/******************************************************************************
* @file svm_functions.h
* @brief Public header file for CMSIS DSP Library
* @version V1.10.0
* @date 08 July 2021
* Target Processor: Cortex-M and Cortex-A cores
******************************************************************************/
/*
* Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef _SVM_FUNCTIONS_H_
#define _SVM_FUNCTIONS_H_
#include "arm_math_types.h"
#include "arm_math_memory.h"
#include "dsp/none.h"
#include "dsp/utils.h"
#include "dsp/svm_defines.h"
#ifdef __cplusplus
extern "C"
{
#endif
#define STEP(x) (x) <= 0 ? 0 : 1
/**
* @defgroup groupSVM SVM Functions
* This set of functions is implementing SVM classification on 2 classes.
* The training must be done from scikit-learn. The parameters can be easily
* generated from the scikit-learn object. Some examples are given in
* DSP/Testing/PatternGeneration/SVM.py
*
* If more than 2 classes are needed, the functions in this folder
* will have to be used, as building blocks, to do multi-class classification.
*
* No multi-class classification is provided in this SVM folder.
*
*/
/**
* @brief Integer exponentiation
* @param[in] x value
* @param[in] nb integer exponent >= 1
* @return x^nb
*
*/
__STATIC_INLINE float32_t arm_exponent_f32(float32_t x, int32_t nb)
{
float32_t r = x;
nb --;
while(nb > 0)
{
r = r * x;
nb--;
}
return(r);
}
/**
* @brief Instance structure for linear SVM prediction function.
*/
typedef struct
{
uint32_t nbOfSupportVectors; /**< Number of support vectors */
uint32_t vectorDimension; /**< Dimension of vector space */
float32_t intercept; /**< Intercept */
const float32_t *dualCoefficients; /**< Dual coefficients */
const float32_t *supportVectors; /**< Support vectors */
const int32_t *classes; /**< The two SVM classes */
} arm_svm_linear_instance_f32;
/**
* @brief Instance structure for polynomial SVM prediction function.
*/
typedef struct
{
uint32_t nbOfSupportVectors; /**< Number of support vectors */
uint32_t vectorDimension; /**< Dimension of vector space */
float32_t intercept; /**< Intercept */
const float32_t *dualCoefficients; /**< Dual coefficients */
const float32_t *supportVectors; /**< Support vectors */
const int32_t *classes; /**< The two SVM classes */
int32_t degree; /**< Polynomial degree */
float32_t coef0; /**< Polynomial constant */
float32_t gamma; /**< Gamma factor */
} arm_svm_polynomial_instance_f32;
/**
* @brief Instance structure for rbf SVM prediction function.
*/
typedef struct
{
uint32_t nbOfSupportVectors; /**< Number of support vectors */
uint32_t vectorDimension; /**< Dimension of vector space */
float32_t intercept; /**< Intercept */
const float32_t *dualCoefficients; /**< Dual coefficients */
const float32_t *supportVectors; /**< Support vectors */
const int32_t *classes; /**< The two SVM classes */
float32_t gamma; /**< Gamma factor */
} arm_svm_rbf_instance_f32;
/**
* @brief Instance structure for sigmoid SVM prediction function.
*/
typedef struct
{
uint32_t nbOfSupportVectors; /**< Number of support vectors */
uint32_t vectorDimension; /**< Dimension of vector space */
float32_t intercept; /**< Intercept */
const float32_t *dualCoefficients; /**< Dual coefficients */
const float32_t *supportVectors; /**< Support vectors */
const int32_t *classes; /**< The two SVM classes */
float32_t coef0; /**< Independent constant */
float32_t gamma; /**< Gamma factor */
} arm_svm_sigmoid_instance_f32;
/**
* @brief SVM linear instance init function
* @param[in] S Parameters for SVM functions
* @param[in] nbOfSupportVectors Number of support vectors
* @param[in] vectorDimension Dimension of vector space
* @param[in] intercept Intercept
* @param[in] dualCoefficients Array of dual coefficients
* @param[in] supportVectors Array of support vectors
* @param[in] classes Array of 2 classes ID
* @return none.
*
*/
void arm_svm_linear_init_f32(arm_svm_linear_instance_f32 *S,
uint32_t nbOfSupportVectors,
uint32_t vectorDimension,
float32_t intercept,
const float32_t *dualCoefficients,
const float32_t *supportVectors,
const int32_t *classes);
/**
* @brief SVM linear prediction
* @param[in] S Pointer to an instance of the linear SVM structure.
