func (classifier *kNNClassifier) Train(trainingData dataset.Dataset) error {
if !trainingData.AllFeaturesFloats() {
return knnerrors.NewNonFloatFeaturesTrainingSetError()
}
if trainingData.NumRows() == 0 {
return knnerrors.NewEmptyTrainingDatasetError()
}
classifier.trainingData = trainingData
return nil
}
func (gdpe *gradientDescentParameterEstimator) Train(ds dataset.Dataset) error {
if !ds.AllFeaturesFloats() {
return gdeErrors.NewNonFloatFeaturesError()
}
if !ds.AllTargetsFloats() {
return gdeErrors.NewNonFloatTargetError()
}
if ds.NumTargets() != 1 {
return gdeErrors.NewInvalidNumberOfTargetsError(ds.NumTargets())
}
if ds.NumFeatures() == 0 {
return gdeErrors.NewNoFeaturesError()
}
gdpe.trainingSet = ds
return nil
}
func (regressor *linearRegressor) Train(trainingData dataset.Dataset) error {
if !trainingData.AllFeaturesFloats() {
return linearerrors.NewNonFloatFeaturesError()
}
if !trainingData.AllTargetsFloats() {
return linearerrors.NewNonFloatTargetsError()
}
if trainingData.NumTargets() != 1 {
return linearerrors.NewInvalidNumberOfTargetsError(trainingData.NumTargets())
}
if trainingData.NumFeatures() == 0 {
return linearerrors.NewNoFeaturesError()
}
estimator, err := gradientdescentestimator.NewGradientDescentParameterEstimator(
defaultLearningRate,
defaultPrecision,
defaultMaxIterations,
gradientdescentestimator.LinearModelLeastSquaresLossGradient,
)
if err != nil {
return linearerrors.NewEstimatorConstructionError(err)
}
err = estimator.Train(trainingData)
if err != nil {
return linearerrors.NewEstimatorTrainingError(err)
}
coefficients, err := estimator.Estimate(defaultInitialCoefficientEstimate(trainingData.NumFeatures()))
if err != nil {
return linearerrors.NewEstimatorEstimationError(err)
}
regressor.coefficients = coefficients
return nil
}
)
var _ = Describe("Dataset", func() {
var ds dataset.Dataset
Describe("AllFeaturesFloats", func() {
Context("When all features are floats", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"1.0", "1.0"})
Ω(err).ShouldNot(HaveOccurred())
ds = dataset.NewDataset([]int{0, 1}, []int{}, columnTypes)
})
It("Returns true", func() {
Ω(ds.AllFeaturesFloats()).Should(BeTrue())
})
})
Context("When not all features are floats", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"x", "1.0"})
Ω(err).ShouldNot(HaveOccurred())
ds = dataset.NewDataset([]int{0, 1}, []int{}, columnTypes)
})
It("Returns true", func() {
Ω(ds.AllFeaturesFloats()).Should(BeFalse())
})
})