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)
}
// Print a pleasant summary of your data.
fmt.Println(rawData)
//Initialises a new KNN classifier
cls := knn.NewKnnClassifier("euclidean", 2)
//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))
}
func main() {
//Parses the infamous Iris data.
cols, rows, _, labels, data := data.ParseCsv("datasets/iris.csv", 4, []int{0, 1, 2})
//Initialises a new KNN classifier
cls := knn.NewKnnClassifier("euclidean")
cls.Fit(labels, data, rows, cols)
for {
//Creates a random array of N float64s between 0 and 7
randArray := util.RandomArray(3, 7)
//Calculates the Euclidean distance and returns the most popular label
labels := cls.Predict(randArray, 3)
fmt.Println(labels)
}
}
func main() {
data, err := base.ParseCSVToInstances("iris_headers.csv", true)
if err != nil {
panic(err)
}
cls := knn.NewKnnClassifier("euclidean", 2)
trainData, testData := base.InstancesTrainTestSplit(data, 0.8)
cls.Fit(trainData)
predictions := cls.Predict(testData)
fmt.Println(predictions)
confusionMat := evaluation.GetConfusionMatrix(testData, predictions)
fmt.Println(evaluation.GetSummary(confusionMat))
}
func NewTestTrial(filename string, split float64) bool {
cls := knn.NewKnnClassifier("euclidean", 2)
data := CSVtoKNNData(filename)
train, test := base.InstancesTrainTestSplit(data, split)
cls.Fit(train)
//Calculates the Euclidean distance and returns the most popular label
predictions := cls.Predict(test)
fmt.Println(predictions)
confusionMat, err := evaluation.GetConfusionMatrix(test, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(confusionMat))
return true
}
func main() {
rawData, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
//Initialises a new KNN classifier
cls := knn.NewKnnClassifier("euclidean", 2)
//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 := evaluation.GetConfusionMatrix(testData, predictions)
fmt.Println(evaluation.GetSummary(confusionMat))
}
func NewKNNClasifierData(data base.FixedDataGrid) *knn.KNNClassifier {
//Initialises a new KNN classifier
cls := knn.NewKnnClassifier("euclidean", 2)
cls.Fit(data)
return cls
}