1 /****************************************************************************** 2 * @file svm_functions_f16.h 3 * @brief Public header file for CMSIS DSP Library 4 * @version V1.10.0 5 * @date 08 July 2021 6 * Target Processor: Cortex-M and Cortex-A cores 7 ******************************************************************************/ 8 /* 9 * Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved. 10 * 11 * SPDX-License-Identifier: Apache-2.0 12 * 13 * Licensed under the Apache License, Version 2.0 (the License); you may 14 * not use this file except in compliance with the License. 15 * You may obtain a copy of the License at 16 * 17 * www.apache.org/licenses/LICENSE-2.0 18 * 19 * Unless required by applicable law or agreed to in writing, software 20 * distributed under the License is distributed on an AS IS BASIS, WITHOUT 21 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 22 * See the License for the specific language governing permissions and 23 * limitations under the License. 24 */ 25 26 27 #ifndef SVM_FUNCTIONS_F16_H_ 28 #define SVM_FUNCTIONS_F16_H_ 29 30 #include "arm_math_types_f16.h" 31 #include "arm_math_memory.h" 32 33 #include "dsp/none.h" 34 #include "dsp/utils.h" 35 #include "dsp/svm_defines.h" 36 37 38 #ifdef __cplusplus 39 extern "C" 40 { 41 #endif 42 43 #if defined(ARM_FLOAT16_SUPPORTED) 44 45 #define STEP(x) (x) <= 0 ? 0 : 1 46 47 /** 48 * @defgroup groupSVM SVM Functions 49 * This set of functions is implementing SVM classification on 2 classes. 50 * The training must be done from scikit-learn. The parameters can be easily 51 * generated from the scikit-learn object. Some examples are given in 52 * DSP/Testing/PatternGeneration/SVM.py 53 * 54 * If more than 2 classes are needed, the functions in this folder 55 * will have to be used, as building blocks, to do multi-class classification. 56 * 57 * No multi-class classification is provided in this SVM folder. 58 * 59 */ 60 61 62 63 /** 64 * @brief Instance structure for linear SVM prediction function. 65 */ 66 typedef struct 67 { 68 uint32_t nbOfSupportVectors; /**< Number of support vectors */ 69 uint32_t vectorDimension; /**< Dimension of vector space */ 70 float16_t intercept; /**< Intercept */ 71 const float16_t *dualCoefficients; /**< Dual coefficients */ 72 const float16_t *supportVectors; /**< Support vectors */ 73 const int32_t *classes; /**< The two SVM classes */ 74 } arm_svm_linear_instance_f16; 75 76 77 /** 78 * @brief Instance structure for polynomial SVM prediction function. 79 */ 80 typedef struct 81 { 82 uint32_t nbOfSupportVectors; /**< Number of support vectors */ 83 uint32_t vectorDimension; /**< Dimension of vector space */ 84 float16_t intercept; /**< Intercept */ 85 const float16_t *dualCoefficients; /**< Dual coefficients */ 86 const float16_t *supportVectors; /**< Support vectors */ 87 const int32_t *classes; /**< The two SVM classes */ 88 int32_t degree; /**< Polynomial degree */ 89 float16_t coef0; /**< Polynomial constant */ 90 float16_t gamma; /**< Gamma factor */ 91 } arm_svm_polynomial_instance_f16; 92 93 94 /** 95 * @brief Instance structure for rbf SVM prediction function. 96 */ 97 typedef struct 98 { 99 uint32_t nbOfSupportVectors; /**< Number of support vectors */ 100 uint32_t vectorDimension; /**< Dimension of vector space */ 101 float16_t intercept; /**< Intercept */ 102 const float16_t *dualCoefficients; /**< Dual coefficients */ 103 const float16_t *supportVectors; /**< Support vectors */ 104 const int32_t *classes; /**< The two SVM classes */ 105 float16_t gamma; /**< Gamma factor */ 106 } arm_svm_rbf_instance_f16; 107 108 109 /** 110 * @brief Instance structure for sigmoid SVM prediction function. 111 */ 112 typedef struct 113 { 114 uint32_t nbOfSupportVectors; /**< Number of support vectors */ 115 uint32_t vectorDimension; /**< Dimension of vector space */ 116 float16_t intercept; /**< Intercept */ 117 const float16_t *dualCoefficients; /**< Dual coefficients */ 118 const float16_t *supportVectors; /**< Support vectors */ 119 const int32_t *classes; /**< The two SVM classes */ 120 float16_t coef0; /**< Independent constant */ 121 float16_t gamma; /**< Gamma factor */ 122 } arm_svm_sigmoid_instance_f16; 123 124 125 /** 126 * @brief SVM linear instance init function 127 * @param[in] S Parameters for SVM functions 128 * @param[in] nbOfSupportVectors Number of support vectors 129 * @param[in] vectorDimension Dimension of vector space 130 * @param[in] intercept Intercept 131 * @param[in] dualCoefficients Array of dual coefficients 132 * @param[in] supportVectors Array of support vectors 133 * @param[in] classes Array of 2 classes ID 134 */ 135 void arm_svm_linear_init_f16(arm_svm_linear_instance_f16 *S, 136 uint32_t nbOfSupportVectors, 137 uint32_t vectorDimension, 138 float16_t intercept, 139 const float16_t *dualCoefficients, 140 const float16_t *supportVectors, 141 const int32_t *classes); 142 143 /** 144 * @brief SVM linear prediction 145 * @param[in] S Pointer to an instance of the linear SVM structure. 