mirror of
https://github.com/sjwhitworth/golearn.git
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78 lines
2.3 KiB
Go
78 lines
2.3 KiB
Go
package knn
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import (
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"testing"
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"github.com/sjwhitworth/golearn/base"
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. "github.com/smartystreets/goconvey/convey"
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)
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func TestKnnClassifierWithoutOptimisationsWithKdtree(t *testing.T) {
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Convey("Given labels, a classifier and data", t, func() {
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trainingData, err := base.ParseCSVToInstances("knn_train_1.csv", false)
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So(err, ShouldBeNil)
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testingData, err := base.ParseCSVToInstances("knn_test_1.csv", false)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("euclidean", "kdtree", 2)
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cls.AllowOptimisations = false
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cls.Fit(trainingData)
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predictions, err := cls.Predict(testingData)
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So(err, ShouldBeNil)
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So(predictions, ShouldNotEqual, nil)
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Convey("When predicting the label for our first vector", func() {
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result := base.GetClass(predictions, 0)
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Convey("The result should be 'blue", func() {
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So(result, ShouldEqual, "blue")
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})
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})
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Convey("When predicting the label for our second vector", func() {
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result2 := base.GetClass(predictions, 1)
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Convey("The result should be 'red", func() {
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So(result2, ShouldEqual, "red")
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})
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})
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})
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}
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func TestKnnClassifierWithTemplatedInstances1WithKdtree(t *testing.T) {
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Convey("Given two basically identical files...", t, func() {
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trainingData, err := base.ParseCSVToInstances("knn_train_2.csv", true)
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So(err, ShouldBeNil)
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testingData, err := base.ParseCSVToTemplatedInstances("knn_test_2.csv", true, trainingData)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("euclidean", "kdtree", 2)
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cls.Fit(trainingData)
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predictions, err := cls.Predict(testingData)
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So(err, ShouldBeNil)
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So(predictions, ShouldNotBeNil)
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})
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}
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func TestKnnClassifierWithTemplatedInstances1SubsetWithKdtree(t *testing.T) {
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Convey("Given two basically identical files...", t, func() {
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trainingData, err := base.ParseCSVToInstances("knn_train_2.csv", true)
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So(err, ShouldBeNil)
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testingData, err := base.ParseCSVToTemplatedInstances("knn_test_2_subset.csv", true, trainingData)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("euclidean", "kdtree", 2)
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cls.Fit(trainingData)
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predictions, err := cls.Predict(testingData)
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So(err, ShouldBeNil)
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So(predictions, ShouldNotBeNil)
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})
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}
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func TestKnnClassifierImplementsClassifierWithKdtree(t *testing.T) {
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cls := NewKnnClassifier("euclidean", "kdtree", 2)
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var c base.Classifier = cls
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if len(c.String()) < 1 {
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t.Fail()
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}
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}
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