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Improved comments. Fixed special case documentation of the public comment, and added better documentation to the working

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of the algorithm
This commit is contained in:
btracey
2014-09-18 18:32:23 -07:00
parent 13e9477173
commit 4cb368c5a0
1 changed files with 17 additions and 8 deletions
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25
stat.go
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@@ -421,8 +421,9 @@ func JensenShannon(p, q []float64) float64 {
// x and y may have different lengths, though len(x) must equal len(xWeights), and
// len(y) must equal len(yWeights).
//
// If len(x) == len(y) == 0, the function returns 0. Otherwise, it returns 1 if
// one of the lengths is zero but not the other.
// Special cases are:
// = 0 if len(x) == len(y) == 0
// = 1 if len(x) == 0, len(y) != 0 or len(x) != 0 and len(y) == 0
func KolmogorovSmirnov(x, xWeights, y, yWeights []float64) float64 {
if xWeights != nil && len(x) != len(xWeights) {
panic("stat: slice length mismatch")
@@ -477,10 +478,15 @@ func KolmogorovSmirnov(x, xWeights, y, yWeights []float64) float64 {
xVal := x[0]
yVal := y[0]
// How to deal with the first case well?
// Step through all the entries in the two vectors. The difference between
// the empirical cdfs only changes when a new x or y comes up.
// Algorithm description:
// The goal is to find the maximum difference in the empirical CDFs for the
// two datasets. The CDFs are piecewise-constant, and thus the distance
// between the CDFs will only change at the values themselves.
//
// To find the maximum distance, step through the data in ascending order
// of value between the two datasets. At each step, compute the empirical CDF
// and compare the local distance with the maximum distance.
// Due to some corner cases, equal data entries must be tallied simultaneously.
for {
switch {
case xVal < yVal:
@@ -510,14 +516,17 @@ func KolmogorovSmirnov(x, xWeights, y, yWeights []float64) float64 {
}
// Both xCdf and yCdf will equal 1 at the end, so if we have reached the
// end of either sample, the distance is as large as it can be, so we can
// just leave
// end of either sample list, the distance is as large as it can be.
if xIdx == len(x) || yIdx == len(y) {
return maxDist
}
}
}
// updateKS gets the next data point from one of the set. In doing so, it combines
// the weight of all the data points of equal value. Upon return, val is the new
// value of the data set, newCdf is the total combined CDF up until this point,
// and newIdx is the index of the next location in that sample to examine.
func updateKS(idx int, cdf, sum float64, values, weights []float64, isNil bool) (val, newCdf float64, newIdx int) {
// Sum up all the weights of consecutive values that are equal
if isNil {