arm_bayes_example_f32.c

/* ----------------------------------------------------------------------
* 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
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* -------------------------------------------------------------------- */

#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
}; 
const float32_t sigma[NB_OF_CLASSES*VECTOR_DIMENSION] = {
  1.0063470889514925f, 0.9038018246524426f, 
  1.0224479953244736f, 0.7768764290432544f, 
  1.1217662403241206f, 1.2303890106020325f
}; 
const float32_t classPriors[NB_OF_CLASSES] = {
  0.3333333333333333f, 0.3333333333333333f, 0.3333333333333333f
}; 
int32_t main(void)
{
  /* Array of input data */
  float32_t in[2];

  /* Result of the classifier */
  float32_t result[NB_OF_CLASSES];
  float32_t temp[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;

  index = arm_gaussian_naive_bayes_predict_f32(&S, in, result,temp);

  maxProba = result[index];

#if defined(SEMIHOSTING)
  printf("Class = %d\n", index);
#endif

  in[0] = -1.5f;
  in[1] = 1.0f;

  index = arm_gaussian_naive_bayes_predict_f32(&S, in, result,temp);

  maxProba = result[index];

#if defined(SEMIHOSTING)
  printf("Class = %d\n", index);
#endif

  in[0] = 0.0f;
  in[1] = -3.0f;

  index = arm_gaussian_naive_bayes_predict_f32(&S, in, result,temp);

  maxProba = result[index];

#if defined(SEMIHOSTING)
  printf("Class = %d\n", index);
#endif

#if !defined(SEMIHOSTING)
  while (1); /* main function does not return */
#endif
}