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Drivers/CMSIS/DSP/Include/dsp/bayes_functions.h

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+/******************************************************************************
+ * @file     bayes_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 _BAYES_FUNCTIONS_H_
+#define _BAYES_FUNCTIONS_H_
+
+#include "arm_math_types.h"
+#include "arm_math_memory.h"
+
+#include "dsp/none.h"
+#include "dsp/utils.h"
+
+#include "dsp/statistics_functions.h"
+
+/**
+ * @defgroup groupBayes Bayesian estimators
+ *
+ * Implement the naive gaussian Bayes estimator.
+ * 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/Bayes.py
+ */
+
+#ifdef   __cplusplus
+extern "C"
+{
+#endif
+
+/**
+ * @brief Instance structure for Naive Gaussian Bayesian estimator.
+ */
+typedef struct
+{
+  uint32_t vectorDimension;  /**< Dimension of vector space */
+  uint32_t numberOfClasses;  /**< Number of different classes  */
+  const float32_t *theta;          /**< Mean values for the Gaussians */
+  const float32_t *sigma;          /**< Variances for the Gaussians */
+  const float32_t *classPriors;    /**< Class prior probabilities */
+  float32_t epsilon;         /**< Additive value to variances */
+} arm_gaussian_naive_bayes_instance_f32;
+
+/**
+ * @brief Naive Gaussian Bayesian Estimator
+ *
+ * @param[in]  S                        points to a naive bayes instance structure
+ * @param[in]  in                       points to the elements of the input vector.
+ * @param[out] *pOutputProbabilities    points to a buffer of length numberOfClasses containing estimated probabilities
+ * @param[out] *pBufferB                points to a temporary buffer of length numberOfClasses
+ * @return The predicted class
+ *
+ */
+
+
+uint32_t arm_gaussian_naive_bayes_predict_f32(const arm_gaussian_naive_bayes_instance_f32 *S, 
+   const float32_t * in, 
+   float32_t *pOutputProbabilities,
+   float32_t *pBufferB);
+
+
+#ifdef   __cplusplus
+}
+#endif
+
+#endif /* ifndef _BAYES_FUNCTIONS_H_ */