1.0.0

agent.py

@@ -28,7 +28,7 @@
 SOFTWARE.
 
 Contributors:
-- Adam Milton-Barker - First version - 2021-5-1
+- Nitin Mane
 
 """
 
@@ -58,7 +58,6 @@ def train(self):
 		self.model.prepare_data()
 		self.model.prepare_network()
 		self.model.train()
-		self.model.evaluate()
 
 	def set_model(self, mtype):
 
@@ -67,7 +66,7 @@ def set_model(self, mtype):
 	def load_model(self):
 		""" Loads the trained model """
 
-		self.model.load()
+		self.model.prepare_network()
 
 	def server(self):
 		""" Loads the API server """

configuration/config.json

@@ -1,8 +1,6 @@
 {
     "agent": {
         "cores": 8,
-        "server": "",
-        "port": 1234,
         "params": [
             "train",
             "classify",

modules/AbstractModel.py

@@ -78,8 +78,7 @@ def http_request(self, img_path):
 		self.helpers.logger.info("Sending request for: " + img_path)
 
 		_, img_encoded = cv2.imencode('.png', cv2.imread(img_path))
-		response = requests.post(
-			self.addr, data=img_encoded.tostring(), headers=self.headers)
+		response = requests.post(self.addr, data=img_encoded.tostring(), headers=self.headers)
 		response = json.loads(response.text)
 
 		return response

modules/data.py

@@ -23,7 +23,7 @@
 SOFTWARE.
 
 Contributors:
-- Adam Milton-Barker
+- Nitin Mane
 
 """
 

modules/model.py

@@ -23,7 +23,6 @@
 SOFTWARE.
 
 Contributors:
-- Adam Milton-Barker
 - Nitin Mane
 
 """
@@ -89,7 +88,7 @@ def prepare_network(self):
 
 		self.load()
 		self.tf_intermediate_layer_model = keras.Model(
-			inputs=self.model.input, outputs=self.model.get_layer(layer_name).output)
+			inputs=self.model.input, outputs=self.model.get_layer(layer_name).output, name="SarsCov2xDNN")
 
 		self.load()
 		self.tf_intermediate_layer_model.summary()
@@ -400,8 +399,7 @@ def test(self):
 				msg = ""
 				status = ""
 				outcome = ""
-				print(prediction)
-				print(testFile)
+
 				if prediction == 1 and "Non-Covid" in testFile:
 					fp += 1
 					status = "incorrectly"
@@ -455,7 +453,6 @@ def test_http(self):
 		for testFile in os.listdir(self.testing_dir):
 			if os.path.splitext(testFile)[1] in self.valid:
 
-				print(self.testing_dir + "/" + testFile)
 				start = time.time()
 				prediction = self.http_request(self.testing_dir + "/" + testFile)
 				end = time.time()

modules/mqtt.py

@@ -1,44 +1,31 @@
 #!/usr/bin/env python
-###################################################################################
-##
-## HIAS iotJumpWay MQTT Module.
-## This module connects devices, applications, robots and software to the
-## HIAS iotJumpWay MQTT Broker.
-##
-###################################################################################
-##
-## Permission is hereby granted, free of charge, to any person obtaining a copy
-## of this software and associated documentation files(the "Software"), to deal
-## in the Software without restriction, including without limitation the rights
-## to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
-## copies of the Software, and to permit persons to whom the Software is
-## furnished to do so, subject to the following conditions:
-##
-## The above copyright notice and this permission notice shall be included in all
-## copies or substantial portions of the Software.
-##
-## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-## FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-## AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-## LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-## OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-## SOFTWARE.
-##
-###################################################################################
-##
-## Project: HIAS Hospital Intelligent Automation Server
-## Author: Adam Milton-Barker
-## Copyright: 2020, Asociacion De Investigacion En Inteligencia Artificial Para La
-##            Leucemia Peter Moss.
-## Credits: ["Adam Milton-Barker"]
-## License: MIT
-## Version: 3.0.0
-## Maintainer: Adam Milton-Barker
-## Website: adammiltonbarker.com
-## Last Modified: 2021-1-9
-##
-###################################################################################
+""" HIAS iotJumpWay MQTT module
+
+This module connects devices, applications, robots  and other softwares to the
+HIAS iotJumpWay MQTT Broker.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Contributors:
+- Adam Milton-Barker
+
+"""
 
 import json
 

modules/server.py

@@ -1,39 +1,30 @@
 #!/usr/bin/env python
-###################################################################################
-##
-## Project:    COVID -19 xDNN Classifier 2020
-## Version:    1.0.0
-## Module:     Server
-## Desription: The COVID -19 xDNN Classifier 2020 server.
-## License:    MIT
-## Copyright:  2021, Asociacion De Investigacion En Inteligencia Artificial Para
-##             La Leucemia Peter Moss.
-## Author:     Nitin Mane
-## Maintainer: Nitin Mane
-##
-## Modified:   2021-2-19
-##
-###################################################################################
-##
-## Permission is hereby granted, free of charge, to any person obtaining a copy
-## of this software and associated documentation files(the "Software"), to deal
-## in the Software without restriction, including without limitation the rights
-## to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
-## copies of the Software, and to permit persons to whom the Software is
-## furnished to do so, subject to the following conditions:
-##
-## The above copyright notice and this permission notice shall be included in all
-## copies or substantial portions of the Software.
-##
-## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-## FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-## AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-## LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-## OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-## SOFTWARE.
-##
-###################################################################################
+""" HIAS AI Agent Server Class.
+
+Class for the HIAS IoT Agent server/API.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Contributors:
+- Nitin Mane
+
+"""
 
