func ChiMBuildFrequencyTable(attr int, inst *base.Instances) []*FrequencyTableEntry {
ret := make([]*FrequencyTableEntry, 0)
var attribute *base.FloatAttribute
attribute, ok := inst.GetAttr(attr).(*base.FloatAttribute)
if !ok {
panic("only use Chi-M on numeric stuff")
}
for i := 0; i < inst.Rows; i++ {
value := inst.Get(i, attr)
valueConv := attribute.GetUsrVal(value)
class := inst.GetClass(i)
// Search the frequency table for the value
found := false
for _, entry := range ret {
if entry.Value == valueConv {
found = true
entry.Frequency[class] += 1
}
}
if !found {
newEntry := &FrequencyTableEntry{
valueConv,
make(map[string]int),
}
newEntry.Frequency[class] = 1
ret = append(ret, newEntry)
}
}
return ret
}
// GetConfusionMatrix builds a ConfusionMatrix from a set of reference (`ref')
// and generate (`gen') Instances.
func GetConfusionMatrix(ref *base.Instances, gen *base.Instances) map[string]map[string]int {
if ref.Rows != gen.Rows {
panic("Row counts should match")
}
ret := make(map[string]map[string]int)
for i := 0; i < ref.Rows; i++ {
referenceClass := ref.GetClass(i)
predictedClass := gen.GetClass(i)
if _, ok := ret[referenceClass]; ok {
ret[referenceClass][predictedClass]++
} else {
ret[referenceClass] = make(map[string]int)
ret[referenceClass][predictedClass] = 1
}
}
return ret
}
