linear_models: More idiomatic

linear_models/linear_models_test.go

@@ -1,34 +1,30 @@
-package linear_models 
+package linear_models
 
 import (
-	"testing"
+	"github.com/sjwhitworth/golearn/base"
 	. "github.com/smartystreets/goconvey/convey"
+	"testing"
 )
 
 func TestLogisticRegression(t *testing.T) {
 	Convey("Given labels, a classifier and data", t, func() {
-		X := [][]float64{
-			{0, 0, 0, 1},
-			{0, 0, 1, 0},
-			{0, 1, 0, 0},
-			{1, 0, 0, 0},
-		}
-		y := []float64{-1, -1, 1, 1}
+		X, err := base.ParseCSVToInstances("train.csv", false)
+		So(err, ShouldEqual, nil)
+		Y, err := base.ParseCSVToInstances("test.csv", false)
+		So(err, ShouldEqual, nil)
 		lr := NewLogisticRegression("l2", 1.0, 1e-6)
-		lr.Fit(X,y)
+		lr.Fit(X)
 
 		Convey("When predicting the label of first vector", func() {
-			pred_x := [][]float64{ {1,1,0,0} }
-			pred_y := lr.Predict(pred_x)
+			Z := lr.Predict(Y)
 			Convey("The result should be 1", func() {
-				So(pred_y[0], ShouldEqual, 1.0)
+				So(Z.Get(0, 0), ShouldEqual, 1.0)
 			})
 		})
 		Convey("When predicting the label of second vector", func() {
-			pred_x := [][]float64{ {0,0,1,1} }
-			pred_y := lr.Predict(pred_x)
+			Z := lr.Predict(Y)
 			Convey("The result should be -1", func() {
-				So(pred_y[0], ShouldEqual, -1.0)
+				So(Z.Get(1, 0), ShouldEqual, -1.0)
 			})
 		})
 	})

linear_models/logistic.go

@@ -1,6 +1,9 @@
 package linear_models
 
-import "fmt"
+import (
+	"fmt"
+	base "github.com/sjwhitworth/golearn/base"
+)
 
 type LogisticRegression struct {
 	param *Parameter
@@ -24,16 +27,51 @@ func NewLogisticRegression(penalty string, C float64, eps float64) *LogisticRegr
 	return &lr
 }
 
-func (lr *LogisticRegression) Fit(X [][]float64, y []float64) {
-	prob := NewProblem(X, y, 0)
+func convertInstancesToProblemVec(X *base.Instances) [][]float64 {
+	problemVec := make([][]float64, X.Rows)
+	for i := 0; i < X.Rows; i++ {
+		problemVecCounter := 0
+		problemVec[i] = make([]float64, X.Cols-1)
+		for j := 0; j < X.Cols; j++ {
+			if j == X.ClassIndex {
+				continue
+			}
+			problemVec[i][problemVecCounter] = X.Get(i, j)
+			problemVecCounter++
+		}
+	}
+	fmt.Println(problemVec, X)
+	return problemVec
+}
+
+func convertInstancesToLabelVec(X *base.Instances) []float64 {
+	labelVec := make([]float64, X.Rows)
+	for i := 0; i < X.Rows; i++ {
+		labelVec[i] = X.Get(i, X.ClassIndex)
+	}
+	return labelVec
+}
+
+func (lr *LogisticRegression) Fit(X *base.Instances) {
+	problemVec := convertInstancesToProblemVec(X)
+	labelVec := convertInstancesToLabelVec(X)
+	prob := NewProblem(problemVec, labelVec, 0)
 	lr.model = Train(prob, lr.param)
 }
 
-func (lr *LogisticRegression) Predict(X [][]float64) []float64 {
-	n_samples := len(X)
-	y := make([]float64, n_samples)
-	for i, x := range X {
-		y[i] = Predict(lr.model, x)
+func (lr *LogisticRegression) Predict(X *base.Instances) *base.Instances {
+	ret := X.GeneratePredictionVector()
+	row := make([]float64, X.Cols-1)
+	for i := 0; i < X.Rows; i++ {
+		rowCounter := 0
+		for j := 0; j < X.Cols; j++ {
+			if j != X.ClassIndex {
+				row[rowCounter] = X.Get(i, j)
+				rowCounter++
+			}
+		}
+		fmt.Println(Predict(lr.model, row), row)
+		ret.Set(i, 0, Predict(lr.model, row))
 	}
-	return y
+	return ret
 }

linear_models/test.csv

@@ -0,0 +1,2 @@
+1.0,1.0,0.0,0.0,1.0
+0.0,0.0,1.0,1.0,-1.0

linear_models/train.csv

@@ -0,0 +1,4 @@
+0.0, 0.0, 0.0, 1.0, -1.0
+0.0, 0.0, 1.0, 0.0, -1.0
+0.0, 1.0, 0.0, 0.0, 1.0
+1.0, 0.0, 0.0, 0.0, 1.0