From 4988a1e8bcf7fbfd2d3ceaf449c186a4be39f689 Mon Sep 17 00:00:00 2001
From: Chavez <matthew@el-chavez.me>
Date: Sun, 21 Dec 2014 19:09:43 -0800
Subject: [PATCH 1/2] Fix example in README

---
 README.md | 53 ++++++++++++++++++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 4164c7a..db5b482 100644
--- a/README.md
+++ b/README.md
@@ -25,31 +25,46 @@ GoLearn implements the scikit-learn interface of Fit/Predict, so you can easily
 GoLearn also includes helper functions for data, like cross validation, and train and test splitting.
 
 ```go
-// Load in a dataset, with headers. Header attributes will be stored.
-// Think of instances as a Data Frame structure in R or Pandas.
-// You can also create instances from scratch.
-data, err := base.ParseCSVToInstances("datasets/iris_headers.csv", true)
+package main
 
-// Print a pleasant summary of your data.
-fmt.Println(data)
+import (
+	"fmt"
 
-// Split your dataframe into a training set, and a test set, with an 80/20 proportion.
-trainTest := base.InstancesTrainTestSplit(rawData, 0.8)
-trainData := trainTest[0]
-testData := trainTest[1]
+	"github.com/sjwhitworth/golearn/base"
+	"github.com/sjwhitworth/golearn/evaluation"
+	"github.com/sjwhitworth/golearn/knn"
+)
 
-// Instantiate a new KNN classifier. Euclidean distance, with 2 neighbours.
-cls := knn.NewKnnClassifier("euclidean", 2)
+func main() {
+	// Load in a dataset, with headers. Header attributes will be stored.
+	// Think of instances as a Data Frame structure in R or Pandas.
+	// You can also create instances from scratch.
+	rawData, err := base.ParseCSVToInstances("datasets/iris.csv", true)
+	if err != nil {
+		panic(err)
+	}
 
-// Fit it on your training data.
-cls.Fit(trainData)
+	// Print a pleasant summary of your data.
+	fmt.Println(rawData)
 
-// Get your predictions against test instances.
-predictions := cls.Predict(testData)
+	//Initialises a new KNN classifier
+	cls := knn.NewKnnClassifier("euclidean", 2)
 
-// Print a confusion matrix with precision and recall metrics.
-confusionMat, _ := evaluation.GetConfusionMatrix(testData, predictions)
-fmt.Println(evaluation.GetSummary(confusionMat))
+	//Do a training-test split
+	trainData, testData := base.InstancesTrainTestSplit(rawData, 0.50)
+	cls.Fit(trainData)
+
+	//Calculates the Euclidean distance and returns the most popular label
+	predictions := cls.Predict(testData)
+	fmt.Println(predictions)
+
+	// Prints precision/recall metrics
+	confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
+	if err != nil {
+		panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
+	}
+	fmt.Println(evaluation.GetSummary(confusionMat))
+}
 ```
 
 ```

From 3d1384b78cf5b3ed24ef3e06bc23fa2ce96bcfcd Mon Sep 17 00:00:00 2001
From: Chavez <matthew@el-chavez.me>
Date: Mon, 22 Dec 2014 08:32:14 -0800
Subject: [PATCH 2/2] Pass false for header on iris dataset in README example

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index db5b482..321c20f 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@ func main() {
 	// Load in a dataset, with headers. Header attributes will be stored.
 	// Think of instances as a Data Frame structure in R or Pandas.
 	// You can also create instances from scratch.
-	rawData, err := base.ParseCSVToInstances("datasets/iris.csv", true)
+	rawData, err := base.ParseCSVToInstances("datasets/iris.csv", false)
 	if err != nil {
 		panic(err)
 	}