func (gdpe *gradientDescentParameterEstimator) Estimate(initialParameters []float64) ([]float64, error) {
if gdpe.trainingSet == nil {
return nil, gdeErrors.NewUntrainedEstimatorError()
}
if len(initialParameters) == 0 {
return nil, gdeErrors.NewEmptyInitialParametersError()
}
gradient := func(guess []float64) ([]float64, error) {
sumLossGradient := make([]float64, len(initialParameters))
for i := 0; i < gdpe.trainingSet.NumRows(); i++ {
row, _ := gdpe.trainingSet.Row(i)
features, _ := row.Features().(slice.FloatSlice)
target, _ := row.Target().(slice.FloatSlice)
x := features.Values()
y := target.Values()[0]
lossGradient, err := gdpe.plgf(guess, x, y)
if err != nil {
return nil, err
}
sumLossGradient = vectorutilities.Add(sumLossGradient, lossGradient)
}
return sumLossGradient, nil
}
return gradientdescent.GradientDescent(initialParameters, gdpe.learningRate, gdpe.precision, gdpe.maxIterations, gradient)
}
var _ = Describe("GradientDescent", func() {
var goodGradient func([]float64) ([]float64, error)
BeforeEach(func() {
goodGradient = func(x []float64) ([]float64, error) {
g := make([]float64, len(x))
for i, xi := range x {
g[i] = 2 * xi
}
return g, nil
}
})
Context("When given an empty initial guess", func() {
It("Returns an error", func() {
_, err := gradientdescent.GradientDescent([]float64{}, 0.05, 0.0005, 100000, goodGradient)
Ω(err).Should(HaveOccurred())
})
})
Context("When the given gradient function returns an error", func() {
It("Returns an error", func() {
badGradient := func(x []float64) ([]float64, error) {
return nil, errors.New("I'm bad")
}
_, err := gradientdescent.GradientDescent([]float64{0.3, -0.4}, 0.05, 0.0005, 100000, badGradient)
Ω(err).Should(HaveOccurred())
})
})
Context("When given reasonable inputs", func() {
