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Merge pull request #103 from mtchavez/fix/readme

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Fix example in README
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Richard Townsend 2014-12-22 19:18:16 +00:00
commit dbf1c9a6b3
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README.md
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@ -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", false)
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))
}
```
```