func (classifier *kNNClassifier) Classify(testRow row.Row) (slice.Slice, error) {
trainingData := classifier.trainingData
if trainingData == nil {
return nil, knnerrors.NewUntrainedClassifierError()
}
numTestRowFeatures := testRow.NumFeatures()
numTrainingDataFeatures := trainingData.NumFeatures()
if numTestRowFeatures != numTrainingDataFeatures {
return nil, knnerrors.NewRowLengthMismatchError(numTestRowFeatures, numTrainingDataFeatures)
}
testFeatures, ok := testRow.Features().(slice.FloatSlice)
if !ok {
return nil, knnerrors.NewNonFloatFeaturesTestRowError()
}
testFeatureValues := testFeatures.Values()
nearestNeighbours := knnutilities.NewKNNTargetCollection(classifier.k)
for i := 0; i < trainingData.NumRows(); i++ {
trainingRow, _ := trainingData.Row(i)
trainingFeatures, _ := trainingRow.Features().(slice.FloatSlice)
trainingFeatureValues := trainingFeatures.Values()
distance := knnutilities.Euclidean(testFeatureValues, trainingFeatureValues, nearestNeighbours.MaxDistance())
if distance < nearestNeighbours.MaxDistance() {
nearestNeighbours.Insert(trainingRow.Target(), distance)
}
}
return nearestNeighbours.Vote(), nil
}
func (regressor *linearRegressor) Predict(testRow row.Row) (float64, error) {
coefficients := regressor.coefficients
if coefficients == nil {
return 0, linearerrors.NewUntrainedRegressorError()
}
numTestRowFeatures := testRow.NumFeatures()
numCoefficients := len(coefficients)
if numCoefficients != numTestRowFeatures+1 {
return 0, linearerrors.NewRowLengthMismatchError(numTestRowFeatures, numCoefficients)
}
testFeatures, ok := testRow.Features().(slice.FloatSlice)
if !ok {
return 0, linearerrors.NewNonFloatFeaturesTestRowError()
}
testFeatureValues := testFeatures.Values()
result := coefficients[numCoefficients-1]
for i, c := range coefficients[:numCoefficients-1] {
result = result + c*testFeatureValues[i]
}
return result, nil
}