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yaokon/Drivers/CMSIS/DSP/Source/StatisticsFunctions/arm_var_q15.c

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+/* ----------------------------------------------------------------------
+ * Project:      CMSIS DSP Library
+ * Title:        arm_var_q15.c
+ * Description:  Variance of an array of Q15 type
+ *
+ * $Date:        27. January 2017
+ * $Revision:    V.1.5.1
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2017 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.
+ */
+
+#include "arm_math.h"
+
+/**
+ * @ingroup groupStats
+ */
+
+/**
+ * @addtogroup variance
+ * @{
+ */
+
+/**
+ * @brief Variance of the elements of a Q15 vector.
+ * @param[in]       *pSrc points to the input vector
+ * @param[in]       blockSize length of the input vector
+ * @param[out]      *pResult variance value returned here
+ * @return none.
+ * @details
+ * <b>Scaling and Overflow Behavior:</b>
+ *
+ * \par
+ * The function is implemented using a 64-bit internal accumulator.
+ * The input is represented in 1.15 format.
+ * Intermediate multiplication yields a 2.30 format, and this
+ * result is added without saturation to a 64-bit accumulator in 34.30 format.
+ * With 33 guard bits in the accumulator, there is no risk of overflow, and the
+ * full precision of the intermediate multiplication is preserved.
+ * Finally, the 34.30 result is truncated to 34.15 format by discarding the lower
+ * 15 bits, and then saturated to yield a result in 1.15 format.
+ */
+
+void arm_var_q15(
+  q15_t * pSrc,
+  uint32_t blockSize,
+  q15_t * pResult)
+{
+  q31_t sum = 0;                                 /* Accumulator */
+  q31_t meanOfSquares, squareOfMean;             /* square of mean and mean of square */
+  uint32_t blkCnt;                               /* loop counter */
+  q63_t sumOfSquares = 0;                        /* Accumulator */
+#if defined (ARM_MATH_DSP)
+  q31_t in;                                      /* input value */
+  q15_t in1;                                     /* input value */
+#else
+  q15_t in;                                      /* input value */
+#endif
+
+  if (blockSize == 1U)
+  {
+    *pResult = 0;
+    return;
+  }
+
+#if defined (ARM_MATH_DSP)
+  /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+  /*loop Unrolling */
+  blkCnt = blockSize >> 2U;
+
+  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.
+   ** a second loop below computes the remaining 1 to 3 samples. */
+  while (blkCnt > 0U)
+  {
+    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1])  */
+    /* Compute Sum of squares of the input samples
+     * and then store the result in a temporary variable, sum. */
+    in = *__SIMD32(pSrc)++;
+    sum += ((in << 16U) >> 16U);
+    sum +=  (in >> 16U);
+    sumOfSquares = __SMLALD(in, in, sumOfSquares);
+    in = *__SIMD32(pSrc)++;
+    sum += ((in << 16U) >> 16U);
+    sum +=  (in >> 16U);
+    sumOfSquares = __SMLALD(in, in, sumOfSquares);
+
+    /* Decrement the loop counter */
+    blkCnt--;
+  }
+
+  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+   ** No loop unrolling is used. */
+  blkCnt = blockSize % 0x4U;
+
+  while (blkCnt > 0U)
+  {
+    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1]) */
+    /* Compute Sum of squares of the input samples
+     * and then store the result in a temporary variable, sum. */
+    in1 = *pSrc++;
+    sumOfSquares = __SMLALD(in1, in1, sumOfSquares);
+    sum += in1;
+
+    /* Decrement the loop counter */
+    blkCnt--;
+  }
+
+  /* Compute Mean of squares of the input samples
+   * and then store the result in a temporary variable, meanOfSquares. */
+  meanOfSquares = (q31_t)(sumOfSquares / (q63_t)(blockSize - 1U));
+
+  /* Compute square of mean */
+  squareOfMean = (q31_t)((q63_t)sum * sum / (q63_t)(blockSize * (blockSize - 1U)));
+
+  /* mean of the squares minus the square of the mean. */
+  *pResult = (meanOfSquares - squareOfMean) >> 15U;
+
+#else
+  /* Run the below code for Cortex-M0 */
+
+  /* Loop over blockSize number of values */
+  blkCnt = blockSize;
+
+  while (blkCnt > 0U)
+  {
+    /* C = (A[0] * A[0] + A[1] * A[1] + ... + A[blockSize-1] * A[blockSize-1]) */
+    /* Compute Sum of squares of the input samples
+     * and then store the result in a temporary variable, sumOfSquares. */
+    in = *pSrc++;
+    sumOfSquares += (in * in);
+
+    /* C = (A[0] + A[1] + A[2] + ... + A[blockSize-1]) */
+    /* Compute sum of all input values and then store the result in a temporary variable, sum. */
+    sum += in;
+
+    /* Decrement the loop counter */
+    blkCnt--;
+  }
+
+  /* Compute Mean of squares of the input samples
+   * and then store the result in a temporary variable, meanOfSquares. */
+  meanOfSquares = (q31_t)(sumOfSquares / (q63_t)(blockSize - 1U));
+
+  /* Compute square of mean */
+  squareOfMean = (q31_t)((q63_t)sum * sum / (q63_t)(blockSize * (blockSize - 1U)));
+
+  /* mean of the squares minus the square of the mean. */
+  *pResult = (meanOfSquares - squareOfMean) >> 15;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+}
+
+/**
+ * @} end of variance group
+ */