mirror of
https://github.com/sjwhitworth/golearn.git
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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
71 lines
1.6 KiB
Go
71 lines
1.6 KiB
Go
package knn
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import (
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"fmt"
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"github.com/sjwhitworth/golearn/base"
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"github.com/sjwhitworth/golearn/evaluation"
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"testing"
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)
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func readMnist() (*base.DenseInstances, *base.DenseInstances) {
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// Create the class Attribute
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classAttrs := make(map[int]base.Attribute)
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classAttrs[0] = base.NewCategoricalAttribute()
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classAttrs[0].SetName("label")
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// Setup the class Attribute to be in its own group
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classAttrGroups := make(map[string]string)
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classAttrGroups["label"] = "ClassGroup"
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// The rest can go in a default group
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attrGroups := make(map[string]string)
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inst1, err := base.ParseCSVToInstancesWithAttributeGroups(
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"../examples/datasets/mnist_train.csv",
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attrGroups,
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classAttrGroups,
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classAttrs,
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true,
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)
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if err != nil {
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panic(err)
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}
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inst2, err := base.ParseCSVToTemplatedInstances(
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"../examples/datasets/mnist_test.csv",
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true,
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inst1,
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)
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if err != nil {
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panic(err)
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}
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return inst1, inst2
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}
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func BenchmarkKNNWithOpts(b *testing.B) {
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// Load
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train, test := readMnist()
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cls := NewKnnClassifier("euclidean", 1)
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cls.AllowOptimisations = true
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cls.Fit(train)
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predictions := cls.Predict(test)
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c, err := evaluation.GetConfusionMatrix(test, predictions)
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if err != nil {
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panic(err)
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}
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fmt.Println(evaluation.GetSummary(c))
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fmt.Println(evaluation.GetAccuracy(c))
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}
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func BenchmarkKNNWithNoOpts(b *testing.B) {
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// Load
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train, test := readMnist()
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cls := NewKnnClassifier("euclidean", 1)
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cls.AllowOptimisations = false
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cls.Fit(train)
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predictions := cls.Predict(test)
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c, err := evaluation.GetConfusionMatrix(test, predictions)
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if err != nil {
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panic(err)
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}
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fmt.Println(evaluation.GetSummary(c))
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fmt.Println(evaluation.GetAccuracy(c))
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}
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