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Jonathan J Lawlor
2014-11-14 15:30:36 -05:00
parent f3c5d5f73a
commit 062f132f95
1 changed files with 4 additions and 4 deletions
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8
stat.go
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@@ -157,7 +157,7 @@ func Correlation(x, y, weights []float64) float64 {
xcompensation += xd xcompensation += xd
ycompensation += yd ycompensation += yd
} }
// xcompensation and ycompensation are from the Chan paper // xcompensation and ycompensation are from Chan, et. al.
// referenced in the MeanVariance function. They are analogous // referenced in the MeanVariance function. They are analogous
// to the second term in (1.7) in that paper. // to the second term in (1.7) in that paper.
sxx -= xcompensation * xcompensation / float64(len(x)) sxx -= xcompensation * xcompensation / float64(len(x))
@@ -184,7 +184,7 @@ func Correlation(x, y, weights []float64) float64 {
ycompensation += wyd ycompensation += wyd
sumWeights += w sumWeights += w
} }
// xcompensation and ycompensation are from the Chan paper // xcompensation and ycompensation are from Chan, et. al.
// referenced in the MeanVariance function. They are analogous // referenced in the MeanVariance function. They are analogous
// to the second term in (1.7) in that paper, except they use // to the second term in (1.7) in that paper, except they use
// the sumWeights instead of the sample count. // the sumWeights instead of the sample count.
@@ -224,7 +224,7 @@ func Covariance(x, y, weights []float64) float64 {
xcompensation += xd xcompensation += xd
ycompensation += yd ycompensation += yd
} }
// xcompensation and ycompensation are from the Chan paper // xcompensation and ycompensation are from Chan, et. al.
// referenced in the MeanVariance function. They are analogous // referenced in the MeanVariance function. They are analogous
// to the second term in (1.7) in that paper. // to the second term in (1.7) in that paper.
return (ss - xcompensation*ycompensation/float64(len(x))) / float64(len(x)-1) return (ss - xcompensation*ycompensation/float64(len(x))) / float64(len(x)-1)
@@ -242,7 +242,7 @@ func Covariance(x, y, weights []float64) float64 {
ycompensation += wyd ycompensation += wyd
sumWeights += w sumWeights += w
} }
// xcompensation and ycompensation are from the Chan paper // xcompensation and ycompensation are from Chan, et. al.
// referenced in the MeanVariance function. They are analogous // referenced in the MeanVariance function. They are analogous
// to the second term in (1.7) in that paper, except they use // to the second term in (1.7) in that paper, except they use
// the sumWeights instead of the sample count. // the sumWeights instead of the sample count.