

#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.    
   */

void arm_lms_norm_f32(
  arm_lms_norm_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 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 */

  /* 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;


#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);

    /* 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;

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

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

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

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

  /* Loop unrolling for (numTaps - 1u)/4 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;

    /* 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;

    /* 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 in the accumulator is stored 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 pCcoeffs pointer */
    pb = pCoeffs;

    /* Loop over numTaps number of values */
    tapCnt = numTaps;

    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;

  /* Copy (numTaps - 1u) samples  */
  tapCnt = (numTaps - 1u);

  /* Copy the remaining q31_t data */
  while(tapCnt > 0u)
  {
    *pStateCurnt++ = *pState++;

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

#endif /*   #ifndef ARM_MATH_CM0_FAMILY */

}

/**    
   * @} end of LMS_NORM group    
   */
