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

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pull/19/head
Christophe Favergeon 6 years ago
parent 585137ad16
commit 88d1328ee4
4 changed files with 283 additions and 5 deletions
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45
Examples/ARM/arm_bayes_example/CMakeLists.txt
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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)

146
Examples/ARM/arm_bayes_example/arm_bayes_example_f32.c
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/* ----------------------------------------------------------------------
* 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);
}

74
Examples/ARM/arm_bayes_example/train.py
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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()

23
Examples/ARM/arm_svm_example/train.py
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@ -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(xa[:,0],xa[:,1],s=1.0)
scatter(x[:,0],x[:,1],s=1.0,color='#FF6B00')
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)
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