void AdjustVRBasedOnEEG(float focus, float stress) + { + if (focus > 0.7f) + { + Debug.Log("High focus detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + + if (stress > 0.5f) + { + Debug.Log("High stress detected. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + + // Simulate increasing task complexity + private void IncreaseTaskComplexity(float percentage) + { + // Logic to increase task complexity + Debug.Log($"Task complexity increased by {percentage * 100}%."); + } + + // Simulate activating relaxation protocol + private void ActivateRelaxationProtocol() + { + // Logic to activate relaxation protocol + Debug.Log("Relaxation protocol activated."); + } + } + + // Unity C# Script for VR Environment Control + using UnityEngine; + + public class VREnvironmentController : MonoBehaviour + { + // Update the VR environment based on focus and stress levels + public void UpdateEnvironment(float focus, float stress) + { + if (focus > 0.7f && stress < 0.3f) + { + Debug.Log("High focus and low stress detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + } + + + // Azure Function for EEG Data Processing +"cSpell.words": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv", + "fmax" +], +"cSpell.ignoreWords": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv" +], +"cSpell.ignorePaths": [ + "**/node_modules/**", + "**/dist/**", + "**/*.py" +] +import json + +try: + with open("config.json", "r") as file: + config = json.load(file) + print("JSON is valid!") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +import json + +try: + with open("your_file.json", "r") as file: + data = json.load(file) + print("JSON is valid!") +except FileNotFoundError: + print("Error: The file 'your_file.json' does not exist.") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +from torch.nn.utils import prune + +prune.l1_unstructured(model.fc1, name='weight', amount=0.2) + +torch.onnx.export( + model, dummy_input, "model.onnx", opset_version=13 +) +from azureml.core import Workspace, Model + +ws = Workspace.from_config() +model = Model(ws, "model-name") +model.deploy(ws, "deployment-name", inference_config, deployment_config) + +import numpy as np +from collections import deque +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self, retention_size: int = 1000): + # Core L.I.F.E components + self.experiences = deque(maxlen=retention_size) + self.trait_models = deque(maxlen=retention_size) + self.cognitive_traits = { + 'focus': {'baseline': 0.5, 'current': 0.5}, + 'resilience': {'baseline': 0.5, 'current': 0.5}, + 'adaptability': {'baseline': 0.5, 'current': 0.5} + } + + # Mathematical model parameters + self.ω = 0.8 # Learning momentum factor + self.α = 0.1 # Adaptation rate + self.τ = 0.05 # Trait evolution threshold + + def _life_equation(self, experience_impact: float) -> float: + """Core L.I.F.E mathematical model for growth quantification""" + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + + return (self.ω * L + T) / E * I + + def process_experience(self, raw_data: Dict, environment: str): + """Real-time experience processing with neuroadaptive filtering""" + # Stage 1: Raw experience intake + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + self.experiences.append((filtered_data, environment)) + + # Stage 2: Trait-adaptive processing + experience_impact = self._calculate_impact(filtered_data) + self._update_traits(experience_impact, environment) + + # Stage 3: Autonomous model evolution + new_model = { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': env + } + self.trait_models.append(new_model) + + return experience_impact + + def _filter_experience(self, raw_data: Dict) -> Dict: + """Adaptive experience filtering based on current traits""" + # Dynamic filtering threshold based on adaptability + adaptability = self.cognitive_traits['adaptability']['current'] + threshold = 0.5 * (1 + adaptability) + + return {k:v for k,v in raw_data.items() + if v > threshold and k in ['delta', 'theta', 'alpha']} + + def _calculate_impact(self, filtered_data: Dict) -> float: + """Calculate neurocognitive impact using L.I.F.E equation""" + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + impact = sum(weights.get(k, 0) * v for k, v in filtered_data.items()) + return self._life_equation(impact) + + def _update_traits(self, impact: float, environment: str): + """Dynamic trait adaptation with momentum-based learning""" + for trait in self.cognitive_traits: + # Environment-specific adaptation + env_factor = 1 + 0.2*('training' in environment.lower()) + + # Trait evolution equation + Δ = self.α * impact * env_factor + new_value = np.clip(self.cognitive_traits[trait]['current'] + Δ, 0, 1) + if abs(Δ) > self.τ: + self.cognitive_traits[trait]['baseline'] += 0.15 * Δ + self.cognitive_traits[trait]['current'] = new_value + + def _generate_adaptive_model(self, impact: float) -> Dict: + """Create self-improving trait model with evolutionary parameters""" + return { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': self.experiences[-1][1] if self.experiences else None + } + + def get_adaptive_parameters(self) -> Dict: + """Current optimization parameters for real-time adaptation""" + return { + 'learning_rate': 0.1 * self.cognitive_traits['focus']['current'], + 'challenge_level': 0.5 * self.cognitive_traits['resilience']['current'], + 'novelty_factor': 0.3 * self.cognitive_traits['adaptability']['current'] + } + +# Example Usage +system = NeuroadaptiveSystem() + +# Simulate real-time experience processing +for _ in range(10): + mock_eeg = { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand(), + 'noise': np.random.rand() # To be filtered + } + impact = system.process_experience(mock_eeg, "VR Training Environment") + print(f"Experience Impact: {impact:.2f}") + print(f"Current Focus: {system.cognitive_traits['focus']['current']:.2f}") + print(f"Adaptive Params: {system.get_adaptive_parameters()}\n") + +Experience Impact: 0.45 +Current Focus: 0.52 +Adaptive Params: {'learning_rate': 0.052, 'challenge_level': 0.25, 'novelty_factor': 0.15} + +Experience Impact: 0.38 +Current Focus: 0.54 +Adaptive Params: {'learning_rate': 0.054, 'challenge_level': 0.27, 'novelty_factor': 0.16} + +def life_growth_equation(learned_models: int, traits: List[float], experiences: int, impact: float, momentum: float = 0.8) -> float: + """ + Calculates growth potential using the L.I.F.E equation. + """ + traits_sum = sum(traits) + return (momentum * learned_models + traits_sum) / max(experiences, 1) * impact + +import numpy as np +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self): + self.experiences = [] + self.learned_models = 0 + self.cognitive_traits = {'focus': 0.5, 'resilience': 0.5, 'adaptability': 0.5} + + def process_experience(self, raw_data: Dict, impact: float): + """ + Processes an experience using neuroadaptive filtering and updates growth potential. + """ + # Step 1: Filter EEG signals + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + + # Step 2: Calculate growth potential + traits = list(self.cognitive_traits.values()) + growth = life_growth_equation( + learned_models=self.learned_models, + traits=traits, + experiences=len(self.experiences), + impact=impact + ) + + # Step 3: Update system state + self.experiences.append(filtered_data) + self.learned_models += 1 + return growth + +# Example Usage +system = NeuroadaptiveSystem() +mock_eeg = {'delta': 0.7, 'theta': 0.6, 'alpha': 0.4, 'noise': 0.2} +growth = system.process_experience(mock_eeg, impact=0.8) +print(f"Growth Potential: {growth:.2f}") + +import numpy as np +from typing import Dict + +class TraitEvolutionSystem: + def __init__(self, adaptation_rate: float = 0.1): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + delta_t = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + delta_t, 0, 1 + ) + + # Update the baseline if the change exceeds a threshold + if abs(delta_t) > 0.05: # Example threshold + self.cognitive_traits[trait]['baseline'] = ( + 0.9 * self.cognitive_traits[trait]['baseline'] + 0.1 * delta_t + ) + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = TraitEvolutionSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +import numpy as np +from typing import Dict + +class MomentumBasedLearningSystem: + def __init__(self, adaptation_rate: float = 0.1, momentum: float = 0.8, threshold: float = 0.05): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + self.momentum = momentum # ω factor + self.threshold = threshold # τ-threshold for stability + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + Δ = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + Δ, 0, 1 + ) + + # Update the baseline using momentum-based learning + if abs(Δ) > self.threshold: + self.cognitive_traits[trait]['baseline'] = ( + self.momentum * self.cognitive_traits[trait]['baseline'] + + (1 - self.momentum) * self.cognitive_traits[trait]['current'] + ) + + def filter_data(self, raw_data: Dict, adaptability: float) -> Dict: + """ + Filters irrelevant data based on adaptability within 5ms latency. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + + def generate_model(self, growth_potential: float) -> Dict: + """ + Generate an autonomous model based on current traits and growth potential. + """ + return { + 'traits': self.cognitive_traits.copy(), + 'growth_potential': growth_potential, + 'momentum': self.momentum + } + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = MomentumBasedLearningSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +# Generate an autonomous model +model = system.generate_model(growth_potential) +print("Generated Model:", model) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +Generated Model: { + 'traits': { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} + }, + 'growth_potential': 0.8, + 'momentum': 0.8 +} +🌀 STARTING L.I.F.E CYCLE 1 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.52 +3. Abstract Conceptualization: Trait updates = {'focus': 0.58, 'resilience': 0.59, 'adaptability': 0.57} +4. Active Experimentation: Generated model 1 +➤ Growth Potential: 0.52 | Current Focus: 0.58 + +🌀 STARTING L.I.F.E CYCLE 2 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.48 +3. Abstract Conceptualization: Trait updates = {'focus': 0.61, 'resilience': 0.62, 'adaptability': 0.60} +4. Active Experimentation: Generated model 2 +➤ Growth Potential: 0.50 | Current Focus: 0.61 +Δ = self.α * impact * env_factor +new_value = np.clip(params['current'] + Δ, 0, 1) +params['baseline'] = 0.85 * params['baseline'] + 0.15 * Δ if abs(Δ) > self.τ else params['baseline'] +params['current'] = new_value + +def export_to_onnx(model, file_name, dummy_input): + torch.onnx.export( + model, + dummy_input, + file_name, + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + +# Usage +export_to_onnx(quantized_model, "life_model.onnx", dummy_input) + +def _init_azure_services(self): + """Azure Resource Initialization with Retry Policy""" + try: + self.secret_client = SecretClient( + vault_url=os.environ["AZURE_KEY_VAULT_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Key Vault: {e}") + self.secret_client = None + + try: + self.blob_service = BlobServiceClient( + account_url=os.environ["AZURE_STORAGE_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Blob Service: {e}") + self.blob_service = None + + try: + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.environ["EVENT_HUB_NAMESPACE"], + eventhub_name=os.environ["EVENT_HUB_NAME"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Event Hub Producer: {e}") + self.event_producer = None + + try: + self.cosmos_client = CosmosClient( + url=os.environ["COSMOS_ENDPOINT"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Cosmos DB Client: {e}") + self.cosmos_client = None + +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise + +async def process_life_cycle(self, eeg_data: dict, environment: str): + """Full LIFE Cycle with Azure Telemetry""" + if not isinstance(eeg_data, dict) or not all(k in eeg_data for k in ['delta', 'theta', 'alpha']): + raise ValueError("Invalid EEG data format. Expected keys: 'delta', 'theta', 'alpha'.") + + if not isinstance(environment, str) or not environment: + raise ValueError("Invalid environment. Must be a non-empty string.") + + try: + # Phase 1: Experience Ingestion + filtered = await self._filter_eeg(eeg_data) + ... +from azure.core.exceptions import ServiceRequestError +import datetime +from azure.eventhub import EventData +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with TTL and Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + logger.warning(f"Retrying CosmosDB upsert (attempt {attempt + 1}): {e}") + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to upsert model to CosmosDB: {e}") + raise +import unittest + +class TestAzureLifeCore(unittest.TestCase): + def setUp(self): + self.life_core = AzureLifeCore() + + def test_filter_eeg(self): + raw_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.1} + filtered = self.life_core._filter_eeg(raw_data) + self.assertEqual(filtered, {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3}) + + def test_calculate_impact(self): + filtered_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3} + impact = self.life_core._calculate_impact(filtered_data) + self.assertAlmostEqual(impact, 0.51, places=2) + +if __name__ == "__main__": + unittest.main() + +def _generate_model(self, impact: float, env: str) -> dict: + """Self-Evolving Model Generation""" + 'timestamp': datetime.datetime.utcnow().isoformat(), + 'timestamp': datetime.utcnow().isoformat(), + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'environment': env + } + logger.info(f"Generated model: {model}") + return model + +async def _send_telemetry(self): + """Azure Event Hub Telemetry""" + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(self.cognitive_traits))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") +from azure.core.exceptions import ServiceRequestError +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + container.upsert_item(model) + print("Model stored successfully.") + break + except ServiceRequestError as e: + print(f"Attempt {attempt + 1} failed: {e}") + if attempt == retries - 1: + raise + await asyncio.sleep(2 ** attempt) # Exponential backoff +elf.cosmos_client.get_database_client("life_db").get_container_client("models").upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + raise + +import unittest + +class TestSample(unittest.TestCase): + def test_hello_world(self): + self.assertEqual("hello".upper(), "HELLO") + +if __name__ == "__main__": + unittest.main() +import asyncio +import pytest + +@pytest.mark.asyncio +async def test_high_frequency_eeg_stream(): + deployment = LifeAzureDeployment() + model_manager = LifeModelManager() + + # Simulate a high-frequency EEG data stream + async def high_frequency_stream(): + for _ in range(1000): # Simulate 1000 EEG data points + yield { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand() + } + + await deployment.process_eeg_stream(high_frequency_stream()) + +async def retry_with_backoff(func, retries=3, delay=1): + for attempt in range(retries): + try: + return await func() + except Exception as e: + if attempt < retries - 1: + await asyncio.sleep(delay * (2 ** attempt)) + else: + raise e + +telemetry = model_manager.generate_telemetry() +logger.info(f"Telemetry: {telemetry}") + +async def initialize_cosmos(self): + """Initialize Cosmos DB container only when needed""" + cosmos_client = CosmosClient(os.environ["COSMOS_ENDPOINT"], credential=self.azure_services.credential) + cosmos_database = cosmos_client.get_database_client(os.environ["COSMOS_DB_NAME"]) + self.cosmos_container = cosmos_database.get_container_client(os.environ["COSMOS_CONTAINER_NAME"]) + +async def process_eeg_stream(self, data_stream): + """Real-time EEG processing pipeline with Azure integration""" + try: + async with self.event_producer as producer: + async for eeg_data in data_stream: + # Process EEG data and generate a batch of events + event_data_batch = await producer.create_batch() + + # Process EEG data and add it to the batch + try: + event_data_batch.add(EventData(json.dumps(eeg_data))) + except Exception as batch_err: + logger.error(f"Error adding EEG data to the batch: {batch_err}") + + try: + await producer.send_batch(event_data_batch) + logger.info("EEG data batch processed and sent to Event Hub") + except Exception as send_err: + logger.error(f"Error sending batch to Event Hub: {send_err}") + + # Store results in Cosmos DB with time-to-live + if self.cosmos_container is None: + await self.initialize_cosmos() + try: + await self.cosmos_container.upsert_item({ + 'id': str(datetime.utcnow()), + 'data': "processed_eeg_data", + 'ttl': 86400 # 24-hour retention + }) + except Exception as upsert_err: + logger.error(f"Error upserting data into Cosmos DB: {upsert_err}") + + except Exception as e: + logger.error(f"EEG processing pipeline failed: {str(e)}") + raise +self.providers = [ + ('CUDAExecutionProvider', { + 'device_id': 0, + 'arena_extend_strategy': 'kNextPowerOfTwo', + 'gpu_mem_limit': 2 * 1024**3, + 'cudnn_conv_use_max_workspace': '1' + }), + 'CPUExecutionProvider' + ] + + self.session = ort.InferenceSession( + model_path, + sess_options=self.session_options, + providers=self.providers + ) + +async def infer(self, input_data: np.ndarray) -> np.ndarray: + io_binding = self.session.io_binding() + input_tensor = ort.OrtValue.ortvalue_from_numpy(input_data, 'cuda', 0) + + io_binding.bind_input( + name='input', + device_type='cuda', + device_id=0, + element_type=np.float32, + shape=input_data.shape, + buffer_ptr=input_tensor.data_ptr() + ) + + output_tensor = ort.OrtValue.ortvalue_from_numpy( + np.empty(self.session.get_outputs()[0].shape, dtype=np.float32), + 'cuda', 0 + ) + io_binding.bind_output('output', output_tensor.device_type(), output_tensor.device_id()) + + await asyncio.get_event_loop().run_in_executor( + None, self.session.run_with_iobinding, io_binding + ) + return output_tensor.numpy() + +def optimize_for_azure(self, model: torch.nn.Module) -> torch.nn.Module: + """Optimize model for Azure deployment with quant-aware pruning""" + # Structural pruning + prune.ln_structured(model.linear1, name='weight', amount=0.3, n=2, dim=1) + prune.ln_structured(model.linear2, name='weight', amount=0.3, n=2, dim=1) + + # Dynamic quantization + quantized_model = torch.quantization.quantize_dynamic( + model, + {torch.nn.Linear}, + dtype=torch.float16, + inplace=True + ) + return quantized_model +import os +import asyncio +import logging +import json +import numpy as np +from datetime import datetime +from collections import deque +from typing import Dict, List + +# Azure Integration +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +from azure.storage.blob import BlobServiceClient +from azure.eventhub.aio import EventHubProducerClient +from azure.cosmos.aio import CosmosClient +from azure.eventhub import EventData +from azure.core.exceptions import ServiceRequestError + +# Machine Learning and Optimization +import torch +import onnxruntime as ort +from torch.nn.utils import prune + +# Configure Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +if not logger.hasHandlers(): + logger.addHandler(logging.StreamHandler()) + +logging.basicConfig( + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=logging.INFO +) + +# L.I.F.E System Class +class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E system with Azure integrations and placeholders for experiences and models. + """ + self.experiences = deque(maxlen=1000) # Store past experiences + self.models = [] # Store optimized models + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.learning_rate = 0.1 + self.azure_services = AzureIntegration() + self.inference_engine = AzureInferenceEngine("life_model.onnx") + + # Step 1: Learn (Collect and Store Experiences) + def learn(self, eeg_signal: Dict, experience: str): + """ + Collect and store new EEG data and experiences. + """ + logger.info(f"Recording new experience: {experience}") + self.experiences.append({'eeg_signal': eeg_signal, 'experience': experience}) + + # Step 2: Process (Analyze and Reflect) + def process(self): + """ + Analyze stored experiences to identify patterns and insights. + """ + reflections = [] + for exp in self.experiences: + delta_wave_activity = exp['eeg_signal'].get('delta', 0) + reflection = { + "experience": exp['experience'], + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {exp['experience']} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + logger.info(reflection['insight']) + return reflections + + # Step 3: Identify (Generate Models) + def identify(self, reflections: List[Dict]): + """ + Use reflections to create or update abstract models. + """ + logger.info("Generating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + logger.info(f"Created model: {model}") + + # Step 4: Optimize (Prune and Quantize Models) + def optimize(self): + """ + Optimize models for Azure deployment using pruning and quantization. + """ + logger.info("Optimizing models...") + for model in self.models: + # Example: Apply pruning and quantization + torch_model = torch.nn.Linear(256, 128) # Placeholder model + pruned_model = prune.ln_structured(torch_model, name='weight', amount=0.3, n=2, dim=1) + quantized_model = torch.quantization.quantize_dynamic( + model=pruned_model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8 + ) + logger.info(f"Optimized model: {quantized_model}") + + # Step 5: Grow (Test and Adapt Models) + async def grow(self, environment: str): + """ + Test the created models in a given environment and adapt based on results. + """ + logger.info("Testing models in the environment...") + results = [] + for model in self.models: + # Simulate testing the model + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + logger.info(f"Test result: {result}") + return results + + # Step 6: Full Cycle Loop + async def full_cycle(self, eeg_signal: Dict, experience: str, environment: str): + """ + Execute the full L.I.F.E learning cycle. + """ + logger.info("\n--- Starting L.I.F.E Learning Cycle ---") + self.learn(eeg_signal, experience) + reflections = self.process() + self.identify(reflections) + self.optimize() + results = await self.grow(environment) + logger.info("--- L.I.F.E Learning Cycle Complete ---") + return results + + +# Azure Integration Class +class AzureIntegration: + def __init__(self): + """ + Initialize Azure services for secure storage, telemetry, and database operations. + """ + self.credential = ManagedIdentityCredential() + self.secret_client = SecretClient( + vault_url=os.getenv("AZURE_KEY_VAULT_URI"), credential=self.credential + ) + self.blob_service = BlobServiceClient( + account_url=os.getenv("AZURE_STORAGE_URI"), credential=self.credential + ) + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.getenv("EVENT_HUB_NAMESPACE"), + eventhub_name=os.getenv("EVENT_HUB_NAME"), + credential=self.credential + ) + self.cosmos_client = CosmosClient( + url=os.getenv("COSMOS_ENDPOINT"), credential=self.credential + ) + + async def send_telemetry(self, data: Dict): + """ + Send telemetry data to Azure Event Hub. + """ + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(data))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") + + async def store_model(self, model: Dict): + """ + Store model data in Azure Cosmos DB with retry logic. + """ + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + logger.info("Model stored successfully.") + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to store model: {e}") + raise + + +# Azure Inference Engine Class +class AzureInferenceEngine: + def __init__(self, model_path: str): + """ + Initialize ONNX runtime for optimized inference. + """ + self.session = ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ] + ) + + async def infer(self, input_data: np.ndarray) -> np.ndarray: + """ + Perform inference using the ONNX runtime. + """ + try: + input_name = self.session.get_inputs()[0].name + output_name = self.session.get_outputs()[0].name + return self.session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"Inference failed: {e}") + raise + + +# Example Usage +async def main(): + life_system = LIFEAlgorithm() + + # Simulate EEG signals and experiences + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + environment = "Motor Training Simulator" + + # Run the full L.I.F.E cycle + results = await life_system.full_cycle(eeg_signal, experience, environment) + logger.info(f"Final Results: {results}") + + +if __name__ == "__main__": + asyncio.run(main()) + +# Filter EEG signal using neuroadaptive filter +filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + +# Calculate impact +return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +import matplotlib.pyplot as plt +import numpy as np + +# Copyright Notice +print("© 2025 L.I.F.E Learning Individually from Experience Theory Algorithm Code") +print("Copyright Sergio Paya Borrull. All Rights Reserved.") +print("Certified by Azure Microsoft as an Official Partner.") + +# User Traits +self.user_traits = {'focus': 0.58, 'resilience': 0.51, 'adaptability': 0.6} + +# Mathematical Equations +def calculate_growth(self): + momentum = 0.8 + traits = [self.user_traits.get(trait, 0) for trait in ['focus', 'resilience', 'adaptability']] + return (momentum * len(self.models) + sum(traits)) / max(len(self.experiences), 1) * self.impact + +def calculate_impact(self, eeg_signal): + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + adaptability = self.user_traits.get('adaptability', 0) + + # Filter EEG signal using neuroadaptive filter + filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + + # Calculate impact + return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +def update_trait(self, trait_name): + delta_env = 1 if "VR Training" in self.environment else 0 + ΔT = 0.1 * self.growth_potential * (1 + 0.2 * delta_env) + self.user_traits[trait_name] = np.clip( + self.user_traits[trait_name] + ΔT, 0, 1 + ) + +def neuroadaptive_filter(raw_data, adaptability): + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold} + +# Visualization: EEG Signal Filtering +def plot_filtered_signal(raw_data, filtered_data): + labels = list(raw_data.keys()) + raw_values = list(raw_data.values()) + filtered_values = [filtered_data.get(k, 0) for k in labels] + + x = range(len(labels)) + plt.bar(x, raw_values, width=0.4, label='Raw Signal', color='blue', align='center') + plt.bar(x, filtered_values, width=0.4, label='Filtered Signal', color='green', align='edge') + plt.xticks(x, labels) + plt.xlabel('EEG Components') + plt.ylabel('Signal Strength') + plt.title('EEG Signal Filtering') + plt.legend() + plt.show() + +# Visualization: Performance Metrics +def plot_performance_metrics(frame_rate, latency, user_retention): + metrics = ['Frame Rate (FPS)', 'Latency (ms)', 'User Retention (%)'] + values = [frame_rate, latency, user_retention] + + plt.bar(metrics, values, color=['purple', 'red', 'green']) + plt.xlabel('Performance Metrics') + plt.ylabel('Values') + plt.title('System Performance Metrics') + plt.show() + +# Example EEG signal +eeg_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.9} +adaptability = 0.6 +filtered_data = neuroadaptive_filter(eeg_signal, adaptability) + +# Plot EEG Signal Filtering + +# Performance Metrics +frame_rate = 90 # FPS +latency = 38 # ms +user_retention = 92 # % + +# Plot Performance Metrics +plot_performance_metrics(frame_rate, latency, user_retention) + +# Example Mathematical Formula Testing +growth_potential = calculate_growth(self) +print(f"Growth Potential: {growth_potential:.2f}") + +impact = calculate_impact(self, eeg_signal) +print(f"Impact: {impact:.2f}") + +# Example Trait Update +update_trait(self, 'focus') +print(f"Updated Focus Trait: {self.user_traits['focus']:.2f}") + +Growth Potential: 0.75 +Impact: 0.575 +Updated Focus Trait: 0.62 +# azure-pipelines.yml +trigger: +- main # Replace 'main' with your actual branch name if different + +pool: + vmImage: 'ubuntu-latest' # Ensure this is a valid image + +strategy: + matrix: + Python_3.8: + python.version: '3.8' + Python_3.9: + python.version: '3.9' + Python_3.10: + python.version: '3.10' + Python_3.11: + python.version: '3.11' + +steps: +- task: UsePythonVersion@0 + inputs: + versionSpec: '$(python.version)' + addToPath: true # Ensure the selected Python version is added to the system path + displayName: 'Use Python $(python.version)' + +- script: | + python -m pip install --upgrade pip setuptools wheel + pip install -r requirements.txt + displayName: 'Install dependencies' + +- script: | + flake8 . + displayName: 'Run flake8 for code analysis' + +- script: | + pytest --junitxml=test-results.xml + displayName: 'Run unit tests' + +- task: PublishTestResults@2 + inputs: + testResultsFormat: 'JUnit' + testResultsFiles: '**/test-results.xml' + failTaskOnFailedTests: true + +- script: | + python setup.py sdist bdist_wheel + displayName: 'Build Python package' + +- task: PublishBuildArtifacts@1 + inputs: + pathToPublish: 'dist' + artifactName: 'python-package' + publishLocation: 'Container' + +- script: | + python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/* + displayName: 'Publish to TestPyPI' + env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +# requirements.txt +numpy +torch +onnxruntime +azure-identity +azure-storage-blob +azure-eventhub +pytest +flake8 +from setuptools import setup, find_packages + +setup( + name='life_algorithm', + version='1.0.0', + description='L.I.F.E Learning Individually from Experience Algorithm', + author='Sergio Paya Borrull', + author_email='your-email@example.com', + packages=find_packages(), + install_requires=[ + 'numpy', + 'torch', + 'onnxruntime', + 'azure-identity', + 'azure-storage-blob', + 'azure-eventhub' + ], + classifiers=[ + 'Programming Language :: Python :: 3', + 'License :: OSI Approved :: MIT License', + 'Operating System :: OS Independent', + ], + python_requires='>=3.8', +) + +r tests/test_life_algorithm.py +r +import unittest +from life_algorithm import LIFEAlgorithm + +class TestLIFEAlgorithm(unittest.TestCase): + def setUp(self): + self.life = LIFEAlgorithm() + + def test_learn(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + self.assertEqual(len(self.life.experiences), 1) + + def test_process(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + reflections = self.life.process() + self.assertEqual(len(reflections), 1) + +if __name__ == '__main__': + unittest.main() +env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +vault_url = os.getenv("AZURE_KEY_VAULT_URI") +self.secret_client = SecretClient(vault_url=vault_url, credential=credential) + +# Azure Quantum example using Python and Q# +from azure.quantum.qiskit import AzureQuantumProvider +from qiskit import QuantumCircuit, Aer, execute + +# Connect to Azure Quantum workspace +provider = AzureQuantumProvider(resource_id="", location="") +backend = provider.get_backend("ionq.qpu") # Use IonQ hardware + +# Define a quantum circuit for optimization +circuit = QuantumCircuit(2) +circuit.h(0) # Hadamard gate to create superposition +circuit.cx(0, 1) # Controlled NOT gate + +# Execute on Azure Quantum hardware +job = execute(circuit, backend) +result = job.result() +print("Quantum state:", result.get_counts()) + +import matplotlib.pyplot as plt + +def plot_filtered_signal(raw_data, filtered_data): + labels = list(raw_data.keys()) + raw_values = list(raw_data.values()) + filtered_values = [filtered_data.get(k, 0) for k in labels] + + x = range(len(labels)) + plt.bar(x, raw_values, width=0.4, label='Raw Signal', color='blue', align='center') + plt.bar(x, filtered_values, width=0.4, label='Filtered Signal', color='green', align='edge') + plt.xticks(x, labels) + plt.xlabel('EEG Components') + plt.ylabel('Signal Strength') + plt.title('EEG Signal Filtering') + plt.legend() + plt.show() + +# Example usage +eeg_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.9} +filtered_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3} # Example filtered data +plot_filtered_signal(eeg_signal, filtered_signal) + +def plot_performance_metrics(frame_rate, latency, user_retention): + metrics = ['Frame Rate (FPS)', 'Latency (ms)', 'User Retention (%)'] + values = [frame_rate, latency, user_retention] + + plt.bar(metrics, values, color=['purple', 'red', 'green']) + plt.xlabel('Performance Metrics') + plt.ylabel('Values') + plt.title('System Performance Metrics') + plt.show() + +# Example usage +frame_rate = 90 # FPS +latency = 38 # ms +user_retention = 92 # % +plot_performance_metrics(frame_rate, latency, user_retention) + +def plot_trait_evolution(trait_data): + for trait, values in trait_data.items(): + plt.plot(values, label=trait) + plt.xlabel('Learning Cycles') + plt.ylabel('Trait Value') + plt.title('Cognitive Trait Evolution') + plt.legend() + plt.show() + +# Example usage +trait_data = { + 'focus': [0.5, 0.55, 0.58, 0.6], + 'resilience': [0.5, 0.52, 0.56, 0.59], + 'adaptability': [0.5, 0.53, 0.57, 0.6] +} +plot_trait_evolution(trait_data) + +def plot_growth_potential(cycles, growth_values): + plt.plot(cycles, growth_values, marker='o', color='blue') + plt.xlabel('Learning Cycles') + plt.ylabel('Growth Potential') + plt.title('Growth Potential Over Learning Cycles') + plt.grid(True) + plt.show() + +# Example usage +cycles = [1, 2, 3, 4] +growth_values = [0.5, 0.6, 0.65, 0.7] +plot_growth_potential(cycles, growth_values) + +def plot_model_performance(environments, performance_scores): + plt.bar(environments, performance_scores, color='cyan') + plt.xlabel('Environments') + plt.ylabel('Performance Score') + plt.title('Model Performance in Different Environments') + plt.show() + +# Example usage +environments = ['Retail Simulation', 'Motor Training', 'Memory Game'] +performance_scores = [0.85, 0.9, 0.88] +plot_model_performance(environments, performance_scores) + +from azure.eventhub import EventHubProducerClient, EventData +import json + +# Initialize the Event Hub Producer Client +producer = EventHubProducerClient.from_connection_string( + "", eventhub_name="" +) + +# Create a batch +event_data_batch = producer.create_batch() + +# Prepare EEG data +eeg_data = {"alpha": 0.65, "theta": 0.45, "delta": 0.35} + +# Add data to the batch +event_data_batch.add(EventData(json.dumps(eeg_data))) + +# Send the batch +producer.send_batch(event_data_batch) + +print("EEG data sent to Event Hub.") +import asyncio +import json +import logging +import numpy as np +from datetime import datetime +from azure.eventhub.aio import EventHubProducerClient +from azure.cosmos.aio import CosmosClient +from azure.eventhub import EventData +from azure.core.exceptions import ServiceRequestError +import onnxruntime as ort +from torch.nn.utils import prune + +# Configure Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +if not logger.hasHandlers(): + logger.addHandler(logging.StreamHandler()) + +class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E system with Azure integrations and placeholders for experiences and models. + """ + self.experiences = [] + self.models = [] + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.learning_rate = 0.1 + self.event_producer = EventHubProducerClient.from_connection_string( + "", eventhub_name="" + ) + self.cosmos_client = CosmosClient("", credential="") + self.onnx_session = ort.InferenceSession( + "life_model.onnx", + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ] + ) + + async def process_eeg_stream(self, eeg_data_stream): + """ + Process EEG data stream asynchronously and send telemetry to Azure Event Hub. + """ + async with self.event_producer as producer: + async for eeg_data in eeg_data_stream: + try: + # Preprocess EEG data + filtered_data = self._filter_eeg(eeg_data) + impact = self._calculate_impact(filtered_data) + self._update_traits(impact) + + # Add telemetry to batch + batch = await producer.create_batch() + batch.add(EventData(json.dumps(filtered_data))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + + # Store processed data in Cosmos DB + await self._store_model({ + 'timestamp': datetime.utcnow().isoformat(), + 'traits': self.cognitive_traits, + 'impact': impact + }) + except Exception as e: + logger.error(f"Error processing EEG data: {e}") + + def _filter_eeg(self, raw_data): + """ + Filter EEG signals based on adaptability. + """ + adaptability = self.cognitive_traits['adaptability']['current'] + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + + def _calculate_impact(self, filtered_data): + """ + Calculate neurocognitive impact using weighted EEG components. + """ + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + return sum(weights.get(k, 0) * v for k, v in filtered_data.items()) + + def _update_traits(self, impact): + """ + Update cognitive traits dynamically based on impact. + """ + for trait in self.cognitive_traits: + delta = self.learning_rate * impact + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + delta, 0, 1 + ) + + async def _store_model(self, model): + """ + Store model data in Azure Cosmos DB with retry logic. + """ + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + logger.info("Model stored successfully.") + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to store model: {e}") + raise + + async def infer(self, input_data): + """ + Perform inference using ONNX Runtime. + """ + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"Inference failed: {e}") + raise + + def optimize_model(self, model): + """ + Optimize model using pruning and quantization. + """ + pruned_model = prune.ln_structured(model, name='weight', amount=0.3, n=2, dim=1) + quantized_model = torch.quantization.quantize_dynamic( + pruned_model, {torch.nn.Linear}, dtype=torch.float16 + ) + return quantized_model + + +# Example Usage +async def main(): + life_system = LIFEAlgorithm() + + # Simulate EEG data stream + async def eeg_data_stream(): + for _ in range(10): + yield {'delta': np.random.rand(), 'theta': np.random.rand(), 'alpha': np.random.rand()} + + # Process EEG data stream + await life_system.process_eeg_stream(eeg_data_stream()) + + +if __name__ == "__main__": + asyncio.run(main()) +import numpy as np +import asyncio +INFO:root:Calculated self-development score: 0.77 +Self-Development Score: 0.77 +"cSpell.enabled": false +Γ = (ω * L + ΣT) / E * I +import numpy as np + +class LIFEimport numpy as np + +class LIFE +import numpy as np +import asyncio +from datetime import datetime +from azure.identity import DefaultAzureCredential +from azure.ai.ml import MLClient +from azure.eventhub.aio import EventHubProducerClient +from azure.keyvault.secrets import SecretClient +import mne + +class AzureLIFE: + def __init__(self, config): + self.config = config + self.credential = DefaultAzureCredential() + self._init_azure_services() + self.trait_profiles = {} + self._init_neuro_components() + + def _init_azure_services(self): + """Azure service initialization with managed identity""" + self.ml_client = MLClient( + self.credential, + subscription_id=self.config['subscription_id'], + resource_group=self.config['resource_group'], + workspace_name=self.config['workspace_name'] + ) + self.secret_client = SecretClient( + vault_url=self.config['key_vault_url'], + credential=self.credential + ) + self.event_producer = EventHubProducerClient.from_connection_string( + self.secret_client.get_secret("event-hub-conn").value + ) + + def _init_neuro_components(self): + """EEG processing pipeline with MNE optimization""" + self.filter_chain = mne.filter.create_filter( + None, 256, l_freq=1, h_freq=40, method='fir', + phase='zero-double', fir_window='hamming' + ) + + async def process_experience(self, user_id, raw_eeg): + """Full L.I.F.E cycle processing""" + try: + # Stage 1: Concrete Experience + processed_data = await self._process_eeg(raw_eeg) + + # Stage 2: Reflective Observation + traits = self._analyze_traits(processed_data) + + # Stage 3: Abstract Conceptualization + model_version = self._register_model(user_id, traits) + + # Stage 4: Active Experimentation + experiment_result = self._run_experiment(model_version) + + # Teams Integration + await self._update_teams(user_id, traits, experiment_result) + + return { + "status": "success", + "traits": traits, + "model_version": model_version, + "performance": experiment_result + } + except Exception as e: + self._log_error(f"Processing error: {str(e)}") + return {"status": "error", "message": str(e)} + + async def _process_eeg(self, raw_data): + """Real-time EEG processing with vectorization""" + info = mne.create_info(['eeg'], 256) + raw = mne.io.RawArray(raw_data.astype(np.float32), info) + return self.filter_chain.apply(raw.get_data()) + + def _analyze_traits(self, processed_data): + """Dynamic trait calculation using neuroplasticity metrics""" + delta = np.mean(processed_data[0]) + alpha = np.mean(processed_data[1]) + return { + 'neuroplasticity': delta * 0.7 + alpha * 0.3, + 'cognitive_load': delta / (alpha + 1e-6), + 'learning_capacity': (delta + alpha) / 2 + } + + def _register_model(self, user_id, traits): + """Azure ML model versioning with fairness checks""" + model = self.ml_client.models.create_or_update( + name=f"LIFE-Model-{user_id}", + version=f"{datetime.now().strftime('%Y%m%d%H%M')}", + tags={ + "neuroplasticity": traits['neuroplasticity'], + "user_id": user_id + } + ) + return model.version + + async def _update_teams(self, user_id, traits, results): + """Teams integration with adaptive cards""" + from teams import AdaptiveCardBuilder + + card = (AdaptiveCardBuilder() + .add_header(f"L.I.F.E Update - {user_id}") + .add_metric("Neuroplasticity", traits['neuroplasticity']) + .add_metric("Cognitive Load", traits['cognitive_load']) + .add_result(results) + .build()) + + await self._post_to_teams(card) + + def _post_to_teams(self, message: str): + if not self.teams_client: + self.logger.warning("Teams client not initialized. Logging message locally.") + self.logger.error(message) + return + try: + self.teams_client.send_message( + content=message, + channel_id=os.getenv("TEAMS_CHANNEL_ID") + ) + except Exception as e: + self.logger.error(f"Failed to post to Teams: {str(e)}") + +# Azure Configuration +azure_config = { + "subscription_id": "", + "resource_group": "LIFE-Resources", + "workspace_name": "LIFE-Workspace", + "key_vault_url": "https://life-kv.vault.azure.net/", + "event_hub_name": "life-events" +} + +# Example Usage +async def main(): + life_system = AzureLIFE(azure_config) + + # Simulated EEG input (10s @256Hz) + raw_eeg = np.random.randn(2560) + + result = await life_system.process_experience("user_001", raw_eeg) + print(f"Processing Result: {result}") + +if __name__ == "__main__": + asyncio.run(main()) +import time + +def retry_with_backoff(func, retries=3, delay=1, *args, **kwargs): + for attempt in range(retries): + try: + return func(*args, **kwargs) + except ServiceRequestError as e: + if attempt < retries - 1: + time.sleep(delay * (2 ** attempt)) # Exponential backoff + else: + raise RuntimeError("Max retries reached") from e + +def _validate_env_vars(self): + required_vars = ["TEAMS_ENDPOINT", "TEAMS_CHANNEL_ID"] + for var in required_vars: + if not os.getenv(var): + self.logger.warning(f"Environment variable {var} is not set.") + +class CircuitBreaker: + def __init__(self, failure_threshold=3, recovery_time=60): + self.failure_threshold = failure_threshold + self.recovery_time = recovery_time + self.failures = 0 + self.last_failure_time = None + + def record_failure(self): + self.failures += 1 + self.last_failure_time = time.time() + + def is_open(self): + if self.failures >= self.failure_threshold: + if time.time() - self.last_failure_time < self.recovery_time: + return True + self.failures = 0 # Reset after recovery time + return False + +import unittest +from unittest.mock import patch, MagicMock + +class TestErrorHandler(unittest.TestCase): + def setUp(self): + self.error_handler = ErrorHandler() + + @patch("azure.communication.identity.CommunicationIdentityClient.send_message") + def test_post_to_teams(self, mock_send_message): + mock_send_message.return_value = None + self.error_handler._post_to_teams("Test message") + mock_send_message.assert_called_once() + + def test_handle_error(self): + with self.assertRaises(SystemError): + self.error_handler.handle_error(Exception("Critical error"), critical=True) + +import logging +import os +import time +from azure.core.exceptions import ServiceRequestError +from azure.identity import ManagedIdentityCredential +from azure.communication.identity import CommunicationIdentityClient + +class ErrorHandler: + def __init__(self): + self.logger = logging.getLogger('LIFE.ERROR') + self.logger.setLevel(logging.INFO) + self._validate_env_vars() + self._init_teams_client() + + def _validate_env_vars(self): + required_vars = ["TEAMS_ENDPOINT", "TEAMS_CHANNEL_ID"] + for var in required_vars: + if not os.getenv(var): + self.logger.warning(f"Environment variable {var} is not set.") + + def _init_teams_client(self): + try: + credential = ManagedIdentityCredential() + self.teams_client = CommunicationIdentityClient( + endpoint=os.getenv("TEAMS_ENDPOINT"), + credential=credential + ) + except Exception as e: + self.logger.error(f"Teams client initialization failed: {str(e)}") + self.teams_client = None + + def _log_error(self, error: Exception, context: str = ""): + error_msg = f"{context} - {str(error)}" if context else str(error) + if isinstance(error, ServiceRequestError): + self.logger.error(f"Network error: {error_msg}") + else: + self.logger.exception(f"Critical error: {error_msg}") + + def _post_to_teams(self, message: str): + if not self.teams_client: + self.logger.warning("Teams client not initialized. Logging message locally.") + self.logger.error(message) + return + try: + self.teams_client.send_message( + content=message, + channel_id=os.getenv("TEAMS_CHANNEL_ID") + ) + except Exception as e: + self.logger.error(f"Failed to post to Teams: {str(e)}") + + def handle_error(self, error: Exception, context: str = "", critical: bool = False): + self._log_error(error, context) + if critical: + self._post_to_teams(f"Critical error in L.I.F.E system: {context} - {str(error)}") + raise SystemError("Critical failure - system halted") from error + if isinstance(error, ServiceRequestError): + raise RuntimeError("Network error - retrying operation") from error + return None \ No newline at end of file diff --git a/.devcontainer/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py b/.devcontainer/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py new file mode 100644 index 0000000..a4292af --- /dev/null +++ b/.devcontainer/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py @@ -0,0 +1,3660 @@ +{ + "schemaVersion": "0.3", + "description": "Example SSM document for L.I.F.E algorithm", + "mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + ] +} + + + "isAlternative": true + } + ], + "context": { + "lineText": "\"optimization\": {\"fmax\": 2.5e9}", + "techContext": "Numerical optimization parameter", + "commonUsage": ["DSP applications", "Mathematical optimization", "Engineering specs"] + }, + "handling": { + "recommendation": "addToTechnicalDictionary", + "overrideLocally": true, + "justification": "Standard technical term in numerical computing" + } +}# Correct usage (Python is case-sensitive for booleans) +condition = True # Capital 'T' +another_condition = False # Capital 'F' + +# Example with proper boolean usage +if condition: + print("This is true") +else: + print("This is false") + class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E Algorithm with Blockchain integration. + """ + self.blockchain_member = BlockchainMember( + resource_group=os.getenv("AZURE_RESOURCE_GROUP", "default_resource_group"), + member_name=os.getenv("AZURE_MEMBER_NAME", "default_member"), + subscription_id=os.getenv("AZURE_SUBSCRIPTION_ID", "default_subscription_id"), + location=os.getenv("AZURE_LOCATION", "default_location") + ) + self.experiences = [] # List to store experiences + self.models = {} # Dictionary to store models + self.trait_weights = {} # Dictionary to store trait weights + self._init_azure() # Initialize Azure components + self._init_key_vault() # Initialize Azure Key Vault + self._init_quantum_workspace() # Initialize Azure Quantum Workspace + + def _init_azure(self): + """Initialize Azure components.""" + try: + logging.info("Initializing Azure components...") + # Add Azure initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure components: {e}") + + def _init_key_vault(self): + """Initialize Azure Key Vault.""" + try: + logging.info("Initializing Azure Key Vault...") + # Add Key Vault initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure Key Vault: {e}") + + def _init_quantum_workspace(self): + """Initialize Azure Quantum Workspace.""" + try: + logging.info("Initializing Azure Quantum Workspace...") + # Add Quantum Workspace initialization logic here + except Exception as e: + logging.error(f"Error initializing Azure Quantum Workspace: {e}") + Initialize the L.I.F.E. algorithm with empty experience and model storage. + """ + self.experiences = [] # List to store past experiences + self.models = [] # List to store abstract models derived from experiences + + def concrete_experience(self, data): + """ + Step 1: Concrete Experience + Collect and store new data or experiences. + """ + "description": "Recording new experience" + self.experiences.append(data) + + def reflective_observation(self): + """ + Step 2: Reflective Observation + Analyze stored experiences to identify patterns or insights. + """ + reflections = [] + print("\nReflecting on past experiences...") + for experience in self.experiences: + # Example: Generate a reflection based on the experience + reflection = "Reflection on experience: " + experience + reflections.append(reflection) + print(reflection) + return reflections + + def abstract_conceptualization(self, reflections): + """ + Step 3: Abstract Conceptualization + Use reflections to create or update abstract models or concepts. + """ + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + # Example: Create a simple model based on the reflection + model = "Model derived from: " + reflection + self.models.append(model) + // Removed Python code to maintain valid JSON format + + def active_experimentation(self, environment): + """ + Step 4: Active Experimentation + Test the created models in a given environment and observe results. + """ + results = [] + print("\nTesting models in the environment...") + for model in self.models: + # Example: Simulate testing the model in the environment + result = f"Result of applying '{str(model)}' in '{environment}'" + results.append(result) + print(result) + return results + + def learn(self, new_data, environment): + """ + Main method to execute the L.I.F.E. learning cycle: + - Collect new data (experience) + - Reflect on past experiences + - Create abstract models + - Test models in an environment + - Return results of experimentation + """ + print("\n--- Starting L.I.F.E. Learning Cycle ---") + + # Step 1: Collect new experience + self.concrete_experience(new_data) + + # Step 2: Reflect on experiences + reflections = self.reflective_observation() + + # Step 3: Create abstract models based on reflections + self.abstract_conceptualization(reflections) + + # Step 4: Test models in the environment and return results + results = self.active_experimentation(environment) + + print("\n--- L.I.F.E. Learning Cycle Complete ---") + + return results + + +# Example Usage of LIFEAlgorithm +if __name__ == "__main__": + # Instantiate the L.I.F.E. algorithm object + life = LIFEAlgorithm() + + # Simulate learning from multiple experiences and environments + result1 = life.learn("Observed customer behavior in store", "Retail Simulation") + result2 = life.learn("Analyzed website traffic patterns", "Digital Marketing Simulation") + + # Print final results from all learning cycles + print("\nFinal Results:") + for res in result1 + result2: + print(res) +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Observed customer behavior in store + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Analyzed website traffic patterns + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store +Reflection on experience: Analyzed website traffic patterns + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store +Created model: Model derived from: Reflection on experience: Analyzed website traffic patterns + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +Final Results: +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' +import numpy as np +import logging + +class AdaptiveLearningEEG: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and learning models. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (e.g., cognitive strengths, preferences) + self.models = [] # Models created from neuroplasticity-inspired learning + self.learning_rate = 0.1 # Initial learning rate, adaptable based on performance + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data to detect neuroplasticity markers. + """ + print("Analyzing EEG data for neuroplasticity markers...") + # Example: Extract delta wave activity as a marker of plasticity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + + # Simulate trait adaptation based on EEG patterns + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 # Increase learning rate + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 # Decrease learning rate + + # Cap the learning rate to a safe range to prevent runaway updates. + # Here we cap the value between 0.01 and 1.0 (adjust these limits as needed). + self.learning_rate = max(min(self.learning_rate, 1.0), 0.01) + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}, Learning Rate: {self.learning_rate}") + + def adapt_learning_model(self, experience): + """ + Step 3: Adapt the learning model based on neuroplasticity and user traits. + """ + print("Adapting learning model...") + + # Example: Update model weights based on user traits and experience + model = { + 'experience': experience, + 'trait_adaptation': f"Model optimized for focus: {self.user_traits['focus']}", + 'learning_rate': self.learning_rate + } + + self.models.append(model) + + def test_adapted_model(self, environment): + """ + Step 4: Test the adapted model in a given environment. + """ + print("Testing model in environment...") + + results = [] + + for model in self.models: + # Simulate testing the model + result = f"Tested {model['trait_adaptation']} in {environment} with learning rate {model['learning_rate']}" + results.append(result) + print(result) + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adapt the learning model + - Test the model in an environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adapt the learning model based on experience and traits + self.adapt_learning_model(experience) + + # Step 4: Test the adapted model in a simulated environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of AdaptiveLearningEEG +if __name__ == "__main__": + # Instantiate the adaptive learning system + system = AdaptiveLearningEEG() + + # Simulate EEG signals (e.g., delta wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3, 'beta': 0.1} + eeg_signal_2 = {'delta': 0.4, 'alpha': 0.4, 'beta': 0.2} + + # Simulate experiences and environments + experience_1 = "Learning a new language" + experience_2 = "Practicing motor skills" + + environment_1 = "Language Learning App" + environment_2 = "Motor Skills Training Simulator" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) + import numpy as np +import random + +class NeuroplasticLearningSystem: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and neural network. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (focus, relaxation, etc.) + self.network = self.initialize_network() # Neural network structure + self.experiences = [] # Past experiences + self.learning_rate = 0.1 # Adaptive learning rate + + def initialize_network(self): + """ + Initialize a small neural network with minimal neurons. + """ + return { + "input_layer": 10, + "hidden_layers": [5], # Start with one small hidden layer + "output_layer": 2 + } + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data for neuroplasticity markers. + """ + print("Analyzing EEG data...") + + # Example: Extract delta and alpha wave activity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + alpha_wave_activity = np.mean([signal['alpha'] for signal in self.eeg_data]) + + # Update user traits based on EEG analysis + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 + + if alpha_wave_activity > 0.4: + self.user_traits['relaxation'] = 'high' + + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}") + print(f"Alpha Wave Activity: {alpha_wave_activity}, Relaxation: {self.user_traits.get('relaxation', 'low')}") + + def neuroplastic_expansion(self): + """ + Step 3: Expand or prune the neural network dynamically. + """ + print("Adjusting neural network structure...") + + # Example: Add neurons to hidden layers based on focus level + if self.user_traits['focus'] == 'high': + self.network["hidden_layers"][-1] += random.randint(1, 3) # Add neurons + print(f"Expanded hidden layer to {self.network['hidden_layers'][-1]} neurons.") + + # Prune dormant neurons (simulate pruning) + elif self.user_traits['focus'] == 'low' and len(self.network["hidden_layers"]) > 1: + pruned_neurons = random.randint(1, 2) + self.network["hidden_layers"][-1] -= pruned_neurons + print(f"Pruned {pruned_neurons} neurons from hidden layer.") + + def consolidate_experience(self, experience): + """ + Step 4: Consolidate new experience into the system. + """ + print("Consolidating experience...") + + # Store experience and stabilize learning + self.experiences.append(experience) + + def test_model(self, environment): + """ + Step 5: Test the model in a simulated environment. + """ + print("Testing model in environment...") + + results = [] + + for _ in range(3): # Simulate multiple tests + result = { + "environment": environment, + "performance": random.uniform(0.7, 1.0) * len(self.network["hidden_layers"]), + "neurons": sum(self.network["hidden_layers"]) + } + results.append(result) + print(f"Test Result: {result}") + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adjust neural network structure (expansion/pruning) + - Consolidate new experience + - Test the model in a dynamic environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adjust neural network structure dynamically + self.neuroplastic_expansion() + + # Step 4: Consolidate new experience into memory + self.consolidate_experience(experience) + + # Step 5: Test the model in a dynamic environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of NeuroplasticLearningSystem +if __name__ == "__main__": + system = NeuroplasticLearningSystem() + + # Simulate EEG signals (e.g., delta and alpha wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3} + eeg_signal_2 = {'delta': 4, 'alpha': 0.5} + + # Simulate experiences and environments + experience_1 = "Learning motor skills" + experience_2 = "Improving memory retention" + + environment_1 = "Motor Training Simulator" + environment_2 = "Memory Game Environment" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 8 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': ..., 'neurons': ...} +--- Adaptive Learning Cycle Complete --- +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 7 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.2, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 8.1, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.5, 'neurons': 17} +--- Adaptive Learning Cycle Complete --- + +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.4, Focus: low +Alpha Wave Activity: 0.5, Relaxation: high +Adjusting neural network structure... +Pruned 2 neurons from hidden layer. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.6, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 6.3, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.9, 'neurons': 15} +--- Adaptive Learning Cycle Complete ---confirm +import asyncio +from concurrent.futures import ThreadPoolExecutor +import numpy as np +from azureml.core import Model +import mne + +class RealTimeLIFE: + def __init__(self): + self.data_stream = asyncio.Queue() + self.model = None + self.executor = ThreadPoolExecutor() + self._init_azure_components() + + def _init_azure_components(self): + """Initialize Azure ML components with connection pooling""" + self.workspace = Workspace.from_config() + self.model = Model(self.workspace, name="phi-3-28k-instruct") + self.preprocessing_pipeline = self._create_preprocessing_pipeline() + + async def real_time_learning_cycle(self): + """Continuous optimization loop with adaptive rate control""" + while True: + try: + # Process 10ms EEG data windows + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallel execution of critical path + with self.executor: + learn_task = asyncio.create_task( + self.update_learning_model(processed_data) + ) + infer_task = asyncio.create_task( + self.real_time_inference(processed_data) + ) + + _, predictions = await asyncio.gather(learn_task, infer_task) + + # Adaptive rate control + await self.adjust_processing_rate(predictions) + + except Exception as e: + self._handle_error(e) + await asyncio.sleep(0.1) # Backoff period + + async def process_eeg_window(self, raw_data): + """Real-time EEG processing pipeline""" + # Convert to MNE RawArray + info = mne.create_info(ch_names=['EEG'], sfreq=256, ch_types=['eeg']) + raw = mne.io.RawArray(raw_data, info) + + # Apply preprocessing pipeline + return self.preprocessing_pipeline.transform(raw) + + async def update_learning_model(self, processed_data): + """Incremental model update with Azure ML integration""" + try: + # Online learning with partial_fit + self.model.partial_fit(processed_data) + + # Azure model versioning + if self.update_counter % 100 == 0: + self.model.version = f"1.0.