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Merge pull request #54 from njern/knn_refactor

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Knn refactor
This commit is contained in:
Stephen Whitworth 2014-07-26 20:18:28 -04:00
commit e6c28efe2d
2 changed files with 32 additions and 36 deletions
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60
knn/knn.go
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@ -44,27 +44,21 @@ func (KNN *KNNClassifier) PredictOne(vector []float64) string {
convertedVector := util.FloatsToMatrix(vector)
// Check what distance function we are using
var distanceFunc pairwiseMetrics.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
{
euclidean := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.TrainingData.GetRowVectorWithoutClass(i)
rowMat := util.FloatsToMatrix(row)
distance := euclidean.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
distanceFunc = pairwiseMetrics.NewEuclidean()
case "manhattan":
{
manhattan := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.TrainingData.GetRowVectorWithoutClass(i)
rowMat := util.FloatsToMatrix(row)
distance := manhattan.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
distanceFunc = pairwiseMetrics.NewManhattan()
default:
panic("unsupported distance function")
}
for i := 0; i < rows; i++ {
row := KNN.TrainingData.GetRowVectorWithoutClass(i)
rowMat := util.FloatsToMatrix(row)
distance := distanceFunc.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
sorted := util.SortIntMap(rownumbers)
@ -125,27 +119,21 @@ 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
switch KNN.DistanceFunc {
case "euclidean":
{
euclidean := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := euclidean.Distance(rowMat, vector)
rownumbers[i] = distance
}
}
distanceFunc = pairwiseMetrics.NewEuclidean()
case "manhattan":
{
manhattan := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := manhattan.Distance(rowMat, vector)
rownumbers[i] = distance
}
}
distanceFunc = pairwiseMetrics.NewManhattan()
default:
panic("unsupported distance function")
}
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := distanceFunc.Distance(rowMat, vector)
rownumbers[i] = distance
}
sorted := util.SortIntMap(rownumbers)

8
metrics/pairwise/pairwise.go
View File

@ -1,2 +1,10 @@
// Package pairwise implements utilities to evaluate pairwise distances or inner product (via kernel).
package pairwise
import (
"github.com/gonum/matrix/mat64"
)
type PairwiseDistanceFunc interface {
Distance(vectorX *mat64.Dense, vectorY *mat64.Dense) float64
}