// SPDX-License-Identifier: Apache-2.0
//
// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
// Copyright 2008-2016 National ICT Australia (NICTA)
//
// 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
// http://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.
// ------------------------------------------------------------------------

//! \addtogroup running_stat
//! @{

template <typename eT>
inline arma_counter<eT>::~arma_counter() {
  arma_debug_sigprint_this(this);
}

template <typename eT>
inline arma_counter<eT>::arma_counter() : d_count(eT(0)), i_count(uword(0)) {
  arma_debug_sigprint_this(this);
}

template <typename eT>
inline const arma_counter<eT>& arma_counter<eT>::operator++() {
  if (i_count < ARMA_MAX_UWORD) {
    i_count++;
  } else {
    d_count += eT(ARMA_MAX_UWORD);
    i_count = 1;
  }

  return *this;
}

template <typename eT>
inline void arma_counter<eT>::operator++(int) {
  operator++();
}

template <typename eT>
inline void arma_counter<eT>::reset() {
  d_count = eT(0);
  i_count = uword(0);
}

template <typename eT>
inline eT arma_counter<eT>::value() const {
  return d_count + eT(i_count);
}

template <typename eT>
inline eT arma_counter<eT>::value_plus_1() const {
  if (i_count < ARMA_MAX_UWORD) {
    return d_count + eT(i_count + 1);
  } else {
    return d_count + eT(ARMA_MAX_UWORD) + eT(1);
  }
}

template <typename eT>
inline eT arma_counter<eT>::value_minus_1() const {
  if (i_count > 0) {
    return d_count + eT(i_count - 1);
  } else {
    return d_count - eT(1);
  }
}

//

template <typename eT>
inline running_stat<eT>::~running_stat() {
  arma_debug_sigprint_this(this);
}

template <typename eT>
inline running_stat<eT>::running_stat()
    : r_mean(eT(0)),
      r_var(typename running_stat<eT>::T(0)),
      min_val(eT(0)),
      max_val(eT(0)),
      min_val_norm(typename running_stat<eT>::T(0)),
      max_val_norm(typename running_stat<eT>::T(0)) {
  arma_debug_sigprint_this(this);
}

//! update statistics to reflect new sample
template <typename eT>
inline void running_stat<eT>::operator()(const typename running_stat<eT>::T sample) {
  arma_debug_sigprint();

  if (arma_isfinite(sample) == false) {
    arma_warn(3, "running_stat: sample ignored as it is non-finite");
    return;
  }

  running_stat_aux::update_stats(*this, sample);
}

//! update statistics to reflect new sample (version for complex numbers)
template <typename eT>
inline void running_stat<eT>::operator()(
    const std::complex<typename running_stat<eT>::T>& sample) {
  arma_debug_sigprint();

  if (arma_isfinite(sample) == false) {
    arma_warn(3, "running_stat: sample ignored as it is non-finite");
    return;
  }

  running_stat_aux::update_stats(*this, sample);
}

//! set all statistics to zero
template <typename eT>
inline void running_stat<eT>::reset() {
  arma_debug_sigprint();

  // typedef typename running_stat<eT>::T T;

  counter.reset();

  r_mean = eT(0);
  r_var = T(0);

  min_val = eT(0);
  max_val = eT(0);

  min_val_norm = T(0);
  max_val_norm = T(0);
}

//! mean or average value
template <typename eT>
inline eT running_stat<eT>::mean() const {
  arma_debug_sigprint();

  return r_mean;
}

//! variance
template <typename eT>
inline typename running_stat<eT>::T running_stat<eT>::var(const uword norm_type) const {
  arma_debug_sigprint();

  const T N = counter.value();

  if (N > T(1)) {
    if (norm_type == 0) {
      return r_var;
    } else {
      const T N_minus_1 = counter.value_minus_1();
      return (N_minus_1 / N) * r_var;
    }
  } else {
    return T(0);
  }
}

