mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-26 13:49:14 +08:00
95 lines
2.9 KiB
Go
95 lines
2.9 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 TestKnnClassifierCov(t *testing.T) {
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Convey("Test predict", t, func() {
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Convey("distance function", 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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Convey("use euclidean", func() {
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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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result := base.GetClass(predictions, 0)
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So(result, ShouldEqual, "blue")
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})
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Convey("use manhattan", func() {
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cls := NewKnnClassifier("manhattan", "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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result := base.GetClass(predictions, 0)
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So(result, ShouldEqual, "blue")
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})
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Convey("use cosine", func() {
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cls := NewKnnClassifier("cosine", "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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result := base.GetClass(predictions, 0)
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So(result, ShouldEqual, "blue")
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})
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Convey("use undefined distance function", func() {
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cls := NewKnnClassifier("abcd", "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(predictions, ShouldBeNil)
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So(err.Error(), ShouldEqual, "unsupported distance function")
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})
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})
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Convey("searching algorithm", 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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Convey("use undefined searching algorithm", func() {
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cls := NewKnnClassifier("cosine", "abcd", 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(predictions, ShouldBeNil)
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So(err.Error(), ShouldEqual, "unsupported searching algorithm")
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})
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})
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Convey("check features", func() {
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Convey("use different dataset", 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_2.csv", false)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("cosine", "linear", 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(predictions, ShouldBeNil)
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So(err.Error(), ShouldEqual, "attributes not compatible")
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})
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})
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})
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}
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