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Refactored function to use mat64.

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Changed most of the actual matrix operations to use the mat64 library.
This commit is contained in:
Nelson Santos 2014-05-02 22:38:15 -04:00
parent 2b6249775f
commit b09a1cecae
1 changed files with 24 additions and 13 deletions
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37
utilities/cross_validation.go
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@ -2,17 +2,25 @@ package utilities
import (
"fmt"
mat "github.com/skelterjohn/go.matrix"
mat "github.com/gonum/matrix/mat64"
"math/rand"
"time"
)
func shuffleMatrix(dataset *mat.DenseMatrix, numGen *rand.Rand) *mat.DenseMatrix {
shuffledSet := dataset.Copy()
func shuffleMatrix(dataset *mat.Dense, numGen *rand.Rand) *mat.Dense {
shuffledSet := mat.DenseCopyOf(dataset)
rowCount, colCount := shuffledSet.Dims()
temp := make([]float64, colCount)
for i := 0; i < shuffledSet.Rows(); i++ {
// Fisher–Yates shuffle
for i := 0; i < rowCount; i++ {
j := numGen.Intn(i + 1)
shuffledSet.SwapRows(i, j)
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)
}
}
return shuffledSet
@ -21,9 +29,9 @@ func shuffleMatrix(dataset *mat.DenseMatrix, numGen *rand.Rand) *mat.DenseMatrix
// 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 ...*mat.DenseMatrix) ([]*mat.DenseMatrix, error) {
func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat.Dense) ([]*mat.Dense, error) {
// Get number of instances (rows).
instanceCount := datasets[0].Rows()
instanceCount, _ := datasets[0].Dims()
// Input should be one or two matrices.
dataCount := len(datasets)
@ -33,9 +41,10 @@ func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat.
if dataCount == 2 {
// Test for consistency.
if datasets[1].Rows() != instanceCount {
labelCount, labelFeatures := datasets[1].Dims()
if labelCount != instanceCount {
return nil, fmt.Errorf("Data and labels must have the same number of instances")
} else if datasets[1].Cols() != 1 {
} else if labelFeatures != 1 {
return nil, fmt.Errorf("Label matrix must have single feature")
}
}
@ -64,14 +73,16 @@ func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat.
numGen := rand.New(randSource)
// Return slice will hold training and test data and optional labels matrix.
var returnDatasets []*mat.DenseMatrix
var returnDatasets []*mat.Dense
for _, dataset := range datasets {
_, featureCount := dataset.Dims()
tempMatrix := shuffleMatrix(dataset, numGen)
// Features count is different on data and labels.
featureCount := tempMatrix.Cols()
returnDatasets = append(returnDatasets, tempMatrix.GetMatrix(0, 0, trainSize, featureCount))
returnDatasets = append(returnDatasets, tempMatrix.GetMatrix(trainSize, 0, testSize, featureCount))
returnDatasets = append(returnDatasets, mat.NewDense(trainSize, featureCount, tempMatrix.RawMatrix().Data[:trainSize*featureCount]))
returnDatasets = append(returnDatasets, mat.NewDense(testSize, featureCount, tempMatrix.RawMatrix().Data[trainSize*featureCount:]))
}
return returnDatasets, nil