mirror of
https://github.com/aportelli/LatAnalyze.git
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268 lines
7.5 KiB
C++
268 lines
7.5 KiB
C++
/*
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* StatArray.hpp, part of LatAnalyze 3
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*
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* Copyright (C) 2013 - 2014 Antonin Portelli
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*
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* LatAnalyze 3 is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LatAnalyze 3 is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LatAnalyze 3. If not, see <http://www.gnu.org/licenses/>.
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*/
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#ifndef Latan_StatArray_hpp_
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#define Latan_StatArray_hpp_
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#include <LatAnalyze/Global.hpp>
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#include <LatAnalyze/Mat.hpp>
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#include <iostream>
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#define FOR_STAT_ARRAY(ar, i) \
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for (Latan::Index i = -(ar).offset; i < (ar).size(); ++i)
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BEGIN_NAMESPACE
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/******************************************************************************
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* Array class with statistics *
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******************************************************************************/
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template <typename T, Index os = 0>
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class StatArray: public Array<T, dynamic, 1>
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{
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private:
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typedef Array<T, dynamic, 1> Base;
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public:
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// constructors
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StatArray(void);
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explicit StatArray(const Index size);
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EIGEN_EXPR_CTOR(StatArray, unique_arg(StatArray<T, os>), Base, ArrayExpr)
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// destructor
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virtual ~StatArray(void) = default;
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// access
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Index size(void) const;
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void resize(const Index size);
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// operators
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T & operator[](const Index s);
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const T & operator[](const Index s) const;
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// statistics
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void bin(Index binSize);
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T mean(const Index pos = 0, const Index n = -1) const;
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T mean(void) const;
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T covariance(const StatArray<T, os> &array, const Index pos = 0,
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const Index n = -1) const;
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T covarianceMatrix(const StatArray<T, os> &array, const Index pos = 0,
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const Index n = -1) const;
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T variance(const Index pos = 0, const Index n = -1) const;
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T varianceMatrix(const Index pos = 0, const Index n = -1) const;
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public:
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static const Index offset = os;
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};
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// reduction operations
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namespace ReducOp
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{
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// general templates
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template <typename T>
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inline T prod(const T &a, const T &b);
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template <typename T>
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inline T tensProd(const T &v1, const T &v2);
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template <typename T>
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inline T sum(const T &a, const T &b);
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// matrix specializations
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template <>
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inline DMat prod(const DMat &a, const DMat &b);
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template <>
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inline DMat tensProd(const DMat &v1, const DMat &v2);
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}
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// Sample types
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#define SAMPLE_OFFSET 1
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const int central = -SAMPLE_OFFSET;
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template <typename T>
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using Sample = StatArray<T, SAMPLE_OFFSET>;
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typedef Sample<double> DSample;
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/******************************************************************************
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* StatArray class template implementation *
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******************************************************************************/
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// constructors ////////////////////////////////////////////////////////////////
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template <typename T, Index os>
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StatArray<T, os>::StatArray(void)
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: Base(static_cast<typename Base::Index>(os))
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{}
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template <typename T, Index os>
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StatArray<T, os>::StatArray(const Index size)
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: Base(static_cast<typename Base::Index>(size + os))
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{}
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// access //////////////////////////////////////////////////////////////////////
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template <typename T, Index os>
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Index StatArray<T, os>::size(void) const
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{
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return Base::size() - os;
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}
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template <typename T, Index os>
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void StatArray<T, os>::resize(const Index size)
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{
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Base::resize(size + os);
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}
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// operators ///////////////////////////////////////////////////////////////////
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template <typename T, Index os>
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T & StatArray<T, os>::operator[](const Index s)
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{
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return Base::operator[](s + os);
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}
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template <typename T, Index os>
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const T & StatArray<T, os>::operator[](const Index s) const
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{
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return Base::operator[](s + os);
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}
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// statistics //////////////////////////////////////////////////////////////////
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template <typename T, Index os>
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void StatArray<T, os>::bin(Index binSize)
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{
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Index q = size()/binSize, r = size()%binSize;
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for (Index i = 0; i < q; ++i)
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{
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(*this)[i] = mean(i*binSize, binSize);
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}
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if (r != 0)
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{
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(*this)[q] = mean(q*binSize, r);
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this->conservativeResize(os + q + 1);
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}
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else
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{
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this->conservativeResize(os + q);
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}
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}
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template <typename T, Index os>
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T StatArray<T, os>::mean(const Index pos, const Index n) const
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{
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T result;
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const Index m = (n >= 0) ? n : size();
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if (m)
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{
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result = this->segment(pos+os, m).redux(&ReducOp::sum<T>);
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}
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return result/static_cast<double>(n);
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}
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template <typename T, Index os>
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T StatArray<T, os>::covariance(const StatArray<T, os> &array, const Index pos,
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const Index n) const
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{
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T s1, s2, prs, res;
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const Index m = (n >= 0) ? n : size();
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if (m)
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{
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auto arraySeg = array.segment(pos+os, m);
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auto thisSeg = this->segment(pos+os, m);
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s1 = thisSeg.redux(&ReducOp::sum<T>);
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s2 = arraySeg.redux(&ReducOp::sum<T>);
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prs = thisSeg.binaryExpr(arraySeg, &ReducOp::prod<T>)
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.redux(&ReducOp::sum<T>);
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res = prs - ReducOp::prod(s1, s2)/static_cast<double>(m);
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}
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return res/static_cast<double>(m - 1);
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}
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template <typename T, Index os>
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T StatArray<T, os>::covarianceMatrix(const StatArray<T, os> &array,
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const Index pos, const Index n) const
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{
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T s1, s2, prs, res;
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const Index m = (n >= 0) ? n : size();
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if (m)
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{
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auto arraySeg = array.segment(pos+os, m);
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auto thisSeg = this->segment(pos+os, m);
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s1 = thisSeg.redux(&ReducOp::sum<T>);
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s2 = arraySeg.redux(&ReducOp::sum<T>);
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prs = thisSeg.binaryExpr(arraySeg, &ReducOp::tensProd<T>)
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.redux(&ReducOp::sum<T>);
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res = prs - ReducOp::tensProd(s1, s2)/static_cast<double>(m);
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}
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return res/static_cast<double>(m - 1);
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}
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template <typename T, Index os>
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T StatArray<T, os>::variance(const Index pos, const Index n) const
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{
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return covariance(*this, pos, n);
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}
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template <typename T, Index os>
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T StatArray<T, os>::varianceMatrix(const Index pos, const Index n) const
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{
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return covarianceMatrix(*this, pos, n);
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}
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// reduction operations ////////////////////////////////////////////////////////
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template <typename T>
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inline T ReducOp::sum(const T &a, const T &b)
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{
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return a + b;
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}
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template <typename T>
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inline T ReducOp::prod(const T &a, const T &b)
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{
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return a*b;
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}
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template <typename T>
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inline T ReducOp::tensProd(const T &v1 __unused, const T &v2 __unused)
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{
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LATAN_ERROR(Implementation,
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"tensorial product not implemented for this type");
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}
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template <>
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inline Mat<double> ReducOp::prod(const Mat<double> &a, const Mat<double> &b)
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{
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return a.cwiseProduct(b);
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}
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template <>
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inline Mat<double> ReducOp::tensProd(const Mat<double> &v1,
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const Mat<double> &v2)
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{
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if ((v1.cols() != 1)||(v2.cols() != 1))
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{
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LATAN_ERROR(Size,
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"tensorial product is only valid with column vectors");
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}
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return v1*v2.transpose();
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}
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END_NAMESPACE
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#endif // Latan_StatArray_hpp_
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