1 /******************************************************************************
2 * @file svm_functions_f16.h
3 * @brief Public header file for CMSIS DSP Library
4 * @version V1.9.0
5 * @date 23 April 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 #ifdef __cplusplus
38 extern "C"
39 {
40 #endif
41
42 #if defined(ARM_FLOAT16_SUPPORTED)
43
44 #define STEP(x) (x) <= 0 ? 0 : 1
45
46 /**
47 * @defgroup groupSVM SVM Functions
48 * This set of functions is implementing SVM classification on 2 classes.
49 * The training must be done from scikit-learn. The parameters can be easily
50 * generated from the scikit-learn object. Some examples are given in
51 * DSP/Testing/PatternGeneration/SVM.py
52 *
53 * If more than 2 classes are needed, the functions in this folder
54 * will have to be used, as building blocks, to do multi-class classification.
55 *
56 * No multi-class classification is provided in this SVM folder.
57 *
58 */
59
60 /**
61 * @brief Integer exponentiation
62 * @param[in] x value
63 * @param[in] nb integer exponent >= 1
64 * @return x^nb
65 *
66 */
arm_exponent_f16(float16_t x,int32_t nb)67 __STATIC_INLINE float16_t arm_exponent_f16(float16_t x, int32_t nb)
68 {
69 float16_t r = x;
70 nb --;
71 while(nb > 0)
72 {
73 r = r * x;
74 nb--;
75 }
76 return(r);
77 }
78
79
80 /**
81 * @brief Instance structure for linear SVM prediction function.
82 */
83 typedef struct
84 {
85 uint32_t nbOfSupportVectors; /**< Number of support vectors */
86 uint32_t vectorDimension; /**< Dimension of vector space */
87 float16_t intercept; /**< Intercept */
88 const float16_t *dualCoefficients; /**< Dual coefficients */
89 const float16_t *supportVectors; /**< Support vectors */
90 const int32_t *classes; /**< The two SVM classes */
91 } arm_svm_linear_instance_f16;
92
93
94 /**
95 * @brief Instance structure for polynomial 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 int32_t degree; /**< Polynomial degree */
106 float16_t coef0; /**< Polynomial constant */
107 float16_t gamma; /**< Gamma factor */
108 } arm_svm_polynomial_instance_f16;
109
110 /**
111 * @brief Instance structure for rbf SVM prediction function.
112 */
113 typedef struct
114 {
115 uint32_t nbOfSupportVectors; /**< Number of support vectors */
116 uint32_t vectorDimension; /**< Dimension of vector space */
117 float16_t intercept; /**< Intercept */
118 const float16_t *dualCoefficients; /**< Dual coefficients */
119 const float16_t *supportVectors; /**< Support vectors */
120 const int32_t *classes; /**< The two SVM classes */
121 float16_t gamma; /**< Gamma factor */
122 } arm_svm_rbf_instance_f16;
123
124 /**
125 * @brief Instance structure for sigmoid SVM prediction function.
126 */
127 typedef struct
128 {
129 uint32_t nbOfSupportVectors; /**< Number of support vectors */
130 uint32_t vectorDimension; /**< Dimension of vector space */
131 float16_t intercept; /**< Intercept */
132 const float16_t *dualCoefficients; /**< Dual coefficients */
133 const float16_t *supportVectors; /**< Support vectors */
134 const int32_t *classes; /**< The two SVM classes */
135 float16_t coef0; /**< Independent constant */
136 float16_t gamma; /**< Gamma factor */
137 } arm_svm_sigmoid_instance_f16;
138
139 /**
140 * @brief SVM linear instance init function
141 * @param[in] S Parameters for SVM functions
142 * @param[in] nbOfSupportVectors Number of support vectors
143 * @param[in] vectorDimension Dimension of vector space
144 * @param[in] intercept Intercept
145 * @param[in] dualCoefficients Array of dual coefficients
146 * @param[in] supportVectors Array of support vectors
147 * @param[in] classes Array of 2 classes ID
148 * @return none.
149 *
150 */
151
152
153 void arm_svm_linear_init_f16(arm_svm_linear_instance_f16 *S,
154 uint32_t nbOfSupportVectors,
155 uint32_t vectorDimension,
156 float16_t intercept,
157 const float16_t *dualCoefficients,
158 const float16_t *supportVectors,
159 const int32_t *classes);
160
161 /**
162 * @brief SVM linear prediction
163 * @param[in] S Pointer to an instance of the linear SVM structure.
164 * @param[in] in Pointer to input vector
165 * @param[out] pResult Decision value
166 * @return none.