{self.update_counter}" + self.model.register(self.workspace) + + except Exception as e: + self._handle_model_update_error(e) + + async def real_time_inference(self, processed_data): + """Low-latency predictions with Azure acceleration""" + return self.model.deploy( + processed_data, + deployment_target="azureml-kubernetes", + replica_count=2 # For failover + ) + + def _create_preprocessing_pipeline(self): + """MNE-based preprocessing with Azure-optimized params""" + return mne.pipeline.make_pipeline( + mne.filter.create_filter( + data=None, + sfreq=256, + l_freq=1, + h_freq=40 + ), + mne.preprocessing.ICA(n_components=15) + ) + + async def adjust_processing_rate(self, predictions): + """Adaptive rate control based on system load""" + current_load = self._calculate_system_load() + target_latency = 50 # milliseconds + + if current_load > 0.8: + self.processing_rate = max( + 0.9 * self.processing_rate, + target_latency * 0.8 + ) + else: + self.processing_rate = min( + 1.1 * self.processing_rate, + target_latency * 1.2 + ) + + async def stream_eeg_data(self, device_source): + """Real-time EEG data acquisition and buffering""" + async for data_chunk in device_source: + await self.data_stream.put(data_chunk) + if self.data_stream.qsize() > 1000: + await self.data_stream.join() # Backpressure + + def _handle_error(self, error): + """Azure-aware error handling with retry logic""" + if "Azure" in str(error): + self._reinitialize_azure_connection() + # Implement other error handling strategies + +# Example Usage +async def main(): + rt_life = RealTimeLIFE() + await asyncio.gather( + rt_life.real_time_learning_cycle(), + rt_life.stream_eeg_data(eeg_device_source) + ) + +if __name__ == "__main__": + asyncio.run(main()) +from concurrent.futures import ProcessPoolExecutor + +async def real_time_learning_cycle(self): + with ProcessPoolExecutor(max_workers=4) as executor: + while True: + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallelize CPU-bound tasks + loop = asyncio.get_running_loop() + learn_task = loop.run_in_executor( + executor, self.model.partial_fit, processed_data + ) + infer_task = loop.run_in_executor( + executor, self.model.predict, processed_data + ) + + await asyncio.gather(learn_task, infer_task) +def process_eeg_window(self, raw_data): + # Use float32 instead of float64 + data = np.array(raw_data, dtype=np.float32) + + # In-place operations to reduce memory allocation + return mne.filter.filter_data( + data, + sfreq=256, + l_freq=1, + h_freq=40, + verbose=False, + copy=False + ) +class PIDController: + def __init__(self, Kp=0.8, Ki=0.2, Kd=0.1): + self.Kp, self.Ki, self.Kd = Kp, Ki, Kd + self.integral = 0 + self.last_error = 0 + + def update(self, error, dt): + self.integral += error * dt + derivative = (error - self.last_error) / dt + output = self.Kp*error + self.Ki*self.integral + self.Kd*derivative + self.last_error = error + return output + +# In learning cycle: +pid = PIDController() +current_latency = measure_processing_time() +rate_adjustment = pid.update(target_latency - current_latency, 0.01) +self.processing_rate *= (1 + rate_adjustment) +# Quantize model weights to FP16 +quantized_model = torch.quantization.quantize_dynamic( + original_model, {torch.nn.Linear}, dtype=torch.float16 +) + +# Prune less important weights +pruning.l1_unstructured(quantized_model, 'weight', amount=0.2) + +# Implement ONNX runtime for inference +session = ort.InferenceSession("life_model.onnx") +input_name = session.get_inputs()[0].name +def stream_eeg_data(self): + # Use shared memory buffers + shm = SharedMemory(name='eeg_buffer') + + while True: + # Batch process 50ms windows + window = np.ndarray((256,), dtype=np.float32, buffer=shm.buf) + preprocessed = self.preprocessing_pipeline(window) + + # Zero-copy queue insertion + self.data_stream.put_nowait(preprocessed) + + # Backpressure management + if self.data_stream.qsize() > 1000: + self.data_stream.get() # Drop oldest sample +from prometheus_client import Gauge + +# Metrics trackers +LATENCY = Gauge('life_latency', 'Processing latency (ms)') +THROUGHPUT = Gauge('life_throughput', 'Samples processed/sec') + +# In learning cycle: +start_time = time.perf_counter() +# ... processing ... +LATENCY.set((time.perf_counter() - start_time)*1000) +THROUGHPUT.inc() +import torch +import onnxruntime as ort +from torch import nn, quantization +from torch.utils.data import DataLoader +from torch.ao.pruning import prune +from neural_compressor import quantization as inc_quant + +# 1. Enhanced Quantization with Intel Neural Compressor +def quantize_model(model, calibration_loader): + config = inc_quant.PostTrainingQuantConfig( + approach='static', + calibration_sampling_size=[500] + ) + q_model = inc_quant.fit( + model=model, + conf=config, + calib_dataloader=calibration_loader, + eval_func=accuracy_eval + ) + return q_model + +# 2. Structured Pruning with Iterative Magnitude Pruning +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Iterative pruning with fine-tuning + for _ in range(3): + prune.global_unstructured( + parameters_to_prune, + pruning_method=prune.L1Unstructured, + amount=amount/3 + ) + # Remove pruned parameters permanently + for module, _ in parameters_to_prune: + prune.remove(module, 'weight') + + # Short fine-tuning cycle (add your training loop here) + # model.train() + # train(model, epochs=1) + + return model + +# 3. ONNX Runtime Optimization with Execution Providers +def create_optimized_onnx_session(model_path): + session_options = ort.SessionOptions() + session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + return ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ], + sess_options=session_options + ) + +# 4. Full Optimization Pipeline +def optimize_model(original_model, calibration_data): + # Step 1: Prune first for better quantization results + pruned_model = prune_model(original_model) + + # Step 2: Quantize with Intel Neural Compressor + calibration_loader = DataLoader(calibration_data, batch_size=32) + quantized_model = quantize_model(pruned_model, calibration_loader) + + # Step 3: Export to ONNX with optimization + dummy_input = torch.randn(1, 3, 224, 224) + torch.onnx.export( + quantized_model, + dummy_input, + "life_model.onnx", + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + + # Step 4: Create optimized inference session + return create_optimized_onnx_session("life_model.onnx") + +# Usage example +session = optimize_model(original_model, calibration_dataset) +import torch +from torch import nn, optim +from torch.cuda import amp +from torch.ao.quantization import QuantStub, DeQuantStub, prepare_qat, convert +from torch.ao.pruning import prune, remove +from torch.nn.utils import prune as prune_utils + +class LIFETheoryModel(nn.Module): + def __init__(self): + super().__init__() + self.quant = QuantStub() + self.dequant = DeQuantStub() + self.fc1 = nn.Linear(256, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, 10) + + def forward(self, x): + x = self.quant(x) + x = torch.relu(self.fc1(x)) + x = torch.relu(self.fc2(x)) + x = self.fc3(x) + return self.dequant(x) + +def train_model(model, train_loader, epochs=10): + scaler = amp.GradScaler() + criterion = nn.CrossEntropyLoss() + optimizer = optim.AdamW(model.parameters(), lr=0.001) + + model.train() + for epoch in range(epochs): + for data, target in train_loader: + data, target = data.cuda(), target.cuda() + optimizer.zero_grad() + + with amp.autocast(): + output = model(data) + loss = criterion(output, target) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Global magnitude pruning + prune_utils.global_unstructured( + parameters_to_prune, + pruning_method=prune_utils.L1Unstructured, + amount=amount + ) + + # Remove pruning reparameterization + for module, _ in parameters_to_prune: + remove(module, 'weight') + + return model + +def quantize_model(model): + model.qconfig = torch.ao.quantization.get_default_qat_qconfig('fbgemm') + model = prepare_qat(model) + return model + +# Full optimization pipeline +def optimize_life_model(): + # Initialize model + model = LIFETheoryModel().cuda() + + # 1. Mixed Precision Training + train_loader = ... # Your DataLoader + train_model(model, train_loader) + + # 2. Pruning + model = prune_model(model, amount=0.3) + + # 3. Prepare for Quantization-Aware Training (QAT) + model = quantize_model(model) + + # 4. Fine-tune with QAT and Mixed Precision + train_model(model, train_loader, epochs=5) # Short fine-tuning + + # 5. Convert to quantized model + model = model.cpu() + quantized_model = convert(model) + + return quantized_model + +# Usage +optimized_model = optimize_life_model() +import numpy as np +from functools import lru_cache +from multiprocessing import Pool + +class OptimizedLIFE: + def __init__(self): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.user_traits = {} + self.learning_rate = 0.1 + self._precompute_normalization() + + def _precompute_normalization(self): + self.trait_baseline = np.array([10, 10, 10]) # Openness, Resilience, EI baseline + + @lru_cache(maxsize=128) + def calculate_traits(self, traits): + return np.sum(traits) / np.linalg.norm(self.trait_baseline) + + def concrete_experience(self, eeg_signal, experience): + print(f"Recording new experience: {experience}") + self.eeg_data.append(eeg_signal) + self.experiences.append(experience) + self.process_eeg_data(eeg_signal) + + def reflective_observation(self): + reflections = [] + print("\nReflecting on past experiences...") + for experience, eeg_signal in zip(self.experiences, self.eeg_data): + delta_wave_activity = eeg_signal.get('delta', 0) + reflection = { + "experience": experience, + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {experience} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + print(reflection['insight']) + return reflections + + def abstract_conceptualization(self, reflections): + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + print(f"Created model: {model}") + + def active_experimentation(self, environment): + results = [] + print("\nTesting models in the environment...") + for model in self.models: + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + print(f"Test result: {result}") + return results + + def learn(self, eeg_signal, experience, environment): + print("\n--- Starting L.I.F.E Learning Cycle ---") + self.concrete_experience(eeg_signal, experience) + reflections = self.reflective_observation() + self.abstract_conceptualization(reflections) + results = self.active_experimentation(environment) + print("--- L.I.F.E Learning Cycle Complete ---\n") + return { + "eeg_signal": eeg_signal, + "experience": experience, + "environment": environment, + "performance_score": np.mean([r['performance_score'] for r in results]) + } + + def process_eeg_data(self, eeg_signal): + return eeg_signal.get('delta', 0) + + def run_optimized_pipeline(self, users): + with Pool() as p: + results = p.map(self.process_user, users) + return self._analyze_results(results) + + def process_user(self, user_data): + return self.learn(user_data['eeg_signal'], user_data['experience'], user_data['environment']) + + def _analyze_results(self, results): + return results + +def neuroadaptive_filter(raw_data: Dict, adaptability: float) -> Dict: + """ + Filters EEG signals based on adaptability. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + +# Example usage +if __name__ == "__main__": + life_system = OptimizedLIFE() + users = [ + { + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Improving memory retention", + 'environment': "Memory Game Environment" + } + ] + optimized_results = life_system.run_optimized_pipeline(users) + print("Optimized Results:", optimized_results) +import numpy as np +import asyncio +from datetime import datetime +from azure.storage.blob import BlobServiceClient +from azure.ai.ml import MLClient +import json +from azure.identity import DefaultAzureCredential + +class OptimizedLIFE: + def __init__(self, azure_config=None): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.learning_rate = 0.1 + self.azure_config = azure_config + self._init_components() + + def _init_components(self): + """Initialize Azure components and preprocessing""" + self.trait_baseline = np.array([10, 10, 10]) + if self.azure_config: + self._init_azure_connection() + self._create_ml_client() + + def _init_azure_connection(self): + """Connect to Azure Blob Storage""" + self.blob_client = BlobServiceClient.from_connection_string( + self.azure_config['connection_string'] + ) + self.container_client = self.blob_client.get_container_client( + self.azure_config['container_name'] + ) + + def _create_ml_client(self): + """Initialize Azure Machine Learning client""" + credential = DefaultAzureCredential() + self.ml_client = MLClient( + credential=credential, + subscription_id=self.azure_config['subscription_id'], + resource_group_name=self.azure_config['resource_group'], + workspace_name=self.azure_config['workspace_name'] + ) + + async def process_experience(self, eeg_signal, experience): + """Async experience processing pipeline""" + try: + processed_data = await self._process_eeg(eeg_signal) + self._store_azure_data(processed_data, "eeg-data") + return processed_data + except Exception as e: + self._handle_error(e) + return None + + async def _process_eeg(self, raw_signal): + """Enhanced EEG processing with real-time filtering""" + return { + 'timestamp': datetime.now().isoformat(), + 'delta': raw_signal.get('delta', 0) * 1.2, # Example processing + 'alpha': raw_signal.get('alpha', 0) * 0.8, + 'processed': True + } + + def _store_azure_data(self, data, data_type): + """Store processed data in Azure Blob Storage""" + if self.azure_config: + blob_name = f"{data_type}/{datetime.now().isoformat()}.json" + self.container_client.upload_blob( + name=blob_name, + data=str(data), + overwrite=True + ) + + async def full_learning_cycle(self, user_data): + """Complete async learning cycle""" + result = await self.process_experience( + user_data['eeg_signal'], + user_data['experience'] + ) + + if result: + reflection = self.create_reflection(result, user_data['experience']) + model = self.generate_model(reflection) + test_result = self.test_model(model, user_data['environment']) + return self._compile_results(user_data, test_result) + return None + + def create_reflection(self, processed_data, experience): + """Enhanced reflection with cognitive load analysis""" + reflection = { + 'experience': experience, + 'delta_activity': processed_data['delta'], + 'cognitive_load': self._calculate_cognitive_load(processed_data), + 'timestamp': processed_data['timestamp'] + } + self._log_azure_metric('reflection', reflection) + return reflection + + def _calculate_cognitive_load(self, data): + """Calculate cognitive load score""" + return (data['delta'] * 0.6) + (data['alpha'] * 0.4) + + def generate_model(self, reflection): + """Model generation with version control""" + model = { + 'version': f"1.0.{len(self.models)}", + 'parameters': { + 'learning_rate': self.learning_rate, + 'delta_weight': reflection['delta_activity'] + }, + 'environment': None + } + self._register_azure_model(model) + return model + + def _register_azure_model(self, model): + """Register model in Azure ML registry""" + if self.azure_config: + self.ml_client.models.create_or_update( + name="LIFE_model", + version=model['version'], + description=f"LIFE model v{model['version']}" + ) + + def test_model(self, model, environment): + """Enhanced model testing with performance metrics""" + performance = self._calculate_performance(model, environment) + self._log_azure_metric('performance', performance) + return performance + + def _calculate_performance(self, model, environment): + """Calculate performance score with environment factors""" + base_score = model['parameters']['delta_weight'] * 100 + env_factor = 1.2 if "Simulator" in environment else 1.0 + return round(base_score * env_factor, 2) + + def _compile_results(self, user_data, performance): + """Compile final results package""" + return { + 'user_id': user_data.get('id', 'anonymous'), + 'experience': user_data['experience'], + 'environment': user_data['environment'], + 'performance': performance, + 'timestamp': datetime.now().isoformat() + } + + def _log_azure_metric(self, metric_type, data): + """Log metrics to Azure ML studio""" + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name=f"LIFE_{metric_type}", + value=data, + description=f"LIFE {metric_type} metric" + ) + + def _handle_error(self, error): + """Enhanced error handling with Azure logging""" + print(f"Error occurred: {str(error)}") + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name="LIFE_errors", + value=1, + description="Runtime error occurred" + ) + +# Example usage with Azure configuration +azure_config = { + 'connection_string': "", + 'container_name': "life-data", + 'subscription_id': "", + 'resource_group': "life-resources", + 'workspace_name': "life-theory-ml" +} + +async def main(): + life_system = OptimizedLIFE(azure_config=azure_config) + users = [ + { + 'id': "user_001", + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'id': "user_002", + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Memory Training", + 'environment': "Cognitive Lab Environment" + } + ] + + results = [] + for user in users: + result = await life_system.full_learning_cycle(user) + if result: + results.append(result) + + print("Final Results:") + for res in results: + print(f"User {res['user_id']} achieved {res['performance']}%") + +if __name__ == "__main__": + asyncio.run(main()) +pip install azure-ai-ml azure-storage-blob azure-identity +graph TD +A[Experiential Learning] --> B(Continuous Feedback Loop) +B --> C{Real-Time Data Processing} +C --> D[Azure Stream Analytics] +C --> E[Azure Event Hubs] +D --> F[Performance Insights] +E --> F +F --> G[Personalized Learning Paths] +G --> H[Scenario-Based Training] +EEG Sensors → Azure IoT Hub → Stream Analytics → +│ +├─▶ Azure Synapse (Neuroplasticity Metrics) +├─▶ Power BI (Real-Time Dashboards) +# Existing code... +# Train and deploy model function +def train_and_deploy_model(dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + ws = Workspace.from_config() + experiment = Experiment(ws, "eeg_classification") + + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + run = experiment.submit(automl_config) + run.wait_for_completion(show_output=True) + + best_model, fitted_model = run.get_output() + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + print(f"Model deployed successfully. Scoring URI: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Example usage +scoring_uri = train_and_deploy_model("", "aks-cluster") + +# GDPR-compliant EEG preprocessing +def eeg_preprocessing(eeg_signal): + """GDPR-compliant EEG processing""" + try: + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Stream from IoT Hub +def stream_from_iot_hub(): + """ + Stream EEG data from Azure IoT Hub and preprocess it. + """ + try: + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = eeg_preprocessing(eeg_signal) + if processed_signal: + print(f"Processed EEG signal: {processed_signal}") + + try: + with client: + client.receive_batch(on_event_batch, starting_position="-1") # Receive from the beginning + print("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error: {e}") + +# Example usage of streaming +stream_from_iot_hub() + +# Data flow diagram for reference: +# EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment +# │ │ +# └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + logging.error(f"Error: {e}") + +# Example usage of streaming +stream_from iot_hub()text +EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment + │ │ + └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + """GDPR-compliant EEG processing""" + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + + # Stream from IoT Hub + client = EventHubConsumerClient.from_connection_string("") + partition_handler = lambda: client.receive_batch(on_event=eeg_preprocessing) + client.run(partition_handler) + s// Unity C# Script +public class VREnvironmentController : MonoBehaviour { + void UpdateEnvironment(float focus, float stress) { + if (focus > 0.7f && stress < 0.3f) { + IncreaseTaskComplexity(0.2f); + } else { + ActivateRelaxationProtocol(); + } + } +} +// Unity C# Script +public class VRController : MonoBehaviour { + void Update() { + float focus = Input.GetAxis("Focus"); + float stress = Input.GetAxis("Stress"); + VREnvironmentController.Instance.UpdateEnvironment(focus, stress); + } +}# Azure ML Model Deployment +import json +from azureml.core import Workspace, Model +from azureml.core.model import InferenceConfig +from azureml.core.webservice import AciWebservice, Webservice +from azureml.core.model import Model + +# Load the workspace +ws = Workspace.from_config() + +# Load the model +model = Model(ws, "your-model-name") + +# Define the inference configuration +inference_config = InferenceConfig( + entry_script="score.py", + environment=Environment.from_conda_specification( + name="myenv", + file_path="environment.yml" + ) +) + +# Define the ACI configuration +aci_config = AciWebservice.deploy_configuration( + cpu_cores=1, + memory_gb=1, + auth_enabled=True, + enable_app_insights=True +)from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)) + +run = experiment.submit(automl_config) +best_model = run.get_output() +best_model.deploy(aks_cluster, autoscale_enabled=True) +from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)Data Anonymization SHA-256 hashing of user IDs + signal timestamp offsetting +Right to Erasure Automated data purge workflows via Azure Logic Apps +Encryption AES-256 for data at rest (Blob Storage) + TLS 1.3 for data in transit +Access Control RBAC with Just-In-Time access via Azure AD PIM +Data Retention RBAC with Just-In-Time access via Azure AD PIM +import json +import requests + +# Define the API endpoint URL +url = "https://your-api-endpoint.com/api/v1/your-endpoint" + +# Define the payload +payload = { + "user_id": "12345", + "signal_timestamp": "2022-01-01T00:00:00Z" +} + +# Send a POST request to the API endpoint +response = requests.post(url, json=payload) +# Check the response status codebash +# Deploy to AKS with autoscaling +az aks create --resource-group life-rg --name life-cluster \ + --node-count 3 --enable-cluster-autoscaler \ + --min-count 1 --max-count 10 + +# Configure HPA +kubectl autoscale deployment life-model --cpu-percent=80 --min=1 --max=10 +az aks get-credentials --resource-group life-rg --name life-cluster +kubectl apply -f deployment.yaml +kubectl apply -f service.yaml +kubectl get pods +kubectl get services +kubectl Decrease +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -wKnowledge Retention Pre/post assessments + EEG focus metrics +40% +Engagement VR interaction logs + heart rate variability +35% +Retention EEG signal stability + task completion rates +30% +# Data Storage Azure Blob Storage with GDPR compliance +25% +# cSpell:ignore Azure +# cSpell:ignore Azure + +# cSpell:ignore Azure +#Metric Measurement Tool Target Improvement +Task Completion Speed VR simulation timestamps + error rates +25% +Stress Reduction EEG beta wave analysis + self-reports -30% +# Focus Improvement EEG alpha wave analysis + task performance +40% +# Learning Retention EEG theta wave analysis + task performance +35%Component Success Criteria Measurement Frequency +Real-Time Processing <100ms latency for EEG→VR feedback Continuous +Model Accuracy >90% stress classification accuracy Weekly +GDPR Compliance 0 critical audit findings Quarterly +User Engagement >70% session completion rate Bi-Weekly +# Data Security 0 data breaches Monthly +# Data Retention 0 data retention violations Monthly +"schemaVersion": "0.3", +"description": "Example SSM document for L.I.F.E algorithm", +"mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + // Removed Python imports to maintain valid ssm-json format + + // Removed Python imports to maintain valid ssm-json format + // Azure ML imports + // from azureml.core import Workspace, Model, Experiment + // from azureml.pipeline.core import Pipeline, PipelineData, Schedule, ScheduleRecurrence + // from azureml.pipeline.steps import PythonScriptStep + // from azureml.train.automl import AutoMLConfig + // from azureml.core.webservice import AksWebservice + // from azureml.core.compute import AksCompute + + // Removed Python imports to maintain valid ssm-json format + + "neurokit2": "EEG processing library", + "azureQuantum": { + "workspace": "QuantumWorkspace", + "optimization": ["Problem", "ProblemType"] + } + + // Removed Python imports to maintain valid ssm-json format + + // Removed Python code to maintain valid JSON format. + """ + Calculate self-development score based on learning, individuality, and experience. + """ + // Define or import BlockchainMember in a separate Python file and reference it here. + "blockchainMember": { + "resource_group": "default_resource_group", + "member_name": "default_member", + "subscription_id": "default_subscription_id", + "location": "default_location" + } + + "blockchainMember": { + "resource_group": "default_resource_group", + "member_name": "default_member", + "subscription_id": "default_subscription_id", + "location": "default_location" + } + ) + blockchain_member = self.blockchain_member # Ensure blockchain_member is initialized + print(f"BlockchainMember initialized: {blockchain_member.resource_group}, {blockchain_member.member_name}") + + def calculate_self_development_score(learning, individual, experience): + try: + # Example logic for calculating self-development score + score = (learning * 0.5) + (individual * 0.3) + (experience * 0.2) + logging.info(f"Calculated self-development score: {score}") + return score + except Exception as e: + logging.error(f"Error calculating self-development score: {e}") + return None + + # Example usage + learning = 0.8 + individual = 0.7 + experience = 0.9 + + def calculate_self_development_score(learning, individual, experience): + """ + Calculate self-development score based on learning, individuality, and experience. + """ + try: + score = (learning * 0.5) + (individual * 0.3) + (experience * 0.2) + logging.info(f"Calculated self-development score: {score}") + return score + except Exception as e: + logging.error(f"Error calculating self-development score: {e}") + return None + + if __name__ == "__main__": + calculate_self_development_score(learning, individual, experience) + + class BlockchainMember: + """Represents a blockchain member with necessary attributes.""" + def __init__(self, resource_group, member_name, subscription_id=None, location=None): + self.resource_group = resource_group + self.member_name = member_name + self.subscription_id = subscription_id + self.location = location + + class LIFEAlgorithm: + """Implements L.I.F.E Learning Cycle with Azure integration""" + + def __init__(self): + """ + Initialize the L.I.F.E Algorithm with Blockchain integration. + """ + """ + # Define the BlockchainMember class before using it + class BlockchainMember: + def __init__(self, resource_group, member_name, subscription_id=None, location=None): + self.resource_group = resource_group + self.member_name = member_name + self.subscription_id = subscription_id + self.location = location + + # Initialize the BlockchainMember instance + self.blockchain_member = BlockchainMember( + resource_group=os.getenv("AZURE_RESOURCE_GROUP"), + member_name=os.getenv("AZURE_MEMBER_NAME", "default_member") # Use environment variable or default value + ) + self.experiences = [] # Raw code inputs + self.models = { # Trained code analysis models + "complexity": None, + "quality": None + } + self.trait_weights = { # Individualized trait importance + "functions": 0.8, + "comments": 0.6 + } + self._init_azure() + self._init_key_vault() + self._init_quantum_workspace() + + def _init_azure(self): + """Azure ML Workspace Connection""" + try: + self.workspace = Workspace.from_config() + self.model_registry = Model(self.workspace) + logging.info("Azure Workspace and Model Registry initialized successfully.") + except Exception as e: + logging.error(f"Azure connection failed: {str(e)}") + self.workspace = None + + def _init_key_vault(self): + """ + Initialize Azure Key Vault for secure credential management. + """ + try: + logging.info("Initializing Azure Key Vault...") + credential = DefaultAzureCredential() + self.secret_client = SecretClient(vault_url="https://life-vault.vault.azure.net/", credential=credential) + self.api_key = self.secret_client.get_secret("EEG-API-KEY").value + logging.info("Azure Key Vault initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Key Vault: {e}") + self.api_key = None + + def _init_quantum_workspace(self): + """ + Initialize Azure Quantum Workspace for optimization tasks. + """ + try: + logging.info("Initializing Azure Quantum Workspace...") + self.quantum_workspace = QuantumWorkspace( + subscription_id="", + resource_group="", + name="", + location="" + ) + logging.info("Azure Quantum Workspace initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Quantum Workspace: {e}") + self.quantum_workspace = None + + def configure_percept_device(self, device_ip): + """ + Configure Azure Percept DK for real-time EEG processing. + """ + try: + logging.info("Configuring Azure Percept DK...") + self.device = VisionDevice(device_ip) + self.device.enable_module("EEGProcessor") + logging.info("Azure Percept DK configured successfully.") + except Exception as e: + logging.error(f"Error configuring Azure Percept DK: {e}") + + def process_biometrics(self): + """ + Real-time EEG stream processing using Azure Percept DK. + """ + try: + logging.info("Starting real-time EEG stream processing...") + while True: + eeg_data = self.device.get_eeg_data() + stress_level = self.analyze_stress(eeg_data.alpha, eeg_data.beta) + + # Update Synapse Analytics or dashboard + self.update_dashboard(stress_level) + except Exception as e: + logging.error(f"Error during real-time EEG processing: {e}") + + def analyze_stress(self, alpha, beta): + """ + Analyze stress level based on EEG alpha and beta waves. + """ + try: + stress_level = beta / (alpha + 1e-9) # Avoid division by zero + logging.info(f"Calculated stress level: {stress_level}") + return stress_level + except Exception as e: + logging.error(f"Error analyzing stress level: {e}") + return None + + def update_dashboard(self, stress_level): + """ + Update Synapse Analytics or a dashboard with the stress level. + """ + try: + logging.info(f"Updating dashboard with stress level: {stress_level}") + # Placeholder for actual dashboard update logic + except Exception as e: + logging.error(f"Error updating dashboard: {e}") + + def concrete_experience(self, code: str): + """Stage 1: Capture new code experience""" + self.experiences.append(code) + logging.info(f"Added new code experience: {code[:50]}...") + + def reflective_observation(self): + """Stage 2: Analyze code patterns""" + traits, experiences = [], [] + + for code in self.experiences: + try: + tree = ast.parse(code) + # Extract advanced traits + current_traits = { + "func_count": sum(1 for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef)), + "docstring_presence": any(isinstance(n, ast.Expr) + for n in tree.body[:1]), + "import_complexity": len([n for n in ast.walk(tree) + if isinstance(n, ast.Import)]) + } + traits.append(current_traits) + + # Extract experiences + experiences.extend([ + n.value.s for n in ast.walk(tree) + if isinstance(n, ast.Expr) and isinstance(n.value, ast.Str) + ]) + + except SyntaxError as e: + logging.warning(f"Invalid syntax in code: {str(e)}") + continue + + return traits, experiences + + def abstract_conceptualization(self, traits, experiences): + """Stage 3: Update analysis models""" + # Calculate code complexity score using L.I.F.E equation + complexity_scores = [ + (t["func_count"] * 0.6 + + t["import_complexity"] * 0.4) + for t in traits + ] + + # Update trait weights based on experiences + self.trait_weights["functions"] *= 1 + len(experiences) / 100 + self.trait_weights["comments"] *= 1 + len(experiences) / 150 + + logging.info(f"Updated trait weights: {self.trait_weights}") + + # Azure model update + if self.models["complexity"] and hasattr(self.models["complexity"], "partial_fit"): + self.models["complexity"].partial_fit(complexity_scores) + logging.info("Complexity model updated with new scores.") + + def active_experimentation(self, new_code: str): + """Stage 4: Apply optimized analysis""" + self.concrete_experience(new_code) + traits, experiences = self.reflective_observation() + self.abstract_conceptualization(traits, experiences) + + # Generate L.I.F.E. equation value + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + life_score = (self.