 import cv2
 import json

modules/xDNN_class.py

@@ -1,7 +1,31 @@
-"""
-Please cite:
-
-Angelov, P., & Soares, E. (2020). Towards explainable deep neural networks (xDNN). Neural Networks.
+""" XDNN Class
+
+Provides the HIAS AI Model with the required required data
+processing functionality.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Citation: Angelov, P., & Soares, E. (2020). Towards explainable deep neural networks (xDNN). Neural Networks.
+
+Contributors:
+- Nitin Mane
+
 """
 
 import math
@@ -22,36 +46,36 @@ def xDNN(Input,Mode):
         Output['xDNNParms']['Parameters'] = Prototypes
         MemberLabels = {}
         for i in range(0,CN+1):
-           MemberLabels[i]=Input['Labels'][Input['Labels']==i] 
+           MemberLabels[i]=Input['Labels'][Input['Labels']==i]
         Output['xDNNParms']['CurrentNumberofClass']=CN+1
         Output['xDNNParms']['OriginalNumberofClass']=CN+1
         Output['xDNNParms']['MemberLabels']=MemberLabels
         return Output
-    
+
     elif Mode == 'Validation':
         Params=Input['xDNNParms']
         datates=Input['Features']
         Test_Results = DecisionMaking(Params,datates)
-        EstimatedLabels = Test_Results['EstimatedLabels'] 
+        EstimatedLabels = Test_Results['EstimatedLabels']
         Scores = Test_Results['Scores']
         Output = {}
         Output['EstLabs'] = EstimatedLabels
         Output['Scores'] = Scores
         Output['ConfMa'] = confusion_matrix(Input['Labels'],Output['EstLabs'])
         Output['ClassAcc'] = np.sum(Output['ConfMa']*np.identity(len(Output['ConfMa'])))/len(Input['Labels'])
         return Output
-        
+
     elif Mode == 'classify':
         Params=Input['xDNNParms']
         datates=Input['Features']
         Test_Results = DecisionMaking(Params,datates)
-        EstimatedLabels = Test_Results['EstimatedLabels'] 
+        EstimatedLabels = Test_Results['EstimatedLabels']
         Scores = Test_Results['Scores']
         Output = {}
         Output['EstLabs'] = EstimatedLabels
         Output['Scores'] = Scores
         return Output
-    
+
 def PrototypesIdentification(Image,GlobalFeature,LABEL,CL):
     data = {}
     image = {}
@@ -67,7 +91,7 @@ def PrototypesIdentification(Image,GlobalFeature,LABEL,CL):
     for i in range(0, CL+1):
         Prototypes[i] = xDNNclassifier(data[i],image[i])
     return Prototypes
-        
+
 
 def xDNNclassifier(Data,Image):
     L, N, W = np.shape(Data)
@@ -95,7 +119,7 @@ def xDNNclassifier(Data,Image):
             distance = cdist(data[i-1,].reshape(1,-1),Centre,'euclidean')[0]
         value,position= distance.max(0),distance.argmax(0)
         value=value**2
-        
+
         if DataDensity > CDmax or DataDensity < CDmin or value > 2*Radius[position]:
             Centre=np.vstack((Centre,data[i-1,]))
             Noc=Noc+1
@@ -106,7 +130,7 @@ def xDNNclassifier(Data,Image):
         else:
             Centre[position,] = Centre[position,]*(Support[position]/Support[position]+1)+data[i-1]/(Support[position]+1)
             Support[position]=Support[position]+1
-            Radius[position]=0.5*Radius[position]+0.5*(X[position,]-sum(Centre[position,]**2))/2  
+            Radius[position]=0.5*Radius[position]+0.5*(X[position,]-sum(Centre[position,]**2))/2
     dic = {}
     dic['Noc'] =  Noc
     dic['Centre'] =  Centre
@@ -116,9 +140,9 @@ def xDNNclassifier(Data,Image):
     dic['Prototype'] = VisualPrototype
     dic['L'] =  L
     dic['X'] =  X
-    return dic  
- 
-    
+    return dic
+
+
 
 def DecisionMaking(Params,datates):
     PARAM=Params['Parameters']
@@ -143,17 +167,15 @@ def DecisionMaking(Params,datates):
         indx = np.argsort(Value)[::-1]
         EstimatedLabels[i-1]=indx[0]
     LABEL1=np.zeros((CurrentNC,1))
-    
-    
-    for i in range(0,CurrentNC): 
+
+
+    for i in range(0,CurrentNC):
         LABEL1[i] = np.unique(LAB[i])
 
     EstimatedLabels = EstimatedLabels.astype(int)
-    EstimatedLabels = LABEL1[EstimatedLabels]   
+    EstimatedLabels = LABEL1[EstimatedLabels]
     dic = {}
     dic['EstimatedLabels'] = EstimatedLabels
     dic['Scores'] = Scores
     return dic
-
-###############################################################################