text: /* ----------------------------------------------------------------------    
  * Copyright (C) 2010-2014 ARM Limited. All rights reserved.    
  *    
  * $Date:        19. March 2015
  * $Revision: 	V.1.4.5
  *    
  * Project: 	    CMSIS DSP Library    
  * Title:	    arm_lms_f32.c    
  *    
  * Description:	Processing function for the floating-point LMS filter.    
  *    
  * Target Processor: Cortex-M4/Cortex-M3/Cortex-M0
  *  
  * Redistribution and use in source and binary forms, with or without 
  * modification, are permitted provided that the following conditions
  * are met:
  *   - Redistributions of source code must retain the above copyright
  *     notice, this list of conditions and the following disclaimer.
  *   - Redistributions in binary form must reproduce the above copyright
  *     notice, this list of conditions and the following disclaimer in
  *     the documentation and/or other materials provided with the 
  *     distribution.
  *   - Neither the name of ARM LIMITED nor the names of its contributors
  *     may be used to endorse or promote products derived from this
  *     software without specific prior written permission.
  *
  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 
  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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  * -------------------------------------------------------------------- */
  
  #include "../../../../../drivers/CMSIS/Include/arm_math.h"
  
  /**    
   * @ingroup groupFilters    
   */
  
  /**    
   * @defgroup LMS Least Mean Square (LMS) Filters    
   *    
   * LMS filters are a class of adaptive filters that are able to "learn" an unknown transfer functions.    
   * LMS filters use a gradient descent method in which the filter coefficients are updated based on the instantaneous error signal.    
   * Adaptive filters are often used in communication systems, equalizers, and noise removal.    
   * The CMSIS DSP Library contains LMS filter functions that operate on Q15, Q31, and floating-point data types.    
   * The library also contains normalized LMS filters in which the filter coefficient adaptation is indepedent of the level of the input signal.    
   *    
   * An LMS filter consists of two components as shown below.    
   * The first component is a standard transversal or FIR filter.    
   * The second component is a coefficient update mechanism.    
   * The LMS filter has two input signals.    
   * The "input" feeds the FIR filter while the "reference input" corresponds to the desired output of the FIR filter.    
   * That is, the FIR filter coefficients are updated so that the output of the FIR filter matches the reference input.    
   * The filter coefficient update mechanism is based on the difference between the FIR filter output and the reference input.    
   * This "error signal" tends towards zero as the filter adapts.    
   * The LMS processing functions accept the input and reference input signals and generate the filter output and error signal.    
   * \image html LMS.gif "Internal structure of the Least Mean Square filter"    
   *    
   * The functions operate on blocks of data and each call to the function processes    
   * <code>blockSize</code> samples through the filter.    
   * <code>pSrc</code> points to input signal, <code>pRef</code> points to reference signal,    
   * <code>pOut</code> points to output signal and <code>pErr</code> points to error signal.    
   * All arrays contain <code>blockSize</code> values.    
   *    
   * The functions operate on a block-by-block basis.    
   * Internally, the filter coefficients <code>b[n]</code> are updated on a sample-by-sample basis.    
   * The convergence of the LMS filter is slower compared to the normalized LMS algorithm.    
   *    
   * \par Algorithm:    
   * The output signal <code>y[n]</code> is computed by a standard FIR filter:    
   * <pre>    
   *     y[n] = b[0] * x[n] + b[1] * x[n-1] + b[2] * x[n-2] + ...+ b[numTaps-1] * x[n-numTaps+1]    
   * </pre>    
   *    
   * \par    
   * The error signal equals the difference between the reference signal <code>d[n]</code> and the filter output:    
   * <pre>    
   *     e[n] = d[n] - y[n].    
   * </pre>    
   *    
   * \par    
   * After each sample of the error signal is computed, the filter coefficients <code>b[k]</code> are updated on a sample-by-sample basis:    
   * <pre>    
   *     b[k] = b[k] + e[n] * mu * x[n-k],  for k=0, 1, ..., numTaps-1    
   * </pre>    
   * where <code>mu</code> is the step size and controls the rate of coefficient convergence.    
   *\par    
   * In the APIs, <code>pCoeffs</code> points to a coefficient array of size <code>numTaps</code>.    
   * Coefficients are stored in time reversed order.    
   * \par    
   * <pre>    
   *    {b[numTaps-1], b[numTaps-2], b[N-2], ..., b[1], b[0]}    
   * </pre>    
   * \par    
   * <code>pState</code> points to a state array of size <code>numTaps + blockSize - 1</code>.    
   * Samples in the state buffer are stored in the order:    
   * \par    
   * <pre>    
   *    {x[n-numTaps+1], x[n-numTaps], x[n-numTaps-1], x[n-numTaps-2]....x[0], x[1], ..., x[blockSize-1]}    
   * </pre>    
   * \par    
   * Note that the length of the state buffer exceeds the length of the coefficient array by <code>blockSize-1</code> samples.    
   * The increased state buffer length allows circular addressing, which is traditionally used in FIR filters,    
   * to be avoided and yields a significant speed improvement.    
   * The state variables are updated after each block of data is processed.    
   * \par Instance Structure    
   * The coefficients and state variables for a filter are stored together in an instance data structure.    
   * A separate instance structure must be defined for each filter and    
   * coefficient and state arrays cannot be shared among instances.    
   * There are separate instance structure declarations for each of the 3 supported data types.    
   *    
   * \par Initialization Functions    
   * There is also an associated initialization function for each data type.    
   * The initialization function performs the following operations:    
   * - Sets the values of the internal structure fields.    
   * - Zeros out the values in the state buffer.    
   * To do this manually without calling the init function, assign the follow subfields of the instance structure:
   * numTaps, pCoeffs, mu, postShift (not for f32), pState. Also set all of the values in pState to zero. 
   *
   * \par    
   * Use of the initialization function is optional.    
   * However, if the initialization function is used, then the instance structure cannot be placed into a const data section.    
   * To place an instance structure into a const data section, the instance structure must be manually initialized.    
   * Set the values in the state buffer to zeros before static initialization.    
   * The code below statically initializes each of the 3 different data type filter instance structures    
   * <pre>    
   *    arm_lms_instance_f32 S = {numTaps, pState, pCoeffs, mu};    
   *    arm_lms_instance_q31 S = {numTaps, pState, pCoeffs, mu, postShift};    
   *    arm_lms_instance_q15 S = {numTaps, pState, pCoeffs, mu, postShift};    
   * </pre>    
   * where <code>numTaps</code> is the number of filter coefficients in the filter; <code>pState</code> is the address of the state buffer;    
   * <code>pCoeffs</code> is the address of the coefficient buffer; <code>mu</code> is the step size parameter; and <code>postShift</code> is the shift applied to coefficients.    
   *    
   * \par Fixed-Point Behavior:    
   * Care must be taken when using the Q15 and Q31 versions of the LMS filter.    
   * The following issues must be considered:    
   * - Scaling of coefficients    
   * - Overflow and saturation    
   *    
   * \par Scaling of Coefficients:    
   * Filter coefficients are represented as fractional values and    
   * coefficients are restricted to lie in the range <code>[-1 +1)</code>.    
   * The fixed-point functions have an additional scaling parameter <code>postShift</code>.    
   * At the output of the filter's accumulator is a shift register which shifts the result by <code>postShift</code> bits.    
   * This essentially scales the filter coefficients by <code>2^postShift</code> and    
   * allows the filter coefficients to exceed the range <code>[+1 -1)</code>.    
   * The value of <code>postShift</code> is set by the user based on the expected gain through the system being modeled.    
   *    
   * \par Overflow and Saturation:    
   * Overflow and saturation behavior of the fixed-point Q15 and Q31 versions are    
   * described separately as part of the function specific documentation below.    
   */
  