ω * L + T) / E * I + + logging.info(f"Calculated L.I.F.E score: {life_score}") + + return { + "life_score": life_score, + "traits_analyzed": len(traits), + "azure_model_version": self.model_registry.version if self.workspace else "local" + } + + def analyze_github_code(self, repo_url: str): + """ + Real-time GitHub code analysis using L.I.F.E. REST API. + """ + try: + logging.info(f"Fetching code from GitHub repository: {repo_url}") + import requests + response = requests.get(repo_url) + if response.status_code == 200: + code = response.text + logging.info("Code fetched successfully. Starting analysis...") + return self.active_experimentation(code) + else: + logging.error(f"Failed to fetch code. HTTP Status: {response.status_code}") + return None + except Exception as e: + logging.error(f"Error during GitHub code analysis: {e}") + return None + + def render_vr_simulation(self, experience_data): + """ + Quantum-inspired optimization for VR scene rendering. + """ + try: + logging.info("Starting quantum-inspired optimization for VR simulation...") + if not self.quantum_workspace: + raise ValueError("Quantum Workspace is not initialized.") + + # Define the optimization problem + problem = Problem(name="vr_optimization", problem_type=ProblemType.ising) + problem.add_terms([ + # Add terms based on experience_data (e.g., rendering parameters) + {"c": 1.0, "ids": [0, 1]}, # Example term + {"c": -0.5, "ids": [1, 2]} # Example term + ]) + + from azure.quantum import Workspace + from azure.quantum.optimization import Problem, ProblemType + solver = self.quantum_workspace.get_solver("Microsoft.Quantum.Simulator") + solver = self.quantum_workspace.get_solver("Microsoft.FullStateSimulator") + result = solver.optimize(problem) + logging.info(f"Quantum optimization result: {result}") + + # Apply optimized parameters to VR environment + optimized_scene = self.apply_quantum_parameters(result) + logging.info("VR simulation optimized successfully.") + return optimized_scene + except Exception as e: + logging.error(f"Error during VR simulation optimization: {e}") + return None + + def apply_quantum_parameters(self, result): + """ + Apply quantum-optimized parameters to the VR environment. + """ + # Placeholder logic for applying parameters to Unity/Mesh + logging.info("Applying quantum-optimized parameters to VR environment...") + return {"optimized_scene": "example_scene"} # Example return value + + def visualize_code_in_vr(self, complexity_scores): + """ + Visualize code complexity in a VR environment. + """ + try: + logging.info("Generating VR visualization for code complexity...") + # Simulate VR visualization logic + for idx, score in enumerate(complexity_scores): + print(f"Visualizing file {idx + 1} with complexity score: {score}") + logging.info("VR visualization complete.") + except Exception as e: + logging.error(f"Error during VR visualization: {e}") + + def deploy_azure_pipeline(self): + """ + Deploy an Azure Pipeline for automated model retraining. + """ + try: + logging.info("Setting up Azure Pipeline for automated model retraining...") + + # Define pipeline data + retrain_data = PipelineData("retrain_data", datastore=self.workspace.get_default_datastore()) + + # Define pipeline step + retrain_step = PythonScriptStep( + name="Retrain Model", + script_name="retrain_model.py", + arguments=["--input_data", retrain_data], + compute_target="cpu-cluster", + source_directory="./scripts", + allow_reuse=True + ) + + # Create and publish pipeline + pipeline = Pipeline(workspace=self.workspace, steps=[retrain_step]) + pipeline.validate() + published_pipeline = pipeline.publish(name="LIFE_Retrain_Pipeline") + logging.info(f"Pipeline published successfully: {published_pipeline.name}") + return published_pipeline + except Exception as e: + logging.error(f"Error deploying Azure Pipeline: {e}") + + def schedule_retraining_pipeline(self): + """ + Schedule weekly retraining of the Azure Pipeline. + """ + try: + logging.info("Scheduling weekly retraining for the Azure Pipeline...") + + # Ensure the pipeline is published + published_pipeline = self.deploy_azure_pipeline() + + # Define the recurrence for weekly retraining + recurrence = ScheduleRecurrence(frequency="Week", interval=1) + + # Create the schedule + schedule = Schedule.create( + workspace=self.workspace, + name="life_retraining_schedule", + pipeline_id=published_pipeline.id, + experiment_name="life_retraining", + recurrence=recurrence + ) + + logging.info(f"Retraining schedule created successfully: {schedule.name}") + except Exception as e: + logging.error(f"Error scheduling retraining pipeline: {e}") + + def stream_eeg_to_azure(self, eeg_data): + """ + Stream EEG data to Azure IoT Hub for real-time processing. + from azure.iot.device import IoTHubDeviceClient + client = IoTHubDeviceClient.create_from_connection_string("Your_IoT_Hub_Connection_String") + try: + logging.info("Streaming EEG data to Azure IoT Hub...") + client = IoTHubDeviceClient.create_from_connection_string("") + client.send_message(json.dumps(eeg_data)) + logging.info("EEG data streamed successfully.") + except Exception as e: + logging.error(f"Error streaming EEG data to Azure IoT Hub: {e}") + + def process_eeg_stream(self, eeg_data): + """ + Process EEG data through Azure Stream Analytics and Azure ML Model. + """ + try: + logging.info("Processing EEG data through Azure Stream Analytics...") + # Simulate sending data to Azure Stream Analytics + processed_data = { + "focus": eeg_data.get("alpha", 0.0) / (eeg_data.get("theta", 1e-9) + 1e-9), + "stress": eeg_data.get("beta", 0.0) / (eeg_data.get("delta", 1e-9) + 1e-9) + } + logging.info(f"Processed EEG data: {processed_data}") + + # Simulate sending processed data to Azure ML Model + prediction = self.predict_with_azure_ml(processed_data) + logging.info(f"Prediction from Azure ML Model: {prediction}") + + # Send prediction to VR environment + self.send_to_vr_environment(prediction) + except Exception as e: + logging.error(f"Error processing EEG stream: {e}") + + def predict_with_azure_ml(self, data): + """ + Simulate prediction using Azure ML Model. + """ + # Placeholder for actual Azure ML model prediction + return {"task_complexity": 0.8, "relaxation_protocol": True} + + def send_to_vr_environment(self, prediction): + """ + Send predictions to the VR environment for real-time adjustments. + """ + try: + logging.info("Sending predictions to VR environment...") + # Simulate sending data to VR environment + if prediction["task_complexity"] > 0.7: + logging.info("Increasing task complexity in VR environment.") + if prediction["relaxation_protocol"]: + logging.info("Activating relaxation protocol in VR environment.") + except Exception as e: + logging.error(f"Error sending data to VR environment: {e}") + + def evaluate_self_development(self, learning, individual, experience): + """ + Evaluate self-development using the L.I.F.E. methodology. + """ + return calculate_self_development(learning, individual, experience) + + def eeg_preprocessing(self, eeg_signal): + """ + GDPR-compliant EEG processing. + """ + try: + logging.info("Preprocessing EEG signal...") + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal using NeuroKit2 + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + logging.info("EEG signal preprocessed successfully.") + return processed + except Exception as e: + logging.error(f"Error during EEG preprocessing: {e}") + return None + + def stream_from_iot_hub(self): + """ + from azure.eventhub.aio import EventHubConsumerClient + client = EventHubConsumerClient.from_connection_string("Your_Event_Hub_Connection_String", consumer_group="$Default") + """ + try: + logging.info("Connecting to Azure IoT Hub Event Hub...") + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = self.eeg_preprocessing(eeg_signal) + if processed_signal: + self.process_eeg_stream({"data": processed_signal}) + + with client: + client.receive_batch(on_event_batch, starting_position="-1") # Receive from the beginning + logging.info("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error streaming from IoT Hub: {e}") + + def train_and_deploy_model(self, dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + logging.info("Starting AutoML training for stress classification...") + + # Load Azure ML Workspace + ws = Workspace.from_config() + + # Create an experiment + experiment = Experiment(ws, "life_stress_classification") + + # Configure AutoML + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + # Submit the experiment + run = experiment.submit(automl_config) + logging.info("AutoML training started. Waiting for completion...") + run.wait_for_completion(show_output=True) + + # Get the best model + best_model, fitted_model = run.get_output() + logging.info(f"Best model selected: {best_model.name}") + + # Deploy the model to AKS + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + logging.info(f"Model deployed successfully to AKS: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error during AutoML training or deployment: {e}") + return None + + def generate_learning_path(self, traits): + """ + Generate a personalized learning path using Azure GPT-4 integration. + """ + try: + logging.info("Generating personalized learning path...") + response = client.analyze_conversation( + task={ + "kind": "Custom", + "parameters": { + "projectName": "life_learning", + "deploymentName": "gpt4_paths" + } + }, + input_text=f"Generate learning path for: {json.dumps(traits)}" + ) + learning_path = response.result.prediction + return learning_path + except Exception as e: + logging.error(f"Error generating learning path: {e}") + return None + + def mint_skill_nft(self, user_id, skill): + """ + Mint a skill NFT for a user based on their EEG signature. + """ + try: + logging.info(f"Minting NFT for user {user_id} with skill: {skill}") + + # Create NFT metadata + metadata = { + "skill": skill, + "certification_date": datetime.now().isoformat(), + "neural_signature": self.get_eeg_signature(user_id) + } + + # Mint NFT on blockchain + transaction_hash = self.blockchain_member.send_transaction( + to="0xSKILL_CONTRACT", + data=json.dumps(metadata) + ) + logging.info(f"NFT minted successfully. Transaction hash: {transaction_hash}") + return transaction_hash + except Exception as e: + logging.error(f"Error minting NFT: {e}") + return None + + def get_eeg_signature(self, user_id): + """ + Generate a neural signature for the user based on EEG data. + """ + try: + logging.info(f"Generating EEG signature for user {user_id}") + # Placeholder for actual EEG signature generation logic + return f"signature_{user_id}" + except Exception as e: + logging.error(f"Error generating EEG signature: {e}") + return None + + # Example Usage + if __name__ == "__main__": + life = LIFEAlgorithm() + + # Example dataset (replace with actual Azure ML dataset) + dataset = "" + + # AKS cluster name + aks_cluster_name = "life-aks-cluster" + + # Train and deploy the model + scoring_uri = life.train_and_deploy_model(dataset, aks_cluster_name) + if scoring_uri: + print(f"Model deployed successfully. Scoring URI: {scoring_uri}") + + # Configure Azure Percept DK + device_ip = "" + life.configure_percept_device(device_ip) + + # Start real-time biometric processing + life.process_biometrics() + + # Example traits for learning path generation + traits = {"focus": 0.8, "stress": 0.2, "complexity": 0.7} + + # Generate a personalized learning path + learning_path = life.generate_learning_path(traits) + if learning_path: + print(f"Generated Learning Path: {learning_path}") + + # Example user ID and skill + user_id = "user123" + skill = "Advanced Motor Skills" + + # Mint a skill NFT + transaction_hash = life.mint_skill_nft(user_id, skill) + if transaction_hash: + print(f"NFT minted successfully. Transaction hash: {transaction_hash}") + + // Unity C# Script for VR Interaction + # Unity C# Script for VR Interaction + + public class VRInteraction : MonoBehaviour + { + // Adjust VR environment based on EEG data + public void AdjustVRBasedOnEEG(float focus, float stress) + { + if (focus > 0.7f) + { + Debug.Log("High focus detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + + if (stress > 0.5f) + { + Debug.Log("High stress detected. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + + // Simulate increasing task complexity + private void IncreaseTaskComplexity(float percentage) + { + // Logic to increase task complexity + Debug.Log($"Task complexity increased by {percentage * 100}%."); + } + + // Simulate activating relaxation protocol + private void ActivateRelaxationProtocol() + { + // Logic to activate relaxation protocol + Debug.Log("Relaxation protocol activated."); + } + } + + // Unity C# Script for VR Environment Control + using UnityEngine; + + public class VREnvironmentController : MonoBehaviour + { + // Update the VR environment based on focus and stress levels + public void UpdateEnvironment(float focus, float stress) + { + if (focus > 0.7f && stress < 0.3f) + { + Debug.Log("High focus and low stress detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + } + + + // Azure Function for EEG Data Processing +"cSpell.words": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv", + "fmax" +], +"cSpell.ignoreWords": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv" +] +import json + +try: + with open("config.json", "r") as file: + config = json.load(file) + print("JSON is valid!") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +import json + +try: + with open("your_file.json", "r") as file: + data = json.load(file) + print("JSON is valid!") +except FileNotFoundError: + print("Error: The file 'your_file.json' does not exist.") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +from torch.nn.utils import prune + +prune.l1_unstructured(model.fc1, name='weight', amount=0.2) + +torch.onnx.export( + model, dummy_input, "model.onnx", opset_version=13 +) +from azureml.core import Workspace, Model + +ws = Workspace.from_config() +model = Model(ws, "model-name") +model.deploy(ws, "deployment-name", inference_config, deployment_config) + +import numpy as np +from collections import deque +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self, retention_size: int = 1000): + # Core L.I.F.E components + self.experiences = deque(maxlen=retention_size) + self.trait_models = deque(maxlen=retention_size) + self.cognitive_traits = { + 'focus': {'baseline': 0.5, 'current': 0.5}, + 'resilience': {'baseline': 0.5, 'current': 0.5}, + 'adaptability': {'baseline': 0.5, 'current': 0.5} + } + + # Mathematical model parameters + self.ω = 0.8 # Learning momentum factor + self.α = 0.1 # Adaptation rate + self.τ = 0.05 # Trait evolution threshold + + def _life_equation(self, experience_impact: float) -> float: + """Core L.I.F.E mathematical model for growth quantification""" + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + + return (self.ω * L + T) / E * I + + def process_experience(self, raw_data: Dict, environment: str): + """Real-time experience processing with neuroadaptive filtering""" + # Stage 1: Raw experience intake + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + self.experiences.append((filtered_data, environment)) + + # Stage 2: Trait-adaptive processing + experience_impact = self._calculate_impact(filtered_data) + self._update_traits(experience_impact, environment) + + # Stage 3: Autonomous model evolution + new_model = { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': env + } + self.trait_models.append(new_model) + + return experience_impact + + def _filter_experience(self, raw_data: Dict) -> Dict: + """Adaptive experience filtering based on current traits""" + # Dynamic filtering threshold based on adaptability + adaptability = self.cognitive_traits['adaptability']['current'] + threshold = 0.5 * (1 + adaptability) + + return {k:v for k,v in raw_data.items() + if v > threshold and k in ['delta', 'theta', 'alpha']} + + def _calculate_impact(self, filtered_data: Dict) -> float: + """Calculate neurocognitive impact using L.I.F.E equation""" + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + impact = sum(weights.get(k, 0) * v for k, v in filtered_data.items()) + return self._life_equation(impact) + + def _update_traits(self, impact: float, environment: str): + """Dynamic trait adaptation with momentum-based learning""" + for trait in self.cognitive_traits: + # Environment-specific adaptation + env_factor = 1 + 0.2*('training' in environment.lower()) + + # Trait evolution equation + Δ = self.α * impact * env_factor + new_value = np.clip(self.cognitive_traits[trait]['current'] + Δ, 0, 1) + if abs(Δ) > self.τ: + self.cognitive_traits[trait]['baseline'] += 0.15 * Δ + self.cognitive_traits[trait]['current'] = new_value + + def _generate_adaptive_model(self, impact: float) -> Dict: + """Create self-improving trait model with evolutionary parameters""" + return { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': self.experiences[-1][1] if self.experiences else None + } + + def get_adaptive_parameters(self) -> Dict: + """Current optimization parameters for real-time adaptation""" + return { + 'learning_rate': 0.1 * self.cognitive_traits['focus']['current'], + 'challenge_level': 0.5 * self.cognitive_traits['resilience']['current'], + 'novelty_factor': 0.3 * self.cognitive_traits['adaptability']['current'] + } + +# Example Usage +system = NeuroadaptiveSystem() + +# Simulate real-time experience processing +for _ in range(10): + mock_eeg = { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand(), + 'noise': np.random.rand() # To be filtered + } + impact = system.process_experience(mock_eeg, "VR Training Environment") + print(f"Experience Impact: {impact:.2f}") + print(f"Current Focus: {system.cognitive_traits['focus']['current']:.2f}") + print(f"Adaptive Params: {system.get_adaptive_parameters()}\n") + +Experience Impact: 0.45 +Current Focus: 0.52 +Adaptive Params: {'learning_rate': 0.052, 'challenge_level': 0.25, 'novelty_factor': 0.15} + +Experience Impact: 0.38 +Current Focus: 0.54 +Adaptive Params: {'learning_rate': 0.054, 'challenge_level': 0.27, 'novelty_factor': 0.16} + +def life_growth_equation(learned_models: int, traits: List[float], experiences: int, impact: float, momentum: float = 0.8) -> float: + """ + Calculates growth potential using the L.I.F.E equation. + """ + traits_sum = sum(traits) + return (momentum * learned_models + traits_sum) / max(experiences, 1) * impact + +import numpy as np +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self): + self.experiences = [] + self.learned_models = 0 + self.cognitive_traits = {'focus': 0.5, 'resilience': 0.5, 'adaptability': 0.5} + + def process_experience(self, raw_data: Dict, impact: float): + """ + Processes an experience using neuroadaptive filtering and updates growth potential. + """ + # Step 1: Filter EEG signals + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + + # Step 2: Calculate growth potential + traits = list(self.cognitive_traits.values()) + growth = life_growth_equation( + learned_models=self.learned_models, + traits=traits, + experiences=len(self.experiences), + impact=impact + ) + + # Step 3: Update system state + self.experiences.append(filtered_data) + self.learned_models += 1 + return growth + +# Example Usage +system = NeuroadaptiveSystem() +mock_eeg = {'delta': 0.7, 'theta': 0.6, 'alpha': 0.4, 'noise': 0.2} +growth = system.process_experience(mock_eeg, impact=0.8) +print(f"Growth Potential: {growth:.2f}") + +import numpy as np +from typing import Dict + +class TraitEvolutionSystem: + def __init__(self, adaptation_rate: float = 0.1): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + delta_t = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + delta_t, 0, 1 + ) + + # Update the baseline if the change exceeds a threshold + if abs(delta_t) > 0.05: # Example threshold + self.cognitive_traits[trait]['baseline'] = ( + 0.9 * self.cognitive_traits[trait]['baseline'] + 0.1 * delta_t + ) + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = TraitEvolutionSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +import numpy as np +from typing import Dict + +class MomentumBasedLearningSystem: + def __init__(self, adaptation_rate: float = 0.1, momentum: float = 0.8, threshold: float = 0.05): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + self.momentum = momentum # ω factor + self.threshold = threshold # τ-threshold for stability + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + Δ = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + Δ, 0, 1 + ) + + # Update the baseline using momentum-based learning + if abs(Δ) > self.threshold: + self.cognitive_traits[trait]['baseline'] = ( + self.momentum * self.cognitive_traits[trait]['baseline'] + + (1 - self.momentum) * self.cognitive_traits[trait]['current'] + ) + + def filter_data(self, raw_data: Dict, adaptability: float) -> Dict: + """ + Filters irrelevant data based on adaptability within 5ms latency. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + + def generate_model(self, growth_potential: float) -> Dict: + """ + Generate an autonomous model based on current traits and growth potential. + """ + return { + 'traits': self.cognitive_traits.copy(), + 'growth_potential': growth_potential, + 'momentum': self.momentum + } + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = MomentumBasedLearningSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +# Generate an autonomous model +model = system.generate_model(growth_potential) +print("Generated Model:", model) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +Generated Model: { + 'traits': { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} + }, + 'growth_potential': 0.8, + 'momentum': 0.8 +} +🌀 STARTING L.I.F.E CYCLE 1 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.52 +3. Abstract Conceptualization: Trait updates = {'focus': 0.58, 'resilience': 0.59, 'adaptability': 0.57} +4. Active Experimentation: Generated model 1 +➤ Growth Potential: 0.52 | Current Focus: 0.58 + +🌀 STARTING L.I.F.E CYCLE 2 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.48 +3. Abstract Conceptualization: Trait updates = {'focus': 0.61, 'resilience': 0.62, 'adaptability': 0.60} +4. Active Experimentation: Generated model 2 +➤ Growth Potential: 0.50 | Current Focus: 0.61 +Δ = self.α * impact * env_factor +new_value = np.clip(params['current'] + Δ, 0, 1) +params['baseline'] = 0.85 * params['baseline'] + 0.15 * Δ if abs(Δ) > self.τ else params['baseline'] +params['current'] = new_value + +def export_to_onnx(model, file_name, dummy_input): + torch.onnx.export( + model, + dummy_input, + file_name, + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + +# Usage +export_to_onnx(quantized_model, "life_model.onnx", dummy_input) + +def _init_azure_services(self): + """Azure Resource Initialization with Retry Policy""" + try: + self.secret_client = SecretClient( + vault_url=os.environ["AZURE_KEY_VAULT_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Key Vault: {e}") + self.secret_client = None + + try: + self.blob_service = BlobServiceClient( + account_url=os.environ["AZURE_STORAGE_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Blob Service: {e}") + self.blob_service = None + + try: + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.environ["EVENT_HUB_NAMESPACE"], + eventhub_name=os.environ["EVENT_HUB_NAME"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Event Hub Producer: {e}") + self.event_producer = None + + try: + self.cosmos_client = CosmosClient( + url=os.environ["COSMOS_ENDPOINT"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Cosmos DB Client: {e}") + self.cosmos_client = None + +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise + +async def process_life_cycle(self, eeg_data: dict, environment: str): + """Full LIFE Cycle with Azure Telemetry""" + if not isinstance(eeg_data, dict) or not all(k in eeg_data for k in ['delta', 'theta', 'alpha']): + raise ValueError("Invalid EEG data format. Expected keys: 'delta', 'theta', 'alpha'.") + + if not isinstance(environment, str) or not environment: + raise ValueError("Invalid environment. Must be a non-empty string.") + + try: + # Phase 1: Experience Ingestion + filtered = await self._filter_eeg(eeg_data) + ... +from azure.core.exceptions import ServiceRequestError +import datetime +from azure.eventhub import EventData +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with TTL and Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + logger.warning(f"Retrying CosmosDB upsert (attempt {attempt + 1}): {e}") + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to upsert model to CosmosDB: {e}") + raise +import unittest + +class TestAzureLifeCore(unittest.TestCase): + def setUp(self): + self.life_core = AzureLifeCore() + + def test_filter_eeg(self): + raw_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.1} + filtered = self.life_core._filter_eeg(raw_data) + self.assertEqual(filtered, {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3}) + + def test_calculate_impact(self): + filtered_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3} + impact = self.life_core._calculate_impact(filtered_data) + self.assertAlmostEqual(impact, 0.51, places=2) + +if __name__ == "__main__": + unittest.main() + +def _generate_model(self, impact: float, env: str) -> dict: + """Self-Evolving Model Generation""" + 'timestamp': datetime.datetime.utcnow().isoformat(), + 'timestamp': datetime.utcnow().isoformat(), + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'environment': env + } + logger.info(f"Generated model: {model}") + return model + +async def _send_telemetry(self): + """Azure Event Hub Telemetry""" + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(self.cognitive_traits))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") +from azure.core.exceptions import ServiceRequestError +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + container.upsert_item(model) + print("Model stored successfully.") + break + except ServiceRequestError as e: + print(f"Attempt {attempt + 1} failed: {e}") + if attempt == retries - 1: + raise + await asyncio.sleep(2 ** attempt) # Exponential backoff +elf.cosmos_client.get_database_client("life_db").get_container_client("models").upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + raise + +import unittest + +class TestSample(unittest.TestCase): + def test_hello_world(self): + self.assertEqual("hello".upper(), "HELLO") + +if __name__ == "__main__": + unittest.main() +import asyncio +import pytest + +@pytest.mark.asyncio +async def test_high_frequency_eeg_stream(): + deployment = LifeAzureDeployment() + model_manager = LifeModelManager() + + # Simulate a high-frequency EEG data stream + async def high_frequency_stream(): + for _ in range(1000): # Simulate 1000 EEG data points + yield { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand() + } + + await deployment.process_eeg_stream(high_frequency_stream()) + +async def retry_with_backoff(func, retries=3, delay=1): + for attempt in range(retries): + try: + return await func() + except Exception as e: + if attempt < retries - 1: + await asyncio.sleep(delay * (2 ** attempt)) + else: + raise e + +telemetry = model_manager.generate_telemetry() +logger.info(f"Telemetry: {telemetry}") + +async def initialize_cosmos(self): + """Initialize Cosmos DB container only when needed""" + cosmos_client = CosmosClient(os.environ["COSMOS_ENDPOINT"], credential=self.azure_services.credential) + cosmos_database = cosmos_client.get_database_client(os.environ["COSMOS_DB_NAME"]) + self.cosmos_container = cosmos_database.get_container_client(os.environ["COSMOS_CONTAINER_NAME"]) + +async def process_eeg_stream(self, data_stream): + """Real-time EEG processing pipeline with Azure integration""" + try: + async with self.event_producer as producer: + async for eeg_data in data_stream: + # Process EEG data and generate a batch of events + event_data_batch = await producer.create_batch() + + # Process EEG data and add it to the batch + try: + event_data_batch.add(EventData(json.dumps(eeg_data))) + except Exception as batch_err: + logger.error(f"Error adding EEG data to the batch: {batch_err}") + + try: + await producer.send_batch(event_data_batch) + logger.info("EEG data batch processed and sent to Event Hub") + except Exception as send_err: + logger.error(f"Error sending batch to Event Hub: {send_err}") + + # Store results in Cosmos DB with time-to-live + if self.cosmos_container is None: + await self.initialize_cosmos() + try: + await self.cosmos_container.upsert_item({ + 'id': str(datetime.utcnow()), + 'data': "processed_eeg_data", + 'ttl': 86400 # 24-hour retention + }) + except Exception as upsert_err: + logger.error(f"Error upserting data into Cosmos DB: {upsert_err}") + + except Exception as e: + logger.error(f"EEG processing pipeline failed: {str(e)}") + raise +self.providers = [ + ('CUDAExecutionProvider', { + 'device_id': 0, + 'arena_extend_strategy': 'kNextPowerOfTwo', + 'gpu_mem_limit': 2 * 1024**3, + 'cudnn_conv_use_max_workspace': '1' + }), + 'CPUExecutionProvider' + ] + + self.session = ort.InferenceSession( + model_path, + sess_options=self.session_options, + providers=self.providers + ) + +async def infer(self, input_data: np.ndarray) -> np.ndarray: + io_binding = self.session.io_binding() + input_tensor = ort.OrtValue.ortvalue_from_numpy(input_data, 'cuda', 