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
}
Example #2
0
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
}
Example #3
0
				Ω(ds.AllTargetsFloats()).Should(BeFalse())
			})
		})
	})

	Describe("NumFeatures and NumTargets", func() {
		BeforeEach(func() {
			columnTypes, err := columntype.StringsToColumnTypes([]string{"1.0", "x", "x", "1.0", "x"})
			Ω(err).ShouldNot(HaveOccurred())

			ds = dataset.NewDataset([]int{1, 3}, []int{0, 2, 4}, columnTypes)
		})

		It("Returns the correct number of features and targets", func() {
			Ω(ds.NumFeatures()).Should(Equal(2))
			Ω(ds.NumTargets()).Should(Equal(3))
		})
	})

	Describe("Adding, Counting, and Getting rows", func() {
		BeforeEach(func() {
			columnTypes, err := columntype.StringsToColumnTypes([]string{"1.0", "x", "x", "1.0", "x"})
			Ω(err).ShouldNot(HaveOccurred())

			ds = dataset.NewDataset([]int{1, 3}, []int{0, 2, 4}, columnTypes)
		})

		Context("When the dataset is empty", func() {
			It("Has 0 rows", func() {
				Ω(ds.NumRows()).To(BeZero())
			})