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Code Issues Releases Wiki Activity

Avoid renaming packages on import

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This commit is contained in:
Amit Kumar Gupta 2014-08-22 09:33:42 +00:00
parent 478b5055c7
commit 529b3bcaa5
9 changed files with 50 additions and 52 deletions
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4
base/domain.go
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@ -6,10 +6,8 @@ import (
"bytes"
"encoding/gob"
"io/ioutil"
)
import (
mat64 "github.com/gonum/matrix/mat64"
"github.com/gonum/matrix/mat64"
)
// An Estimator is object that can ingest some data and train on it.

14
cross_validation/cross_validation.go
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@ -2,17 +2,17 @@ package cross_validation
import (
"fmt"
mat "github.com/gonum/matrix/mat64"
"github.com/gonum/matrix/mat64"
"math/rand"
"sync"
"time"
)
func shuffleMatrix(returnDatasets []*mat.Dense, dataset mat.Matrix, testSize int, seed int64, wg *sync.WaitGroup) {
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 := mat.DenseCopyOf(dataset)
shuffledSet := mat64.DenseCopyOf(dataset)
rowCount, colCount := shuffledSet.Dims()
temp := make([]float64, colCount)
@ -27,8 +27,8 @@ func shuffleMatrix(returnDatasets []*mat.Dense, dataset mat.Matrix, testSize int
}
}
trainSize := rowCount - testSize
returnDatasets[0] = mat.NewDense(trainSize, colCount, shuffledSet.RawMatrix().Data[:trainSize*colCount])
returnDatasets[1] = mat.NewDense(testSize, colCount, shuffledSet.RawMatrix().Data[trainSize*colCount:])
returnDatasets[0] = mat64.NewDense(trainSize, colCount, shuffledSet.RawMatrix().Data[:trainSize*colCount])
returnDatasets[1] = mat64.NewDense(testSize, colCount, shuffledSet.RawMatrix().Data[trainSize*colCount:])
wg.Done()
}
@ -36,7 +36,7 @@ func shuffleMatrix(returnDatasets []*mat.Dense, dataset mat.Matrix, testSize int
// 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.Dense) ([]*mat.Dense, error) {
func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat64.Dense) ([]*mat64.Dense, error) {
// Get number of instances (rows).
instanceCount, _ := datasets[0].Dims()
@ -82,7 +82,7 @@ func TrainTestSplit(size interface{}, randomState interface{}, datasets ...*mat.
wg.Add(dataCount)
// Return slice will hold training and test data and optional labels matrix.
returnDatasets := make([]*mat.Dense, 2*dataCount)
returnDatasets := make([]*mat64.Dense, 2*dataCount)
for i, dataset := range datasets {
// Send proper returnDataset slice.

8
cross_validation/cross_validation_test.go
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@ -2,7 +2,7 @@ package cross_validation
import (
//. "github.com/smartystreets/goconvey/convey"
mat "github.com/gonum/matrix/mat64"
"github.com/gonum/matrix/mat64"
"math/rand"
"testing"
"time"
@ -10,7 +10,7 @@ import (
var (
flatValues, flatLabels []float64
values, labels *mat.Dense
values, labels *mat64.Dense
)
func init() {
@ -23,8 +23,8 @@ func init() {
flatLabels[int(i/4)] = float64(rand.Intn(2))
}
values = mat.NewDense(20, 4, flatValues)
labels = mat.NewDense(20, 1, flatLabels)
values = mat64.NewDense(20, 4, flatValues)
labels = mat64.NewDense(20, 1, flatLabels)
}
func TestTrainTrainTestSplit(t *testing.T) {

6
ensemble/randomforest_test.go
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@ -2,7 +2,7 @@ package ensemble
import (
"github.com/sjwhitworth/golearn/base"
eval "github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/filters"
"testing"
)
@ -25,9 +25,9 @@ func TestRandomForest1(t *testing.T) {
rf := NewRandomForest(10, 3)
rf.Fit(trainData)
predictions := rf.Predict(testData)
confusionMat, err := eval.GetConfusionMatrix(testData, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}

14
examples/trees/trees.go
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@ -6,7 +6,7 @@ import (
"fmt"
"github.com/sjwhitworth/golearn/base"
"github.com/sjwhitworth/golearn/ensemble"
eval "github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/filters"
"github.com/sjwhitworth/golearn/trees"
"math/rand"
@ -50,11 +50,11 @@ func main() {
// Evaluate
fmt.Println("ID3 Performance")
cf, err := eval.GetConfusionMatrix(testData, predictions)
cf, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(eval.GetSummary(cf))
fmt.Println(evaluation.GetSummary(cf))
//
// Next up, Random Trees
@ -65,11 +65,11 @@ func main() {
tree.Fit(testData)
predictions = tree.Predict(testData)
fmt.Println("RandomTree Performance")
cf, err = eval.GetConfusionMatrix(testData, predictions)
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(eval.GetSummary(cf))
fmt.Println(evaluation.GetSummary(cf))
//
// Finally, Random Forests
@ -78,9 +78,9 @@ func main() {
tree.Fit(trainData)
predictions = tree.Predict(testData)
fmt.Println("RandomForest Performance")
cf, err = eval.GetConfusionMatrix(testData, predictions)
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(eval.GetSummary(cf))
fmt.Println(evaluation.GetSummary(cf))
}