  /**    
   * @addtogroup LMS    
   * @{    
   */
  
  /**           
   * @details           
   * This function operates on floating-point data types.       
   *    
   * @brief Processing function for floating-point LMS filter.    
   * @param[in]  *S points to an instance of the floating-point LMS filter structure.    
   * @param[in]  *pSrc points to the block of input data.    
   * @param[in]  *pRef points to the block of reference data.    
   * @param[out] *pOut points to the block of output data.    
   * @param[out] *pErr points to the block of error data.    
   * @param[in]  blockSize number of samples to process.    
   * @return     none.    
   */
  
  void arm_lms_f32(
    const arm_lms_instance_f32 * S,
    float32_t * pSrc,
    float32_t * pRef,
    float32_t * pOut,
    float32_t * pErr,
    uint32_t blockSize)
  {
    float32_t *pState = S->pState;                 /* State pointer */
    float32_t *pCoeffs = S->pCoeffs;               /* Coefficient pointer */
    float32_t *pStateCurnt;                        /* Points to the current sample of the state */
    float32_t *px, *pb;                            /* Temporary pointers for state and coefficient buffers */
    float32_t mu = S->mu;                          /* Adaptive factor */
    uint32_t numTaps = S->numTaps;                 /* Number of filter coefficients in the filter */
    uint32_t tapCnt, blkCnt;                       /* Loop counters */
    float32_t sum, e, d;                           /* accumulator, error, reference data sample */
    float32_t w = 0.0f;                            /* weight factor */
  
    e = 0.0f;
    d = 0.0f;
  
    /* S->pState points to state array which contains previous frame (numTaps - 1) samples */
    /* pStateCurnt points to the location where the new input data should be written */
    pStateCurnt = &(S->pState[(numTaps - 1u)]);
  
    blkCnt = blockSize;
  
  
  #ifndef ARM_MATH_CM0_FAMILY
  
    /* Run the below code for Cortex-M4 and Cortex-M3 */
  
    while(blkCnt > 0u)
    {
      /* Copy the new input sample into the state buffer */
      *pStateCurnt++ = *pSrc++;
  