* @param[in] in Pointer to input vector
* @param[out] pResult Decision value
* @return none.
*
*/
void arm_svm_linear_predict_f32(const arm_svm_linear_instance_f32 *S,
const float32_t * in,
int32_t * pResult);
/**
* @brief SVM polynomial instance init function
* @param[in] S points to an instance of the polynomial SVM structure.
* @param[in] nbOfSupportVectors Number of support vectors
* @param[in] vectorDimension Dimension of vector space
* @param[in] intercept Intercept
* @param[in] dualCoefficients Array of dual coefficients
* @param[in] supportVectors Array of support vectors
* @param[in] classes Array of 2 classes ID
* @param[in] degree Polynomial degree
* @param[in] coef0 coeff0 (scikit-learn terminology)
* @param[in] gamma gamma (scikit-learn terminology)
* @return none.
*
*/
void arm_svm_polynomial_init_f32(arm_svm_polynomial_instance_f32 *S,
uint32_t nbOfSupportVectors,
uint32_t vectorDimension,
float32_t intercept,
const float32_t *dualCoefficients,
const float32_t *supportVectors,
const int32_t *classes,
int32_t degree,
float32_t coef0,
float32_t gamma
);
/**
* @brief SVM polynomial prediction
* @param[in] S Pointer to an instance of the polynomial SVM structure.
* @param[in] in Pointer to input vector
* @param[out] pResult Decision value
* @return none.
*
*/
void arm_svm_polynomial_predict_f32(const arm_svm_polynomial_instance_f32 *S,
const float32_t * in,
int32_t * pResult);
/**
* @brief SVM radial basis function instance init function
* @param[in] S points to an instance of the polynomial SVM structure.
* @param[in] nbOfSupportVectors Number of support vectors
* @param[in] vectorDimension Dimension of vector space
* @param[in] intercept Intercept
* @param[in] dualCoefficients Array of dual coefficients
* @param[in] supportVectors Array of support vectors
* @param[in] classes Array of 2 classes ID
* @param[in] gamma gamma (scikit-learn terminology)
* @return none.
*
*/
void arm_svm_rbf_init_f32(arm_svm_rbf_instance_f32 *S,
uint32_t nbOfSupportVectors,
uint32_t vectorDimension,
float32_t intercept,
const float32_t *dualCoefficients,
const float32_t *supportVectors,
const int32_t *classes,
float32_t gamma
);
/**
* @brief SVM rbf prediction
* @param[in] S Pointer to an instance of the rbf SVM structure.
* @param[in] in Pointer to input vector
* @param[out] pResult decision value
* @return none.
*
*/
void arm_svm_rbf_predict_f32(const arm_svm_rbf_instance_f32 *S,
const float32_t * in,
int32_t * pResult);
/**
* @brief SVM sigmoid instance init function
* @param[in] S points to an instance of the rbf SVM structure.
* @param[in] nbOfSupportVectors Number of support vectors
* @param[in] vectorDimension Dimension of vector space
* @param[in] intercept Intercept
* @param[in] dualCoefficients Array of dual coefficients
* @param[in] supportVectors Array of support vectors
* @param[in] classes Array of 2 classes ID
* @param[in] coef0 coeff0 (scikit-learn terminology)
* @param[in] gamma gamma (scikit-learn terminology)
* @return none.
*
*/
void arm_svm_sigmoid_init_f32(arm_svm_sigmoid_instance_f32 *S,
uint32_t nbOfSupportVectors,
uint32_t vectorDimension,
float32_t intercept,
const float32_t *dualCoefficients,
const float32_t *supportVectors,
const int32_t *classes,
float32_t coef0,
float32_t gamma
);
/**
* @brief SVM sigmoid prediction
* @param[in] S Pointer to an instance of the rbf SVM structure.
* @param[in] in Pointer to input vector
* @param[out] pResult Decision value
* @return none.
*
*/
void arm_svm_sigmoid_predict_f32(const arm_svm_sigmoid_instance_f32 *S,
const float32_t * in,
int32_t * pResult);
#ifdef __cplusplus
}
#endif
#endif /* ifndef _SVM_FUNCTIONS_H_ */