Merge pull request #2 from timkaye11/master

added some comments on readme & cars example
fxsjy · Jul 2, 2014 · fc68bab · fc68bab
2 parents 15cce47 + b63cd6d
commit fc68bab
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diff --git a/README.md b/README.md
@@ -16,3 +16,7 @@ RF.go
 * RF.go supports both Classification and Regression. The examples can be found in the repository.
 
 * RF.go supports dumpping and loading the forest data structure between RAM and disk, in a JSON format file
+
+### Installation
+1. [Install Go](http://www.golang.org) 
+2. ```$ go get github.com/fxsjy/RF.go/RF ``` This will put the binary in ```$GOROOT/bin```
diff --git a/example_iris/test_iris.go b/example_iris/test_iris.go
@@ -1,87 +1,84 @@
 package main
 
 import (
+	"../RF"
 	"fmt"
 	"io/ioutil"
-	"strings"
-	"strconv"
 	"os"
-
+	"strconv"
+	"strings"
 	"time"
-
-	//"math"
-	"../RF"
 )
 
-
-func main(){
+func main() {
 
 	start := time.Now()
-	f,_ := os.Open("iris2.data")
+	f, _ := os.Open("iris2.data")
 	defer f.Close()
-	content,_ := ioutil.ReadAll(f)
+	content, _ := ioutil.ReadAll(f)
 	s_content := string(content)
-	lines := strings.Split(s_content,"\n")
-
-	inputs := make([][]interface{},0)
-	targets := make([]string,0)
-	for _,line := range lines{
+	lines := strings.Split(s_content, "\n")
 
-		line = strings.TrimRight(line,"\r\n")
+	// set up variables for random forest
+	inputs := make([][]interface{}, 0)
+	targets := make([]string, 0)
+	for _, line := range lines {
 
-		if len(line)==0{
+		line = strings.TrimRight(line, "\r\n")
+
+		if len(line) == 0 {
 			continue
 		}
-		tup := strings.Split(line,",")
+		tup := strings.Split(line, ",")
 		pattern := tup[:len(tup)-1]
 		target := tup[len(tup)-1]
-		X := make([]interface{},0)
-		for _,x := range pattern{
-			f_x,_:= strconv.ParseFloat(x,64)
-			X = append(X,f_x)
+		X := make([]interface{}, 0)
+		for _, x := range pattern {
+			f_x, _ := strconv.ParseFloat(x, 64)
+			X = append(X, f_x)
 		}
-		inputs = append(inputs,X)
-	
-		targets = append(targets,target)
+		inputs = append(inputs, X)
+
+		targets = append(targets, target)
 	}
-	train_inputs := make([][]interface{},0)
-	train_targets := make([]string,0)
+	train_inputs := make([][]interface{}, 0)
+	train_targets := make([]string, 0)
 
-	test_inputs := make([][]interface{},0)
-	test_targets := make([]string,0)
+	test_inputs := make([][]interface{}, 0)
+	test_targets := make([]string, 0)
 
-	for i,x := range inputs{
-		if i%3==0{
+	for i, x := range inputs {
+		if i%3 == 0 {
 			test_inputs = append(test_inputs, x)
-		}else{
+		} else {
 			train_inputs = append(train_inputs, x)
 		}
 	}
 
-	for i,y := range targets{
-		if i%3==0{
-			test_targets = append(test_targets,y)
-		}else{
-			train_targets = append(train_targets,y)
+	for i, y := range targets {
+		if i%3 == 0 {
+			test_targets = append(test_targets, y)
+		} else {
+			train_targets = append(train_targets, y)
 		}
 	}
 
-	forest := RF.DefaultForest(inputs,targets,100)//100 trees
+	forest := RF.DefaultForest(inputs, targets, 100) //100 trees
 
-	RF.DumpForest(forest,"rf.bin")
+	RF.DumpForest(forest, "rf.bin")
 
 	forest = RF.LoadForest("rf.bin")
 
 	err_count := 0.0
-	for i:=0;i<len(test_inputs);i++{
+	for i := 0; i < len(test_inputs); i++ {
 		output := forest.Predicate(test_inputs[i])
 		expect := test_targets[i]
-		fmt.Println(output,expect)
-		if output!=expect{
+		fmt.Println(output, expect)
+		if output != expect {
 			err_count += 1
 		}
 	}
-	fmt.Println("success rate:",1.0 - err_count/float64(len(test_inputs)))
+	fmt.Println("success rate:", 1.0-err_count/float64(len(test_inputs)))
 
 	fmt.Println(time.Since(start))