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7357da958d
By taking advantage of some gonum functions, it is possible to remove much of the code related to centering the columns. Also tidied up many of the comments.
72 lines
1.8 KiB
Go
72 lines
1.8 KiB
Go
// Copyright ©2014 The gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package stat
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import (
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"github.com/gonum/floats"
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"github.com/gonum/matrix/mat64"
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)
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// CovarianceMatrix calculates a covariance matrix (also known as a
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// variance-covariance matrix) from a matrix of data, using a two-pass
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// algorithm. The matrix returned will be symmetric, square, and
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// positive-semidefinite.
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//
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// The weights wts should have the length equal to the number of rows in
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// input data matrix x. cov should either be a square matrix with the same
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// number of columns as the input data matrix x, or nil in which case a new
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// Dense matrix will be constructed.
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func CovarianceMatrix(cov *mat64.Dense, x mat64.Matrix, wts []float64) *mat64.Dense {
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// This is the matrix version of the two-pass algorithm. It doesn't use
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// the correction found in the Covariance and Variance functions.
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r, c := x.Dims()
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// TODO(jonlawlor): indicate that the resulting matrix is symmetric, which
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// should improve performance.
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if cov == nil {
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cov = mat64.NewDense(c, c, nil)
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} else if covr, covc := cov.Dims(); covr != covc || covc != c {
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panic(mat64.ErrShape)
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}
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var xt mat64.Dense
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xt.TCopy(x)
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// Subtract the mean of each of the columns.
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for i := 0; i < c; i++ {
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v := xt.RowView(i)
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mean := Mean(v, wts)
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floats.AddConst(-mean, v)
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}
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var xc mat64.Dense
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xc.TCopy(&xt)
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var N, scale float64
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if wts != nil {
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if wr := len(wts); wr != r {
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panic(mat64.ErrShape)
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}
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// Weight the rows.
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for i := 0; i < c; i++ {
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v := xt.RowView(i)
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floats.Mul(v, wts)
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}
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// Calculate the normalization factor.
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N = floats.Sum(wts)
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} else {
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N = float64(r)
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}
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cov.Mul(&xt, &xc)
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// Scale by the sample size.
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scale = 1 / (N - 1)
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cov.Scale(scale, cov)
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return cov
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}
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