mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-26 13:49:14 +08:00
215 lines
6.4 KiB
Go
215 lines
6.4 KiB
Go
package neural
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import (
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"github.com/gonum/matrix/mat64"
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"github.com/sjwhitworth/golearn/base"
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. "github.com/smartystreets/goconvey/convey"
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"testing"
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)
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func TestLayerStructureNoHidden(t *testing.T) {
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Convey("Creating a network...", t, func() {
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XORData, err := base.ParseCSVToInstances("xor.csv", false)
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So(err, ShouldEqual, nil)
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Convey("Create a MultiLayerNet with no layers...", func() {
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net := NewMultiLayerNet(make([]int, 0))
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net.MaxIterations = 0
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net.Fit(XORData)
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Convey("The network should be the right size...", func() {
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So(net.network.size, ShouldEqual, 3)
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})
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Convey("The right nodes should be connected in the network...", func() {
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So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(3, 3), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(1, 3), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(2, 3), ShouldNotAlmostEqual, 0.000)
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})
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})
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Convey("Create a multilayer net with two hidden layers...", func() {
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net := NewMultiLayerNet([]int{3, 2})
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net.MaxIterations = 0
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net.Fit(XORData)
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Convey("The network should be the right size...", func() {
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So(net.network.size, ShouldEqual, 8)
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})
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Convey("The right nodes should be connected in the network...", func() {
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So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
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for i := 3; i <= 8; i++ {
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So(net.network.GetWeight(i, i), ShouldAlmostEqual, 0.000)
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}
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for i := 1; i <= 2; i++ {
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for j := 3; j <= 5; j++ {
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So(net.network.GetWeight(i, j), ShouldNotAlmostEqual, 0.000)
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}
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}
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for i := 3; i <= 5; i++ {
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for j := 6; j <= 7; j++ {
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So(net.network.GetWeight(i, j), ShouldNotAlmostEqual, 0.000)
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}
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}
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for i := 6; i <= 7; i++ {
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So(net.network.GetWeight(i, 8), ShouldNotAlmostEqual, 0.000)
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}
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for i := 8; i > 0; i-- {
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for j := i - 1; j > 0; j-- {
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So(net.network.GetWeight(i, j), ShouldAlmostEqual, 0.000)
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}
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}
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})
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})
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Convey("Create a MultiLayerNet with 1 hidden layer...", func() {
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net := NewMultiLayerNet([]int{3})
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net.LearningRate = 0.9
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net.MaxIterations = 0
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net.Fit(XORData)
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Convey("The network should be the right size...", func() {
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So(net.network.size, ShouldEqual, 6)
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})
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Convey("The right nodes should be connected in the network...", func() {
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So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(1, 3), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(1, 4), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(1, 5), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(2, 3), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(2, 4), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(2, 5), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(3, 3), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(3, 4), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(3, 5), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(4, 4), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(4, 3), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(4, 5), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(5, 5), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(5, 3), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(5, 4), ShouldAlmostEqual, 0.000)
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So(net.network.GetWeight(3, 6), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(4, 6), ShouldNotAlmostEqual, 0.000)
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So(net.network.GetWeight(5, 6), ShouldNotAlmostEqual, 0.000)
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for i := 1; i <= 6; i++ {
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So(net.network.GetWeight(6, i), ShouldAlmostEqual, 0.000)
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}
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})
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})
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})
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}
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func TestLayeredXOR(t *testing.T) {
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Convey("Given an XOR dataset...", t, func() {
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XORData, err := base.ParseCSVToInstances("xor.csv", false)
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So(err, ShouldEqual, nil)
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net := NewMultiLayerNet([]int{3})
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net.MaxIterations = 20000
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net.Fit(XORData)
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Convey("After running for 20000 iterations, should have some predictive power...", func() {
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Convey("The right nodes should be connected in the network...", func() {
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So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
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for i := 1; i <= 6; i++ {
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So(net.network.GetWeight(6, i), ShouldAlmostEqual, 0.000)
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}
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})
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out := mat64.NewDense(6, 1, []float64{1.0, 0.0, 0.0, 0.0, 0.0, 0.0})
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net.network.Activate(out, 2)
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So(out.At(5, 0), ShouldAlmostEqual, 1.0, 0.1)
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Convey("And Predict() should do OK too...", func() {
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pred := net.Predict(XORData)
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for _, a := range pred.AllAttributes() {
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af, ok := a.(*base.FloatAttribute)
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So(ok, ShouldBeTrue)
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af.Precision = 1
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}
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So(base.GetClass(pred, 0), ShouldEqual, "0.0")
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So(base.GetClass(pred, 1), ShouldEqual, "1.0")
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So(base.GetClass(pred, 2), ShouldEqual, "1.0")
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So(base.GetClass(pred, 3), ShouldEqual, "0.0")
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})
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})
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})
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}
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func TestLayeredXORInline(t *testing.T) {
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Convey("Given an inline XOR dataset...", t, func() {
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data := mat64.NewDense(4, 3, []float64{
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1, 0, 1,
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0, 1, 1,
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0, 0, 0,
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1, 1, 0,
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})
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XORData := base.InstancesFromMat64(4, 3, data)
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classAttr := base.GetAttributeByName(XORData, "2")
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XORData.AddClassAttribute(classAttr)
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net := NewMultiLayerNet([]int{3})
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net.MaxIterations = 20000
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net.Fit(XORData)
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Convey("After running for 20000 iterations, should have some predictive power...", func() {
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Convey("The right nodes should be connected in the network...", func() {
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So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
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So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
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for i := 1; i <= 6; i++ {
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So(net.network.GetWeight(6, i), ShouldAlmostEqual, 0.000)
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}
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})
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out := mat64.NewDense(6, 1, []float64{1.0, 0.0, 0.0, 0.0, 0.0, 0.0})
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net.network.Activate(out, 2)
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So(out.At(5, 0), ShouldAlmostEqual, 1.0, 0.1)
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Convey("And Predict() should do OK too...", func() {
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pred := net.Predict(XORData)
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for _, a := range pred.AllAttributes() {
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af, ok := a.(*base.FloatAttribute)
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So(ok, ShouldBeTrue)
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af.Precision = 1
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}
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So(base.GetClass(pred, 0), ShouldEqual, "1.0")
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So(base.GetClass(pred, 1), ShouldEqual, "1.0")
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So(base.GetClass(pred, 2), ShouldEqual, "0.0")
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So(base.GetClass(pred, 3), ShouldEqual, "0.0")
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})
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})
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})
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}
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