/* ----------------------------------------------------------------------
 * Project:      CMSIS DSP Library
 * Title:        arm_lms_norm_f32.c
 * Description:  Processing function for the floating-point NLMS filter
 *
 * $Date:        18. March 2019
 * $Revision:    V1.6.0
 *
 * Target Processor: Cortex-M cores
 * -------------------------------------------------------------------- */
/*
 * Copyright (C) 2010-2019 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 groupFilters
 */

/**
  @defgroup LMS_NORM Normalized LMS Filters

  This set of functions implements a commonly used adaptive filter.
  It is related to the Least Mean Square (LMS) adaptive filter and includes an additional normalization
  factor which increases the adaptation rate of the filter.
  The CMSIS DSP Library contains normalized LMS filter functions that operate on Q15, Q31, and floating-point data types.

  A normalized least mean square (NLMS) 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 NLMS 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 NLMS 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 NLMS adaptive 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 instanteous energy of the filter state variables is calculated:
  <pre>
     E = x[n]^2 + x[n-1]^2 + ... + x[n-numTaps+1]^2.
  </pre>
                   The filter coefficients <code>b[k]</code> are then updated on a sample-by-sample basis:
  <pre>
      b[k] = b[k] + e[n] * (mu/E) * 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, energy, x0, pState. Also set all of the values in pState to zero.
                   For Q7, Q15, and Q31 the following fields must also be initialized;
                   recipTable, postShift
 @par
                   Instance structure cannot be placed into a const data section and it is recommended to use the initialization function.
 @par            Fixed-Point Behavior
                   Care must be taken when using the Q15 and Q31 versions of the normalised 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_NORM
  @{
 */

/**
  @brief         Processing function for floating-point normalized LMS filter.
  @param[in]     S         points to an instance of the floating-point normalized 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
 */

#if defined(ARM_MATH_NEON)
void arm_lms_norm_f32(
  arm_lms_norm_instance_f32 * S,
  const 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 energy;                              /* Energy of the input */
  float32_t sum, e, d;                           /* accumulator, error, reference data sample */
  float32_t w, x0, in;                           /* weight factor, temporary variable to hold input sample and state */

  float32x4_t tempV, sumV, xV, bV;
  float32x2_t tempV2;

  /* Initializations of error,  difference, Coefficient update */
  e = 0.0f;
  d = 0.0f;
  w = 0.0f;

  energy = S->energy;
  x0 = S->x0;

  /* S->pState points to buffer 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)]);

  /* Loop over blockSize number of values */
  blkCnt = blockSize;

  while (blkCnt > 0U)
  {
    /* Copy the new input sample into the state buffer */
    *pStateCurnt++ = *pSrc;

    /* Initialize pState pointer */
    px = pState;

    /* Initialize coeff pointer */
    pb = (pCoeffs);

    /* Read the sample from input buffer */
    in = *pSrc++;

    /* Update the energy calculation */
    energy -= x0 * x0;
    energy += in * in;

    /* Set the accumulator to zero */
    sum = 0.0f;
    sumV = vdupq_n_f32(0.0);

    /* Process 4 taps at a time. */
    tapCnt = numTaps >> 2;

    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      xV = vld1q_f32(px);
      bV = vld1q_f32(pb);
      sumV = vmlaq_f32(sumV, xV, bV);

      px += 4; 
      pb += 4;

      /* Decrement the loop counter */
      tapCnt--;
    }
    tempV2 = vpadd_f32(vget_low_f32(sumV),vget_high_f32(sumV));
    sum = tempV2[0] + tempV2[1];

    /* 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 updating filter coefficients */
    /* epsilon value 0.000000119209289f */
    w = (e * mu) / (energy + 0.000000119209289f);

    /* Initialize pState pointer */
    px = pState;

    /* Initialize coeff pointer */
    pb = (pCoeffs);

    /* Process 4 taps at a time. */
    tapCnt = numTaps >> 2;

    /* Update filter coefficients */
    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      xV = vld1q_f32(px);
      bV = vld1q_f32(pb);
      px += 4;
      bV = vmlaq_n_f32(bV,xV,w);

      vst1q_f32(pb,bV); 
      pb += 4;


      /* 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 += w * (*px++);
      pb++;

      /* Decrement the loop counter */
      tapCnt--;
    }

    x0 = *pState;

    /* Advance state pointer by 1 for the next sample */
    pState = pState + 1;

    /* Decrement the loop counter */
    blkCnt--;
  }

  S->energy = energy;
  S->x0 = x0;

  /* 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;

  /* Process 4 taps at a time for (numTaps - 1U)/4 samples copy */
  tapCnt = (numTaps - 1U) >> 2U;

  /* copy data */
  while (tapCnt > 0U)
  {
    tempV = vld1q_f32(pState);
    vst1q_f32(pStateCurnt,tempV); 
    pState += 4;
    pStateCurnt += 4;

