mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-28 13:48:56 +08:00
527c6476e1
This patch also:
* Completes removal of the edf/ package
* Corrects an erroneous print statement
* Introduces two new CSV functions
* ParseCSVToInstancesTemplated makes sure that
reading a second CSV file maintains strict Attribute
compatibility with an existing DenseInstances
* ParseCSVToInstancesWithAttributeGroups gives more control
over where Attributes end up in memory, important for
gaining predictable control over the KNN optimisation
* Decouples BinaryAttributeGroup from FixedAttributeGroup for
better casting support
68 lines
1.9 KiB
Go
68 lines
1.9 KiB
Go
package knn
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import (
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"github.com/sjwhitworth/golearn/base"
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. "github.com/smartystreets/goconvey/convey"
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"testing"
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)
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func TestKnnClassifierWithoutOptimisations(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.csv", false)
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So(err, ShouldBeNil)
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testingData, err := base.ParseCSVToInstances("knn_test.csv", false)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("euclidean", 2)
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cls.AllowOptimisations = false
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cls.Fit(trainingData)
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predictions := cls.Predict(testingData)
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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 TestKnnClassifierWithOptimisations(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.csv", false)
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So(err, ShouldBeNil)
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testingData, err := base.ParseCSVToInstances("knn_test.csv", false)
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So(err, ShouldBeNil)
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cls := NewKnnClassifier("euclidean", 2)
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cls.AllowOptimisations = true
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cls.Fit(trainingData)
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predictions := cls.Predict(testingData)
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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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