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Diffstat (limited to 'Drivers/CMSIS/DSP/Include/dsp/svm_functions.h')
| -rw-r--r-- | Drivers/CMSIS/DSP/Include/dsp/svm_functions.h | 299 |
1 files changed, 299 insertions, 0 deletions
diff --git a/Drivers/CMSIS/DSP/Include/dsp/svm_functions.h b/Drivers/CMSIS/DSP/Include/dsp/svm_functions.h new file mode 100644 index 0000000..3acc621 --- /dev/null +++ b/Drivers/CMSIS/DSP/Include/dsp/svm_functions.h @@ -0,0 +1,299 @@ +/****************************************************************************** + * @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_ */ |