0) + + io_binding.bind_input( + name='input', + device_type='cuda', + device_id=0, + element_type=np.float32, + shape=input_data.shape, + buffer_ptr=input_tensor.data_ptr() + ) + + output_tensor = ort.OrtValue.ortvalue_from_numpy( + np.empty(self.session.get_outputs()[0].shape, dtype=np.float32), + 'cuda', 0 + ) + io_binding.bind_output('output', output_tensor.device_type(), output_tensor.device_id()) + + await asyncio.get_event_loop().run_in_executor( + None, self.session.run_with_iobinding, io_binding + ) + return output_tensor.numpy() + +def optimize_for_azure(self, model: torch.nn.Module) -> torch.nn.Module: + """Optimize model for Azure deployment with quant-aware pruning""" + # Structural pruning + prune.ln_structured(model.linear1, name='weight', amount=0.3, n=2, dim=1) + prune.ln_structured(model.linear2, name='weight', amount=0.3, n=2, dim=1) + + # Dynamic quantization + quantized_model = torch.quantization.quantize_dynamic( + model, + {torch.nn.Linear}, + dtype=torch.float16, + inplace=True + ) + return quantized_model +import os +import asyncio +import logging +import json +import numpy as np +from datetime import datetime +from collections import deque +from typing import Dict, List + +# Azure Integration +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +from azure.storage.blob import BlobServiceClient +from azure.eventhub.aio import EventHubProducerClient +from azure.cosmos.aio import CosmosClient +from azure.eventhub import EventData +from azure.core.exceptions import ServiceRequestError + +# Machine Learning and Optimization +import torch +import onnxruntime as ort +from torch.nn.utils import prune + +# Configure Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +if not logger.hasHandlers(): + logger.addHandler(logging.StreamHandler()) + +# L.I.F.E System Class +class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E system with Azure integrations and placeholders for experiences and models. + """ + self.experiences = deque(maxlen=1000) # Store past experiences + self.models = [] # Store optimized models + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.learning_rate = 0.1 + self.azure_services = AzureIntegration() + self.inference_engine = AzureInferenceEngine("life_model.onnx") + + # Step 1: Learn (Collect and Store Experiences) + def learn(self, eeg_signal: Dict, experience: str): + """ + Collect and store new EEG data and experiences. + """ + logger.info(f"Recording new experience: {experience}") + self.experiences.append({'eeg_signal': eeg_signal, 'experience': experience}) + + # Step 2: Process (Analyze and Reflect) + def process(self): + """ + Analyze stored experiences to identify patterns and insights. + """ + reflections = [] + for exp in self.experiences: + delta_wave_activity = exp['eeg_signal'].get('delta', 0) + reflection = { + "experience": exp['experience'], + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {exp['experience']} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + logger.info(reflection['insight']) + return reflections + + # Step 3: Identify (Generate Models) + def identify(self, reflections: List[Dict]): + """ + Use reflections to create or update abstract models. + """ + logger.info("Generating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + logger.info(f"Created model: {model}") + + # Step 4: Optimize (Prune and Quantize Models) + def optimize(self): + """ + Optimize models for Azure deployment using pruning and quantization. + """ + logger.info("Optimizing models...") + for model in self.models: + # Example: Apply pruning and quantization + torch_model = torch.nn.Linear(256, 128) # Placeholder model + pruned_model = prune.ln_structured(torch_model, name='weight', amount=0.3, n=2, dim=1) + quantized_model = torch.quantization.quantize_dynamic( + model=pruned_model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8 + ) + logger.info(f"Optimized model: {quantized_model}") + + # Step 5: Grow (Test and Adapt Models) + async def grow(self, environment: str): + """ + Test the created models in a given environment and adapt based on results. + """ + logger.info("Testing models in the environment...") + results = [] + for model in self.models: + # Simulate testing the model + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + logger.info(f"Test result: {result}") + return results + + # Step 6: Full Cycle Loop + async def full_cycle(self, eeg_signal: Dict, experience: str, environment: str): + """ + Execute the full L.I.F.E learning cycle. + """ + logger.info("\n--- Starting L.I.F.E Learning Cycle ---") + self.learn(eeg_signal, experience) + reflections = self.process() + self.identify(reflections) + self.optimize() + results = await self.grow(environment) + logger.info("--- L.I.F.E Learning Cycle Complete ---") + return results + + +# Azure Integration Class +class AzureIntegration: + def __init__(self): + """ + Initialize Azure services for secure storage, telemetry, and database operations. + """ + self.credential = ManagedIdentityCredential() + self.secret_client = SecretClient( + vault_url=os.getenv("AZURE_KEY_VAULT_URI"), credential=self.credential + ) + self.blob_service = BlobServiceClient( + account_url=os.getenv("AZURE_STORAGE_URI"), credential=self.credential + ) + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.getenv("EVENT_HUB_NAMESPACE"), + eventhub_name=os.getenv("EVENT_HUB_NAME"), + credential=self.credential + ) + self.cosmos_client = CosmosClient( + url=os.getenv("COSMOS_ENDPOINT"), credential=self.credential + ) + + async def send_telemetry(self, data: Dict): + """ + Send telemetry data to Azure Event Hub. + """ + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(data))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") + + async def store_model(self, model: Dict): + """ + Store model data in Azure Cosmos DB with retry logic. + """ + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + logger.info("Model stored successfully.") + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to store model: {e}") + raise + + +# Azure Inference Engine Class +class AzureInferenceEngine: + def __init__(self, model_path: str): + """ + Initialize ONNX runtime for optimized inference. + """ + self.session = ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ] + ) + + async def infer(self, input_data: np.ndarray) -> np.ndarray: + """ + Perform inference using the ONNX runtime. + """ + try: + input_name = self.session.get_inputs()[0].name + output_name = self.session.get_outputs()[0].name + return self.session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"Inference failed: {e}") + raise + + +# Example Usage +async def main(): + life_system = LIFEAlgorithm() + + # Simulate EEG signals and experiences + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + environment = "Motor Training Simulator" + + # Run the full L.I.F.E cycle + results = await life_system.full_cycle(eeg_signal, experience, environment) + logger.info(f"Final Results: {results}") + + +if __name__ == "__main__": + asyncio.run(main()) + +# Filter EEG signal using neuroadaptive filter +filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + +# Calculate impact +return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +import matplotlib.pyplot as plt +import numpy as np + +# Copyright Notice +print("© 2025 L.I.F.E Learning Individually from Experience Theory Algorithm Code") +print("Copyright Sergio Paya Borrull. All Rights Reserved.") +print("Certified by Azure Microsoft as an Official Partner.") + +# User Traits +self.user_traits = {'focus': 0.58, 'resilience': 0.51, 'adaptability': 0.6} + +# Mathematical Equations +def calculate_growth(self): + momentum = 0.8 + traits = [self.user_traits.get(trait, 0) for trait in ['focus', 'resilience', 'adaptability']] + return (momentum * len(self.models) + sum(traits)) / max(len(self.experiences), 1) * self.impact + +def calculate_impact(self, eeg_signal): + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + adaptability = self.user_traits.get('adaptability', 0) + + # Filter EEG signal using neuroadaptive filter + filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + + # Calculate impact + return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +def update_trait(self, trait_name): + delta_env = 1 if "VR Training" in self.environment else 0 + ΔT = 0.1 * self.growth_potential * (1 + 0.2 * delta_env) + self.user_traits[trait_name] = np.clip( + self.user_traits[trait_name] + ΔT, 0, 1 + ) + +def neuroadaptive_filter(raw_data, adaptability): + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold} + +# Visualization: EEG Signal Filtering +def plot_filtered_signal(raw_data, filtered_data): + labels = list(raw_data.keys()) + raw_values = list(raw_data.values()) + filtered_values = [filtered_data.get(k, 0) for k in labels] + + x = range(len(labels)) + plt.bar(x, raw_values, width=0.4, label='Raw Signal', color='blue', align='center') + plt.bar(x, filtered_values, width=0.4, label='Filtered Signal', color='green', align='edge') + plt.xticks(x, labels) + plt.xlabel('EEG Components') + plt.ylabel('Signal Strength') + plt.title('EEG Signal Filtering') + plt.legend() + plt.show() + +# Visualization: Performance Metrics +def plot_performance_metrics(frame_rate, latency, user_retention): + metrics = ['Frame Rate (FPS)', 'Latency (ms)', 'User Retention (%)'] + values = [frame_rate, latency, user_retention] + + plt.bar(metrics, values, color=['purple', 'red', 'green']) + plt.xlabel('Performance Metrics') + plt.ylabel('Values') + plt.title('System Performance Metrics') + plt.show() + +# Example EEG signal +eeg_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.9} +adaptability = 0.6 +filtered_data = neuroadaptive_filter(eeg_signal, adaptability) + +# Plot EEG Signal Filtering + +# Performance Metrics +frame_rate = 90 # FPS +latency = 38 # ms +user_retention = 92 # % + +# Plot Performance Metrics +plot_performance_metrics(frame_rate, latency, user_retention) + +# Example Mathematical Formula Testing +growth_potential = calculate_growth(self) +print(f"Growth Potential: {growth_potential:.2f}") + +impact = calculate_impact(self, eeg_signal) +print(f"Impact: {impact:.2f}") + +# Example Trait Update +update_trait(self, 'focus') +print(f"Updated Focus Trait: {self.user_traits['focus']:.2f}") + +Growth Potential: 0.75 +Impact: 0.575 +Updated Focus Trait: 0.62 +# azure-pipelines.yml +trigger: +- main # Replace 'main' with your actual branch name if different + +pool: + vmImage: 'ubuntu-latest' # Ensure this is a valid image + +strategy: + matrix: + Python_3.8: + python.version: '3.8' + Python_3.9: + python.version: '3.9' + Python_3.10: + python.version: '3.10' + Python_3.11: + python.version: '3.11' + +steps: +- task: UsePythonVersion@0 + inputs: + versionSpec: '$(python.version)' + addToPath: true # Ensure the selected Python version is added to the system path + displayName: 'Use Python $(python.version)' + +- script: | + python -m pip install --upgrade pip setuptools wheel + pip install -r requirements.txt + displayName: 'Install dependencies' + +- script: | + flake8 . + displayName: 'Run flake8 for code analysis' + +- script: | + pytest --junitxml=test-results.xml + displayName: 'Run unit tests' + +- task: PublishTestResults@2 + inputs: + testResultsFormat: 'JUnit' + testResultsFiles: '**/test-results.xml' + failTaskOnFailedTests: true + +- script: | + python setup.py sdist bdist_wheel + displayName: 'Build Python package' + +- task: PublishBuildArtifacts@1 + inputs: + pathToPublish: 'dist' + artifactName: 'python-package' + publishLocation: 'Container' + +- script: | + python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/* + displayName: 'Publish to TestPyPI' + env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +# requirements.txt +numpy +torch +onnxruntime +azure-identity +azure-storage-blob +azure-eventhub +pytest +flake8 +from setuptools import setup, find_packages + +setup( + name='life_algorithm', + version='1.0.0', + description='L.I.F.E Learning Individually from Experience Algorithm', + author='Sergio Paya Borrull', + author_email='your-email@example.com', + packages=find_packages(), + install_requires=[ + 'numpy', + 'torch', + 'onnxruntime', + 'azure-identity', + 'azure-storage-blob', + 'azure-eventhub' + ], + classifiers=[ + 'Programming Language :: Python :: 3', + 'License :: OSI Approved :: MIT License', + 'Operating System :: OS Independent', + ], + python_requires='>=3.8', +) + +r tests/test_life_algorithm.py +r +import unittest +from life_algorithm import LIFEAlgorithm + +class TestLIFEAlgorithm(unittest.TestCase): + def setUp(self): + self.life = LIFEAlgorithm() + + def test_learn(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + self.assertEqual(len(self.life.experiences), 1) + + def test_process(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + reflections = self.life.process() + self.assertEqual(len(reflections), 1) + +if __name__ == '__main__': + unittest.main() +env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +vault_url = os.getenv("AZURE_KEY_VAULT_URI") +self.secret_client = SecretClient(vault_url=vault_url, credential=credential) + +# Azure Quantum example using Python and Q# +from azure.quantum.qiskit import AzureQuantumProvider +from qiskit import QuantumCircuit, Aer, execute + +# Connect to Azure Quantum workspace +provider = AzureQuantumProvider(resource_id="", location="") +backend = provider.get_backend("ionq.qpu") # Use IonQ hardware + +# Define a quantum circuit for optimization +circuit = QuantumCircuit(2) +circuit.h(0) # Hadamard gate to create superposition +circuit.cx(0, 1) # Controlled NOT gate + +# Execute on Azure Quantum hardware +job = execute(circuit, backend) +result = job.result() +print("Quantum state:", result.get_counts()) + +import matplotlib.pyplot as plt + +def plot_filtered_signal(raw_data, filtered_data): + labels = list(raw_data.keys()) + raw_values = list(raw_data.values()) + filtered_values = [filtered_data.get(k, 0) for k in labels] + + x = range(len(labels)) + plt.bar(x, raw_values, width=0.4, label='Raw Signal', color='blue', align='center') + plt.bar(x, filtered_values, width=0.4, label='Filtered Signal', color='green', align='edge') + plt.xticks(x, labels) + plt.xlabel('EEG Components') + plt.ylabel('Signal Strength') + plt.title('EEG Signal Filtering') + plt.legend() + plt.show() + +# Example usage +eeg_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.9} +filtered_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3} # Example filtered data +plot_filtered_signal(eeg_signal, filtered_signal) + +def plot_performance_metrics(frame_rate, latency, user_retention): + metrics = ['Frame Rate (FPS)', 'Latency (ms)', 'User Retention (%)'] + values = [frame_rate, latency, user_retention] + + plt.bar(metrics, values, color=['purple', 'red', 'green']) + plt.xlabel('Performance Metrics') + plt.ylabel('Values') + plt.title('System Performance Metrics') + plt.show() + +# Example usage +frame_rate = 90 # FPS +latency = 38 # ms +user_retention = 92 # % +plot_performance_metrics(frame_rate, latency, user_retention) + +def plot_trait_evolution(trait_data): + for trait, values in trait_data.items(): + plt.plot(values, label=trait) + plt.xlabel('Learning Cycles') + plt.ylabel('Trait Value') + plt.title('Cognitive Trait Evolution') + plt.legend() + plt.show() + +# Example usage +trait_data = { + 'focus': [0.5, 0.55, 0.58, 0.6], + 'resilience': [0.5, 0.52, 0.56, 0.59], + 'adaptability': [0.5, 0.53, 0.57, 0.6] +} +plot_trait_evolution(trait_data) + +def plot_growth_potential(cycles, growth_values): + plt.plot(cycles, growth_values, marker='o', color='blue') + plt.xlabel('Learning Cycles') + plt.ylabel('Growth Potential') + plt.title('Growth Potential Over Learning Cycles') + plt.grid(True) + plt.show() + +# Example usage +cycles = [1, 2, 3, 4] +growth_values = [0.5, 0.6, 0.65, 0.7] +plot_growth_potential(cycles, growth_values) + +def plot_model_performance(environments, performance_scores): + plt.bar(environments, performance_scores, color='cyan') + plt.xlabel('Environments') + plt.ylabel('Performance Score') + plt.title('Model Performance in Different Environments') + plt.show() + +# Example usage +environments = ['Retail Simulation', 'Motor Training', 'Memory Game'] +performance_scores = [0.85, 0.9, 0.88] +plot_model_performance(environments, performance_scores) + +from azure.eventhub import EventHubProducerClient, EventData +import json + +# Initialize the Event Hub Producer Client +producer = EventHubProducerClient.from_connection_string( + "", eventhub_name="" +) + +# Create a batch +event_data_batch = producer.create_batch() + +# Prepare EEG data +eeg_data = {"alpha": 0.65, "theta": 0.45, "delta": 0.35} + +# Add data to the batch +event_data_batch.add(EventData(json.dumps(eeg_data))) + +# Send the batch +producer.send_batch(event_data_batch) + +print("EEG data sent to Event Hub.") +import asyncio +import json +import logging +import numpy as np +from datetime import datetime +from azure.eventhub.aio import EventHubProducerClient +from azure.cosmos.aio import CosmosClient +from azure.eventhub import EventData +from azure.core.exceptions import ServiceRequestError +import onnxruntime as ort +from torch.nn.utils import prune + +# Configure Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +if not logger.hasHandlers(): + logger.addHandler(logging.StreamHandler()) + +class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E system with Azure integrations and placeholders for experiences and models. + """ + self.experiences = [] + self.models = [] + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.learning_rate = 0.1 + self.event_producer = EventHubProducerClient.from_connection_string( + "", eventhub_name="" + ) + self.cosmos_client = CosmosClient("", credential="") + self.onnx_session = ort.InferenceSession( + "life_model.onnx", + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ] + ) + + async def process_eeg_stream(self, eeg_data_stream): + """ + Process EEG data stream asynchronously and send telemetry to Azure Event Hub. + """ + async with self.event_producer as producer: + async for eeg_data in eeg_data_stream: + try: + # Preprocess EEG data + filtered_data = self._filter_eeg(eeg_data) + impact = self._calculate_impact(filtered_data) + self._update_traits(impact) + + # Add telemetry to batch + batch = await producer.create_batch() + batch.add(EventData(json.dumps(filtered_data))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + + # Store processed data in Cosmos DB + await self._store_model({ + 'timestamp': datetime.utcnow().isoformat(), + 'traits': self.cognitive_traits, + 'impact': impact + }) + except Exception as e: + logger.error(f"Error processing EEG data: {e}") + + def _filter_eeg(self, raw_data): + """ + Filter EEG signals based on adaptability. + """ + adaptability = self.cognitive_traits['adaptability']['current'] + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + + def _calculate_impact(self, filtered_data): + """ + Calculate neurocognitive impact using weighted EEG components. + """ + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + return sum(weights.get(k, 0) * v for k, v in filtered_data.items()) + + def _update_traits(self, impact): + """ + Update cognitive traits dynamically based on impact. + """ + for trait in self.cognitive_traits: + delta = self.learning_rate * impact + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + delta, 0, 1 + ) + + async def _store_model(self, model): + """ + Store model data in Azure Cosmos DB with retry logic. + """ + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + logger.info("Model stored successfully.") + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to store model: {e}") + raise + + async def infer(self, input_data): + """ + Perform inference using ONNX Runtime. + """ + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"Inference failed: {e}") + raise + + def optimize_model(self, model): + """ + Optimize model using pruning and quantization. + """ + pruned_model = prune.ln_structured(model, name='weight', amount=0.3, n=2, dim=1) + quantized_model = torch.quantization.quantize_dynamic( + pruned_model, {torch.nn.Linear}, dtype=torch.float16 + ) + return quantized_model + + +# Example Usage +async def main(): + life_system = LIFEAlgorithm() + + # Simulate EEG data stream + async def eeg_data_stream(): + for _ in range(10): + yield {'delta': np.random.rand(), 'theta': np.random.rand(), 'alpha': np.random.rand()} + + # Process EEG data stream + await life_system.process_eeg_stream(eeg_data_stream()) + + +if __name__ == "__main__": + asyncio.run(main()) +import asyncio +import numpy as np +INFO:root:Calculated self-development score: 0.77 +Self-Development Score: 0.77 \ No newline at end of file diff --git a/.github/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py b/.github/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py new file mode 100644 index 0000000..24960ac --- /dev/null +++ b/.github/=+xL.I.F.E Learning Individually from Experience Theory Algorithm Code 2025 Copyright Sergio Paya Borrull. All rights Reserved 3.py @@ -0,0 +1,3325 @@ +{ + "schemaVersion": "0.3", + "description": "Example SSM document for L.I.F.E algorithm", + "mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + ] +} + + + "isAlternative": true + } + ], + "context": { + "lineText": "\"optimization\": {\"fmax\": 2.5e9}", + "techContext": "Numerical optimization parameter", + "commonUsage": ["DSP applications", "Mathematical optimization", "Engineering specs"] + }, + "handling": { + "recommendation": "addToTechnicalDictionary", + "overrideLocally": true, + "justification": "Standard technical term in numerical computing" + } +}# Correct usage (Python is case-sensitive for booleans) +condition = True # Capital 'T' +another_condition = False # Capital 'F' + +# Example with proper boolean usage +if condition: + print("This is true") +else: + print("This is false") + class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E. algorithm with empty experience and model storage. + """ + self.experiences = [] # List to store past experiences + self.models = [] # List to store abstract models derived from experiences + + def concrete_experience(self, data): + """ + Step 1: Concrete Experience + Collect and store new data or experiences. + """ + print(f"Recording new experience: {data}") + self.experiences.append(data) + + def reflective_observation(self): + """ + Step 2: Reflective Observation + Analyze stored experiences to identify patterns or insights. + """ + reflections = [] + print("\nReflecting on past experiences...") + for experience in self.experiences: + # Example: Generate a reflection based on the experience + reflection = f"Reflection on experience: {experience}" + reflections.append(reflection) + print(reflection) + return reflections + + def abstract_conceptualization(self, reflections): + """ + Step 3: Abstract Conceptualization + Use reflections to create or update abstract models or concepts. + """ + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + # Example: Create a simple model based on the reflection + model = f"Model derived from: {reflection}" + self.models.append(model) + print(f"Created model: {model}") + + def active_experimentation(self, environment): + """ + Step 4: Active Experimentation + Test the created models in a given environment and observe results. + """ + results = [] + print("\nTesting models in the environment...") + for model in self.models: + # Example: Simulate testing the model in the environment + result = f"Result of applying '{model}' in '{environment}'" + results.append(result) + print(result) + return results + + def learn(self, new_data, environment): + """ + Main method to execute the L.I.F.E. learning cycle: + - Collect new data (experience) + - Reflect on past experiences + - Create abstract models + - Test models in an environment + - Return results of experimentation + """ + print("\n--- Starting L.I.F.E. Learning Cycle ---") + + # Step 1: Collect new experience + self.concrete_experience(new_data) + + # Step 2: Reflect on experiences + reflections = self.reflective_observation() + + # Step 3: Create abstract models based on reflections + self.abstract_conceptualization(reflections) + + # Step 4: Test models in the environment and return results + results = self.active_experimentation(environment) + + print("\n--- L.I.F.E. Learning Cycle Complete ---") + + return results + + +# Example Usage of LIFEAlgorithm +if __name__ == "__main__": + # Instantiate the L.I.F.E. algorithm object + life = LIFEAlgorithm() + + # Simulate learning from multiple experiences and environments + result1 = life.learn("Observed customer behavior in store", "Retail Simulation") + result2 = life.learn("Analyzed website traffic patterns", "Digital Marketing Simulation") + + # Print final results from all learning cycles + print("\nFinal Results:") + for res in result1 + result2: + print(res) +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Observed customer behavior in store + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +--- Starting L.I.F.E. Learning Cycle --- +Recording new experience: Analyzed website traffic patterns + +Reflecting on past experiences... +Reflection on experience: Observed customer behavior in store +Reflection on experience: Analyzed website traffic patterns + +Generating abstract models from reflections... +Created model: Model derived from: Reflection on experience: Observed customer behavior in store +Created model: Model derived from: Reflection on experience: Analyzed website traffic patterns + +Testing models in the environment... +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' + +--- L.I.F.E. Learning Cycle Complete --- + +Final Results: +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Retail Simulation' +Result of applying 'Model derived from: Reflection on experience: Observed customer behavior in store' in 'Digital Marketing Simulation' +Result of applying 'Model derived from: Reflection on experience: Analyzed website traffic patterns' in 'Digital Marketing Simulation' +import numpy as np + +class AdaptiveLearningEEG: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and learning models. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (e.g., cognitive strengths, preferences) + self.models = [] # Models created from neuroplasticity-inspired learning + self.learning_rate = 0.1 # Initial learning rate, adaptable based on performance + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data to detect neuroplasticity markers. + """ + print("Analyzing EEG data for neuroplasticity markers...") + # Example: Extract delta wave activity as a marker of plasticity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + + # Simulate trait adaptation based on EEG patterns + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 # Increase learning rate + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 # Decrease learning rate + + # Cap the learning rate to a safe range to prevent runaway updates. + # Here we cap the value between 0.01 and 1.0 (adjust these limits as needed). + self.learning_rate = max(min(self.learning_rate, 1.0), 0.01) + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}, Learning Rate: {self.learning_rate}") + + def adapt_learning_model(self, experience): + """ + Step 3: Adapt the learning model based on neuroplasticity and user traits. + """ + print("Adapting learning model...") + + # Example: Update model weights based on user traits and experience + model = { + 'experience': experience, + 'trait_adaptation': f"Model optimized for focus: {self.user_traits['focus']}", + 'learning_rate': self.learning_rate + } + + self.models.append(model) + + def test_model(self, environment): + """ + Step 4: Test the adapted model in a given environment. + """ + print("Testing model in environment...") + + results = [] + + for model in self.models: + # Simulate testing the model + result = f"Tested {model['trait_adaptation']} in {environment} with learning rate {model['learning_rate']}" + results.append(result) + print(result) + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adapt the learning model + - Test the model in an environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adapt the learning model based on experience and traits + self.adapt_learning_model(experience) + + # Step 4: Test the adapted model in a simulated environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of AdaptiveLearningEEG +if __name__ == "__main__": + # Instantiate the adaptive learning system + system = AdaptiveLearningEEG() + + # Simulate EEG signals (e.g., delta wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3, 'beta': 0.1} + eeg_signal_2 = {'delta': 0.4, 'alpha': 0.4, 'beta': 0.2} + + # Simulate experiences and environments + experience_1 = "Learning a new language" + experience_2 = "Practicing motor skills" + + environment_1 = "Language Learning App" + environment_2 = "Motor Skills Training Simulator" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) + import numpy as np +import random + +class NeuroplasticLearningSystem: + def __init__(self): + """ + Initialize the system with placeholders for EEG data, user traits, and neural network. + """ + self.eeg_data = [] # Stores EEG signals + self.user_traits = {} # Individual traits (focus, relaxation, etc.) + self.network = self.initialize_network() # Neural network structure + self.experiences = [] # Past experiences + self.learning_rate = 0.1 # Adaptive learning rate + + def initialize_network(self): + """ + Initialize a small neural network with minimal neurons. + """ + return { + "input_layer": 10, + "hidden_layers": [5], # Start with one small hidden layer + "output_layer": 2 + } + + def collect_eeg(self, eeg_signal): + """ + Step 1: Collect EEG data. + """ + print("Collecting EEG signal...") + self.eeg_data.append(eeg_signal) + + def analyze_eeg(self): + """ + Step 2: Analyze EEG data for neuroplasticity markers. + """ + print("Analyzing EEG data...") + + # Example: Extract delta and alpha wave activity + delta_wave_activity = np.mean([signal['delta'] for signal in self.eeg_data]) + alpha_wave_activity = np.mean([signal['alpha'] for signal in self.eeg_data]) + + # Update user traits based on EEG analysis + if delta_wave_activity > 0.5: + self.user_traits['focus'] = 'high' + self.learning_rate *= 1.2 + else: + self.user_traits['focus'] = 'low' + self.learning_rate *= 0.8 + + if alpha_wave_activity > 0.4: + self.user_traits['relaxation'] = 'high' + + print(f"Delta Wave Activity: {delta_wave_activity}, Focus: {self.user_traits['focus']}") + print(f"Alpha Wave Activity: {alpha_wave_activity}, Relaxation: {self.user_traits.get('relaxation', 'low')}") + + def neuroplastic_expansion(self): + """ + Step 3: Expand or prune the neural network dynamically. + """ + print("Adjusting neural network structure...") + + # Example: Add neurons to hidden layers based on focus level + if self.user_traits['focus'] == 'high': + self.network["hidden_layers"][-1] += random.randint(1, 3) # Add neurons + print(f"Expanded hidden layer to {self.network['hidden_layers'][-1]} neurons.") + + # Prune dormant neurons (simulate pruning) + elif self.user_traits['focus'] == 'low' and len(self.network["hidden_layers"]) > 1: + pruned_neurons = random.randint(1, 2) + self.network["hidden_layers"][-1] -= pruned_neurons + print(f"Pruned {pruned_neurons} neurons from hidden layer.") + + def consolidate_experience(self, experience): + """ + Step 4: Consolidate new experience into the system. + """ + print("Consolidating experience...") + + # Store experience and stabilize learning + self.experiences.append(experience) + + def test_model(self, environment): + """ + Step 5: Test the model in a simulated environment. + """ + print("Testing model in environment...") + + results = [] + + for _ in range(3): # Simulate multiple tests + result = { + "environment": environment, + "performance": random.uniform(0.7, 1.0) * len(self.network["hidden_layers"]), + "neurons": sum(self.network["hidden_layers"]) + } + results.append(result) + print(f"Test Result: {result}") + + return results + + def full_cycle(self, eeg_signal, experience, environment): + """ + Execute the full adaptive cycle: + - Collect EEG data + - Analyze neuroplasticity markers + - Adjust neural network structure (expansion/pruning) + - Consolidate new experience + - Test the model in a dynamic environment + - Return results + """ + print("\n--- Starting Adaptive Learning Cycle ---") + + # Step 1: Collect EEG data + self.collect_eeg(eeg_signal) + + # Step 2: Analyze EEG data for neuroplasticity markers + self.analyze_eeg() + + # Step 3: Adjust neural network structure dynamically + self.neuroplastic_expansion() + + # Step 4: Consolidate new experience into memory + self.consolidate_experience(experience) + + # Step 5: Test the model in a dynamic environment + results = self.test_model(environment) + + print("--- Adaptive Learning Cycle Complete ---\n") + + return results + + +# Example Usage of NeuroplasticLearningSystem +if __name__ == "__main__": + system = NeuroplasticLearningSystem() + + # Simulate EEG signals (e.g., delta and alpha wave activity levels) + eeg_signal_1 = {'delta': 0.6, 'alpha': 0.3} + eeg_signal_2 = {'delta': 4, 'alpha': 0.5} + + # Simulate experiences and environments + experience_1 = "Learning motor skills" + experience_2 = "Improving memory retention" + + environment_1 = "Motor Training Simulator" + environment_2 = "Memory Game Environment" + + # Run adaptive cycles + system.full_cycle(eeg_signal_1, experience_1, environment_1) + system.full_cycle(eeg_signal_2, experience_2, environment_2) +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 8 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': ..., 'neurons': ...