Golang ParameterEstimator Examples

Example #1

File: gradientdescentestimator_test.go Project: c4e8ece0/goodlearn

			It("Does not return an error", func() {
				_, err := gradientdescentestimator.NewGradientDescentParameterEstimator(
					0.3,
					0.3,
					100,
					lossGradient,
				)

				Ω(err).ShouldNot(HaveOccurred())
			})
		})
	})

	Describe("Train", func() {
		var trainingSet dataset.Dataset
		var estimator parameterestimator.ParameterEstimator

		BeforeEach(func() {
			var err error
			estimator, err = gradientdescentestimator.NewGradientDescentParameterEstimator(
				0.3,
				0.3,
				100,
				lossGradient,
			)
			Ω(err).ShouldNot(HaveOccurred())
		})

		Context("Given a dataset with non-float features", func() {
			BeforeEach(func() {
				columnTypes, err := columntype.StringsToColumnTypes([]string{"x", "1.0"})

Example #2

File: linearmodelleastsquareslossgradient_test.go Project: c4e8ece0/goodlearn

import (
	"github.com/amitkgupta/goodlearn/data/columntype"
	"github.com/amitkgupta/goodlearn/data/dataset"
	"github.com/amitkgupta/goodlearn/parameterestimator"
	"github.com/amitkgupta/goodlearn/parameterestimator/gradientdescentestimator"

	"fmt"
	"math/rand"

	. "github.com/onsi/ginkgo"
	. "github.com/onsi/gomega"
)

var _ = Describe("Linear Model Least Squares Parameter Estimation", func() {
	var estimator parameterestimator.ParameterEstimator
	var trueParameters []float64

	Context("Given reasonable learning rate, precision, maximum number of iterations, and training set", func() {
		BeforeEach(func() {
			trueParameters = []float64{2, -3, 4}

			var err error
			estimator, err = gradientdescentestimator.NewGradientDescentParameterEstimator(
				0.001,
				0.000005,
				1000,
				gradientdescentestimator.LinearModelLeastSquaresLossGradient,
			)
			Ω(err).ShouldNot(HaveOccurred())