299 lines
9.8 KiB
C
299 lines
9.8 KiB
C
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/******************************************************************************
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* @file svm_functions_f16.h
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* @brief Public header file for CMSIS DSP Library
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* @version V1.9.0
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* @date 23 April 2021
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* Target Processor: Cortex-M and Cortex-A cores
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******************************************************************************/
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/*
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* Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef _SVM_FUNCTIONS_F16_H_
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#define _SVM_FUNCTIONS_F16_H_
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#include "arm_math_types_f16.h"
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#include "arm_math_memory.h"
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#include "dsp/none.h"
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#include "dsp/utils.h"
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#include "dsp/svm_defines.h"
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#ifdef __cplusplus
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extern "C"
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{
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#endif
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#if defined(ARM_FLOAT16_SUPPORTED)
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#define STEP(x) (x) <= 0 ? 0 : 1
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/**
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* @defgroup groupSVM SVM Functions
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* This set of functions is implementing SVM classification on 2 classes.
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* The training must be done from scikit-learn. The parameters can be easily
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* generated from the scikit-learn object. Some examples are given in
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* DSP/Testing/PatternGeneration/SVM.py
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*
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* If more than 2 classes are needed, the functions in this folder
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* will have to be used, as building blocks, to do multi-class classification.
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*
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* No multi-class classification is provided in this SVM folder.
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*
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*/
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/**
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* @brief Integer exponentiation
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* @param[in] x value
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* @param[in] nb integer exponent >= 1
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* @return x^nb
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*
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*/
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__STATIC_INLINE float16_t arm_exponent_f16(float16_t x, int32_t nb)
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{
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float16_t r = x;
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nb --;
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while(nb > 0)
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{
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r = r * x;
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nb--;
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}
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return(r);
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}
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/**
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* @brief Instance structure for linear SVM prediction function.
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*/
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typedef struct
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{
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uint32_t nbOfSupportVectors; /**< Number of support vectors */
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uint32_t vectorDimension; /**< Dimension of vector space */
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float16_t intercept; /**< Intercept */
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const float16_t *dualCoefficients; /**< Dual coefficients */
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const float16_t *supportVectors; /**< Support vectors */
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const int32_t *classes; /**< The two SVM classes */
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} arm_svm_linear_instance_f16;
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/**
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* @brief Instance structure for polynomial SVM prediction function.
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*/
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typedef struct
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{
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uint32_t nbOfSupportVectors; /**< Number of support vectors */
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uint32_t vectorDimension; /**< Dimension of vector space */
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float16_t intercept; /**< Intercept */
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const float16_t *dualCoefficients; /**< Dual coefficients */
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const float16_t *supportVectors; /**< Support vectors */
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const int32_t *classes; /**< The two SVM classes */
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int32_t degree; /**< Polynomial degree */
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float16_t coef0; /**< Polynomial constant */
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float16_t gamma; /**< Gamma factor */
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} arm_svm_polynomial_instance_f16;
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/**
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* @brief Instance structure for rbf SVM prediction function.
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*/
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typedef struct
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{
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uint32_t nbOfSupportVectors; /**< Number of support vectors */
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uint32_t vectorDimension; /**< Dimension of vector space */
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float16_t intercept; /**< Intercept */
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const float16_t *dualCoefficients; /**< Dual coefficients */
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const float16_t *supportVectors; /**< Support vectors */
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const int32_t *classes; /**< The two SVM classes */
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float16_t gamma; /**< Gamma factor */
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} arm_svm_rbf_instance_f16;
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/**
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* @brief Instance structure for sigmoid SVM prediction function.
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*/
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typedef struct
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{
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uint32_t nbOfSupportVectors; /**< Number of support vectors */
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uint32_t vectorDimension; /**< Dimension of vector space */
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float16_t intercept; /**< Intercept */
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const float16_t *dualCoefficients; /**< Dual coefficients */
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const float16_t *supportVectors; /**< Support vectors */
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const int32_t *classes; /**< The two SVM classes */
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float16_t coef0; /**< Independent constant */
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float16_t gamma; /**< Gamma factor */
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} arm_svm_sigmoid_instance_f16;
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/**
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* @brief SVM linear instance init function
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* @param[in] S Parameters for SVM functions
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* @param[in] nbOfSupportVectors Number of support vectors
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* @param[in] vectorDimension Dimension of vector space
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* @param[in] intercept Intercept
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* @param[in] dualCoefficients Array of dual coefficients
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* @param[in] supportVectors Array of support vectors
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* @param[in] classes Array of 2 classes ID
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* @return none.