146 * @param[in] in Pointer to input vector 147 * @param[out] pResult Decision value 148 */ 149 void arm_svm_linear_predict_f16(const arm_svm_linear_instance_f16 *S, 150 const float16_t * in, 151 int32_t * pResult); 152 153 154 /** 155 * @brief SVM polynomial instance init function 156 * @param[in] S points to an instance of the polynomial SVM structure. 157 * @param[in] nbOfSupportVectors Number of support vectors 158 * @param[in] vectorDimension Dimension of vector space 159 * @param[in] intercept Intercept 160 * @param[in] dualCoefficients Array of dual coefficients 161 * @param[in] supportVectors Array of support vectors 162 * @param[in] classes Array of 2 classes ID 163 * @param[in] degree Polynomial degree 164 * @param[in] coef0 coeff0 (scikit-learn terminology) 165 * @param[in] gamma gamma (scikit-learn terminology) 166 */ 167 void arm_svm_polynomial_init_f16(arm_svm_polynomial_instance_f16 *S, 168 uint32_t nbOfSupportVectors, 169 uint32_t vectorDimension, 170 float16_t intercept, 171 const float16_t *dualCoefficients, 172 const float16_t *supportVectors, 173 const int32_t *classes, 174 int32_t degree, 175 float16_t coef0, 176 float16_t gamma 177 ); 178 179 180 /** 181 * @brief SVM polynomial prediction 182 * @param[in] S Pointer to an instance of the polynomial SVM structure. 183 * @param[in] in Pointer to input vector 184 * @param[out] pResult Decision value 185 */ 186 void arm_svm_polynomial_predict_f16(const arm_svm_polynomial_instance_f16 *S, 187 const float16_t * in, 188 int32_t * pResult); 189 190 191 /** 192 * @brief SVM radial basis function instance init function 193 * @param[in] S points to an instance of the polynomial SVM structure. 194 * @param[in] nbOfSupportVectors Number of support vectors 195 * @param[in] vectorDimension Dimension of vector space 196 * @param[in] intercept Intercept 197 * @param[in] dualCoefficients Array of dual coefficients 198 * @param[in] supportVectors Array of support vectors 199 * @param[in] classes Array of 2 classes ID 200 * @param[in] gamma gamma (scikit-learn terminology) 201 */ 202 void arm_svm_rbf_init_f16(arm_svm_rbf_instance_f16 *S, 203 uint32_t nbOfSupportVectors, 204 uint32_t vectorDimension, 205 float16_t intercept, 206 const float16_t *dualCoefficients, 207 const float16_t *supportVectors, 208 const int32_t *classes, 209 float16_t gamma 210 ); 211 212 213 /** 214 * @brief SVM rbf prediction 215 * @param[in] S Pointer to an instance of the rbf SVM structure. 216 * @param[in] in Pointer to input vector 217 * @param[out] pResult decision value 218 */ 219 void arm_svm_rbf_predict_f16(const arm_svm_rbf_instance_f16 *S, 220 const float16_t * in, 221 int32_t * pResult); 222 223 224 /** 225 * @brief SVM sigmoid instance init function 226 * @param[in] S points to an instance of the rbf SVM structure. 227 * @param[in] nbOfSupportVectors Number of support vectors 228 * @param[in] vectorDimension Dimension of vector space 229 * @param[in] intercept Intercept 230 * @param[in] dualCoefficients Array of dual coefficients 231 * @param[in] supportVectors Array of support vectors 232 * @param[in] classes Array of 2 classes ID 233 * @param[in] coef0 coeff0 (scikit-learn terminology) 234 * @param[in] gamma gamma (scikit-learn terminology) 235 */ 236 void arm_svm_sigmoid_init_f16(arm_svm_sigmoid_instance_f16 *S, 237 uint32_t nbOfSupportVectors, 238 uint32_t vectorDimension, 239 float16_t intercept, 240 const float16_t *dualCoefficients, 241 const float16_t *supportVectors, 242 const int32_t *classes, 243 float16_t coef0, 244 float16_t gamma 245 ); 246 247 248 /** 249 * @brief SVM sigmoid prediction 250 * @param[in] S Pointer to an instance of the rbf SVM structure. 251 * @param[in] in Pointer to input vector 252 * @param[out] pResult Decision value 253 */ 254 void arm_svm_sigmoid_predict_f16(const arm_svm_sigmoid_instance_f16 *S, 255 const float16_t * in, 256 int32_t * pResult); 257 258 259 260 #endif /*defined(ARM_FLOAT16_SUPPORTED)*/ 261 #ifdef __cplusplus 262 } 263 #endif 264 265 #endif /* ifndef _SVM_FUNCTIONS_F16_H_ */ 266
