464 lines
8.3 KiB
C++
464 lines
8.3 KiB
C++
// SPDX-License-Identifier: Apache-2.0
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//
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// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
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// Copyright 2008-2016 National ICT Australia (NICTA)
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ------------------------------------------------------------------------
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//! \addtogroup running_stat
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//! @{
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template<typename eT>
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inline
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arma_counter<eT>::~arma_counter()
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{
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arma_debug_sigprint_this(this);
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}
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template<typename eT>
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inline
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arma_counter<eT>::arma_counter()
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: d_count( eT(0))
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, i_count(uword(0))
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{
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arma_debug_sigprint_this(this);
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}
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template<typename eT>
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inline
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const arma_counter<eT>&
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arma_counter<eT>::operator++()
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{
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if(i_count < ARMA_MAX_UWORD)
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{
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i_count++;
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}
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else
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{
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d_count += eT(ARMA_MAX_UWORD);
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i_count = 1;
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}
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return *this;
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}
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template<typename eT>
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inline
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void
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arma_counter<eT>::operator++(int)
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{
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operator++();
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}
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template<typename eT>
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inline
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void
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arma_counter<eT>::reset()
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{
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d_count = eT(0);
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i_count = uword(0);
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}
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template<typename eT>
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inline
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eT
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arma_counter<eT>::value() const
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{
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return d_count + eT(i_count);
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}
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template<typename eT>
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inline
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eT
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arma_counter<eT>::value_plus_1() const
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{
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if(i_count < ARMA_MAX_UWORD)
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{
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return d_count + eT(i_count + 1);
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}
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else
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{
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return d_count + eT(ARMA_MAX_UWORD) + eT(1);
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}
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}
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template<typename eT>
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inline
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eT
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arma_counter<eT>::value_minus_1() const
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{
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if(i_count > 0)
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{
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return d_count + eT(i_count - 1);
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}
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else
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{
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return d_count - eT(1);
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}
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}
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//
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template<typename eT>
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inline
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running_stat<eT>::~running_stat()
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{
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arma_debug_sigprint_this(this);
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}
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template<typename eT>
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inline
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running_stat<eT>::running_stat()
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: r_mean ( eT(0))
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, r_var (typename running_stat<eT>::T(0))
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, min_val ( eT(0))
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, max_val ( eT(0))
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, min_val_norm(typename running_stat<eT>::T(0))
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, max_val_norm(typename running_stat<eT>::T(0))
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{
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arma_debug_sigprint_this(this);
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}
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//! update statistics to reflect new sample
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template<typename eT>
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inline
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void
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running_stat<eT>::operator() (const typename running_stat<eT>::T sample)
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{
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arma_debug_sigprint();
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if(arma_isnonfinite(sample))
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{
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arma_warn(3, "running_stat: non-finite sample ignored" );
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return;
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}
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running_stat_aux::update_stats(*this, sample);
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}
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//! update statistics to reflect new sample (version for complex numbers)
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template<typename eT>
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inline
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void
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running_stat<eT>::operator() (const std::complex< typename running_stat<eT>::T >& sample)
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{
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arma_debug_sigprint();
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if(arma_isnonfinite(sample))
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{
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arma_warn(3, "running_stat: non-finite sample ignored" );
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return;
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}
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running_stat_aux::update_stats(*this, sample);
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}
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//! set all statistics to zero
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template<typename eT>
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inline
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void
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running_stat<eT>::reset()
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{
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arma_debug_sigprint();
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// typedef typename running_stat<eT>::T T;
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counter.reset();
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r_mean = eT(0);
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r_var = T(0);
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min_val = eT(0);
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max_val = eT(0);
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min_val_norm = T(0);
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max_val_norm = T(0);
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}
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//! mean or average value
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template<typename eT>
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inline
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eT
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running_stat<eT>::mean() const
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{
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arma_debug_sigprint();
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return r_mean;
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}
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//! variance
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template<typename eT>
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inline
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typename running_stat<eT>::T
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running_stat<eT>::var(const uword norm_type) const
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{
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arma_debug_sigprint();
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const T N = counter.value();
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if(N > T(1))
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{