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*
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*/
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void arm_svm_linear_init_f16(arm_svm_linear_instance_f16 *S,
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uint32_t nbOfSupportVectors,
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uint32_t vectorDimension,
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float16_t intercept,
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const float16_t *dualCoefficients,
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const float16_t *supportVectors,
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const int32_t *classes);
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/**
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* @brief SVM linear prediction
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* @param[in] S Pointer to an instance of the linear SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult Decision value
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* @return none.
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*
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*/
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void arm_svm_linear_predict_f16(const arm_svm_linear_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult);
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/**
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* @brief SVM polynomial instance init function
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* @param[in] S points to an instance of the polynomial SVM structure.
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* @param[in] nbOfSupportVectors Number of support vectors
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* @param[in] vectorDimension Dimension of vector space
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* @param[in] intercept Intercept
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* @param[in] dualCoefficients Array of dual coefficients
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* @param[in] supportVectors Array of support vectors
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* @param[in] classes Array of 2 classes ID
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* @param[in] degree Polynomial degree
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* @param[in] coef0 coeff0 (scikit-learn terminology)
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* @param[in] gamma gamma (scikit-learn terminology)
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* @return none.
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*
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*/
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void arm_svm_polynomial_init_f16(arm_svm_polynomial_instance_f16 *S,
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uint32_t nbOfSupportVectors,
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uint32_t vectorDimension,
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float16_t intercept,
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const float16_t *dualCoefficients,
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const float16_t *supportVectors,
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const int32_t *classes,
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int32_t degree,
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float16_t coef0,
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float16_t gamma
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);
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/**
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* @brief SVM polynomial prediction
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* @param[in] S Pointer to an instance of the polynomial SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult Decision value
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* @return none.
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*
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*/
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void arm_svm_polynomial_predict_f16(const arm_svm_polynomial_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult);
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/**
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* @brief SVM radial basis function instance init function
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* @param[in] S points to an instance of the polynomial SVM structure.
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* @param[in] nbOfSupportVectors Number of support vectors
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* @param[in] vectorDimension Dimension of vector space
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* @param[in] intercept Intercept
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* @param[in] dualCoefficients Array of dual coefficients
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* @param[in] supportVectors Array of support vectors
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* @param[in] classes Array of 2 classes ID
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* @param[in] gamma gamma (scikit-learn terminology)
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* @return none.
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*
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*/
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void arm_svm_rbf_init_f16(arm_svm_rbf_instance_f16 *S,
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uint32_t nbOfSupportVectors,
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uint32_t vectorDimension,
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float16_t intercept,
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const float16_t *dualCoefficients,
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const float16_t *supportVectors,
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const int32_t *classes,
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float16_t gamma
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);
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/**
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* @brief SVM rbf prediction
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* @param[in] S Pointer to an instance of the rbf SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult decision value
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* @return none.
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*
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*/
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void arm_svm_rbf_predict_f16(const arm_svm_rbf_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult);
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/**
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* @brief SVM sigmoid instance init function
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* @param[in] S points to an instance of the rbf SVM structure.
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* @param[in] nbOfSupportVectors Number of support vectors
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* @param[in] vectorDimension Dimension of vector space
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* @param[in] intercept Intercept
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* @param[in] dualCoefficients Array of dual coefficients
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* @param[in] supportVectors Array of support vectors
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* @param[in] classes Array of 2 classes ID
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* @param[in] coef0 coeff0 (scikit-learn terminology)
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* @param[in] gamma gamma (scikit-learn terminology)
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* @return none.
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*
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*/
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void arm_svm_sigmoid_init_f16(arm_svm_sigmoid_instance_f16 *S,
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uint32_t nbOfSupportVectors,
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uint32_t vectorDimension,
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float16_t intercept,
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const float16_t *dualCoefficients,
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const float16_t *supportVectors,
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const int32_t *classes,
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float16_t coef0,
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float16_t gamma
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);
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/**
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* @brief SVM sigmoid prediction
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* @param[in] S Pointer to an instance of the rbf SVM structure.
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* @param[in] in Pointer to input vector
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* @param[out] pResult Decision value
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* @return none.
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*
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*/
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void arm_svm_sigmoid_predict_f16(const arm_svm_sigmoid_instance_f16 *S,
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const float16_t * in,
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int32_t * pResult);
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#endif /*defined(ARM_FLOAT16_SUPPORTED)*/
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#ifdef __cplusplus
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}
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#endif
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#endif /* ifndef _SVM_FUNCTIONS_F16_H_ */
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