func main() {
// Load and parse the data from csv files
fmt.Println("Loading data...")
trainData, err := base.ParseCSVToInstances("data/mnist_train.csv", true)
if err != nil {
panic(err)
}
testData, err := base.ParseCSVToInstances("data/mnist_test.csv", true)
if err != nil {
panic(err)
}
// Create a new linear SVC with some good default values
classifier, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
if err != nil {
panic(err)
}
// Don't output information on each iteration
base.Silent()
// Train the linear SVC
fmt.Println("Training...")
classifier.Fit(trainData)
// Make predictions for the test data
fmt.Println("Predicting...")
predictions, err := classifier.Predict(testData)
if err != nil {
panic(err)
}
// Get a confusion matrix and print out some accuracy stats for our predictions
confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(confusionMat))
}
func TestOneVsAllModel(t *testing.T) {
classifierFunc := func(c string) base.Classifier {
m, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
if err != nil {
panic(err)
}
return m
}
Convey("Given data", t, func() {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
So(err, ShouldBeNil)
X, Y := base.InstancesTrainTestSplit(inst, 0.4)
m := NewOneVsAllModel(classifierFunc)
m.Fit(X)
Convey("The maximum class index should be 2", func() {
So(m.maxClassVal, ShouldEqual, 2)
})
Convey("There should be three of everything...", func() {
So(len(m.filters), ShouldEqual, 3)
So(len(m.classifiers), ShouldEqual, 3)
})
Convey("Predictions should work...", func() {
predictions, err := m.Predict(Y)
So(err, ShouldEqual, nil)
cf, err := evaluation.GetConfusionMatrix(Y, predictions)
So(err, ShouldEqual, nil)
fmt.Println(evaluation.GetAccuracy(cf))
fmt.Println(evaluation.GetSummary(cf))
})
})
}
