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significant optimisation of covariance routines + checks

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Antonin Portelli
2021-12-20 01:25:13 +01:00
parent c796187d1e
commit 57c6004797
4 changed files with 288 additions and 33 deletions
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76
lib/Statistics/MatSample.hpp
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@ -103,6 +103,10 @@ public:
const Index nCol);
// resize all matrices
void resizeMat(const Index nRow, const Index nCol);
// covariance matrix
Mat<T> covarianceMatrix(const MatSample<T> &sample) const;
Mat<T> varianceMatrix(void) const;
Mat<T> correlationMatrix(void) const;
};
// non-member operators
@ -379,6 +383,78 @@ void MatSample<T>::resizeMat(const Index nRow, const Index nCol)
}
}
// covariance matrix ///////////////////////////////////////////////////////////
template <typename T>
Mat<T> MatSample<T>::covarianceMatrix(const MatSample<T> &sample) const
{
if (((*this)[central].cols() != 1) or (sample[central].cols() != 1))
{
LATAN_ERROR(Size, "samples have more than one column");
}
Index n1 = (*this)[central].rows(), n2 = sample[central].rows();
Index nSample = this->size();
Mat<T> tmp1(n1, nSample), tmp2(n2, nSample), res(n1, n2);
Mat<T> s1(n1, 1), s2(n2, 1), one(nSample, 1);
one.fill(1.);
s1.fill(0.);
s2.fill(0.);
for (unsigned int s = 0; s < nSample; ++s)
{
s1 += (*this)[s];
tmp1.col(s) = (*this)[s];
}
tmp1 -= s1*one.transpose()/static_cast<double>(nSample);
for (unsigned int s = 0; s < nSample; ++s)
{
s2 += sample[s];
tmp2.col(s) = sample[s];
}
tmp2 -= s2*one.transpose()/static_cast<double>(nSample);
res = tmp1*tmp2.transpose()/static_cast<double>(nSample - 1);
return res;
}
template <typename T>
Mat<T> MatSample<T>::varianceMatrix(void) const
{
if ((*this)[central].cols() != 1)
{
LATAN_ERROR(Size, "samples have more than one column");
}
Index n1 = (*this)[central].rows();
Index nSample = this->size();
Mat<T> tmp1(n1, nSample), res(n1, n1);
Mat<T> s1(n1, 1), one(nSample, 1);
one.fill(1.);
s1.fill(0.);
for (unsigned int s = 0; s < nSample; ++s)
{
s1 += (*this)[s];
tmp1.col(s) = (*this)[s];
}
tmp1 -= s1*one.transpose()/static_cast<double>(nSample);
res = tmp1*tmp1.transpose()/static_cast<double>(nSample - 1);
return res;
}
template <typename T>
Mat<T> MatSample<T>::correlationMatrix(void) const
{
Mat<T> res = varianceMatrix();
Mat<T> invDiag(res.rows(), 1);
invDiag = res.diagonal();
invDiag = invDiag.cwiseInverse().cwiseSqrt();
res = (invDiag*invDiag.transpose()).cwiseProduct(res);
return res;
}
END_LATAN_NAMESPACE