167 *
168 */
169
170 void arm_svm_linear_predict_f16(const arm_svm_linear_instance_f16 *S,
171 const float16_t * in,
172 int32_t * pResult);
173
174
175 /**
176 * @brief SVM polynomial instance init function
177 * @param[in] S points to an instance of the polynomial SVM structure.
178 * @param[in] nbOfSupportVectors Number of support vectors
179 * @param[in] vectorDimension Dimension of vector space
180 * @param[in] intercept Intercept
181 * @param[in] dualCoefficients Array of dual coefficients
182 * @param[in] supportVectors Array of support vectors
183 * @param[in] classes Array of 2 classes ID
184 * @param[in] degree Polynomial degree
185 * @param[in] coef0 coeff0 (scikit-learn terminology)
186 * @param[in] gamma gamma (scikit-learn terminology)
187 * @return none.
188 *
189 */
190
191
192 void arm_svm_polynomial_init_f16(arm_svm_polynomial_instance_f16 *S,
193 uint32_t nbOfSupportVectors,
194 uint32_t vectorDimension,
195 float16_t intercept,
196 const float16_t *dualCoefficients,
197 const float16_t *supportVectors,
198 const int32_t *classes,
199 int32_t degree,
200 float16_t coef0,
201 float16_t gamma
202 );
203
204 /**
205 * @brief SVM polynomial prediction
206 * @param[in] S Pointer to an instance of the polynomial SVM structure.
207 * @param[in] in Pointer to input vector
208 * @param[out] pResult Decision value
209 * @return none.
210 *
211 */
212 void arm_svm_polynomial_predict_f16(const arm_svm_polynomial_instance_f16 *S,
213 const float16_t * in,
214 int32_t * pResult);
215
216
217 /**
218 * @brief SVM radial basis function instance init function
219 * @param[in] S points to an instance of the polynomial SVM structure.
220 * @param[in] nbOfSupportVectors Number of support vectors
221 * @param[in] vectorDimension Dimension of vector space
222 * @param[in] intercept Intercept
223 * @param[in] dualCoefficients Array of dual coefficients
224 * @param[in] supportVectors Array of support vectors
225 * @param[in] classes Array of 2 classes ID
226 * @param[in] gamma gamma (scikit-learn terminology)
227 * @return none.
228 *
229 */
230
231 void arm_svm_rbf_init_f16(arm_svm_rbf_instance_f16 *S,
232 uint32_t nbOfSupportVectors,
233 uint32_t vectorDimension,
234 float16_t intercept,
235 const float16_t *dualCoefficients,
236 const float16_t *supportVectors,
237 const int32_t *classes,
238 float16_t gamma
239 );
240
241 /**
242 * @brief SVM rbf prediction
243 * @param[in] S Pointer to an instance of the rbf SVM structure.
244 * @param[in] in Pointer to input vector
245 * @param[out] pResult decision value
246 * @return none.
247 *
248 */
249 void arm_svm_rbf_predict_f16(const arm_svm_rbf_instance_f16 *S,
250 const float16_t * in,
251 int32_t * pResult);
252
253 /**
254 * @brief SVM sigmoid instance init function
255 * @param[in] S points to an instance of the rbf SVM structure.
256 * @param[in] nbOfSupportVectors Number of support vectors
257 * @param[in] vectorDimension Dimension of vector space
258 * @param[in] intercept Intercept
259 * @param[in] dualCoefficients Array of dual coefficients
260 * @param[in] supportVectors Array of support vectors
261 * @param[in] classes Array of 2 classes ID
262 * @param[in] coef0 coeff0 (scikit-learn terminology)
263 * @param[in] gamma gamma (scikit-learn terminology)
264 * @return none.
265 *
266 */
267
268 void arm_svm_sigmoid_init_f16(arm_svm_sigmoid_instance_f16 *S,
269 uint32_t nbOfSupportVectors,
270 uint32_t vectorDimension,
271 float16_t intercept,
272 const float16_t *dualCoefficients,
273 const float16_t *supportVectors,
274 const int32_t *classes,
275 float16_t coef0,
276 float16_t gamma
277 );
278
279 /**
280 * @brief SVM sigmoid prediction
281 * @param[in] S Pointer to an instance of the rbf SVM structure.
282 * @param[in] in Pointer to input vector
283 * @param[out] pResult Decision value
284 * @return none.
285 *
286 */
287 void arm_svm_sigmoid_predict_f16(const arm_svm_sigmoid_instance_f16 *S,
288 const float16_t * in,
289 int32_t * pResult);
290
291
292
293 #endif /*defined(ARM_FLOAT16_SUPPORTED)*/
294 #ifdef __cplusplus
295 }
296 #endif
297
298 #endif /* ifndef _SVM_FUNCTIONS_F16_H_ */
299
