CMSIS-DSP: Added example for the naive gaussian bayes classifier

Examples/ARM/arm_bayes_example/CMakeLists.txt

@@ -0,0 +1,45 @@
+cmake_minimum_required (VERSION 3.6)
+project (arm_bayes_example VERSION 0.1)
+
+
+# Needed to include the configBoot module
+# Define the path to CMSIS-DSP (ROOT is defined on command line when using cmake)
+set(ROOT ${CMAKE_CURRENT_SOURCE_DIR}/../../../../..)
+set(DSP ${ROOT}/CMSIS/DSP)
+
+# Add DSP folder to module path
+list(APPEND CMAKE_MODULE_PATH ${DSP})
+
+################################### 
+#
+# LIBRARIES
+#
+###################################
+
+########### 
+#
+# CMSIS DSP
+#
+
+add_subdirectory(../../../Source bin_dsp)
+
+
+################################### 
+#
+# TEST APPLICATION
+#
+###################################
+
+
+add_executable(arm_bayes_example)
+
+
+include(config)
+configApp(arm_bayes_example ${ROOT})
+
+target_sources(arm_bayes_example PRIVATE arm_bayes_example_f32.c)
+
+### Sources and libs
+
+target_link_libraries(arm_bayes_example PRIVATE CMSISDSP)
+

Examples/ARM/arm_bayes_example/arm_bayes_example_f32.c

@@ -0,0 +1,146 @@
+/* ----------------------------------------------------------------------
+* Copyright (C) 2019-2020 ARM Limited. All rights reserved.
+*
+* $Date:         09. December 2019
+* $Revision:     V1.0.0
+*
+* Project:       CMSIS DSP Library
+* Title:         arm_bayes_example_f32.c
+*
+* Description:   Example code demonstrating how to use Bayes functions.
+*
+* Target Processor: Cortex-M/Cortex-A
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+*   - Redistributions of source code must retain the above copyright
+*     notice, this list of conditions and the following disclaimer.
+*   - Redistributions in binary form must reproduce the above copyright
+*     notice, this list of conditions and the following disclaimer in
+*     the documentation and/or other materials provided with the
+*     distribution.
+*   - Neither the name of ARM LIMITED nor the names of its contributors
+*     may be used to endorse or promote products derived from this
+*     software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup BayesExample Bayes Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates the use of Bayesian classifier functions. It is complementing the tutorial
+ * about classical ML with CMSIS-DSP and python scikit-learn.
+ *
+ *
+ */
+
+
+/** \example arm_bayes_example_f32.c
+  */
+
+#include <math.h>
+#include <stdio.h>
+#include "arm_math.h"
+
+/* 
+
+Those parameters can be generated with the python library scikit-learn.
+
+*/
+arm_gaussian_naive_bayes_instance_f32 S;
+
+#define NB_OF_CLASSES 3
+#define VECTOR_DIMENSION 2
+
+const float32_t theta[NB_OF_CLASSES*VECTOR_DIMENSION] = {
+  1.4539529436590528f, 0.8722776016801852f, 
+  -1.5267934452462473f, 0.903204577814203f, 
+  -0.15338006360932258f, -2.9997913665803964f
+};          /**< Mean values for the Gaussians */
+
+const float32_t sigma[NB_OF_CLASSES*VECTOR_DIMENSION] = {
+  1.0063470889514925f, 0.9038018246524426f, 
+  1.0224479953244736f, 0.7768764290432544f, 
+  1.1217662403241206f, 1.2303890106020325f
+};          /**< Variances for the Gaussians */
+
+const float32_t classPriors[NB_OF_CLASSES] = {
+  0.3333333333333333f, 0.3333333333333333f, 0.3333333333333333f
+};    /**< Class prior probabilities */
+
+
+int32_t main(void)
+{
+  /* Array of input data */
+  float32_t in[2];
+
+  /* Result of the classifier */
+  float32_t result[NB_OF_CLASSES];
+  float32_t maxProba;
+  uint32_t index;
+  
+  S.vectorDimension = VECTOR_DIMENSION; 
+  S.numberOfClasses = NB_OF_CLASSES; 
+  S.theta = theta;          
+  S.sigma = sigma;         
+  S.classPriors = classPriors;    
+  S.epsilon=4.328939296523643e-09f; 
+
+  in[0] = 1.5f;
+  in[1] = 1.0f;
+
+  arm_gaussian_naive_bayes_predict_f32(&S, in, result);
+
+  arm_max_f32(result,
+        NB_OF_CLASSES,
+        &maxProba,
+        &index);
+
+  printf("Class = %d\n",index);
+
+  in[0] = -1.5f;
+  in[1] = 1.0f;
+
+  arm_gaussian_naive_bayes_predict_f32(&S, in, result);
+
+  arm_max_f32(result,
+        NB_OF_CLASSES,
+        &maxProba,
+        &index);
+
+  printf("Class = %d\n",index);
+
+  in[0] = 0.0f;
+  in[1] = -3.0f;
+
+  arm_gaussian_naive_bayes_predict_f32(&S, in, result);
+
+  arm_max_f32(result,
+        NB_OF_CLASSES,
+        &maxProba,
+        &index);
+
+  printf("Class = %d\n",index);
+}
+
+
+