    /* 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
void arm_lms_norm_f32(
        arm_lms_norm_instance_f32 * S,
  const 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 */
        float32_t acc, e;                              /* Accumulator, error */
        float32_t w;                                   /* Weight factor */
        uint32_t numTaps = S->numTaps;                 /* Number of filter coefficients in the filter */
        uint32_t tapCnt, blkCnt;                       /* Loop counters */
        float32_t energy;                              /* Energy of the input */
        float32_t x0, in;                              /* Temporary variable to hold input sample and state */

  /* Initializations of error,  difference, Coefficient update */
  e = 0.0f;
  w = 0.0f;

  energy = S->energy;
  x0 = S->x0;

  /* S->pState points to buffer 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)]);

  /* initialise loop count */
  blkCnt = blockSize;

  while (blkCnt > 0U)
  {
    /* Copy the new input sample into the state buffer */
    *pStateCurnt++ = *pSrc;

    /* Initialize pState pointer */
    px = pState;

    /* Initialize coefficient pointer */
    pb = pCoeffs;

    /* Read the sample from input buffer */
    in = *pSrc++;

    /* Update the energy calculation */
    energy -= x0 * x0;
    energy += in * in;

    /* Set the accumulator to zero */
    acc = 0.0f;

#if defined (ARM_MATH_LOOPUNROLL)

    /* Loop unrolling: Compute 4 taps at a time. */
    tapCnt = numTaps >> 2U;

    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      acc += (*px++) * (*pb++);

      acc += (*px++) * (*pb++);

      acc += (*px++) * (*pb++);

      acc += (*px++) * (*pb++);

      /* Decrement loop counter */
      tapCnt--;
    }

    /* Loop unrolling: Compute remaining taps */
    tapCnt = numTaps % 0x4U;

#else

    /* Initialize tapCnt with number of samples */
    tapCnt = numTaps;

#endif /* #if defined (ARM_MATH_LOOPUNROLL) */

    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      acc += (*px++) * (*pb++);

      /* Decrement the loop counter */
      tapCnt--;
    }

    /* Store the result from accumulator into the destination buffer. */
    *pOut++ = acc;

    /* Compute and store error */
    e = (float32_t) *pRef++ - acc;
    *pErr++ = e;

    /* Calculation of Weighting factor for updating filter coefficients */
    /* epsilon value 0.000000119209289f */
    w = (e * mu) / (energy + 0.000000119209289f);

    /* Initialize pState pointer */
    px = pState;

    /* Initialize coefficient pointer */
    pb = pCoeffs;

#if defined (ARM_MATH_LOOPUNROLL)

    /* Loop unrolling: Compute 4 taps at a time. */
    tapCnt = numTaps >> 2U;

    /* Update filter coefficients */
    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      *pb += w * (*px++);
      pb++;

      *pb += w * (*px++);
      pb++;

      *pb += w * (*px++);
      pb++;

      *pb += w * (*px++);
      pb++;

      /* Decrement loop counter */
      tapCnt--;
    }

    /* Loop unrolling: Compute remaining taps */
    tapCnt = numTaps % 0x4U;

#else

    /* Initialize tapCnt with number of samples */
    tapCnt = numTaps;

#endif /* #if defined (ARM_MATH_LOOPUNROLL) */

    while (tapCnt > 0U)
    {
      /* Perform the multiply-accumulate */
      *pb += w * (*px++);
      pb++;

      /* Decrement loop counter */
      tapCnt--;
    }

    x0 = *pState;

    /* Advance state pointer by 1 for the next sample */
    pState = pState + 1;

    /* Decrement loop counter */
    blkCnt--;
  }

  /* Save energy and x0 values for the next frame */
  S->energy = energy;
  S->x0 = x0;

  /* 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 data */
#if defined (ARM_MATH_LOOPUNROLL)

  /* Loop unrolling: Compute 4 taps at a time. */
  tapCnt = (numTaps - 1U) >> 2U;

  while (tapCnt > 0U)
  {
    *pStateCurnt++ = *pState++;
    *pStateCurnt++ = *pState++;
    *pStateCurnt++ = *pState++;
    *pStateCurnt++ = *pState++;

    /* Decrement loop counter */
    tapCnt--;
  }

  /* Loop unrolling: Compute remaining taps */
  tapCnt = (numTaps - 1U) % 0x4U;

#else

  /* Initialize tapCnt with number of samples */
  tapCnt = (numTaps - 1U);

#endif /* #if defined (ARM_MATH_LOOPUNROLL) */

  while (tapCnt > 0U)
  {
    *pStateCurnt++ = *pState++;

    /* Decrement loop counter */
    tapCnt--;
  }

}
#endif /* #if defined(ARM_MATH_NEON) */
/**
  @} end of LMS_NORM group
 */