Default to applying min-coverage filter based on training set only.

[lightning.git] / glm.go

diff --git a/glm.go b/glm.go
index 20721a6d503316ac4e31ffe48dc61dae17d54047..b68bdd1898419ffab3c15d5516c8259ad4646073 100644 (file)
--- a/glm.go
+++ b/glm.go
@@ -35,7 +35,7 @@ func normalize(a []float64) {
 // onehot is the observed outcome, in same order as sampleInfo, but
 // shorter because it only has entries for samples with
 // isTraining==true.
-func glmPvalueFunc(sampleInfo []sampleInfo, nPCA int, minFrequency float64) func(onehot []bool) float64 {
+func glmPvalueFunc(sampleInfo []sampleInfo, nPCA int) func(onehot []bool) float64 {
        pcaNames := make([]string, 0, nPCA)
        data := make([][]statmodel.Dtype, 0, nPCA)
        for pca := 0; pca < nPCA; pca++ {
@@ -86,21 +86,16 @@ func glmPvalueFunc(sampleInfo []sampleInfo, nPCA int, minFrequency float64) func
 
                variant := make([]statmodel.Dtype, 0, len(sampleInfo))
                row := 0
-               ones := 0
                for _, si := range sampleInfo {
                        if si.isTraining {
                                if onehot[row] {
                                        variant = append(variant, 1)
-                                       ones++
                                } else {
                                        variant = append(variant, 0)
                                }
                                row++
                        }
                }
-               if float64(ones) < float64(len(variant))*minFrequency {
-                       return math.NaN()
-               }
 
                data := append([][]statmodel.Dtype{data[0], variant}, data[1:]...)
                names := append([]string{"outcome", "variant"}, names[1:]...)