C0

linear_models/linearsvc.go

@@ -264,7 +264,6 @@ func (lr *LinearSVC) SaveWithPrefix(writer *base.ClassifierSerializer, prefix st
 	defer func() {
 		f.Close()
 	}()
-
 	err = Export(lr.model, f.Name())
 	if err != nil {
 		return base.DescribeError("Error exporting model", err)

linear_models/linearsvc_test.go

@@ -0,0 +1,57 @@
+package linear_models
+
+import (
+	//"github.com/sjwhitworth/golearn/base"
+	. "github.com/smartystreets/goconvey/convey"
+	"testing"
+)
+
+func TestLinearSVC(t *testing.T) {
+	Convey("Doing a LinearSVC test", t, func(){
+		_, err := NewLinearSVC("l1", "l1", false, 1.0, -1e6)
+		So(err, ShouldNotBeNil)
+		_, err = NewLinearSVC("l0", "l1", false, 1.0, -1e6)
+		So(err, ShouldNotBeNil)
+		_, err = NewLinearSVC("l1", "l0", false, 1.0, -1e6)
+		So(err, ShouldNotBeNil)
+		_, err = NewLinearSVC("l1", "l2", false, 1.0, -1e6)
+		So(err, ShouldNotBeNil)
+		SVC, err := NewLinearSVC("l1", "l2", true, 1.0, -1e6)
+		So(SVC, ShouldNotBeNil)
+		So(err, ShouldBeNil)
+		_, err = NewLinearSVC("l2", "l2", false, 1.0, -1e6)
+		So(err, ShouldBeNil)
+		_, err = NewLinearSVC("l2", "l2", true, 1.0, -1e6)
+		So(err, ShouldBeNil)
+		_, err = NewLinearSVC("l2", "l1", false, 1.0, -1e6)
+		So(err, ShouldBeNil)
+		_, err = NewLinearSVC("l2", "l1", true, 1.0, -1e6)
+		So(err, ShouldNotBeNil)
+		So(func(){ SVC.GetMetadata() } , ShouldNotPanic)
+		//err = SVC.Save("tmp")
+
+		params := &LinearSVCParams{0, []float64{0.0}, 1.0, -1e6, false, false}
+		params = params.Copy()
+		So(params, ShouldNotBeNil)
+
+		g := [][]float64{{1, 2}, {1, 2}, {1, 2}}
+		v := []float64{1, 2}
+		var bias float64
+		problem := NewProblem(g[:], v[:], bias)
+		param := NewParameter(0, 1.0, -1e6)
+		model := Train(problem, param)
+		So(model, ShouldNotBeNil)
+		err = Export(model, "tmp")
+		So(err, ShouldBeNil)
+		err = Load(model, "tmp")
+		So(err, ShouldBeNil)
+
+		SVC.model = model
+		err = SVC.Save("tmp")
+		So(err, ShouldBeNil)
+		err = SVC.Load("tmp")
+		So(err, ShouldBeNil)
+		s := SVC.String()
+		So(s, ShouldEqual, "LogisticSVC")
+	})
+}

linear_models/logistic_test.go

@@ -0,0 +1,38 @@
+package linear_models
+
+import (
+	"github.com/sjwhitworth/golearn/base"
+	. "github.com/smartystreets/goconvey/convey"
+	"testing"
+)
+
+func TestLogistic(t *testing.T) {
+	Convey("Doing a logistic test", t, func(){
+		X, err := base.ParseCSVToInstances("train.csv", false)
+		So(err, ShouldEqual, nil)
+		Y, err := base.ParseCSVToInstances("test.csv", false)
+		So(err, ShouldEqual, nil)
+		_, err = NewLogisticRegression("l0", 1.0, 1e-6)
+		So(err, ShouldNotBeNil)
+		lr, err := NewLogisticRegression("l1", 1.0, 1e-6)
+		So(err, ShouldBeNil)
+
+		lr.Fit(X)
+
+		Convey("When predicting the label of first vector", func() {
+			Z, err := lr.Predict(Y)
+			So(err, ShouldEqual, nil)
+			Convey("The result should be 1", func() {
+				So(Z.RowString(0), ShouldEqual, "-1.0")
+			})
+		})
+		Convey("When predicting the label of second vector", func() {
+			Z, err := lr.Predict(Y)
+			So(err, ShouldEqual, nil)
+			Convey("The result should be -1", func() {
+				So(Z.RowString(1), ShouldEqual, "-1.0")
+			})
+		})
+		So((*lr).String(), ShouldEqual, "LogisticRegression")
+	})
+}