Beispiel #1
0
func (classifier *MaxEntClassifier) Predict(instance *data.Instance) data.InstanceOutput {
	output := data.InstanceOutput{}

	// 当使用NamedFeatures时转化为Features
	if instance.NamedFeatures != nil {
		if classifier.FeatureDictionary == nil {
			return output
		}
		instance.Features = util.NewSparseVector()
		// 第0个feature始终是1
		instance.Features.Set(0, 1.0)

		for k, v := range instance.NamedFeatures {
			id := classifier.FeatureDictionary.TranslateIdFromName(k)
			instance.Features.Set(id, v)
		}
	}

	output.LabelDistribution = util.NewVector(classifier.NumLabels)
	output.LabelDistribution.Set(0, 1.0)

	z := float64(1)
	mostPossibleLabel := 0
	mostPossibleLabelWeight := float64(1)
	for iLabel := 1; iLabel < classifier.NumLabels; iLabel++ {
		sum := float64(0)
		for _, k := range classifier.Weights.GetValues(iLabel - 1).Keys() {
			sum += classifier.Weights.Get(iLabel-1, k) * instance.Features.Get(k)
		}
		exp := math.Exp(sum)
		if exp > mostPossibleLabelWeight {
			mostPossibleLabel = iLabel
			mostPossibleLabelWeight = exp
		}
		z += exp
		output.LabelDistribution.Set(iLabel, exp)
	}
	output.LabelDistribution.Scale(1 / z)
	output.Label = mostPossibleLabel

	if classifier.LabelDictionary != nil {
		output.LabelString = classifier.LabelDictionary.GetNameFromId(output.Label)
	}

	return output
}
Beispiel #2
0
// 使用当前训练出的模型对一个样本的输出进行预测
func (classifier *OnlineSGDClassifier) Predict(instance *data.Instance) data.InstanceOutput {
	output := data.InstanceOutput{}

	predictedLabel := 0
	maxWeight := float64(0)
	for iLabel := 1; iLabel < classifier.weights.NumLabels()+1; iLabel++ {
		sum := float64(0)
		for _, k := range instance.Features.Keys() {
			sum += classifier.weights.Get(iLabel-1, k) * instance.Features.Get(k)
		}
		if sum > maxWeight {
			predictedLabel = iLabel
			maxWeight = sum
		}
	}
	output.Label = predictedLabel

	return output
}