go

Author: yusugomori

c/LogisticRegression.c

@@ -94,7 +94,7 @@ void test_lr(void) {
   int i, j, epoch;
 
   double learning_rate = 0.1;
-  double n_epochs = 500;
+  int n_epochs = 500;
 
   int train_N = 6;
   int test_N = 2;

cpp/LogisticRegression.cpp

@@ -86,30 +86,12 @@ void test_lr() {
   srand(0);
 
   double learning_rate = 0.1;
-  double n_epochs = 500;
+  int n_epochs = 500;
 
   int train_N = 6;
   int test_N = 2;
   int n_in = 6;
   int n_out = 2;
-  // int **train_X;
-  // int **train_Y;
-  // int **test_X;
-  // double **test_Y;
-
-  // train_X = new int*[train_N];
-  // train_Y = new int*[train_N];
-  // for(i=0; i<train_N; i++){
-  //   train_X[i] = new int[n_in];
-  //   train_Y[i] = new int[n_out];
-  // };
-
-  // test_X = new int*[test_N];
-  // test_Y = new double*[test_N];
-  // for(i=0; i<test_N; i++){
-  //   test_X[i] = new int[n_in];
-  //   test_Y[i] = new double[n_out];
-  // }
 
 
   // training data

go/LogisticRegression.go

@@ -0,0 +1,150 @@
+package main
+
+import (
+	"fmt"
+	"math"
+)
+
+type LogisticRegression struct {
+	N int
+	n_in int
+	n_out int
+	W [][]float64
+	b []float64
+}
+
+
+func LogisticRegression__construct(this *LogisticRegression, N int, n_in int, n_out int) {
+	this.N = N
+	this.n_in = n_in
+	this.n_out = n_out
+
+	this.W = make([][]float64, n_out)
+	for i := 0; i < n_out; i++ { this.W[i] = make([]float64, n_in) }
+	
+	this.b = make([]float64, n_out)
+}
+
+func LogisticRegression_train(this *LogisticRegression, x []int, y []int, lr float64) {
+	p_y_given_x := make([]float64, this.n_out)
+	dy := make([]float64, this.n_out)
+	
+	for i := 0; i < this.n_out; i++ {
+		p_y_given_x[i] = 0
+		for j := 0; j < this.n_in; j++ {
+			p_y_given_x[i] += this.W[i][j] * float64(x[j])
+		}
+		p_y_given_x[i] += this.b[i]
+	}
+	LogisticRegression_softmax(this, p_y_given_x)
+	
+	for i := 0; i < this.n_out; i++ {
+		dy[i] = float64(y[i]) - p_y_given_x[i]
+		
+		for j := 0; j < this.n_in; j++ {
+			this.W[i][j] += lr * dy[i] * float64(x[j]) / float64(this.N)
+		}
+
+		this.b[i] += lr * dy[i] / float64(this.N)
+	}
+	
+}
+
+func LogisticRegression_softmax(this *LogisticRegression, x []float64) {
+	var (
+		max float64
+		sum float64
+	)
+
+	for i := 0; i < this.n_out; i++ { if max < x[i] {max = x[i]} }
+	for i := 0; i < this.n_out; i++ {
+		x[i] = math.Exp(x[i] - max)
+		sum += x[i]
+	}
+
+	for i := 0; i < this.n_out; i++ { x[i] /= sum }
+}
+
+func LogisticRegression_predict(this *LogisticRegression, x []int, y []float64) {
+	for i := 0; i < this.n_out; i++ {
+		y[i] = 0
+		for j := 0; j < this.n_in; j++ {
+			y[i] += this.W[i][j] * float64(x[j])
+		}
+		y[i] += this.b[i]
+	}
+
+	LogisticRegression_softmax(this, y)
+}
+
+
+
+func test_lr() {
+	
+	learning_rate := 0.1
+	n_epochs := 500
+
+	train_N := 6
+	test_N := 2
+	n_in := 6
+	n_out := 2
+
+	
+	// training data
+	train_X := [][]int {
+    {1, 1, 1, 0, 0, 0},
+    {1, 0, 1, 0, 0, 0},
+    {1, 1, 1, 0, 0, 0},
+    {0, 0, 1, 1, 1, 0},
+    {0, 0, 1, 1, 0, 0},
+    {0, 0, 1, 1, 1, 0},
+	}
+
+
+	train_Y := [][]int {
+    {1, 0},
+    {1, 0},
+    {1, 0},
+    {0, 1},
+    {0, 1},
+    {0, 1},
+	}
+
+	
+	// construct LogisticRegression
+	var classifier LogisticRegression
+	LogisticRegression__construct(&classifier, train_N, n_in, n_out)
+
+	// train
+	for epoch := 0; epoch < n_epochs; epoch++ {
+		for i := 0; i < train_N; i++ {
+			LogisticRegression_train(&classifier, train_X[i], train_Y[i], learning_rate)
+		}
+	}
+	
+	// test data
+	test_X := [][]int {
+    {1, 0, 1, 0, 0, 0},
+    {0, 0, 1, 1, 1, 0},
+	}
+	
+	test_Y := make([][]float64, test_N)
+	for i := 0; i < test_N; i++ { test_Y[i] = make([]float64, n_out) }
+
+
+	// test
+	for i := 0; i < test_N; i++ {
+		LogisticRegression_predict(&classifier, test_X[i], test_Y[i])
+		for j := 0; j < n_out; j++ {
+			fmt.Printf("%f ", test_Y[i][j])
+		}
+		fmt.Printf("\n")
+	}
+	
+}
+
+
+func main() {
+	test_lr()
+}
+

java/LogisticRegression/src/LogisticRegression.java

@@ -67,7 +67,7 @@ public void predict(int[] x, double[] y) {
 
 	private static void test_lr() {
 		double learning_rate = 0.1;
-		double n_epochs = 500;
+		int n_epochs = 500;
 
 		int train_N = 6;
 		int test_N = 2;