Remove unused cross_validation package

cross_validation/cross_validation.go

@@ -1,96 +0,0 @@
-package cross_validation
-
-import (
-	"fmt"
-	"github.com/gonum/matrix/mat64"
-	"math/rand"
-	"sync"
-	"time"
-)
-
-func shuffleMatrix(returnDatasets []*mat64.Dense, dataset mat64.Matrix, testSize int, seed int64, wg *sync.WaitGroup) {
-	numGen := rand.New(rand.NewSource(seed))
-
-	// We don't want to alter the original dataset.
-	shuffledSet := mat64.DenseCopyOf(dataset)
-	rowCount, colCount := shuffledSet.Dims()
-	temp := make([]float64, colCount)
-
-	// Fisher–Yates shuffle
-	for i := 0; i < rowCount; i++ {
-		j := numGen.Intn(i + 1)
-		if j != i {
-			// Make a "hard" copy to avoid pointer craziness.
-			copy(temp, shuffledSet.RowView(i))
-			shuffledSet.SetRow(i, shuffledSet.RowView(j))
-			shuffledSet.SetRow(j, temp)
-		}
-	}
-	trainSize := rowCount - testSize
-	returnDatasets[0] = mat64.NewDense(trainSize, colCount, shuffledSet.RawMatrix().Data[:trainSize*colCount])
-	returnDatasets[1] = mat64.NewDense(testSize, colCount, shuffledSet.RawMatrix().Data[trainSize*colCount:])
-
-	wg.Done()
-}
-
-// TrainTestSplit splits input DenseMatrix into subsets for testing.
-// The function expects a test size number (int) or percentage (float64), and a random state or nil to get "random" shuffle.
-// It returns a list containing the train-test split and an error status.
-func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat64.Dense) ([]*mat64.Dense, error) {
-	// Get number of instances (rows).
-	instanceCount, _ := datasets[0].Dims()
-
-	// Input should be one or two matrices.
-	dataCount := len(datasets)
-	if dataCount > 2 {
-		return nil, fmt.Errorf("expected 1 or 2 datasets, got %d\n", dataCount)
-	}
-
-	if dataCount == 2 {
-		// Test for consistency.
-		labelCount, labelFeatures := datasets[1].Dims()
-		if labelCount != instanceCount {
-			return nil, fmt.Errorf("data and labels must have the same number of instances")
-		} else if labelFeatures != 1 {
-			return nil, fmt.Errorf("label matrix must have single feature")
-		}
-	}
-
-	var testSize int
-	switch size := size.(type) {
-	// If size is an integer, treat it as the test data instance count.
-	case int:
-		testSize = size
-	case float64:
-		// If size is a float, treat it as a percentage of the instances to be allocated to the test set.
-		testSize = int(float64(instanceCount)*size + 0.5)
-	default:
-		return nil, fmt.Errorf("expected a test instance count (int) or percentage (float64)")
-	}
-
-	var randSeed int64
-	// Create a deterministic shuffle, or a "random" one based on current time.
-	if seed, ok := randomState.(int); ok {
-		randSeed = int64(seed)
-	} else {
-		// Use seconds since epoch as seed
-		randSeed = time.Now().Unix()
-	}
-
-	// Wait group for goroutine syncronization.
-	wg := new(sync.WaitGroup)
-	wg.Add(dataCount)
-
-	// Return slice will hold training and test data and optional labels matrix.
-	returnDatasets := make([]*mat64.Dense, 2*dataCount)
-
-	for i, dataset := range datasets {
-		// Send proper returnDataset slice.
-		// This is needed so goroutine doesn't mess up the expected return order.
-		// Perhaps returning a map is a better solution...
-		go shuffleMatrix(returnDatasets[i:i+2], dataset, testSize, randSeed, wg)
-	}
-	wg.Wait()
-
-	return returnDatasets, nil
-}

cross_validation/cross_validation_test.go

@@ -1,61 +0,0 @@
-package cross_validation
-
-import (
-	//. "github.com/smartystreets/goconvey/convey"
-	"github.com/gonum/matrix/mat64"
-	"math/rand"
-	"testing"
-	"time"
-)
-
-var (
-	flatValues, flatLabels []float64
-	values, labels         *mat64.Dense
-)
-
-func init() {
-	flatValues = make([]float64, 80)
-	flatLabels = make([]float64, 20)
-
-	for i := 0; i < 80; i++ {
-		flatValues[i] = float64(i + 1)
-		// Replaces labels four times per run but who cares.
-		flatLabels[int(i/4)] = float64(rand.Intn(2))
-	}
-
-	values = mat64.NewDense(20, 4, flatValues)
-	labels = mat64.NewDense(20, 1, flatLabels)
-}
-
-func TestTrainTrainTestSplit(t *testing.T) {
-	nolab1, err := TrainTestSplit(4, nil, values)
-	if err != nil {
-		t.Error(err)
-	}
-
-	// Make sure the random generator gets a new seed (time).
-	time.Sleep(time.Second)
-
-	nolab2, _ := TrainTestSplit(4, nil, values)
-	if nolab1[0].Equals(nolab2[0]) {
-		t.Errorf("Shuffle with different seed returned same matrix")
-	}
-
-	nolab1, _ = TrainTestSplit(4, 1, values)
-	nolab2, _ = TrainTestSplit(4, 1, values)
-	// Comparing the determinants does not guarantee uniqueness, but it will do for now.
-	if !nolab1[0].Equals(nolab2[0]) {
-		t.Errorf("Shuffle with same seed returned different matrix")
-	}
-
-	// Same thing for data with labels.
-	lab1, err := TrainTestSplit(0.1, 10, values, labels)
-	if err != nil {
-		t.Error(err)
-	}
-
-	lab2, _ := TrainTestSplit(0.1, 10, values, labels)
-	if !lab1[0].Equals(lab2[0]) {
-		t.Errorf("Shuffle with same seed returned different determinants")
-	}
-}