// Fit creates n filtered datasets (where n is the number of values // a CategoricalAttribute can take) and uses them to train the // underlying classifiers. func (m *OneVsAllModel) Fit(using base.FixedDataGrid) { var classAttr *base.CategoricalAttribute // Do some validation classAttrs := using.AllClassAttributes() for _, a := range classAttrs { if c, ok := a.(*base.CategoricalAttribute); !ok { panic("Unsupported ClassAttribute type") } else { classAttr = c } } attrs := m.generateAttributes(using) // Find the highest stored value val := uint64(0) classVals := classAttr.GetValues() for _, s := range classVals { cur := base.UnpackBytesToU64(classAttr.GetSysValFromString(s)) if cur > val { val = cur } } if val == 0 { panic("Must have more than one class!") } m.maxClassVal = val // Create individual filtered instances for training filters := make([]*oneVsAllFilter, val+1) classifiers := make([]base.Classifier, val+1) for i := uint64(0); i <= val; i++ { f := &oneVsAllFilter{ attrs, classAttr, i, } filters[i] = f classifiers[i] = m.NewClassifierFunction(classVals[int(i)]) classifiers[i].Fit(base.NewLazilyFilteredInstances(using, f)) } m.filters = filters m.classifiers = classifiers }