26
knn/knn.go
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@ -6,8 +6,8 @@ package knn
import (
"github.com/gonum/matrix/mat64"
"github.com/sjwhitworth/golearn/base"
pairwiseMetrics "github.com/sjwhitworth/golearn/metrics/pairwise"
util "github.com/sjwhitworth/golearn/utilities"
"github.com/sjwhitworth/golearn/metrics/pairwise"
"github.com/sjwhitworth/golearn/utilities"
)
// A KNNClassifier consists of a data matrix, associated labels in the same order as the matrix, and a distance function.
@ -36,12 +36,12 @@ func (KNN *KNNClassifier) Fit(trainingData base.FixedDataGrid) {
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Check what distance function we are using
var distanceFunc pairwiseMetrics.PairwiseDistanceFunc
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwiseMetrics.NewEuclidean()
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwiseMetrics.NewManhattan()
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
@ -85,7 +85,7 @@ func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
}
predMat := util.FloatsToMatrix(predRowBuf)
predMat := utilities.FloatsToMatrix(predRowBuf)
// Find the closest match in the training data
KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
@ -96,12 +96,12 @@ func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
}
// Compute the distance
trainMat := util.FloatsToMatrix(trainRowBuf)
trainMat := utilities.FloatsToMatrix(trainRowBuf)
distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
return true, nil
})
sorted := util.SortIntMap(distances)
sorted := utilities.SortIntMap(distances)
values := sorted[:KNN.NearestNeighbours]
// Reset maxMap
@ -167,24 +167,24 @@ func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
labels := make([]float64, 0)
// Check what distance function we are using
var distanceFunc pairwiseMetrics.PairwiseDistanceFunc
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwiseMetrics.NewEuclidean()
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwiseMetrics.NewManhattan()
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
rowMat := utilities.FloatsToMatrix(row)
distance := distanceFunc.Distance(rowMat, vector)
rownumbers[i] = distance
}
sorted := util.SortIntMap(rownumbers)
sorted := utilities.SortIntMap(rownumbers)
values := sorted[:K]
var sum float64

6
meta/bagging_test.go
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@ -2,7 +2,7 @@ package meta
import (
"github.com/sjwhitworth/golearn/base"
eval "github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/filters"
"github.com/sjwhitworth/golearn/trees"
"math/rand"
@ -84,9 +84,9 @@ func TestRandomForest1(t *testing.T) {
rf.Fit(trainDataf)
predictions := rf.Predict(testDataf)
confusionMat, err := eval.GetConfusionMatrix(testDataf, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testDataf, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}

6
trees/id3.go
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@ -4,7 +4,7 @@ import (
"bytes"
"fmt"
"github.com/sjwhitworth/golearn/base"
eval "github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/evaluation"
"sort"
)
@ -153,8 +153,8 @@ func (d *DecisionTreeNode) String() string {
// computeAccuracy is a helper method for Prune()
func computeAccuracy(predictions base.FixedDataGrid, from base.FixedDataGrid) float64 {
cf, _ := eval.GetConfusionMatrix(from, predictions)
return eval.GetAccuracy(cf)
cf, _ := evaluation.GetConfusionMatrix(from, predictions)
return evaluation.GetAccuracy(cf)
}
// Prune eliminates branches which hurt accuracy

18
trees/tree_test.go
View File

@ -2,7 +2,7 @@ package trees
import (
"github.com/sjwhitworth/golearn/base"
eval "github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/filters"
"math"
"testing"
@ -48,11 +48,11 @@ func TestRandomTreeClassification(t *testing.T) {
root := InferID3Tree(trainDataF, r)
predictions := root.Predict(testDataF)
confusionMat, err := eval.GetConfusionMatrix(testDataF, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testDataF, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}
func TestRandomTreeClassification2(t *testing.T) {
@ -74,11 +74,11 @@ func TestRandomTreeClassification2(t *testing.T) {
root.Fit(trainDataF)
predictions := root.Predict(testDataF)
confusionMat, err := eval.GetConfusionMatrix(testDataF, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testDataF, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}
func TestPruning(t *testing.T) {
@ -102,11 +102,11 @@ func TestPruning(t *testing.T) {
root.Prune(fittestData)
predictions := root.Predict(testDataF)
confusionMat, err := eval.GetConfusionMatrix(testDataF, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testDataF, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}
func TestInformationGain(t *testing.T) {
@ -196,11 +196,11 @@ func TestID3Classification(t *testing.T) {
root := InferID3Tree(trainData, rule)
predictions := root.Predict(testData)
confusionMat, err := eval.GetConfusionMatrix(testData, predictions)
confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
}
_ = eval.GetSummary(confusionMat)
_ = evaluation.GetSummary(confusionMat)
}
func TestID3(t *testing.T) {