Knn (#85)

* knn

Signed-off-by: Andrey Parfenov <[email protected]>

CMakeLists.txt

@@ -196,6 +196,7 @@ set (ML_MODULE_SRC
     ${CMAKE_HOME_DIRECTORY}/src/ml/ml_module.cpp
     ${CMAKE_HOME_DIRECTORY}/src/ml/concentration_regression_classifier.cpp
     ${CMAKE_HOME_DIRECTORY}/third_party/libsvm/svm.cpp
+    ${CMAKE_HOME_DIRECTORY}/src/ml/concentration_knn_classifier.cpp
 )
 
 add_library (
@@ -252,6 +253,7 @@ target_include_directories (
     ${CMAKE_HOME_DIRECTORY}/src/ml/inc
     ${CMAKE_HOME_DIRECTORY}/third_party/libsvm
     ${CMAKE_HOME_DIRECTORY}/third_party/json
+    ${CMAKE_HOME_DIRECTORY}/third_party/kdtree
 )
 
 set_target_properties (${BOARD_CONTROLLER_NAME}
@@ -282,13 +284,6 @@ set_target_properties (${ML_MODULE_NAME}
     RUNTIME_OUTPUT_DIRECTORY ${CMAKE_HOME_DIRECTORY}/compiled
 )
 
-# link openmp if found
-find_package (OpenMP)
-if (OpenMP_CXX_FOUND)
-    target_link_libraries (${DATA_HANDLER_NAME} PRIVATE OpenMP::OpenMP_CXX)
-    target_link_libraries (${ML_MODULE_NAME} PRIVATE OpenMP::OpenMP_CXX)
-endif()
-
 # dont link pthread for Android
 if (UNIX AND NOT ANDROID)
     target_link_libraries (${BOARD_CONTROLLER_NAME} PRIVATE pthread dl)

csharp-package/brainflow/brainflow/ml_module_library.cs

@@ -16,6 +16,7 @@ public enum BrainFlowMetrics
     public enum BrainFlowClassifiers
     {
         REGRESSION = 0,
+        KNN = 1
     };
 
     public static class MLModuleLibrary64

java-package/brainflow/src/main/java/brainflow/BrainFlowClassifiers.java

@@ -6,7 +6,8 @@
 public enum BrainFlowClassifiers
 {
 
-    REGRESSION (0);
+    REGRESSION (0),
+    KNN (1);
 
     private final int protocol;
     private static final Map<Integer, BrainFlowClassifiers> cl_map = new HashMap<Integer, BrainFlowClassifiers> ();

julia-package/brainflow/src/ml_model.jl

@@ -15,6 +15,7 @@ end
 @enum BrainFlowClassifiers begin
 
     REGRESSION = 0
+    KNN = 1
 
 end
 

matlab-package/brainflow/BrainFlowClassifiers.m

@@ -1,5 +1,6 @@
 classdef BrainFlowClassifiers < int32
     enumeration
         REGRESSION (0)
+        KNN (1)
     end
 end

python-package/brainflow/ml_model.py

@@ -27,6 +27,7 @@ class BrainFlowClassifiers (enum.Enum):
     """Enum to store all supported classifiers"""
 
     REGRESSION = 0 #:
+    KNN = 1 #:
 
 
 class BrainFlowModelParams (object):

src/data_handler/data_handler.cpp

@@ -6,10 +6,6 @@
 #include <string>
 #include <vector>
 
-#ifdef _OPENMP
-#include <omp.h>
-#endif
-
 #include "brainflow_constants.h"
 #include "data_handler.h"
 #include "downsample_operators.h"
@@ -920,8 +916,7 @@ int get_avg_band_powers (double *raw_data, int rows, int cols, int sampling_rate
             bands[i][j] = 0.0;
         }
     }
-
-#pragma omp parallel for
+    // todo parallel using c++ threads
     for (int i = 0; i < rows; i++)
     {
         double *ampls = new double[nfft / 2 + 1];