Examples/ARM/arm_bayes_example/train.py

@@ -0,0 +1,74 @@
+from sklearn.naive_bayes import GaussianNB
+import random
+import numpy as np
+import math
+
+from pylab import scatter,figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show,semilogx, semilogy
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+
+# Generation of data to train the  classifier
+# 100 vectors are generated. Vector have dimension 2 so can be represented as points
+NBVECS = 100
+VECDIM = 2
+
+# 3 cluster of points are generated
+ballRadius = 1.0
+x1 = [1.5, 1] +  ballRadius * np.random.randn(NBVECS,VECDIM)
+x2 = [-1.5, 1] + ballRadius * np.random.randn(NBVECS,VECDIM)
+x3 = [0, -3] + ballRadius * np.random.randn(NBVECS,VECDIM)
+
+# All points are concatenated
+X_train=np.concatenate((x1,x2,x3))
+
+# The classes are 0,1 and 2.
+Y_train=np.concatenate((np.zeros(NBVECS),np.ones(NBVECS),2*np.ones(NBVECS)))
+
+gnb = GaussianNB()
+gnb.fit(X_train, Y_train)
+
+print("Testing")
+y_pred = gnb.predict([[1.5,1.0]])
+print(y_pred)
+
+y_pred = gnb.predict([[-1.5,1.0]])
+print(y_pred)
+
+y_pred = gnb.predict([[0,-3.0]])
+print(y_pred)
+
+# Dump of data for CMSIS-DSP
+
+print("Parameters")
+# Gaussian averages
+print("Theta = ",list(np.reshape(gnb.theta_,np.size(gnb.theta_))))
+
+# Gaussian variances
+print("Sigma = ",list(np.reshape(gnb.sigma_,np.size(gnb.sigma_))))
+
+# Class priors
+print("Prior = ",list(np.reshape(gnb.class_prior_,np.size(gnb.class_prior_))))
+
+print("Epsilon = ",gnb.epsilon_)
+
+
+# Some bounds are computed for the graphical representation
+x_min = X_train[:, 0].min()
+x_max = X_train[:, 0].max()
+y_min = X_train[:, 1].min()
+y_max = X_train[:, 1].max()
+
+font = FontProperties()
+font.set_size(20)
+
+r=plt.figure()
+plt.axis('off')
+plt.text(1.5,1.0,"A", verticalalignment='center', horizontalalignment='center',fontproperties=font)
+plt.text(-1.5,1.0,"B",verticalalignment='center', horizontalalignment='center', fontproperties=font)
+plt.text(0,-3,"C", verticalalignment='center', horizontalalignment='center',fontproperties=font)
+scatter(x1[:,0],x1[:,1],s=1.0,color='#FF6B00')
+scatter(x2[:,0],x2[:,1],s=1.0,color='#95D600')
+scatter(x3[:,0],x3[:,1],s=1.0,color='#00C1DE')
+#r.savefig('fig.jpeg')
+#plt.close(r)
+show()

Examples/ARM/arm_svm_example/train.py

@@ -6,7 +6,7 @@ import math
 from pylab import scatter,figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show,semilogx, semilogy
 import matplotlib.pyplot as plt
 from matplotlib.ticker import MaxNLocator
-from matplotlib.colors import BoundaryNorm
+from matplotlib.colors import BoundaryNorm,ListedColormap
 
 # Generation of data to train the SVM classifier
 # 100 vectors are generated. Vector have dimension 2 so can be represented as points
@@ -86,6 +86,7 @@ print(supportVectors)
 # Graphical representation to display the cluster of points
 # and the SVM boundary
 r=plt.figure()
+plt.axis('off')
 XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
 Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
 
@@ -94,17 +95,29 @@ Z = Z.reshape(XX.shape)
 
 levels = MaxNLocator(nbins=15).tick_values(Z.min(), Z.max())
 
-cmap = plt.get_cmap('gray')
+#cmap = plt.get_cmap('gray')
+newcolors = ['#FFFFFF','#FFFFFF']
+cmap = ListedColormap(newcolors)
 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
 
-plt.pcolormesh(XX, YY, Z > 0, cmap=plt.get_cmap('gray'),norm=norm)
+plt.pcolormesh(XX, YY, Z > 0, cmap=cmap,norm=norm)
 plt.contour(XX, YY, Z, colors=['k', 'k', 'k'],
                 linestyles=['--', '-', '--'], levels=[-.5, 0, .5])
 
-scatter(x[:,0],x[:,1],s=1.0)
+scatter(x[:,0],x[:,1],s=1.0,color='#FF6B00')
-scatter(xa[:,0],xa[:,1],s=1.0)
+scatter(xa[:,0],xa[:,1],s=1.0,color='#95D600')
 
 # The test points are displayed in red.
 scatter(test1[:,0],test1[:,1],s=6.0,color='Red')
 scatter(test2[:,0],test2[:,1],s=6.0,color='Red')
+#r.savefig('fig1.jpeg')
+#plt.close(r)
 show()
+
+
+#r=plt.figure()
+#plt.axis('off')
+#scatter(x[:,0],x[:,1],s=1.0,color='#FF6B00')
+#scatter(xa[:,0],xa[:,1],s=1.0,color='#95D600')
+#r.savefig('fig2.jpeg')
+#plt.close(r)