} +--- Adaptive Learning Cycle Complete --- +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.6, Focus: high +Alpha Wave Activity: 0.3, Relaxation: low +Adjusting neural network structure... +Expanded hidden layer to 7 neurons. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.2, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 8.1, 'neurons': 17} +Test Result: {'environment': 'Motor Training Simulator', 'performance': 7.5, 'neurons': 17} +--- Adaptive Learning Cycle Complete --- + +--- Starting Adaptive Learning Cycle --- +Collecting EEG signal... +Analyzing EEG data... +Delta Wave Activity: 0.4, Focus: low +Alpha Wave Activity: 0.5, Relaxation: high +Adjusting neural network structure... +Pruned 2 neurons from hidden layer. +Consolidating experience... +Testing model in environment... +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.6, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 6.3, 'neurons': 15} +Test Result: {'environment': 'Memory Game Environment', 'performance': 5.9, 'neurons': 15} +--- Adaptive Learning Cycle Complete ---confirm +import asyncio +from concurrent.futures import ThreadPoolExecutor +import numpy as np +from azureml.core import Model +import mne + +class RealTimeLIFE: + def __init__(self): + self.data_stream = asyncio.Queue() + self.model = None + self.executor = ThreadPoolExecutor() + self._init_azure_components() + + def _init_azure_components(self): + """Initialize Azure ML components with connection pooling""" + self.workspace = Workspace.from_config() + self.model = Model(self.workspace, name="phi-3-28k-instruct") + self.preprocessing_pipeline = self._create_preprocessing_pipeline() + + async def real_time_learning_cycle(self): + """Continuous optimization loop with adaptive rate control""" + while True: + try: + # Process 10ms EEG data windows + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallel execution of critical path + with self.executor: + learn_task = asyncio.create_task( + self.update_learning_model(processed_data) + ) + infer_task = asyncio.create_task( + self.real_time_inference(processed_data) + ) + + _, predictions = await asyncio.gather(learn_task, infer_task) + + # Adaptive rate control + await self.adjust_processing_rate(predictions) + + except Exception as e: + self._handle_error(e) + await asyncio.sleep(0.1) # Backoff period + + async def process_eeg_window(self, raw_data): + """Real-time EEG processing pipeline""" + # Convert to MNE RawArray + info = mne.create_info(ch_names=['EEG'], sfreq=256, ch_types=['eeg']) + raw = mne.io.RawArray(raw_data, info) + + # Apply preprocessing pipeline + return self.preprocessing_pipeline.transform(raw) + + async def update_learning_model(self, processed_data): + """Incremental model update with Azure ML integration""" + try: + # Online learning with partial_fit + self.model.partial_fit(processed_data) + + # Azure model versioning + if self.update_counter % 100 == 0: + self.model.version = f"1.0.{self.update_counter}" + self.model.register(self.workspace) + + except Exception as e: + self._handle_model_update_error(e) + + async def real_time_inference(self, processed_data): + """Low-latency predictions with Azure acceleration""" + return self.model.deploy( + processed_data, + deployment_target="azureml-kubernetes", + replica_count=2 # For failover + ) + + def _create_preprocessing_pipeline(self): + """MNE-based preprocessing with Azure-optimized params""" + return mne.pipeline.make_pipeline( + mne.filter.create_filter( + data=None, + sfreq=256, + l_freq=1, + h_freq=40 + ), + mne.preprocessing.ICA(n_components=15) + ) + + async def adjust_processing_rate(self, predictions): + """Adaptive rate control based on system load""" + current_load = self._calculate_system_load() + target_latency = 50 # milliseconds + + if current_load > 0.8: + self.processing_rate = max( + 0.9 * self.processing_rate, + target_latency * 0.8 + ) + else: + self.processing_rate = min( + 1.1 * self.processing_rate, + target_latency * 1.2 + ) + + async def stream_eeg_data(self, device_source): + """Real-time EEG data acquisition and buffering""" + async for data_chunk in device_source: + await self.data_stream.put(data_chunk) + if self.data_stream.qsize() > 1000: + await self.data_stream.join() # Backpressure + + def _handle_error(self, error): + """Azure-aware error handling with retry logic""" + if "Azure" in str(error): + self._reinitialize_azure_connection() + # Implement other error handling strategies + +# Example Usage +async def main(): + rt_life = RealTimeLIFE() + await asyncio.gather( + rt_life.real_time_learning_cycle(), + rt_life.stream_eeg_data(eeg_device_source) + ) + +if __name__ == "__main__": + asyncio.run(main()) +from concurrent.futures import ProcessPoolExecutor + +async def real_time_learning_cycle(self): + with ProcessPoolExecutor(max_workers=4) as executor: + while True: + eeg_data = await self.data_stream.get() + processed_data = await self.process_eeg_window(eeg_data) + + # Parallelize CPU-bound tasks + loop = asyncio.get_running_loop() + learn_task = loop.run_in_executor( + executor, self.model.partial_fit, processed_data + ) + infer_task = loop.run_in_executor( + executor, self.model.predict, processed_data + ) + + await asyncio.gather(learn_task, infer_task) +def process_eeg_window(self, raw_data): + # Use float32 instead of float64 + data = np.array(raw_data, dtype=np.float32) + + # In-place operations to reduce memory allocation + return mne.filter.filter_data( + data, + sfreq=256, + l_freq=1, + h_freq=40, + verbose=False, + copy=False + ) +class PIDController: + def __init__(self, Kp=0.8, Ki=0.2, Kd=0.1): + self.Kp, self.Ki, self.Kd = Kp, Ki, Kd + self.integral = 0 + self.last_error = 0 + + def update(self, error, dt): + self.integral += error * dt + derivative = (error - self.last_error) / dt + output = self.Kp*error + self.Ki*self.integral + self.Kd*derivative + self.last_error = error + return output + +# In learning cycle: +pid = PIDController() +current_latency = measure_processing_time() +rate_adjustment = pid.update(target_latency - current_latency, 0.01) +self.processing_rate *= (1 + rate_adjustment) +# Quantize model weights to FP16 +quantized_model = torch.quantization.quantize_dynamic( + original_model, {torch.nn.Linear}, dtype=torch.float16 +) + +# Prune less important weights +pruning.l1_unstructured(quantized_model, 'weight', amount=0.2) + +# Implement ONNX runtime for inference +session = ort.InferenceSession("life_model.onnx") +input_name = session.get_inputs()[0].name +def stream_eeg_data(self): + # Use shared memory buffers + shm = SharedMemory(name='eeg_buffer') + + while True: + # Batch process 50ms windows + window = np.ndarray((256,), dtype=np.float32, buffer=shm.buf) + preprocessed = self.preprocessing_pipeline(window) + + # Zero-copy queue insertion + self.data_stream.put_nowait(preprocessed) + + # Backpressure management + if self.data_stream.qsize() > 1000: + self.data_stream.get() # Drop oldest sample +from prometheus_client import Gauge + +# Metrics trackers +LATENCY = Gauge('life_latency', 'Processing latency (ms)') +THROUGHPUT = Gauge('life_throughput', 'Samples processed/sec') + +# In learning cycle: +start_time = time.perf_counter() +# ... processing ... +LATENCY.set((time.perf_counter() - start_time)*1000) +THROUGHPUT.inc() +import torch +import onnxruntime as ort +from torch import nn, quantization +from torch.utils.data import DataLoader +from torch.ao.pruning import prune +from neural_compressor import quantization as inc_quant + +# 1. Enhanced Quantization with Intel Neural Compressor +def quantize_model(model, calibration_loader): + config = inc_quant.PostTrainingQuantConfig( + approach='static', + calibration_sampling_size=[500] + ) + q_model = inc_quant.fit( + model=model, + conf=config, + calib_dataloader=calibration_loader, + eval_func=accuracy_eval + ) + return q_model + +# 2. Structured Pruning with Iterative Magnitude Pruning +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Iterative pruning with fine-tuning + for _ in range(3): + prune.global_unstructured( + parameters_to_prune, + pruning_method=prune.L1Unstructured, + amount=amount/3 + ) + # Remove pruned parameters permanently + for module, _ in parameters_to_prune: + prune.remove(module, 'weight') + + # Short fine-tuning cycle (add your training loop here) + # model.train() + # train(model, epochs=1) + + return model + +# 3. ONNX Runtime Optimization with Execution Providers +def create_optimized_onnx_session(model_path): + session_options = ort.SessionOptions() + session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + return ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ], + sess_options=session_options + ) + +# 4. Full Optimization Pipeline +def optimize_model(original_model, calibration_data): + # Step 1: Prune first for better quantization results + pruned_model = prune_model(original_model) + + # Step 2: Quantize with Intel Neural Compressor + calibration_loader = DataLoader(calibration_data, batch_size=32) + quantized_model = quantize_model(pruned_model, calibration_loader) + + # Step 3: Export to ONNX with optimization + dummy_input = torch.randn(1, 3, 224, 224) + torch.onnx.export( + quantized_model, + dummy_input, + "life_model.onnx", + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + + # Step 4: Create optimized inference session + return create_optimized_onnx_session("life_model.onnx") + +# Usage example +session = optimize_model(original_model, calibration_dataset) +import torch +from torch import nn, optim +from torch.cuda import amp +from torch.ao.quantization import QuantStub, DeQuantStub, prepare_qat, convert +from torch.ao.pruning import prune, remove +from torch.nn.utils import prune as prune_utils + +class LIFETheoryModel(nn.Module): + def __init__(self): + super().__init__() + self.quant = QuantStub() + self.dequant = DeQuantStub() + self.fc1 = nn.Linear(256, 128) + self.fc2 = nn.Linear(128, 64) + self.fc3 = nn.Linear(64, 10) + + def forward(self, x): + x = self.quant(x) + x = torch.relu(self.fc1(x)) + x = torch.relu(self.fc2(x)) + x = self.fc3(x) + return self.dequant(x) + +def train_model(model, train_loader, epochs=10): + scaler = amp.GradScaler() + criterion = nn.CrossEntropyLoss() + optimizer = optim.AdamW(model.parameters(), lr=0.001) + + model.train() + for epoch in range(epochs): + for data, target in train_loader: + data, target = data.cuda(), target.cuda() + optimizer.zero_grad() + + with amp.autocast(): + output = model(data) + loss = criterion(output, target) + + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + +def prune_model(model, amount=0.2): + parameters_to_prune = [ + (module, 'weight') for module in model.modules() + if isinstance(module, nn.Linear) + ] + + # Global magnitude pruning + prune_utils.global_unstructured( + parameters_to_prune, + pruning_method=prune_utils.L1Unstructured, + amount=amount + ) + + # Remove pruning reparameterization + for module, _ in parameters_to_prune: + remove(module, 'weight') + + return model + +def quantize_model(model): + model.qconfig = torch.ao.quantization.get_default_qat_qconfig('fbgemm') + model = prepare_qat(model) + return model + +# Full optimization pipeline +def optimize_life_model(): + # Initialize model + model = LIFETheoryModel().cuda() + + # 1. Mixed Precision Training + train_loader = ... # Your DataLoader + train_model(model, train_loader) + + # 2. Pruning + model = prune_model(model, amount=0.3) + + # 3. Prepare for Quantization-Aware Training (QAT) + model = quantize_model(model) + + # 4. Fine-tune with QAT and Mixed Precision + train_model(model, train_loader, epochs=5) # Short fine-tuning + + # 5. Convert to quantized model + model = model.cpu() + quantized_model = convert(model) + + return quantized_model + +# Usage +optimized_model = optimize_life_model() +import numpy as np +from functools import lru_cache +from multiprocessing import Pool + +class OptimizedLIFE: + def __init__(self): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.user_traits = {} + self.learning_rate = 0.1 + self._precompute_normalization() + + def _precompute_normalization(self): + self.trait_baseline = np.array([10, 10, 10]) # Openness, Resilience, EI baseline + + @lru_cache(maxsize=128) + def calculate_traits(self, traits): + return np.sum(traits) / np.linalg.norm(self.trait_baseline) + + def concrete_experience(self, eeg_signal, experience): + print(f"Recording new experience: {experience}") + self.eeg_data.append(eeg_signal) + self.experiences.append(experience) + self.process_eeg_data(eeg_signal) + + def reflective_observation(self): + reflections = [] + print("\nReflecting on past experiences...") + for experience, eeg_signal in zip(self.experiences, self.eeg_data): + delta_wave_activity = eeg_signal.get('delta', 0) + reflection = { + "experience": experience, + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {experience} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + print(reflection['insight']) + return reflections + + def abstract_conceptualization(self, reflections): + print("\nGenerating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + print(f"Created model: {model}") + + def active_experimentation(self, environment): + results = [] + print("\nTesting models in the environment...") + for model in self.models: + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + print(f"Test result: {result}") + return results + + def learn(self, eeg_signal, experience, environment): + print("\n--- Starting L.I.F.E Learning Cycle ---") + self.concrete_experience(eeg_signal, experience) + reflections = self.reflective_observation() + self.abstract_conceptualization(reflections) + results = self.active_experimentation(environment) + print("--- L.I.F.E Learning Cycle Complete ---\n") + return { + "eeg_signal": eeg_signal, + "experience": experience, + "environment": environment, + "performance_score": np.mean([r['performance_score'] for r in results]) + } + + def process_eeg_data(self, eeg_signal): + return eeg_signal.get('delta', 0) + + def run_optimized_pipeline(self, users): + with Pool() as p: + results = p.map(self.process_user, users) + return self._analyze_results(results) + + def process_user(self, user_data): + return self.learn(user_data['eeg_signal'], user_data['experience'], user_data['environment']) + + def _analyze_results(self, results): + return results + +def neuroadaptive_filter(raw_data: Dict, adaptability: float) -> Dict: + """ + Filters EEG signals based on adaptability. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + +# Example usage +if __name__ == "__main__": + life_system = OptimizedLIFE() + users = [ + { + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Improving memory retention", + 'environment': "Memory Game Environment" + } + ] + optimized_results = life_system.run_optimized_pipeline(users) + print("Optimized Results:", optimized_results) +import numpy as np +import asyncio +from datetime import datetime +from azure.storage.blob import BlobServiceClient +from azure.ai.ml import MLClient +import json +from azure.identity import DefaultAzureCredential + +class OptimizedLIFE: + def __init__(self, azure_config=None): + self.experiences = [] + self.eeg_data = [] + self.models = [] + self.learning_rate = 0.1 + self.azure_config = azure_config + self._init_components() + + def _init_components(self): + """Initialize Azure components and preprocessing""" + self.trait_baseline = np.array([10, 10, 10]) + if self.azure_config: + self._init_azure_connection() + self._create_ml_client() + + def _init_azure_connection(self): + """Connect to Azure Blob Storage""" + self.blob_client = BlobServiceClient.from_connection_string( + self.azure_config['connection_string'] + ) + self.container_client = self.blob_client.get_container_client( + self.azure_config['container_name'] + ) + + def _create_ml_client(self): + """Initialize Azure Machine Learning client""" + credential = DefaultAzureCredential() + self.ml_client = MLClient( + credential=credential, + subscription_id=self.azure_config['subscription_id'], + resource_group_name=self.azure_config['resource_group'], + workspace_name=self.azure_config['workspace_name'] + ) + + async def process_experience(self, eeg_signal, experience): + """Async experience processing pipeline""" + try: + processed_data = await self._process_eeg(eeg_signal) + self._store_azure_data(processed_data, "eeg-data") + return processed_data + except Exception as e: + self._handle_error(e) + return None + + async def _process_eeg(self, raw_signal): + """Enhanced EEG processing with real-time filtering""" + return { + 'timestamp': datetime.now().isoformat(), + 'delta': raw_signal.get('delta', 0) * 1.2, # Example processing + 'alpha': raw_signal.get('alpha', 0) * 0.8, + 'processed': True + } + + def _store_azure_data(self, data, data_type): + """Store processed data in Azure Blob Storage""" + if self.azure_config: + blob_name = f"{data_type}/{datetime.now().isoformat()}.json" + self.container_client.upload_blob( + name=blob_name, + data=str(data), + overwrite=True + ) + + async def full_learning_cycle(self, user_data): + """Complete async learning cycle""" + result = await self.process_experience( + user_data['eeg_signal'], + user_data['experience'] + ) + + if result: + reflection = self.create_reflection(result, user_data['experience']) + model = self.generate_model(reflection) + test_result = self.test_model(model, user_data['environment']) + return self._compile_results(user_data, test_result) + return None + + def create_reflection(self, processed_data, experience): + """Enhanced reflection with cognitive load analysis""" + reflection = { + 'experience': experience, + 'delta_activity': processed_data['delta'], + 'cognitive_load': self._calculate_cognitive_load(processed_data), + 'timestamp': processed_data['timestamp'] + } + self._log_azure_metric('reflection', reflection) + return reflection + + def _calculate_cognitive_load(self, data): + """Calculate cognitive load score""" + return (data['delta'] * 0.6) + (data['alpha'] * 0.4) + + def generate_model(self, reflection): + """Model generation with version control""" + model = { + 'version': f"1.0.{len(self.models)}", + 'parameters': { + 'learning_rate': self.learning_rate, + 'delta_weight': reflection['delta_activity'] + }, + 'environment': None + } + self._register_azure_model(model) + return model + + def _register_azure_model(self, model): + """Register model in Azure ML registry""" + if self.azure_config: + self.ml_client.models.create_or_update( + name="LIFE_model", + version=model['version'], + description=f"LIFE model v{model['version']}" + ) + + def test_model(self, model, environment): + """Enhanced model testing with performance metrics""" + performance = self._calculate_performance(model, environment) + self._log_azure_metric('performance', performance) + return performance + + def _calculate_performance(self, model, environment): + """Calculate performance score with environment factors""" + base_score = model['parameters']['delta_weight'] * 100 + env_factor = 1.2 if "Simulator" in environment else 1.0 + return round(base_score * env_factor, 2) + + def _compile_results(self, user_data, performance): + """Compile final results package""" + return { + 'user_id': user_data.get('id', 'anonymous'), + 'experience': user_data['experience'], + 'environment': user_data['environment'], + 'performance': performance, + 'timestamp': datetime.now().isoformat() + } + + def _log_azure_metric(self, metric_type, data): + """Log metrics to Azure ML studio""" + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name=f"LIFE_{metric_type}", + value=data, + description=f"LIFE {metric_type} metric" + ) + + def _handle_error(self, error): + """Enhanced error handling with Azure logging""" + print(f"Error occurred: {str(error)}") + if self.azure_config: + self.ml_client.metrics_logger.log( + metric_name="LIFE_errors", + value=1, + description="Runtime error occurred" + ) + +# Example usage with Azure configuration +azure_config = { + 'connection_string': "", + 'container_name': "life-data", + 'subscription_id': "", + 'resource_group': "life-resources", + 'workspace_name': "life-theory-ml" +} + +async def main(): + life_system = OptimizedLIFE(azure_config=azure_config) + users = [ + { + 'id': "user_001", + 'eeg_signal': {'delta': 0.7, 'alpha': 0.3}, + 'experience': "Motor Training", + 'environment': "Motor Training Simulator" + }, + { + 'id': "user_002", + 'eeg_signal': {'delta': 0.4, 'alpha': 0.6}, + 'experience': "Memory Training", + 'environment': "Cognitive Lab Environment" + } + ] + + results = [] + for user in users: + result = await life_system.full_learning_cycle(user) + if result: + results.append(result) + + print("Final Results:") + for res in results: + print(f"User {res['user_id']} achieved {res['performance']}%") + +if __name__ == "__main__": + asyncio.run(main()) +pip install azure-ai-ml azure-storage-blob azure-identity +graph TD +A[Experiential Learning] --> B(Continuous Feedback Loop) +B --> C{Real-Time Data Processing} +C --> D[Azure Stream Analytics] +C --> E[Azure Event Hubs] +D --> F[Performance Insights] +E --> F +F --> G[Personalized Learning Paths] +G --> H[Scenario-Based Training] +EEG Sensors → Azure IoT Hub → Stream Analytics → +│ +├─▶ Azure Synapse (Neuroplasticity Metrics) +├─▶ Power BI (Real-Time Dashboards) +# Existing code... +# Train and deploy model function +def train_and_deploy_model(dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + ws = Workspace.from_config() + experiment = Experiment(ws, "eeg_classification") + + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + run = experiment.submit(automl_config) + run.wait_for_completion(show_output=True) + + best_model, fitted_model = run.get_output() + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + print(f"Model deployed successfully. Scoring URI: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Example usage +scoring_uri = train_and_deploy_model("", "aks-cluster") + +# GDPR-compliant EEG preprocessing +def eeg_preprocessing(eeg_signal): + """GDPR-compliant EEG processing""" + try: + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + except Exception as e: + logging.error(f"Error: {e}") + return None + +# Stream from IoT Hub +def stream_from_iot_hub(): + """ + Stream EEG data from Azure IoT Hub and preprocess it. + """ + try: + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = eeg_preprocessing(eeg_signal) + if processed_signal: + print(f"Processed EEG signal: {processed_signal}") + + with client: + client.receive_batch(on_event_batch, starting_position="-1") # Receive from the beginning + print("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error: {e}") + +# Example usage of streaming +stream_from iot_hub()text +EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment + │ │ + └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + logging.error(f"Error: {e}") + +# Example usage of streaming +stream_from iot_hub()text +EEG Device → [Azure IoT Hub] → [Azure Stream Analytics] → [Azure ML Model] → [AKS Cluster] → VR Environment + │ │ + └──[GDPR-Compliant Storage]←──────[Feedback Loop]←──┘ + + """GDPR-compliant EEG processing""" + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + return processed + + # Stream from IoT Hub + client = EventHubConsumerClient.from_connection_string("") + partition_handler = lambda: client.receive_batch(on_event=eeg_preprocessing) + client.run(partition_handler) + s// Unity C# Script +public class VREnvironmentController : MonoBehaviour { + void UpdateEnvironment(float focus, float stress) { + if (focus > 0.7f && stress < 0.3f) { + IncreaseTaskComplexity(0.2f); + } else { + ActivateRelaxationProtocol(); + } + } +} +// Unity C# Script +public class VRController : MonoBehaviour { + void Update() { + float focus = Input.GetAxis("Focus"); + float stress = Input.GetAxis("Stress"); + VREnvironmentController.Instance.UpdateEnvironment(focus, stress); + } +}# Azure ML Model Deployment +import json +from azureml.core import Workspace, Model +from azureml.core.model import InferenceConfig +from azureml.core.webservice import AciWebservice, Webservice +from azureml.core.model import Model + +# Load the workspace +ws = Workspace.from_config() + +# Load the model +model = Model(ws, "your-model-name") + +# Define the inference configuration +inference_config = InferenceConfig( + entry_script="score.py", + environment=Environment.from_conda_specification( + name="myenv", + file_path="environment.yml" + ) +) + +# Define the ACI configuration +aci_config = AciWebservice.deploy_configuration( + cpu_cores=1, + memory_gb=1, + auth_enabled=True, + enable_app_insights=True +)from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)) + +run = experiment.submit(automl_config) +best_model = run.get_output() +best_model.deploy(aks_cluster, autoscale_enabled=True) +from azureml.core import Workspace, Experiment +from azureml.train.automl import AutoMLConfig + +ws = Workspace.from_config() +experiment = Experiment(ws, "life_stress_classification") + +automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" +)Data Anonymization SHA-256 hashing of user IDs + signal timestamp offsetting +Right to Erasure Automated data purge workflows via Azure Logic Apps +Encryption AES-256 for data at rest (Blob Storage) + TLS 1.3 for data in transit +Access Control RBAC with Just-In-Time access via Azure AD PIM +Data Retention RBAC with Just-In-Time access via Azure AD PIM +import json +import requests + +# Define the API endpoint URL +url = "https://your-api-endpoint.com/api/v1/your-endpoint" + +# Define the payload +payload = { + "user_id": "12345", + "signal_timestamp": "2022-01-01T00:00:00Z" +} + +# Send a POST request to the API endpoint +response = requests.post(url, json=payload) +# Check the response status codebash +# Deploy to AKS with autoscaling +az aks create --resource-group life-rg --name life-cluster \ + --node-count 3 --enable-cluster-autoscaler \ + --min-count 1 --max-count 10 + +# Configure HPA +kubectl autoscale deployment life-model --cpu-percent=80 --min=1 --max=10 +az aks get-credentials --resource-group life-rg --name life-cluster +kubectl apply -f deployment.yaml +kubectl apply -f service.yaml +kubectl get pods +kubectl get services +kubectl Decrease +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -w +kubectl get events --sort-by=.metadata.creationTimestamp -wKnowledge Retention Pre/post assessments + EEG focus metrics +40% +Engagement VR interaction logs + heart rate variability +35% +Retention EEG signal stability + task completion rates +30% +# Data Storage Azure Blob Storage with GDPR compliance +25% +# cSpell:ignore Azure +# cSpell:ignore Azure + +# cSpell:ignore Azure +#Metric Measurement Tool Target Improvement +Task Completion Speed VR simulation timestamps + error rates +25% +Stress Reduction EEG beta wave analysis + self-reports -30% +# Focus Improvement EEG alpha wave analysis + task performance +40% +# Learning Retention EEG theta wave analysis + task performance +35%Component Success Criteria Measurement Frequency +Real-Time Processing <100ms latency for EEG→VR feedback Continuous +Model Accuracy >90% stress classification accuracy Weekly +GDPR Compliance 0 critical audit findings Quarterly +User Engagement >70% session completion rate Bi-Weekly +# Data Security 0 data breaches Monthly +# Data Retention 0 data retention violations Monthly +"schemaVersion": "0.3", +"description": "Example SSM document for L.I.F.E algorithm", +"mainSteps": [ + { + "action": "aws:runCommand", + "name": "RunLIFEAlgorithm", + "inputs": { + "DocumentName": "LIFEAlgorithmExecution", + "Parameters": { + "eeg_signal": ["{\"delta\": 0.6, \"alpha\": 0.3}"], + "experience": ["Learning motor skills"], + "environment": ["Motor Training Simulator"] + } + } + } + # Python code starts here + import ast + import logging + import requests + import json + from azureml.core import Workspace, Model, Experiment + from azureml.pipeline.core import Pipeline, PipelineData, Schedule, ScheduleRecurrence + from azureml.pipeline.steps import PythonScriptStep + from azureml.train.automl import AutoMLConfig + from azureml.core.webservice import AksWebservice + from azureml.core.compute import AksCompute + import numpy as np + from azure.iot.device import IoTHubDeviceClient + from