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https://github.com/sjwhitworth/golearn.git
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e30ff6580a
* Only numeric and categorical ARFF attributes are currently supported.
* Only the dense version of the ARFF format is supported.
* Compressed format is .tar.gz file which should allow extensibility.
* Attributes stored using JSON representations.
* Also offers smarter estimation of the precision of numeric Attributes.
* Also adds support for writing instances to CSV
39 lines
976 B
Go
39 lines
976 B
Go
package linear_models
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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 TestLogisticRegression(t *testing.T) {
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Convey("Given labels, a classifier and data", t, func() {
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// Load data
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X, err := base.ParseCSVToInstances("train.csv", false)
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So(err, ShouldEqual, nil)
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Y, err := base.ParseCSVToInstances("test.csv", false)
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So(err, ShouldEqual, nil)
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// Setup the problem
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lr, err := NewLogisticRegression("l2", 1.0, 1e-6)
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So(err, ShouldBeNil)
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lr.Fit(X)
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Convey("When predicting the label of first vector", func() {
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Z, err := lr.Predict(Y)
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So(err, ShouldEqual, nil)
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Convey("The result should be 1", func() {
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So(Z.RowString(0), ShouldEqual, "1.0")
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})
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})
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Convey("When predicting the label of second vector", func() {
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Z, err := lr.Predict(Y)
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So(err, ShouldEqual, nil)
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Convey("The result should be -1", func() {
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So(Z.RowString(1), ShouldEqual, "-1.0")
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})
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})
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})
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}
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