src/ml/concentration_knn_classifier.cpp

@@ -0,0 +1,94 @@
+#include <algorithm>
+#include <cmath>
+#include <stdlib.h>
+
+#include "brainflow_constants.h"
+#include "concentration_knn_classifier.h"
+#include "focus_dataset.h"
+
+
+int ConcentrationKNNClassifier::prepare ()
+{
+    if (kdtree != NULL)
+    {
+        return (int)BrainFlowExitCodes::ANOTHER_CLASSIFIER_IS_PREPARED_ERROR;
+    }
+    if (!params.other_info.empty ())
+    {
+        try
+        {
+            num_neighbors = std::stoi (params.other_info);
+        }
+        catch (const std::exception &e)
+        {
+            return (int)BrainFlowExitCodes::INVALID_ARGUMENTS_ERROR;
+        }
+    }
+    if ((num_neighbors < 1) || (num_neighbors > 100))
+    {
+        return (int)BrainFlowExitCodes::INVALID_ARGUMENTS_ERROR;
+    }
+
+    int dataset_len = sizeof (brainflow_focus_y) / sizeof (brainflow_focus_y[0]);
+    for (int i = 0; i < dataset_len; i++)
+    {
+        FocusPoint point (brainflow_focus_x[i], 10, brainflow_focus_y[i]);
+        // decrease weight for stddev, 0.2 - experimental vlaue
+        for (int j = 5; j < 10; j++)
+        {
+            point[j] *= 0.2;
+        }
+        dataset.push_back (point);
+    }
+    kdtree = new kdt::KDTree<FocusPoint> (dataset);
+    return (int)BrainFlowExitCodes::STATUS_OK;
+}
+
+int ConcentrationKNNClassifier::predict (double *data, int data_len, double *output)
+{
+    if ((data_len < 5) || (data == NULL) || (output == NULL))
+    {
+        return (int)BrainFlowExitCodes::INVALID_BUFFER_SIZE_ERROR;
+    }
+    if (kdtree == NULL)
+    {
+        return (int)BrainFlowExitCodes::CLASSIFIER_IS_NOT_PREPARED_ERROR;
+    }
+
+    double feature_vector[10] = {0.0};
+    for (int i = 0; i < data_len; i++)
+    {
+        if (i >= 5)
+        {
+            feature_vector[i] = data[i] * 0.2;
+        }
+        else
+        {
+            feature_vector[i] = data[i];
+        }
+    }
+
+    FocusPoint sample_to_predict (feature_vector, 10, 0.0);
+    const std::vector<int> knn_ids = kdtree->knnSearch (sample_to_predict, num_neighbors);
+    int num_ones = 0;
+    for (int i = 0; i < knn_ids.size (); i++)
+    {
+        if (dataset[knn_ids[i]].value == 1)
+        {
+            num_ones++;
+        }
+    }
+
+    double score = ((double)num_ones) / num_neighbors;
+    *output = score;
+
+    return (int)BrainFlowExitCodes::STATUS_OK;
+}
+
+int ConcentrationKNNClassifier::release ()
+{
+    delete kdtree;
+    kdtree = NULL;
+    dataset.clear ();
+    return (int)BrainFlowExitCodes::STATUS_OK;
+}

src/ml/inc/concentration_knn_classifier.h

@@ -0,0 +1,33 @@
+#pragma once
+
+#include <vector>
+
+#include "base_classifier.h"
+#include "focus_point.h"
+
+#include "kdtree.h"
+
+
+class ConcentrationKNNClassifier : public BaseClassifier
+{
+public:
+    ConcentrationKNNClassifier (struct BrainFlowModelParams params) : BaseClassifier (params)
+    {
+        num_neighbors = 5;
+        kdtree = NULL;
+    }
+
+    virtual ~ConcentrationKNNClassifier ()
+    {
+        release ();
+    }
+
+    virtual int prepare ();
+    virtual int predict (double *data, int data_len, double *output);
+    virtual int release ();
+
+private:
+    std::vector<FocusPoint> dataset;
+    kdt::KDTree<FocusPoint> *kdtree;
+    int num_neighbors;
+};