neural: check that the new dense instances type works...

base/mat.go

@@ -21,6 +21,7 @@ func InstancesFromMat64(rows, cols int, data *mat64.Dense) *Mat64Instances {
 			i)))
 	}
 
+	ret.classAttrs = make(map[int]bool)
 	ret.Data = data
 	ret.rows = rows
 	return &ret

neural/layered_test.go

@@ -155,3 +155,60 @@ func TestLayeredXOR(t *testing.T) {
 	})
 
 }
+
+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")
+
+			})
+		})
+
+	})
+
+}