//! standard deviation
template <typename eT>
inline typename running_stat<eT>::T running_stat<eT>::stddev(
    const uword norm_type) const {
  arma_debug_sigprint();

  return std::sqrt((*this).var(norm_type));
}

//! minimum value
template <typename eT>
inline eT running_stat<eT>::min() const {
  arma_debug_sigprint();

  return min_val;
}

//! maximum value
template <typename eT>
inline eT running_stat<eT>::max() const {
  arma_debug_sigprint();

  return max_val;
}

template <typename eT>
inline eT running_stat<eT>::range() const {
  arma_debug_sigprint();

  return (max_val - min_val);
}

//! number of samples so far
template <typename eT>
inline typename get_pod_type<eT>::result running_stat<eT>::count() const {
  arma_debug_sigprint();

  return counter.value();
}

//! update statistics to reflect new sample (version for non-complex numbers, non-complex
//! sample)
template <typename eT>
inline void running_stat_aux::update_stats(running_stat<eT>& x, const eT sample,
                                           const typename arma_not_cx<eT>::result* junk) {
  arma_debug_sigprint();
  arma_ignore(junk);

  typedef typename running_stat<eT>::T T;

  const T N = x.counter.value();

  if (N > T(0)) {
    if (sample < x.min_val) {
      x.min_val = sample;
    }

    if (sample > x.max_val) {
      x.max_val = sample;
    }

    const T N_plus_1 = x.counter.value_plus_1();
    const T N_minus_1 = x.counter.value_minus_1();

    // note: variance has to be updated before the mean

    const eT tmp = sample - x.r_mean;

    x.r_var = N_minus_1 / N * x.r_var + (tmp * tmp) / N_plus_1;

    x.r_mean = x.r_mean + (sample - x.r_mean) / N_plus_1;
    // x.r_mean = (N/N_plus_1)*x.r_mean + sample/N_plus_1;
    // x.r_mean = (x.r_mean + sample/N) * N/N_plus_1;
  } else {
    x.r_mean = sample;
    x.min_val = sample;
    x.max_val = sample;

    // r_var is initialised to zero
    // in the constructor and reset()
  }

  x.counter++;
}

//! update statistics to reflect new sample (version for non-complex numbers, complex
//! sample)
template <typename eT>
inline void running_stat_aux::update_stats(running_stat<eT>& x,
                                           const std::complex<eT>& sample,
                                           const typename arma_not_cx<eT>::result* junk) {
  arma_debug_sigprint();
  arma_ignore(junk);

  running_stat_aux::update_stats(x, std::real(sample));
}

//! update statistics to reflect new sample (version for complex numbers, non-complex
//! sample)
template <typename eT>
inline void running_stat_aux::update_stats(
    running_stat<eT>& x, const typename eT::value_type sample,
    const typename arma_cx_only<eT>::result* junk) {
  arma_debug_sigprint();
  arma_ignore(junk);

  typedef typename eT::value_type T;

  running_stat_aux::update_stats(x, std::complex<T>(sample));
}

//! alter statistics to reflect new sample (version for complex numbers, complex sample)
template <typename eT>
inline void running_stat_aux::update_stats(
    running_stat<eT>& x, const eT& sample,
    const typename arma_cx_only<eT>::result* junk) {
  arma_debug_sigprint();
  arma_ignore(junk);

  typedef typename eT::value_type T;

  const T sample_norm = std::norm(sample);
  const T N = x.counter.value();

  if (N > T(0)) {
    if (sample_norm < x.min_val_norm) {
      x.min_val_norm = sample_norm;
      x.min_val = sample;
    }

    if (sample_norm > x.max_val_norm) {
      x.max_val_norm = sample_norm;
      x.max_val = sample;
    }

    const T N_plus_1 = x.counter.value_plus_1();
    const T N_minus_1 = x.counter.value_minus_1();

    x.r_var = N_minus_1 / N * x.r_var + std::norm(sample - x.r_mean) / N_plus_1;

    x.r_mean = x.r_mean + (sample - x.r_mean) / N_plus_1;
    // x.r_mean = (N/N_plus_1)*x.r_mean + sample/N_plus_1;
    // x.r_mean = (x.r_mean + sample/N) * N/N_plus_1;
  } else {
    x.r_mean = sample;
    x.min_val = sample;
    x.max_val = sample;
    x.min_val_norm = sample_norm;
    x.max_val_norm = sample_norm;

    // r_var is initialised to zero
    // in the constructor and reset()
  }

  x.counter++;
}

//! @}
