| 1 | /******************************************************************************
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| 2 | * @file bayes_functions.h
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| 3 | * @brief Public header file for CMSIS DSP Library
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| 4 | * @version V1.10.0
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| 5 | * @date 08 July 2021
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| 6 | * Target Processor: Cortex-M and Cortex-A cores
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| 7 | ******************************************************************************/
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| 8 | /*
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| 9 | * Copyright (c) 2010-2020 Arm Limited or its affiliates. All rights reserved.
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| 10 | *
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| 11 | * SPDX-License-Identifier: Apache-2.0
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| 12 | *
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| 13 | * Licensed under the Apache License, Version 2.0 (the License); you may
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| 14 | * not use this file except in compliance with the License.
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| 15 | * You may obtain a copy of the License at
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| 16 | *
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| 17 | * www.apache.org/licenses/LICENSE-2.0
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| 18 | *
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| 19 | * Unless required by applicable law or agreed to in writing, software
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| 20 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT
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| 21 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 22 | * See the License for the specific language governing permissions and
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| 23 | * limitations under the License.
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| 24 | */
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| 25 |
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| 26 |
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| 27 | #ifndef _BAYES_FUNCTIONS_H_
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| 28 | #define _BAYES_FUNCTIONS_H_
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| 29 |
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| 30 | #include "arm_math_types.h"
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| 31 | #include "arm_math_memory.h"
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| 32 |
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| 33 | #include "dsp/none.h"
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| 34 | #include "dsp/utils.h"
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| 35 |
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| 36 | #include "dsp/statistics_functions.h"
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| 37 |
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| 38 | /**
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| 39 | * @defgroup groupBayes Bayesian estimators
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| 40 | *
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| 41 | * Implement the naive gaussian Bayes estimator.
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| 42 | * The training must be done from scikit-learn.
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| 43 | *
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| 44 | * The parameters can be easily
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| 45 | * generated from the scikit-learn object. Some examples are given in
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| 46 | * DSP/Testing/PatternGeneration/Bayes.py
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| 47 | */
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| 48 |
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| 49 | #ifdef __cplusplus
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| 50 | extern "C"
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| 51 | {
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| 52 | #endif
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| 53 |
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| 54 | /**
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| 55 | * @brief Instance structure for Naive Gaussian Bayesian estimator.
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| 56 | */
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| 57 | typedef struct
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| 58 | {
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| 59 | uint32_t vectorDimension; /**< Dimension of vector space */
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| 60 | uint32_t numberOfClasses; /**< Number of different classes */
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| 61 | const float32_t *theta; /**< Mean values for the Gaussians */
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| 62 | const float32_t *sigma; /**< Variances for the Gaussians */
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| 63 | const float32_t *classPriors; /**< Class prior probabilities */
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| 64 | float32_t epsilon; /**< Additive value to variances */
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| 65 | } arm_gaussian_naive_bayes_instance_f32;
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| 66 |
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| 67 | /**
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| 68 | * @brief Naive Gaussian Bayesian Estimator
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| 69 | *
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| 70 | * @param[in] S points to a naive bayes instance structure
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| 71 | * @param[in] in points to the elements of the input vector.
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| 72 | * @param[out] *pOutputProbabilities points to a buffer of length numberOfClasses containing estimated probabilities
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| 73 | * @param[out] *pBufferB points to a temporary buffer of length numberOfClasses
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| 74 | * @return The predicted class
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| 75 | *
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| 76 | */
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| 77 |
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| 78 |
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| 79 | uint32_t arm_gaussian_naive_bayes_predict_f32(const arm_gaussian_naive_bayes_instance_f32 *S,
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| 80 | const float32_t * in,
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| 81 | float32_t *pOutputProbabilities,
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| 82 | float32_t *pBufferB);
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| 83 |
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| 84 |
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| 85 | #ifdef __cplusplus
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| 86 | }
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| 87 | #endif
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| 88 |
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| 89 | #endif /* ifndef _BAYES_FUNCTIONS_H_ */
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