azure.keyvault.secrets import SecretClient + from azure.identity import DefaultAzureCredential + from azure.eventhub import EventHubConsumerClient + import neurokit2 as nk + from azure.quantum import Workspace as QuantumWorkspace + from azure.quantum.optimization import Problem, ProblemType + from azure.iot.percept import VisionDevice + from azure.ai.language.conversations import ConversationAnalysisClient, AnalyzeConversationResult + from azure.blockchain import BlockchainMember + from datetime import datetime + + def calculate_self_development(learning, individual, experience): + """ + Calculate self-development score based on learning, individuality, and experience. + """ + try: + self_development_score = (learning + individual) / (experience + 1e-9) # Avoid division by zero + logging.info(f"Calculated self-development score: {self_development_score}") + return self_development_score + except Exception as e: + logging.error(f"Error calculating self-development score: {e}") + return None + + class LIFEAlgorithm: + """Implements L.I.F.E Learning Cycle with Azure integration""" + + def __init__(self): + """ + Initialize the L.I.F.E Algorithm with Blockchain integration. + """ + self.blockchain_member = BlockchainMember( + resource_group = os.getenv("AZURE_RESOURCE_GROUP"), + blockchain_name = os.getenv("AZURE_BLOCKCHAIN_NAME"), + member_name="" + ) + self.experiences = [] # Raw code inputs + self.models = { # Trained code analysis models + "complexity": None, + "quality": None + } + self.trait_weights = { # Individualized trait importance + "functions": 0.8, + "comments": 0.6 + } + self._init_azure() + self._init_key_vault() + self._init_quantum_workspace() + + def _init_azure(self): + """Azure ML Workspace Connection""" + try: + self.workspace = Workspace.from_config() + self.model_registry = Model(self.workspace) + logging.info("Azure Workspace and Model Registry initialized successfully.") + except Exception as e: + logging.error(f"Azure connection failed: {str(e)}") + self.workspace = None + + def _init_key_vault(self): + """ + Initialize Azure Key Vault for secure credential management. + """ + try: + logging.info("Initializing Azure Key Vault...") + credential = DefaultAzureCredential() + self.secret_client = SecretClient(vault_url="https://life-vault.vault.azure.net/", credential=credential) + self.api_key = self.secret_client.get_secret("EEG-API-KEY").value + logging.info("Azure Key Vault initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Key Vault: {e}") + self.api_key = None + + def _init_quantum_workspace(self): + """ + Initialize Azure Quantum Workspace for optimization tasks. + """ + try: + logging.info("Initializing Azure Quantum Workspace...") + self.quantum_workspace = QuantumWorkspace( + subscription_id="", + resource_group="", + name="", + location="" + ) + logging.info("Azure Quantum Workspace initialized successfully.") + except Exception as e: + logging.error(f"Error initializing Azure Quantum Workspace: {e}") + self.quantum_workspace = None + + def configure_percept_device(self, device_ip): + """ + Configure Azure Percept DK for real-time EEG processing. + """ + try: + logging.info("Configuring Azure Percept DK...") + self.device = VisionDevice(device_ip) + self.device.enable_module("EEGProcessor") + logging.info("Azure Percept DK configured successfully.") + except Exception as e: + logging.error(f"Error configuring Azure Percept DK: {e}") + + def process_biometrics(self): + """ + Real-time EEG stream processing using Azure Percept DK. + """ + try: + logging.info("Starting real-time EEG stream processing...") + while True: + eeg_data = self.device.get_eeg_data() + stress_level = self.analyze_stress(eeg_data.alpha, eeg_data.beta) + + # Update Synapse Analytics or dashboard + self.update_dashboard(stress_level) + except Exception as e: + logging.error(f"Error during real-time EEG processing: {e}") + + def analyze_stress(self, alpha, beta): + """ + Analyze stress level based on EEG alpha and beta waves. + """ + try: + stress_level = beta / (alpha + 1e-9) # Avoid division by zero + logging.info(f"Calculated stress level: {stress_level}") + return stress_level + except Exception as e: + logging.error(f"Error analyzing stress level: {e}") + return None + + def update_dashboard(self, stress_level): + """ + Update Synapse Analytics or a dashboard with the stress level. + """ + try: + logging.info(f"Updating dashboard with stress level: {stress_level}") + # Placeholder for actual dashboard update logic + except Exception as e: + logging.error(f"Error updating dashboard: {e}") + + def concrete_experience(self, code: str): + """Stage 1: Capture new code experience""" + self.experiences.append(code) + logging.info(f"Added new code experience: {code[:50]}...") + + def reflective_observation(self): + """Stage 2: Analyze code patterns""" + traits, experiences = [], [] + + for code in self.experiences: + try: + tree = ast.parse(code) + # Extract advanced traits + current_traits = { + "func_count": sum(1 for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef)), + "docstring_presence": any(isinstance(n, ast.Expr) + for n in tree.body[:1]), + "import_complexity": len([n for n in ast.walk(tree) + if isinstance(n, ast.Import)]) + } + traits.append(current_traits) + + # Extract experiences + experiences.extend([ + n.value.s for n in ast.walk(tree) + if isinstance(n, ast.Expr) and isinstance(n.value, ast.Str) + ]) + + except SyntaxError as e: + logging.warning(f"Invalid syntax in code: {str(e)}") + continue + + return traits, experiences + + def abstract_conceptualization(self, traits, experiences): + """Stage 3: Update analysis models""" + # Calculate code complexity score using L.I.F.E equation + complexity_scores = [ + (t["func_count"] * 0.6 + + t["import_complexity"] * 0.4) + for t in traits + ] + + # Update trait weights based on experiences + self.trait_weights["functions"] *= 1 + len(experiences) / 100 + self.trait_weights["comments"] *= 1 + len(experiences) / 150 + + logging.info(f"Updated trait weights: {self.trait_weights}") + + # Azure model update + if self.models["complexity"] and hasattr(self.models["complexity"], "partial_fit"): + self.models["complexity"].partial_fit(complexity_scores) + logging.info("Complexity model updated with new scores.") + + def active_experimentation(self, new_code: str): + """Stage 4: Apply optimized analysis""" + self.concrete_experience(new_code) + traits, experiences = self.reflective_observation() + self.abstract_conceptualization(traits, experiences) + + # Generate L.I.F.E. equation value + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + life_score = (self.ω * L + T) / E * I + + logging.info(f"Calculated L.I.F.E score: {life_score}") + + return { + "life_score": life_score, + "traits_analyzed": len(traits), + "azure_model_version": self.model_registry.version if self.workspace else "local" + } + + def analyze_github_code(self, repo_url: str): + """ + Real-time GitHub code analysis using L.I.F.E. REST API. + """ + try: + logging.info(f"Fetching code from GitHub repository: {repo_url}") + response = requests.get(repo_url) + if response.status_code == 200: + code = response.text + logging.info("Code fetched successfully. Starting analysis...") + return self.active_experimentation(code) + else: + logging.error(f"Failed to fetch code. HTTP Status: {response.status_code}") + return None + except Exception as e: + logging.error(f"Error during GitHub code analysis: {e}") + return None + + def render_vr_simulation(self, experience_data): + """ + Quantum-inspired optimization for VR scene rendering. + """ + try: + logging.info("Starting quantum-inspired optimization for VR simulation...") + if not self.quantum_workspace: + raise ValueError("Quantum Workspace is not initialized.") + + # Define the optimization problem + problem = Problem(name="vr_optimization", problem_type=ProblemType.ising) + problem.add_terms([ + # Add terms based on experience_data (e.g., rendering parameters) + {"c": 1.0, "ids": [0, 1]}, # Example term + {"c": -0.5, "ids": [1, 2]} # Example term + ]) + + # Submit the problem to the quantum solver + solver = self.quantum_workspace.get_solver("Microsoft.FullStateSimulator") + result = solver.optimize(problem) + logging.info(f"Quantum optimization result: {result}") + + # Apply optimized parameters to VR environment + optimized_scene = self.apply_quantum_parameters(result) + logging.info("VR simulation optimized successfully.") + return optimized_scene + except Exception as e: + logging.error(f"Error during VR simulation optimization: {e}") + return None + + def apply_quantum_parameters(self, result): + """ + Apply quantum-optimized parameters to the VR environment. + """ + # Placeholder logic for applying parameters to Unity/Mesh + logging.info("Applying quantum-optimized parameters to VR environment...") + return {"optimized_scene": "example_scene"} # Example return value + + def visualize_code_in_vr(self, complexity_scores): + """ + Visualize code complexity in a VR environment. + """ + try: + logging.info("Generating VR visualization for code complexity...") + # Simulate VR visualization logic + for idx, score in enumerate(complexity_scores): + print(f"Visualizing file {idx + 1} with complexity score: {score}") + logging.info("VR visualization complete.") + except Exception as e: + logging.error(f"Error during VR visualization: {e}") + + def deploy_azure_pipeline(self): + """ + Deploy an Azure Pipeline for automated model retraining. + """ + try: + logging.info("Setting up Azure Pipeline for automated model retraining...") + + # Define pipeline data + retrain_data = PipelineData("retrain_data", datastore=self.workspace.get_default_datastore()) + + # Define pipeline step + retrain_step = PythonScriptStep( + name="Retrain Model", + script_name="retrain_model.py", + arguments=["--input_data", retrain_data], + compute_target="cpu-cluster", + source_directory="./scripts", + allow_reuse=True + ) + + # Create and publish pipeline + pipeline = Pipeline(workspace=self.workspace, steps=[retrain_step]) + pipeline.validate() + published_pipeline = pipeline.publish(name="LIFE_Retrain_Pipeline") + logging.info(f"Pipeline published successfully: {published_pipeline.name}") + return published_pipeline + except Exception as e: + logging.error(f"Error deploying Azure Pipeline: {e}") + + def schedule_retraining_pipeline(self): + """ + Schedule weekly retraining of the Azure Pipeline. + """ + try: + logging.info("Scheduling weekly retraining for the Azure Pipeline...") + + # Ensure the pipeline is published + published_pipeline = self.deploy_azure_pipeline() + + # Define the recurrence for weekly retraining + recurrence = ScheduleRecurrence(frequency="Week", interval=1) + + # Create the schedule + schedule = Schedule.create( + workspace=self.workspace, + name="life_retraining_schedule", + pipeline_id=published_pipeline.id, + experiment_name="life_retraining", + recurrence=recurrence + ) + + logging.info(f"Retraining schedule created successfully: {schedule.name}") + except Exception as e: + logging.error(f"Error scheduling retraining pipeline: {e}") + + def stream_eeg_to_azure(self, eeg_data): + """ + Stream EEG data to Azure IoT Hub for real-time processing. + """ + try: + logging.info("Streaming EEG data to Azure IoT Hub...") + client = IoTHubDeviceClient.create_from_connection_string("") + client.send_message(json.dumps(eeg_data)) + logging.info("EEG data streamed successfully.") + except Exception as e: + logging.error(f"Error streaming EEG data to Azure IoT Hub: {e}") + + def process_eeg_stream(self, eeg_data): + """ + Process EEG data through Azure Stream Analytics and Azure ML Model. + """ + try: + logging.info("Processing EEG data through Azure Stream Analytics...") + # Simulate sending data to Azure Stream Analytics + processed_data = { + "focus": eeg_data.get("alpha", 0.0) / (eeg_data.get("theta", 1e-9) + 1e-9), + "stress": eeg_data.get("beta", 0.0) / (eeg_data.get("delta", 1e-9) + 1e-9) + } + logging.info(f"Processed EEG data: {processed_data}") + + # Simulate sending processed data to Azure ML Model + prediction = self.predict_with_azure_ml(processed_data) + logging.info(f"Prediction from Azure ML Model: {prediction}") + + # Send prediction to VR environment + self.send_to_vr_environment(prediction) + except Exception as e: + logging.error(f"Error processing EEG stream: {e}") + + def predict_with_azure_ml(self, data): + """ + Simulate prediction using Azure ML Model. + """ + # Placeholder for actual Azure ML model prediction + return {"task_complexity": 0.8, "relaxation_protocol": True} + + def send_to_vr_environment(self, prediction): + """ + Send predictions to the VR environment for real-time adjustments. + """ + try: + logging.info("Sending predictions to VR environment...") + # Simulate sending data to VR environment + if prediction["task_complexity"] > 0.7: + logging.info("Increasing task complexity in VR environment.") + if prediction["relaxation_protocol"]: + logging.info("Activating relaxation protocol in VR environment.") + except Exception as e: + logging.error(f"Error sending data to VR environment: {e}") + + def evaluate_self_development(self, learning, individual, experience): + """ + Evaluate self-development using the L.I.F.E. methodology. + """ + return calculate_self_development(learning, individual, experience) + + def eeg_preprocessing(self, eeg_signal): + """ + GDPR-compliant EEG processing. + """ + try: + logging.info("Preprocessing EEG signal...") + # Anonymize data + anonymized_signal = {**eeg_signal, "user_id": hash(eeg_signal["user_id"])} + + # Preprocess signal using NeuroKit2 + processed = nk.eeg_clean(anonymized_signal["data"], sampling_rate=128) + logging.info("EEG signal preprocessed successfully.") + return processed + except Exception as e: + logging.error(f"Error during EEG preprocessing: {e}") + return None + + def stream_from_iot_hub(self): + """ + Stream EEG data from Azure IoT Hub and preprocess it. + """ + try: + logging.info("Connecting to Azure IoT Hub Event Hub...") + client = EventHubConsumerClient.from_connection_string("", consumer_group="$Default") + + def on_event_batch(partition_context, events): + for event in events: + eeg_signal = json.loads(event.body_as_str()) + processed_signal = self.eeg_preprocessing(eeg_signal) + if processed_signal: + self.process_eeg_stream({"data": processed_signal}) + + with client: + client.receive_batch(on_event_batch, starting_position="-1") # Receive from the beginning + logging.info("Streaming EEG data from IoT Hub...") + except Exception as e: + logging.error(f"Error streaming from IoT Hub: {e}") + + def train_and_deploy_model(self, dataset, aks_cluster_name): + """ + Train a classification model using Azure AutoML and deploy it to an AKS cluster. + """ + try: + logging.info("Starting AutoML training for stress classification...") + + # Load Azure ML Workspace + ws = Workspace.from_config() + + # Create an experiment + experiment = Experiment(ws, "life_stress_classification") + + # Configure AutoML + automl_config = AutoMLConfig( + task="classification", + training_data=dataset, + label_column_name="stress_level", + iterations=30, + primary_metric="accuracy", + enable_early_stopping=True, + featurization="auto" + ) + + # Submit the experiment + run = experiment.submit(automl_config) + logging.info("AutoML training started. Waiting for completion...") + run.wait_for_completion(show_output=True) + + # Get the best model + best_model, fitted_model = run.get_output() + logging.info(f"Best model selected: {best_model.name}") + + # Deploy the model to AKS + aks_target = AksCompute(ws, aks_cluster_name) + deployment_config = AksWebservice.deploy_configuration(autoscale_enabled=True) + try: + service = best_model.deploy( + workspace=ws, + name="life-stress-classification-service", + deployment_config=deployment_config, + deployment_target=aks_target + ) + service.wait_for_deployment(show_output=True) + except Exception as e: + logger.error(f"Model deployment failed: {e}") + logging.info(f"Model deployed successfully to AKS: {service.scoring_uri}") + return service.scoring_uri + except Exception as e: + logging.error(f"Error during AutoML training or deployment: {e}") + return None + + def generate_learning_path(self, traits): + """ + Generate a personalized learning path using Azure GPT-4 integration. + """ + try: + logging.info("Generating personalized learning path...") + response = client.analyze_conversation( + task={ + "kind": "Custom", + "parameters": { + "projectName": "life_learning", + "deploymentName": "gpt4_paths" + } + }, + input_text=f"Generate learning path for: {json.dumps(traits)}" + ) + learning_path = response.result.prediction + return learning_path + except Exception as e: + logging.error(f"Error generating learning path: {e}") + return None + + def mint_skill_nft(self, user_id, skill): + """ + Mint a skill NFT for a user based on their EEG signature. + """ + try: + logging.info(f"Minting NFT for user {user_id} with skill: {skill}") + + # Create NFT metadata + metadata = { + "skill": skill, + "certification_date": datetime.now().isoformat(), + "neural_signature": self.get_eeg_signature(user_id) + } + + # Mint NFT on blockchain + transaction_hash = self.blockchain_member.send_transaction( + to="0xSKILL_CONTRACT", + data=json.dumps(metadata) + ) + logging.info(f"NFT minted successfully. Transaction hash: {transaction_hash}") + return transaction_hash + except Exception as e: + logging.error(f"Error minting NFT: {e}") + return None + + def get_eeg_signature(self, user_id): + """ + Generate a neural signature for the user based on EEG data. + """ + try: + logging.info(f"Generating EEG signature for user {user_id}") + # Placeholder for actual EEG signature generation logic + return f"signature_{user_id}" + except Exception as e: + logging.error(f"Error generating EEG signature: {e}") + return None + + # Example Usage + if __name__ == "__main__": + life = LIFEAlgorithm() + + # Example dataset (replace with actual Azure ML dataset) + dataset = "" + + # AKS cluster name + aks_cluster_name = "life-aks-cluster" + + # Train and deploy the model + scoring_uri = life.train_and_deploy_model(dataset, aks_cluster_name) + if scoring_uri: + print(f"Model deployed successfully. Scoring URI: {scoring_uri}") + + # Configure Azure Percept DK + device_ip = "" + life.configure_percept_device(device_ip) + + # Start real-time biometric processing + life.process_biometrics() + + # Example traits for learning path generation + traits = {"focus": 0.8, "stress": 0.2, "complexity": 0.7} + + # Generate a personalized learning path + learning_path = life.generate_learning_path(traits) + if learning_path: + print(f"Generated Learning Path: {learning_path}") + + # Example user ID and skill + user_id = "user123" + skill = "Advanced Motor Skills" + + # Mint a skill NFT + transaction_hash = life.mint_skill_nft(user_id, skill) + if transaction_hash: + print(f"NFT minted successfully. Transaction hash: {transaction_hash}") + + // Unity C# Script for VR Interaction + # Unity C# Script for VR Interaction + + public class VRInteraction : MonoBehaviour + { + // Adjust VR environment based on EEG data + public void AdjustVRBasedOnEEG(float focus, float stress) + { + if (focus > 0.7f) + { + Debug.Log("High focus detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + + if (stress > 0.5f) + { + Debug.Log("High stress detected. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + + // Simulate increasing task complexity + private void IncreaseTaskComplexity(float percentage) + { + // Logic to increase task complexity + Debug.Log($"Task complexity increased by {percentage * 100}%."); + } + + // Simulate activating relaxation protocol + private void ActivateRelaxationProtocol() + { + // Logic to activate relaxation protocol + Debug.Log("Relaxation protocol activated."); + } + } + + // Unity C# Script for VR Environment Control + using UnityEngine; + + public class VREnvironmentController : MonoBehaviour + { + // Update the VR environment based on focus and stress levels + public void UpdateEnvironment(float focus, float stress) + { + if (focus > 0.7f && stress < 0.3f) + { + Debug.Log("High focus and low stress detected. Increasing task complexity by 20%."); + IncreaseTaskComplexity(0.2f); // Increase complexity by 20% + } + else + { + Debug.Log("Stress level is high or focus is low. Activating relaxation protocol."); + ActivateRelaxationProtocol(); + } + } + } + + + // Azure Function for EEG Data Processing +"cSpell.words": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv" +], +"cSpell.ignoreWords": [ + "Neuroplastic", + "ndarray", + "nowait", + "myenv", + "codebash", + "numpy", + "getenv" +] +import json + +try: + with open("config.json", "r") as file: + config = json.load(file) + print("JSON is valid!") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +import json + +try: + with open("your_file.json", "r") as file: + data = json.load(file) + print("JSON is valid!") +except FileNotFoundError: + print("Error: The file 'your_file.json' does not exist.") +except json.JSONDecodeError as e: + print(f"JSON error: {e}") +from torch.nn.utils import prune + +prune.l1_unstructured(model.fc1, name='weight', amount=0.2) + +torch.onnx.export( + model, dummy_input, "model.onnx", opset_version=13 +) +from azureml.core import Workspace, Model + +ws = Workspace.from_config() +model = Model(ws, "model-name") +model.deploy(ws, "deployment-name", inference_config, deployment_config) + +import numpy as np +from collections import deque +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self, retention_size: int = 1000): + # Core L.I.F.E components + self.experiences = deque(maxlen=retention_size) + self.trait_models = deque(maxlen=retention_size) + self.cognitive_traits = { + 'focus': {'baseline': 0.5, 'current': 0.5}, + 'resilience': {'baseline': 0.5, 'current': 0.5}, + 'adaptability': {'baseline': 0.5, 'current': 0.5} + } + + # Mathematical model parameters + self.ω = 0.8 # Learning momentum factor + self.α = 0.1 # Adaptation rate + self.τ = 0.05 # Trait evolution threshold + + def _life_equation(self, experience_impact: float) -> float: + """Core L.I.F.E mathematical model for growth quantification""" + L = len(self.trait_models) + T = sum(t['current'] for t in self.cognitive_traits.values()) + E = max(len(self.experiences), 1) + I = np.mean([m['impact'] for m in self.trait_models[-10:]]) if self.trait_models else 0.5 + + return (self.ω * L + T) / E * I + + def process_experience(self, raw_data: Dict, environment: str): + """Real-time experience processing with neuroadaptive filtering""" + # Stage 1: Raw experience intake + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + self.experiences.append((filtered_data, environment)) + + # Stage 2: Trait-adaptive processing + experience_impact = self._calculate_impact(filtered_data) + self._update_traits(experience_impact, environment) + + # Stage 3: Autonomous model evolution + new_model = { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': env + } + self.trait_models.append(new_model) + + return experience_impact + + def _filter_experience(self, raw_data: Dict) -> Dict: + """Adaptive experience filtering based on current traits""" + # Dynamic filtering threshold based on adaptability + adaptability = self.cognitive_traits['adaptability']['current'] + threshold = 0.5 * (1 + adaptability) + + return {k:v for k,v in raw_data.items() + if v > threshold and k in ['delta', 'theta', 'alpha']} + + def _calculate_impact(self, filtered_data: Dict) -> float: + """Calculate neurocognitive impact using L.I.F.E equation""" + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + impact = sum(weights.get(k, 0) * v for k, v in filtered_data.items()) + return self._life_equation(impact) + + def _update_traits(self, impact: float, environment: str): + """Dynamic trait adaptation with momentum-based learning""" + for trait in self.cognitive_traits: + # Environment-specific adaptation + env_factor = 1 + 0.2*('training' in environment.lower()) + + # Trait evolution equation + Δ = self.α * impact * env_factor + new_value = np.clip(self.cognitive_traits[trait]['current'] + Δ, 0, 1) + if abs(Δ) > self.τ: + self.cognitive_traits[trait]['baseline'] += 0.15 * Δ + self.cognitive_traits[trait]['current'] = new_value + + def _generate_adaptive_model(self, impact: float) -> Dict: + """Create self-improving trait model with evolutionary parameters""" + return { + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'velocity': self.ω * impact, + 'environment': self.experiences[-1][1] if self.experiences else None + } + + def get_adaptive_parameters(self) -> Dict: + """Current optimization parameters for real-time adaptation""" + return { + 'learning_rate': 0.1 * self.cognitive_traits['focus']['current'], + 'challenge_level': 0.5 * self.cognitive_traits['resilience']['current'], + 'novelty_factor': 0.3 * self.cognitive_traits['adaptability']['current'] + } + +# Example Usage +system = NeuroadaptiveSystem() + +# Simulate real-time experience processing +for _ in range(10): + mock_eeg = { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand(), + 'noise': np.random.rand() # To be filtered + } + impact = system.process_experience(mock_eeg, "VR Training Environment") + print(f"Experience Impact: {impact:.2f}") + print(f"Current Focus: {system.cognitive_traits['focus']['current']:.2f}") + print(f"Adaptive Params: {system.get_adaptive_parameters()}\n") + +Experience Impact: 0.45 +Current Focus: 0.52 +Adaptive Params: {'learning_rate': 0.052, 'challenge_level': 0.25, 'novelty_factor': 0.15} + +Experience Impact: 0.38 +Current Focus: 0.54 +Adaptive Params: {'learning_rate': 0.054, 'challenge_level': 0.27, 'novelty_factor': 0.16} + +def life_growth_equation(learned_models: int, traits: List[float], experiences: int, impact: float, momentum: float = 0.8) -> float: + """ + Calculates growth potential using the L.I.F.E equation. + """ + traits_sum = sum(traits) + return (momentum * learned_models + traits_sum) / max(experiences, 1) * impact + +import numpy as np +from typing import Dict, List + +class NeuroadaptiveSystem: + def __init__(self): + self.experiences = [] + self.learned_models = 0 + self.cognitive_traits = {'focus': 0.5, 'resilience': 0.5, 'adaptability': 0.5} + + def process_experience(self, raw_data: Dict, impact: float): + """ + Processes an experience using neuroadaptive filtering and updates growth potential. + """ + # Step 1: Filter EEG signals + adaptability = self.cognitive_traits['adaptability']['current'] + filter_threshold = 0.4 + 0.3 * adaptability + filtered_data = {k: v for k, v in raw_data.items() if v > filter_threshold and k in ['delta', 'theta', 'alpha']} + + # Step 2: Calculate growth potential + traits = list(self.cognitive_traits.values()) + growth = life_growth_equation( + learned_models=self.learned_models, + traits=traits, + experiences=len(self.experiences), + impact=impact + ) + + # Step 3: Update system state + self.experiences.append(filtered_data) + self.learned_models += 1 + return growth + +# Example Usage +system = NeuroadaptiveSystem() +mock_eeg = {'delta': 0.7, 'theta': 0.6, 'alpha': 0.4, 'noise': 0.2} +growth = system.process_experience(mock_eeg, impact=0.8) +print(f"Growth Potential: {growth:.2f}") + +import numpy as np +from typing import Dict + +class TraitEvolutionSystem: + def __init__(self, adaptation_rate: float = 0.1): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + delta_t = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + delta_t, 0, 1 + ) + + # Update the baseline if the change exceeds a threshold + if abs(delta_t) > 0.05: # Example threshold + self.cognitive_traits[trait]['baseline'] = ( + 0.9 * self.cognitive_traits[trait]['baseline'] + 0.1 * delta_t + ) + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = TraitEvolutionSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +import numpy as np +from typing import Dict + +class MomentumBasedLearningSystem: + def __init__(self, adaptation_rate: float = 0.1, momentum: float = 0.8, threshold: float = 0.05): + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.adaptation_rate = adaptation_rate # α in the equation + self.momentum = momentum # ω factor + self.threshold = threshold # τ-threshold for stability + + def update_traits(self, growth_potential: float, environment: str): + """ + Update cognitive traits based on growth potential and environment. + """ + # Determine environmental factor + delta_env = 1 if 'training' in environment.lower() else 0 + + for trait in self.cognitive_traits: + # Calculate ΔT (change in trait) + Δ = self.adaptation_rate * growth_potential * (1 + 0.2 * delta_env) + + # Update the current trait value + self.cognitive_traits[trait]['current'] = np.clip( + self.cognitive_traits[trait]['current'] + Δ, 0, 1 + ) + + # Update the baseline using momentum-based learning + if abs(Δ) > self.threshold: + self.cognitive_traits[trait]['baseline'] = ( + self.momentum * self.cognitive_traits[trait]['baseline'] + + (1 - self.momentum) * self.cognitive_traits[trait]['current'] + ) + + def filter_data(self, raw_data: Dict, adaptability: float) -> Dict: + """ + Filters irrelevant data based on adaptability within 5ms latency. + """ + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold and k in ['delta', 'theta', 'alpha']} + + def generate_model(self, growth_potential: float) -> Dict: + """ + Generate an autonomous model based on current traits and growth potential. + """ + return { + 'traits': self.cognitive_traits.copy(), + 'growth_potential': growth_potential, + 'momentum': self.momentum + } + + def get_traits(self) -> Dict: + """ + Return the current state of cognitive traits. + """ + return self.cognitive_traits + +# Example Usage +system = MomentumBasedLearningSystem() + +# Simulate growth potential and environment +growth_potential = 0.8 # Example value from L.I.F.E equation +environment = "VR Training Environment" + +# Update traits +system.update_traits(growth_potential, environment) + +# Display updated traits +print("Updated Cognitive Traits:", system.get_traits()) + +# Generate an autonomous model +model = system.generate_model(growth_potential) +print("Generated Model:", model) + +Updated Cognitive Traits: { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} +} +Generated Model: { + 'traits': { + 'focus': {'current': 0.58, 'baseline': 0.508}, + 'resilience': {'current': 0.58, 'baseline': 0.508}, + 'adaptability': {'current': 0.58, 'baseline': 0.508} + }, + 'growth_potential': 0.8, + 'momentum': 0.8 +} +🌀 STARTING L.I.F.E CYCLE 1 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.52 +3. Abstract Conceptualization: Trait updates = {'focus': 0.58, 'resilience': 0.59, 'adaptability': 0.57} +4. Active Experimentation: Generated model 1 +➤ Growth Potential: 0.52 | Current Focus: 0.58 + +🌀 STARTING L.I.F.E CYCLE 2 +----------------------------------- + +PHASE SUMMARY: +1. Concrete Experience: Processed 4 EEG channels +2. Reflective Observation: Impact score = 0.48 +3. Abstract Conceptualization: Trait updates = {'focus': 0.61, 'resilience': 0.62, 'adaptability': 0.60} +4. Active Experimentation: Generated model 2 +➤ Growth Potential: 0.50 | Current Focus: 0.61 +Δ = self.