+ cmd.chi2Cases = nil
+ cmd.trainingSetSize = 0
+ for i := range cmd.cgnames {
+ if cmd.samples[i].isTraining {
+ cmd.trainingSet[i] = cmd.trainingSetSize
+ cmd.trainingSetSize++
+ cmd.chi2Cases = append(cmd.chi2Cases, cmd.samples[i].isCase)
+ } else {
+ cmd.trainingSet[i] = -1
+ }
+ }
+ if cmd.pvalue == nil {
+ cmd.pvalue = func(onehot []bool) float64 {
+ return pvalue(onehot, cmd.chi2Cases)
+ }
+ }
+ }
+
+ if cmd.minCoverageAll {
+ cmd.minCoverage = len(cmd.cgnames)
+ } else {
+ cmd.minCoverage = cmd.trainingSetSize
+ }
+ if cmd.filter.MinCoverage < 1 {
+ cmd.minCoverage = int(math.Ceil(cmd.filter.MinCoverage * float64(cmd.minCoverage)))
+ }
+
+ if len(cmd.samples[0].pcaComponents) > 0 {
+ cmd.pvalue = glmPvalueFunc(cmd.samples, cmd.pcaComponents)
+ // Unfortunately, statsmodel/glm lib logs stuff to
+ // os.Stdout when it panics on an unsolvable
+ // problem. We recover() from the panic in glm.go, but
+ // we also need to commandeer os.Stdout to avoid
+ // producing large quantities of logs.
+ stdoutWas := os.Stdout
+ defer func() { os.Stdout = stdoutWas }()
+ os.Stdout, err = os.Open(os.DevNull)