      /* Initialize pState pointer */
      px = pState;
  
      /* Initialize coeff pointer */
      pb = (pCoeffs);
  
      /* Set the accumulator to zero */
      sum = 0.0f;
  
      /* Loop unrolling.  Process 4 taps at a time. */
      tapCnt = numTaps >> 2;
  
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        sum += (*px++) * (*pb++);
        sum += (*px++) * (*pb++);
        sum += (*px++) * (*pb++);
        sum += (*px++) * (*pb++);
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* If the filter length is not a multiple of 4, compute the remaining filter taps */
      tapCnt = numTaps % 0x4u;
  
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        sum += (*px++) * (*pb++);
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* The result in the accumulator, store in the destination buffer. */
      *pOut++ = sum;
  
      /* Compute and store error */
      d = (float32_t) (*pRef++);
      e = d - sum;
      *pErr++ = e;
  
      /* Calculation of Weighting factor for the updating filter coefficients */
      w = e * mu;
  
      /* Initialize pState pointer */
      px = pState;
  
      /* Initialize coeff pointer */
      pb = (pCoeffs);
  
      /* Loop unrolling.  Process 4 taps at a time. */
      tapCnt = numTaps >> 2;
  
      /* Update filter coefficients */
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        *pb = *pb + (w * (*px++));
        pb++;
  
        *pb = *pb + (w * (*px++));
        pb++;
  
        *pb = *pb + (w * (*px++));
        pb++;
  
        *pb = *pb + (w * (*px++));
        pb++;
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* If the filter length is not a multiple of 4, compute the remaining filter taps */
      tapCnt = numTaps % 0x4u;
  
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        *pb = *pb + (w * (*px++));
        pb++;
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* Advance state pointer by 1 for the next sample */
      pState = pState + 1;
  
      /* Decrement the loop counter */
      blkCnt--;
    }
  
  
    /* Processing is complete. Now copy the last numTaps - 1 samples to the    
       satrt of the state buffer. This prepares the state buffer for the    
       next function call. */
  
    /* Points to the start of the pState buffer */
    pStateCurnt = S->pState;
  
    /* Loop unrolling for (numTaps - 1u) samples copy */
    tapCnt = (numTaps - 1u) >> 2u;
  
    /* copy data */
    while(tapCnt > 0u)
    {
      *pStateCurnt++ = *pState++;
      *pStateCurnt++ = *pState++;
      *pStateCurnt++ = *pState++;
      *pStateCurnt++ = *pState++;
  
      /* Decrement the loop counter */
      tapCnt--;
    }
  
    /* Calculate remaining number of copies */
    tapCnt = (numTaps - 1u) % 0x4u;
  
    /* Copy the remaining q31_t data */
    while(tapCnt > 0u)
    {
      *pStateCurnt++ = *pState++;
  
      /* Decrement the loop counter */
      tapCnt--;
    }
  
  #else
  
    /* Run the below code for Cortex-M0 */
  
    while(blkCnt > 0u)
    {
      /* Copy the new input sample into the state buffer */
      *pStateCurnt++ = *pSrc++;
  
      /* Initialize pState pointer */
      px = pState;
  
      /* Initialize pCoeffs pointer */
      pb = pCoeffs;
  
      /* Set the accumulator to zero */
      sum = 0.0f;
  
      /* Loop over numTaps number of values */
      tapCnt = numTaps;
  
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        sum += (*px++) * (*pb++);
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* The result is stored in the destination buffer. */
      *pOut++ = sum;
  
      /* Compute and store error */
      d = (float32_t) (*pRef++);
      e = d - sum;
      *pErr++ = e;
  
      /* Weighting factor for the LMS version */
      w = e * mu;
  
      /* Initialize pState pointer */
      px = pState;
  
      /* Initialize pCoeffs pointer */
      pb = pCoeffs;
  
      /* Loop over numTaps number of values */
      tapCnt = numTaps;
  
      while(tapCnt > 0u)
      {
        /* Perform the multiply-accumulate */
        *pb = *pb + (w * (*px++));
        pb++;
  
        /* Decrement the loop counter */
        tapCnt--;
      }
  
      /* Advance state pointer by 1 for the next sample */
      pState = pState + 1;
  
      /* Decrement the loop counter */
      blkCnt--;
    }
  
  
    /* Processing is complete. Now copy the last numTaps - 1 samples to the        
     * start of the state buffer. This prepares the state buffer for the        
     * next function call. */
  
    /* Points to the start of the pState buffer */
    pStateCurnt = S->pState;
  
    /*  Copy (numTaps - 1u) samples  */
    tapCnt = (numTaps - 1u);
  
    /* Copy the data */
    while(tapCnt > 0u)
    {
      *pStateCurnt++ = *pState++;
  
      /* Decrement the loop counter */
      tapCnt--;
    }
  
  #endif /*   #ifndef ARM_MATH_CM0_FAMILY */
  
  }
  
  /**    
     * @} end of LMS group    
     */
  