untabify go

go/LogisticRegression.go

@@ -1,118 +1,118 @@
 package main
 
 import (
-	"fmt"
-	"math"
+  "fmt"
+  "math"
 )
 
 type LogisticRegression struct {
-	N int
-	n_in int
-	n_out int
-	W [][]float64
-	b []float64
+  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)
+  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)
-	}
-	
+  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 }
+  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)
+  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 {
+  
+  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 {
+  train_Y := [][]int {
     {1, 0},
     {1, 0},
     {1, 0},
     {0, 1},
     {0, 1},
     {0, 1},
-	}
+  }
 
-	
-	// construct LogisticRegression
-	var classifier LogisticRegression
+  
+  // construct LogisticRegression
+  var classifier LogisticRegression
   LogisticRegression__construct(&classifier, train_N, n_in, n_out)
 
   // train