func TestLayeredXORInline(t *testing.T) {
Convey("Given an inline XOR dataset...", t, func() {
data := mat64.NewDense(4, 3, []float64{
1, 0, 1,
0, 1, 1,
0, 0, 0,
1, 1, 0,
})
XORData := base.InstancesFromMat64(4, 3, data)
classAttr := base.GetAttributeByName(XORData, "2")
XORData.AddClassAttribute(classAttr)
net := NewMultiLayerNet([]int{3})
net.MaxIterations = 20000
net.Fit(XORData)
Convey("After running for 20000 iterations, should have some predictive power...", func() {
Convey("The right nodes should be connected in the network...", func() {
So(net.network.GetWeight(1, 1), ShouldAlmostEqual, 1.000)
So(net.network.GetWeight(2, 2), ShouldAlmostEqual, 1.000)
for i := 1; i <= 6; i++ {
So(net.network.GetWeight(6, i), ShouldAlmostEqual, 0.000)
}
})
out := mat64.NewDense(6, 1, []float64{1.0, 0.0, 0.0, 0.0, 0.0, 0.0})
net.network.Activate(out, 2)
So(out.At(5, 0), ShouldAlmostEqual, 1.0, 0.1)
Convey("And Predict() should do OK too...", func() {
pred := net.Predict(XORData)
for _, a := range pred.AllAttributes() {
af, ok := a.(*base.FloatAttribute)
So(ok, ShouldBeTrue)
af.Precision = 1
}
So(base.GetClass(pred, 0), ShouldEqual, "1.0")
So(base.GetClass(pred, 1), ShouldEqual, "1.0")
So(base.GetClass(pred, 2), ShouldEqual, "0.0")
So(base.GetClass(pred, 3), ShouldEqual, "0.0")
})
})
})
}
func TestPRIVATEgetNumericAttributeEntropy(t *testing.T) {
Convey("Checking a particular split...", t, func() {
instances, err := base.ParseCSVToInstances("../examples/datasets/c45-numeric.csv", true)
So(err, ShouldBeNil)
Convey("Fetching the right Attribute", func() {
attr := base.GetAttributeByName(instances, "Attribute2")
So(attr, ShouldNotEqual, nil)
Convey("Finding the threshold...", func() {
_, threshold := getNumericAttributeEntropy(instances, attr.(*base.FloatAttribute))
So(threshold, ShouldAlmostEqual, 82.5)
})
})
})
}