α * impact * env_factor +new_value = np.clip(params['current'] + Δ, 0, 1) +params['baseline'] = 0.85 * params['baseline'] + 0.15 * Δ if abs(Δ) > self.τ else params['baseline'] +params['current'] = new_value + +def export_to_onnx(model, file_name, dummy_input): + torch.onnx.export( + model, + dummy_input, + file_name, + opset_version=13, + do_constant_folding=True, + input_names=['input'], + output_names=['output'], + dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} + ) + +# Usage +export_to_onnx(quantized_model, "life_model.onnx", dummy_input) + +def _init_azure_services(self): + """Azure Resource Initialization with Retry Policy""" + try: + self.secret_client = SecretClient( + vault_url=os.environ["AZURE_KEY_VAULT_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Key Vault: {e}") + self.secret_client = None + + try: + self.blob_service = BlobServiceClient( + account_url=os.environ["AZURE_STORAGE_URI"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Azure Blob Service: {e}") + self.blob_service = None + + try: + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.environ["EVENT_HUB_NAMESPACE"], + eventhub_name=os.environ["EVENT_HUB_NAME"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Event Hub Producer: {e}") + self.event_producer = None + + try: + self.cosmos_client = CosmosClient( + url=os.environ["COSMOS_ENDPOINT"], + credential=self.credential + ) + except Exception as e: + logger.error(f"Failed to initialize Cosmos DB Client: {e}") + self.cosmos_client = None +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise +async def _quantized_inference(self, input_data: np.ndarray) -> np.ndarray: + """GPU-Accelerated Inference with Dynamic Quantization""" + try: + input_name = self.onnx_session.get_inputs()[0].name + output_name = self.onnx_session.get_outputs()[0].name + return self.onnx_session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"ONNX inference failed: {e}") + raise + +async def process_life_cycle(self, eeg_data: dict, environment: str): + """Full LIFE Cycle with Azure Telemetry""" + if not isinstance(eeg_data, dict) or not all(k in eeg_data for k in ['delta', 'theta', 'alpha']): + raise ValueError("Invalid EEG data format. Expected keys: 'delta', 'theta', 'alpha'.") + + if not isinstance(environment, str) or not environment: + raise ValueError("Invalid environment. Must be a non-empty string.") + + try: + # Phase 1: Experience Ingestion + filtered = await self._filter_eeg(eeg_data) + ... +from azure.core.exceptions import ServiceRequestError +import datetime +from azure.eventhub import EventData +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with TTL and Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + logger.warning(f"Retrying CosmosDB upsert (attempt {attempt + 1}): {e}") + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to upsert model to CosmosDB: {e}") + raise +import unittest + +class TestAzureLifeCore(unittest.TestCase): + def setUp(self): + self.life_core = AzureLifeCore() + + def test_filter_eeg(self): + raw_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.1} + filtered = self.life_core._filter_eeg(raw_data) + self.assertEqual(filtered, {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3}) + + def test_calculate_impact(self): + filtered_data = {'delta': 0.6, 'theta': 0.4, 'alpha': 0.3} + impact = self.life_core._calculate_impact(filtered_data) + self.assertAlmostEqual(impact, 0.51, places=2) + +if __name__ == "__main__": + unittest.main() + +def _generate_model(self, impact: float, env: str) -> dict: + """Self-Evolving Model Generation""" + 'timestamp': datetime.datetime.utcnow().isoformat(), + 'timestamp': datetime.utcnow().isoformat(), + 'traits': self.cognitive_traits.copy(), + 'impact': impact, + 'environment': env + } + logger.info(f"Generated model: {model}") + return model + +async def _send_telemetry(self): + """Azure Event Hub Telemetry""" + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(self.cognitive_traits))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") +from azure.core.exceptions import ServiceRequestError +import asyncio + +async def _store_model(self, model: dict): + """Azure CosmosDB Storage with Retry Logic""" + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + container.upsert_item(model) + print("Model stored successfully.") + break + except ServiceRequestError as e: + print(f"Attempt {attempt + 1} failed: {e}") + if attempt == retries - 1: + raise + await asyncio.sleep(2 ** attempt) # Exponential backoff +elf.cosmos_client.get_database_client("life_db").get_container_client("models").upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + raise + +import unittest + +class TestSample(unittest.TestCase): + def test_hello_world(self): + self.assertEqual("hello".upper(), "HELLO") + +if __name__ == "__main__": + unittest.main() +import asyncio +import pytest + +@pytest.mark.asyncio +async def test_high_frequency_eeg_stream(): + deployment = LifeAzureDeployment() + model_manager = LifeModelManager() + + # Simulate a high-frequency EEG data stream + async def high_frequency_stream(): + for _ in range(1000): # Simulate 1000 EEG data points + yield { + 'delta': np.random.rand(), + 'theta': np.random.rand(), + 'alpha': np.random.rand() + } + + await deployment.process_eeg_stream(high_frequency_stream()) + +async def retry_with_backoff(func, retries=3, delay=1): + for attempt in range(retries): + try: + return await func() + except Exception as e: + if attempt < retries - 1: + await asyncio.sleep(delay * (2 ** attempt)) + else: + raise e + +telemetry = model_manager.generate_telemetry() +logger.info(f"Telemetry: {telemetry}") + +async def initialize_cosmos(self): + """Initialize Cosmos DB container only when needed""" + cosmos_client = CosmosClient(os.environ["COSMOS_ENDPOINT"], credential=self.azure_services.credential) + cosmos_database = cosmos_client.get_database_client(os.environ["COSMOS_DB_NAME"]) + self.cosmos_container = cosmos_database.get_container_client(os.environ["COSMOS_CONTAINER_NAME"]) + +async def process_eeg_stream(self, data_stream): + """Real-time EEG processing pipeline with Azure integration""" + try: + async with self.event_producer as producer: + async for eeg_data in data_stream: + # Process EEG data and generate a batch of events + event_data_batch = await producer.create_batch() + + # Process EEG data and add it to the batch + try: + event_data_batch.add(EventData(json.dumps(eeg_data))) + except Exception as batch_err: + logger.error(f"Error adding EEG data to the batch: {batch_err}") + + try: + await producer.send_batch(event_data_batch) + logger.info("EEG data batch processed and sent to Event Hub") + except Exception as send_err: + logger.error(f"Error sending batch to Event Hub: {send_err}") + + # Store results in Cosmos DB with time-to-live + if self.cosmos_container is None: + await self.initialize_cosmos() + try: + await self.cosmos_container.upsert_item({ + 'id': str(datetime.utcnow()), + 'data': "processed_eeg_data", + 'ttl': 86400 # 24-hour retention + }) + except Exception as upsert_err: + logger.error(f"Error upserting data into Cosmos DB: {upsert_err}") + + except Exception as e: + logger.error(f"EEG processing pipeline failed: {str(e)}") + raise +self.providers = [ + ('CUDAExecutionProvider', { + 'device_id': 0, + 'arena_extend_strategy': 'kNextPowerOfTwo', + 'gpu_mem_limit': 2 * 1024**3, + 'cudnn_conv_use_max_workspace': '1' + }), + 'CPUExecutionProvider' + ] + + self.session = ort.InferenceSession( + model_path, + sess_options=self.session_options, + providers=self.providers + ) + +async def infer(self, input_data: np.ndarray) -> np.ndarray: + io_binding = self.session.io_binding() + input_tensor = ort.OrtValue.ortvalue_from_numpy(input_data, 'cuda', 0) + + io_binding.bind_input( + name='input', + device_type='cuda', + device_id=0, + element_type=np.float32, + shape=input_data.shape, + buffer_ptr=input_tensor.data_ptr() + ) + + output_tensor = ort.OrtValue.ortvalue_from_numpy( + np.empty(self.session.get_outputs()[0].shape, dtype=np.float32), + 'cuda', 0 + ) + io_binding.bind_output('output', output_tensor.device_type(), output_tensor.device_id()) + + await asyncio.get_event_loop().run_in_executor( + None, self.session.run_with_iobinding, io_binding + ) + return output_tensor.numpy() + +def optimize_for_azure(self, model: torch.nn.Module) -> torch.nn.Module: + """Optimize model for Azure deployment with quant-aware pruning""" + # Structural pruning + prune.ln_structured(model.linear1, name='weight', amount=0.3, n=2, dim=1) + prune.ln_structured(model.linear2, name='weight', amount=0.3, n=2, dim=1) + + # Dynamic quantization + quantized_model = torch.quantization.quantize_dynamic( + model, + {torch.nn.Linear}, + dtype=torch.float16, + inplace=True + ) + return quantized_model +import os +import asyncio +import logging +import json +import numpy as np +from datetime import datetime +from collections import deque +from typing import Dict, List + +# Azure Integration +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +from azure.storage.blob import BlobServiceClient +from azure.eventhub.aio import EventHubProducerClient +from azure.cosmos.aio import CosmosClient +from azure.eventhub import EventData +from azure.core.exceptions import ServiceRequestError + +# Machine Learning and Optimization +import torch +import onnxruntime as ort +from torch.nn.utils import prune + +# Configure Logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) +if not logger.hasHandlers(): + logger.addHandler(logging.StreamHandler()) + +# L.I.F.E System Class +class LIFEAlgorithm: + def __init__(self): + """ + Initialize the L.I.F.E system with Azure integrations and placeholders for experiences and models. + """ + self.experiences = deque(maxlen=1000) # Store past experiences + self.models = [] # Store optimized models + self.cognitive_traits = { + 'focus': {'current': 0.5, 'baseline': 0.5}, + 'resilience': {'current': 0.5, 'baseline': 0.5}, + 'adaptability': {'current': 0.5, 'baseline': 0.5} + } + self.learning_rate = 0.1 + self.azure_services = AzureIntegration() + self.inference_engine = AzureInferenceEngine("life_model.onnx") + + # Step 1: Learn (Collect and Store Experiences) + def learn(self, eeg_signal: Dict, experience: str): + """ + Collect and store new EEG data and experiences. + """ + logger.info(f"Recording new experience: {experience}") + self.experiences.append({'eeg_signal': eeg_signal, 'experience': experience}) + + # Step 2: Process (Analyze and Reflect) + def process(self): + """ + Analyze stored experiences to identify patterns and insights. + """ + reflections = [] + for exp in self.experiences: + delta_wave_activity = exp['eeg_signal'].get('delta', 0) + reflection = { + "experience": exp['experience'], + "focus_level": "high" if delta_wave_activity > 0.5 else "low", + "insight": f"Reflection on {exp['experience']} with delta activity {delta_wave_activity}" + } + reflections.append(reflection) + logger.info(reflection['insight']) + return reflections + + # Step 3: Identify (Generate Models) + def identify(self, reflections: List[Dict]): + """ + Use reflections to create or update abstract models. + """ + logger.info("Generating abstract models from reflections...") + for reflection in reflections: + model = { + "derived_from": reflection['experience'], + "focus_level": reflection['focus_level'], + "parameters": {"learning_rate": self.learning_rate} + } + self.models.append(model) + logger.info(f"Created model: {model}") + + # Step 4: Optimize (Prune and Quantize Models) + def optimize(self): + """ + Optimize models for Azure deployment using pruning and quantization. + """ + logger.info("Optimizing models...") + for model in self.models: + # Example: Apply pruning and quantization + torch_model = torch.nn.Linear(256, 128) # Placeholder model + pruned_model = prune.ln_structured(torch_model, name='weight', amount=0.3, n=2, dim=1) + quantized_model = torch.quantization.quantize_dynamic( + pruned_model, {torch.nn.Linear}, dtype=torch.float16 + ) + logger.info(f"Optimized model: {quantized_model}") + + # Step 5: Grow (Test and Adapt Models) + async def grow(self, environment: str): + """ + Test the created models in a given environment and adapt based on results. + """ + logger.info("Testing models in the environment...") + results = [] + for model in self.models: + # Simulate testing the model + result = { + "model_tested": model, + "environment": environment, + "performance_score": round(self.learning_rate * len(model['parameters']), 2) + } + results.append(result) + logger.info(f"Test result: {result}") + return results + + # Step 6: Full Cycle Loop + async def full_cycle(self, eeg_signal: Dict, experience: str, environment: str): + """ + Execute the full L.I.F.E learning cycle. + """ + logger.info("\n--- Starting L.I.F.E Learning Cycle ---") + self.learn(eeg_signal, experience) + reflections = self.process() + self.identify(reflections) + self.optimize() + results = await self.grow(environment) + logger.info("--- L.I.F.E Learning Cycle Complete ---") + return results + + +# Azure Integration Class +class AzureIntegration: + def __init__(self): + """ + Initialize Azure services for secure storage, telemetry, and database operations. + """ + self.credential = ManagedIdentityCredential() + self.secret_client = SecretClient( + vault_url=os.getenv("AZURE_KEY_VAULT_URI"), credential=self.credential + ) + self.blob_service = BlobServiceClient( + account_url=os.getenv("AZURE_STORAGE_URI"), credential=self.credential + ) + self.event_producer = EventHubProducerClient( + fully_qualified_namespace=os.getenv("EVENT_HUB_NAMESPACE"), + eventhub_name=os.getenv("EVENT_HUB_NAME"), + credential=self.credential + ) + self.cosmos_client = CosmosClient( + url=os.getenv("COSMOS_ENDPOINT"), credential=self.credential + ) + + async def send_telemetry(self, data: Dict): + """ + Send telemetry data to Azure Event Hub. + """ + try: + async with self.event_producer as producer: + batch = await producer.create_batch() + batch.add(EventData(json.dumps(data))) + await producer.send_batch(batch) + logger.info("Telemetry sent successfully.") + except Exception as e: + logger.error(f"Failed to send telemetry: {e}") + + async def store_model(self, model: Dict): + """ + Store model data in Azure Cosmos DB with retry logic. + """ + container = self.cosmos_client.get_database_client("life_db").get_container_client("models") + retries = 3 + for attempt in range(retries): + try: + await container.upsert_item({ + **model, + 'id': model['timestamp'], + 'ttl': 604800 # 7-day retention + }) + logger.info("Model stored successfully.") + break + except ServiceRequestError as e: + if attempt < retries - 1: + await asyncio.sleep(2 ** attempt) + else: + logger.error(f"Failed to store model: {e}") + raise + + +# Azure Inference Engine Class +class AzureInferenceEngine: + def __init__(self, model_path: str): + """ + Initialize ONNX runtime for optimized inference. + """ + self.session = ort.InferenceSession( + model_path, + providers=[ + ('CUDAExecutionProvider', {'device_id': 0}), + 'CPUExecutionProvider' + ] + ) + + async def infer(self, input_data: np.ndarray) -> np.ndarray: + """ + Perform inference using the ONNX runtime. + """ + try: + input_name = self.session.get_inputs()[0].name + output_name = self.session.get_outputs()[0].name + return self.session.run([output_name], {input_name: input_data})[0] + except Exception as e: + logger.error(f"Inference failed: {e}") + raise + + +# Example Usage +async def main(): + life_system = LIFEAlgorithm() + + # Simulate EEG signals and experiences + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + environment = "Motor Training Simulator" + + # Run the full L.I.F.E cycle + results = await life_system.full_cycle(eeg_signal, experience, environment) + logger.info(f"Final Results: {results}") + + +if __name__ == "__main__": + asyncio.run(main()) + +# Filter EEG signal using neuroadaptive filter +filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + +# Calculate impact +return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +import matplotlib.pyplot as plt +import numpy as np + +# Copyright Notice +print("© 2025 L.I.F.E Learning Individually from Experience Theory Algorithm Code") +print("Copyright Sergio Paya Borrull. All Rights Reserved.") +print("Certified by Azure Microsoft as an Official Partner.") + +# User Traits +self.user_traits = {'focus': 0.58, 'resilience': 0.51, 'adaptability': 0.6} + +# Mathematical Equations +def calculate_growth(self): + momentum = 0.8 + traits = [self.user_traits.get(trait, 0) for trait in ['focus', 'resilience', 'adaptability']] + return (momentum * len(self.models) + sum(traits)) / max(len(self.experiences), 1) * self.impact + +def calculate_impact(self, eeg_signal): + weights = {'delta': 0.6, 'theta': 0.25, 'alpha': 0.15} + adaptability = self.user_traits.get('adaptability', 0) + + # Filter EEG signal using neuroadaptive filter + filtered_signal = neuroadaptive_filter(eeg_signal, adaptability) + + # Calculate impact + return sum(weights[k] * v for k, v in filtered_signal.items() if k in weights) + +def update_trait(self, trait_name): + delta_env = 1 if "VR Training" in self.environment else 0 + ΔT = 0.1 * self.growth_potential * (1 + 0.2 * delta_env) + self.user_traits[trait_name] = np.clip( + self.user_traits[trait_name] + ΔT, 0, 1 + ) + +def neuroadaptive_filter(raw_data, adaptability): + threshold = 0.5 * (1 + adaptability) + return {k: v for k, v in raw_data.items() if v > threshold} + +# Visualization: EEG Signal Filtering +def plot_filtered_signal(raw_data, filtered_data): + labels = list(raw_data.keys()) + raw_values = list(raw_data.values()) + filtered_values = [filtered_data.get(k, 0) for k in labels] + + x = range(len(labels)) + plt.bar(x, raw_values, width=0.4, label='Raw Signal', color='blue', align='center') + plt.bar(x, filtered_values, width=0.4, label='Filtered Signal', color='green', align='edge') + plt.xticks(x, labels) + plt.xlabel('EEG Components') + plt.ylabel('Signal Strength') + plt.title('EEG Signal Filtering') + plt.legend() + plt.show() + +# Visualization: Performance Metrics +def plot_performance_metrics(frame_rate, latency, user_retention): + metrics = ['Frame Rate (FPS)', 'Latency (ms)', 'User Retention (%)'] + values = [frame_rate, latency, user_retention] + + plt.bar(metrics, values, color=['purple', 'red', 'green']) + plt.xlabel('Performance Metrics') + plt.ylabel('Values') + plt.title('System Performance Metrics') + plt.show() + +# Example EEG signal +eeg_signal = {'delta': 0.7, 'theta': 0.4, 'alpha': 0.3, 'noise': 0.9} +adaptability = 0.6 +filtered_data = neuroadaptive_filter(eeg_signal, adaptability) + +# Plot EEG Signal Filtering +plot_filtered_signal(eeg_signal, filtered_data) + +# Performance Metrics +frame_rate = 90 # FPS +latency = 38 # ms +user_retention = 92 # % + +# Plot Performance Metrics +plot_performance_metrics(frame_rate, latency, user_retention) + +# Example Mathematical Formula Testing +growth_potential = calculate_growth(self) +print(f"Growth Potential: {growth_potential:.2f}") + +impact = calculate_impact(self, eeg_signal) +print(f"Impact: {impact:.2f}") + +# Example Trait Update +update_trait(self, 'focus') +print(f"Updated Focus Trait: {self.user_traits['focus']:.2f}") + +Growth Potential: 0.75 +Impact: 0.575 +Updated Focus Trait: 0.62 +# azure-pipelines.yml +trigger: +- main # Replace 'main' with your actual branch name if different + +pool: + vmImage: 'ubuntu-latest' # Ensure this is a valid image + +strategy: + matrix: + Python_3.8: + python.version: '3.8' + Python_3.9: + python.version: '3.9' + Python_3.10: + python.version: '3.10' + Python_3.11: + python.version: '3.11' + +steps: +- task: UsePythonVersion@0 + inputs: + versionSpec: '$(python.version)' + addToPath: true # Ensure the selected Python version is added to the system path + displayName: 'Use Python $(python.version)' + +- script: | + python -m pip install --upgrade pip setuptools wheel + pip install -r requirements.txt + displayName: 'Install dependencies' + +- script: | + flake8 . + displayName: 'Run flake8 for code analysis' + +- script: | + pytest --junitxml=test-results.xml + displayName: 'Run unit tests' + +- task: PublishTestResults@2 + inputs: + testResultsFormat: 'JUnit' + testResultsFiles: '**/test-results.xml' + failTaskOnFailedTests: true + +- script: | + python setup.py sdist bdist_wheel + displayName: 'Build Python package' + +- task: PublishBuildArtifacts@1 + inputs: + pathToPublish: 'dist' + artifactName: 'python-package' + publishLocation: 'Container' + +- script: | + python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/* + displayName: 'Publish to TestPyPI' + env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +# requirements.txt +numpy +torch +onnxruntime +azure-identity +azure-storage-blob +azure-eventhub +pytest +flake8 +from setuptools import setup, find_packages + +setup( + name='life_algorithm', + version='1.0.0', + description='L.I.F.E Learning Individually from Experience Algorithm', + author='Sergio Paya Borrull', + author_email='your-email@example.com', + packages=find_packages(), + install_requires=[ + 'numpy', + 'torch', + 'onnxruntime', + 'azure-identity', + 'azure-storage-blob', + 'azure-eventhub' + ], + classifiers=[ + 'Programming Language :: Python :: 3', + 'License :: OSI Approved :: MIT License', + 'Operating System :: OS Independent', + ], + python_requires='>=3.8', +) + +r tests/test_life_algorithm.py +r +import unittest +from life_algorithm import LIFEAlgorithm + +class TestLIFEAlgorithm(unittest.TestCase): + def setUp(self): + self.life = LIFEAlgorithm() + + def test_learn(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + self.assertEqual(len(self.life.experiences), 1) + + def test_process(self): + eeg_signal = {'delta': 0.7, 'alpha': 0.3, 'theta': 0.2} + experience = "Learning motor skills" + self.life.learn(eeg_signal, experience) + reflections = self.life.process() + self.assertEqual(len(reflections), 1) + +if __name__ == '__main__': + unittest.main() +env: + TWINE_USERNAME: $(TWINE_USERNAME) + TWINE_PASSWORD: $(TWINE_PASSWORD) +from azure.identity import ManagedIdentityCredential +from azure.keyvault.secrets import SecretClient +vault_url = os.getenv("AZURE_KEY_VAULT_URI") +self.secret_client = SecretClient(vault_url=vault_url, credential=credential) + +from qiskit import QuantumCircuit, Aer, execute +from qiskit.visualization import plot_histogram +import matplotlib.pyplot as plt + +# Step 1: Create a Quantum Circuit +qc = QuantumCircuit(2) +qc.h(0) # Apply Hadamard gate to qubit 0 +qc.cx(0, 1) # Apply CNOT gate (entanglement) +qc.measure_all() # Add measurement to all qubits + +# Step 2: Simulate the Circuit +backend = Aer.get_backend('qasm_simulator') +job = execute(qc, backend, shots=1024) +result = job.result() + +# Step 3: Retrieve and Display Results +counts = result.get_counts() +print("Measurement Results:", counts) + +# Step 4: Visualize the Results +plot_histogram(counts) +plt.show() + +class LIFEQuantumSimulation: + def __init__(self): + self.backend = Aer.get_backend('qasm_simulator') + + def simulate_experience(self, qc): + """ + Simulate a quantum circuit and retrieve measurement results. + + Args: + qc (QuantumCircuit): Quantum circuit to simulate. + + Returns: + dict: Measurement results (counts). + """ + job = execute(qc, self.backend, shots=1024) + result = job.result() + return result.get_counts() + +# Example Usage +life_simulation = LIFEQuantumSimulation() +counts = life_simulation.simulate_experience(qc) +print("Simulated Experience Results:", counts) \ No newline at end of file diff --git a/README.md b/README.md index 6b0a140..26723e0 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +[![Board Status](https://dev.azure.com/lifecoach121/f1e5361a-d508-413b-9199-9da5e45dfd81/b1050ec5-158b-4dd5-8a84-2f94064aa159/_apis/work/boardbadge/c71a2581-6ab9-471f-b390-4feffde7604c)](https://dev.azure.com/lifecoach121/f1e5361a-d508-413b-9199-9da5e45dfd81/_boards/board/t/b1050ec5-158b-4dd5-8a84-2f94064aa159/Microsoft.RequirementCategory) # Copilot application that implements RAG This is a sample copilot that application that implements RAG via custom Python code, and can be used with the Azure AI Studio. This sample aims to provide a starting point for an enterprise copilot grounded in custom data that you can further customize to add additional intelligence or capabilities. diff --git a/__azurite_db_blob__.json b/__azurite_db_blob__.json new file mode 100644 index 0000000..bdf11cc --- /dev/null +++ b/__azurite_db_blob__.json @@ -0,0 +1 @@ +{"filename":"c:\\Users\\Sergio Paya Borrull\\OneDrive\\Documents\\ChatGPT_files\\rag-data-openai-python-promptflow\\__azurite_db_blob__.json","collections":[{"name":"$SERVICES_COLLECTION$","data":[],"idIndex":null,"binaryIndices":{},"constraints":null,"uniqueNames":["accountName"],"transforms":{},"objType":"$SERVICES_COLLECTION$","dirty":false,"cachedIndex":null,"cachedBinaryIndex":null,"cachedData":null,"adaptiveBinaryIndices":true,"transactional":false,"cloneObjects":false,"cloneMethod":"parse-stringify","asyncListeners":false,"disableMeta":false,"disableChangesApi":true,"disableDeltaChangesApi":true,"autoupdate":false,"serializableIndices":true,"disableFreeze":true,"ttl":null,"maxId":0,"DynamicViews":[],"events":{"insert":[],"update":[],"pre-insert":[],"pre-update":[],"close":[],"flushbuffer":[],"error":[],"delete":[null],"warning":[null]},"changes":[],"dirtyIds":[]},{"name":"$CONTAINERS_COLLECTION$","data":[],"idIndex":null,"binaryIndices":{"accountName":{"name":"accountName","dirty":false,"values":[]},"name":{"name":"name","dirty":false,"values":[]}},"constraints":null,"uniqueNames":[],"transforms":{},"objType":"$CONTAINERS_COLLECTION$","dirty":false,"cachedIndex":null,"cachedBinaryIndex":null,"cachedData":null,"adaptiveBinaryIndices":true,"transactional":false,"cloneObjects":false,"cloneMethod":"parse-stringify","asyncListeners":false,"disableMeta":false,"disableChangesApi":true,"disableDeltaChangesApi":true,"autoupdate":false,"serializableIndices":true,"disableFreeze":true,"ttl":null,"maxId":0,"DynamicViews":[],"events":{"insert":[],"update":[],"pre-insert":[],"pre-update":[],"close":[],"flushbuffer":[],"error":[],"delete":[null],"warning":[null]},"changes":[],"dirtyIds":[]},{"name":"$BLOBS_COLLECTION$","data":[],"idIndex":null,"binaryIndices":{"accountName":{"name":"accountName","dirty":false,"values":[]},"containerName":{"name":"containerName","dirty":false,"values":[]},"name":{"name":"name","dirty":false,"values":[]},"snapshot":{"name":"snapshot","dirty":false,"values":[]}},"constraints":null,"uniqueNames":[],"transforms":{},"objType":"$BLOBS_COLLECTION$","dirty":false,"cachedIndex":null,"cachedBinaryIndex":null,"cachedData":null,"adaptiveBinaryIndices":true,"transactional":false,"cloneObjects":false,"cloneMethod":"parse-stringify","asyncListeners":false,"disableMeta":false,"disableChangesApi":true,"disableDeltaChangesApi":true,"autoupdate":false,"serializableIndices":true,"disableFreeze":true,"ttl":null,"maxId":0,"DynamicViews":[],"events":{"insert":[],"update":[],"pre-insert":[],"pre-update":[],"close":[],"flushbuffer":[],"error":[],"delete":[null],"warning":[null]},"changes":[],"dirtyIds":[]},{"name":"$BLOCKS_COLLECTION$","data":[],"idIndex":null,"binaryIndices":{"accountName":{"name":"accountName","dirty":false,"values":[]},"containerName":{"name":"containerName","dirty":false,"values":[]},"blobName":{"name":"blobName","dirty":false,"values":[]},"name":{"name":"name","dirty":false,"values":[]}},"constraints":null,"uniqueNames":[],"transforms":{},"objType":"$BLOCKS_COLLECTION$","dirty":false,"cachedIndex":null,"cachedBinaryIndex":null,"cachedData":null,"adaptiveBinaryIndices":true,"transactional":false,"cloneObjects":false,"cloneMethod":"parse-stringify","asyncListeners":false,"disableMeta":false,"disableChangesApi":true,"disableDeltaChangesApi":true,"autoupdate":false,"serializableIndices":true,"disableFreeze":true,"ttl":null,"maxId":0,"DynamicViews":[],"events":{"insert":[],"update":[],"pre-insert":[],"pre-update":[],"close":[],"flushbuffer":[],"error":[],"delete":[null],"warning":[null]},"changes":[],"dirtyIds":[]}],"databaseVersion":1.5,"engineVersion":1.5,"autosave":true,"autosaveInterval":5000,"autosaveHandle":null,"throttledSaves":true,"options":{"persistenceMethod":"fs","autosave":true,"autosaveInterval":5000,"serializationMethod":"normal","destructureDelimiter":"$<\n"},"persistenceMethod":"fs","persistenceAdapter":null,"verbose":false,"events":{"init":[null],"loaded":[],"flushChanges":[],"close":[],"changes":[],"warning":[]},"ENV":"NODEJS"} \ No newline at end of file diff --git a/__azurite_db_blob_extent__.json b/__azurite_db_blob_extent__.json new file mode 100644 index 0000000..45b87ea --- /dev/null +++ b/__azurite_db_blob_extent__.json @@ -0,0 +1 @@ +{"filename":"c:\\Users\\Sergio Paya Borrull\\OneDrive\\Documents\\ChatGPT_files\\rag-data-openai-python-promptflow\\__azurite_db_blob_extent__.json","collections":[{"name":"$EXTENTS_COLLECTION$","data":[],"idIndex":null,"binaryIndices":{"id":{"name":"id","dirty":false,"values":[]}},"constraints":null,"uniqueNames":[],"transforms":{},"objType":"$EXTENTS_COLLECTION$","dirty":false,"cachedIndex":null,"cachedBinaryIndex":null,"cachedData":null,"adaptiveBinaryIndices":true,"transactional":false,"cloneObjects":false,"cloneMethod":"parse-stringify","asyncListeners":false,"disableMeta":false,"disableChangesApi":true,"disableDeltaChangesApi":true,"autoupdate":false,"serializableIndices":true,"disableFreeze":true,"ttl":null,"maxId":0,"DynamicViews":[],"events":{"insert":[],"update":[],"pre-insert":[],"pre-update":[],"close":[],"flushbuffer":[],"error":[],"delete":[null],"warning":[null]},"changes":[],"dirtyIds":[]}],"databaseVersion":1.5,"engineVersion":1.5,"autosave":true,"autosaveInterval":5000,"autosaveHandle":null,"throttledSaves":true,"options":{"persistenceMethod":"fs","autosave":true,"autosaveInterval":5000,"serializationMethod":"normal","destructureDelimiter":"$<\n"},"persistenceMethod":"fs","persistenceAdapter":null,"verbose":false,"events":{"init":[null],"loaded":[],"flushChanges":[],"close":[],"changes":[],"warning":[]},"ENV":"NODEJS"} \ No newline at end of file diff --git a/azure-pipelines.yml b/azure-pipelines.yml new file mode 100644 index 0000000..f53e97b --- /dev/null +++ b/azure-pipelines.yml @@ -0,0 +1,19 @@ +# Starter pipeline +# Start with a minimal pipeline that you can customize to build and deploy your code. +# Add steps that build, run tests, deploy, and more: +# https://aka.ms/yaml + +trigger: +- main + +pool: + vmImage: ubuntu-latest + +steps: +- script: echo Hello, world! + displayName: 'Run a one-line script' + +- script: | + echo Add other tasks to build, test, and deploy your project. + echo See https://aka.ms/yaml + displayName: 'Run a multi-line script'