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if(norm_type == 0)
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{
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return r_var;
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}
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else
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{
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const T N_minus_1 = counter.value_minus_1();
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return (N_minus_1/N) * r_var;
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}
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}
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else
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{
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return T(0);
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}
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}
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//! standard deviation
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template<typename eT>
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inline
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typename running_stat<eT>::T
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running_stat<eT>::stddev(const uword norm_type) const
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{
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arma_debug_sigprint();
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return std::sqrt( (*this).var(norm_type) );
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}
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//! minimum value
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template<typename eT>
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inline
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eT
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running_stat<eT>::min() const
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{
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arma_debug_sigprint();
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return min_val;
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}
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//! maximum value
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template<typename eT>
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inline
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eT
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running_stat<eT>::max() const
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{
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arma_debug_sigprint();
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return max_val;
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}
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template<typename eT>
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inline
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eT
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running_stat<eT>::range() const
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{
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arma_debug_sigprint();
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return (max_val - min_val);
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}
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//! number of samples so far
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template<typename eT>
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inline
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typename get_pod_type<eT>::result
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running_stat<eT>::count() const
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{
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arma_debug_sigprint();
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return counter.value();
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}
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//! update statistics to reflect new sample (version for non-complex numbers, non-complex sample)
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template<typename eT>
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inline
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void
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running_stat_aux::update_stats(running_stat<eT>& x, const eT sample, const typename arma_not_cx<eT>::result* junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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typedef typename running_stat<eT>::T T;
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const T N = x.counter.value();
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if(N > T(0))
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{
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if(sample < x.min_val)
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{
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x.min_val = sample;
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}
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if(sample > x.max_val)
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{
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x.max_val = sample;
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}
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const T N_plus_1 = x.counter.value_plus_1();
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const T N_minus_1 = x.counter.value_minus_1();
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// note: variance has to be updated before the mean
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const eT tmp = sample - x.r_mean;
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x.r_var = N_minus_1/N * x.r_var + (tmp*tmp)/N_plus_1;
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x.r_mean = x.r_mean + (sample - x.r_mean)/N_plus_1;
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//x.r_mean = (N/N_plus_1)*x.r_mean + sample/N_plus_1;
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//x.r_mean = (x.r_mean + sample/N) * N/N_plus_1;
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}
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else
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{
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x.r_mean = sample;
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x.min_val = sample;
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x.max_val = sample;
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// r_var is initialised to zero
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// in the constructor and reset()
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}
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x.counter++;
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}
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//! update statistics to reflect new sample (version for non-complex numbers, complex sample)
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template<typename eT>
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inline
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void
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running_stat_aux::update_stats(running_stat<eT>& x, const std::complex<eT>& sample, const typename arma_not_cx<eT>::result* junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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running_stat_aux::update_stats(x, std::real(sample));
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}
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//! update statistics to reflect new sample (version for complex numbers, non-complex sample)
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template<typename eT>
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inline
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void
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running_stat_aux::update_stats(running_stat<eT>& x, const typename eT::value_type sample, const typename arma_cx_only<eT>::result* junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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typedef typename eT::value_type T;
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running_stat_aux::update_stats(x, std::complex<T>(sample));
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}
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//! alter statistics to reflect new sample (version for complex numbers, complex sample)
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template<typename eT>
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inline
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void
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running_stat_aux::update_stats(running_stat<eT>& x, const eT& sample, const typename arma_cx_only<eT>::result* junk)
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{
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arma_debug_sigprint();
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arma_ignore(junk);
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typedef typename eT::value_type T;
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const T sample_norm = std::norm(sample);
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const T N = x.counter.value();
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if(N > T(0))
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{
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if(sample_norm < x.min_val_norm)
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{
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x.min_val_norm = sample_norm;
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x.min_val = sample;
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}
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if(sample_norm > x.max_val_norm)
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{
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x.max_val_norm = sample_norm;
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x.max_val = sample;
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}
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const T N_plus_1 = x.counter.value_plus_1();
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const T N_minus_1 = x.counter.value_minus_1();
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x.r_var = N_minus_1/N * x.r_var + std::norm(sample - x.r_mean)/N_plus_1;
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x.r_mean = x.r_mean + (sample - x.r_mean)/N_plus_1;
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//x.r_mean = (N/N_plus_1)*x.r_mean + sample/N_plus_1;
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//x.r_mean = (x.r_mean + sample/N) * N/N_plus_1;
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}
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else
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{
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x.r_mean = sample;
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x.min_val = sample;
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x.max_val = sample;
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x.min_val_norm = sample_norm;
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x.max_val_norm = sample_norm;
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// r_var is initialised to zero
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// in the constructor and reset()
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}
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x.counter++;
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}
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//! @}
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