// Returns a classification for the vector, based on a vector input, using the KNN algorithm.
// See http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
func (KNN *KNNClassifier) Predict(vector []float64, K int) string {
convertedVector := util.FloatsToMatrix(vector)
// Get the number of rows
rows, _ := KNN.Data.Dims()
rownumbers := make(map[int]float64)
labels := make([]string, 0)
maxmap := make(map[string]int)
// Check what distance function we are using
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, convertedVector)
rownumbers[i] = distance
}
}
case "manhattan":
{
manhattan := pairwiseMetrics.NewEuclidean()
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
rowMat := util.FloatsToMatrix(row)
distance := manhattan.Distance(rowMat, convertedVector)
rownumbers[i] = distance
}
}
}
sorted := util.SortIntMap(rownumbers)
values := sorted[:K]
for _, elem := range values {
// It's when we access this map
labels = append(labels, KNN.Labels[elem])
if _, ok := maxmap[KNN.Labels[elem]]; ok {
maxmap[KNN.Labels[elem]] += 1
} else {
maxmap[KNN.Labels[elem]] = 1
}
}
sortedlabels := util.SortStringMap(maxmap)
label := sortedlabels[0]
return label
}
// Returns a classification for the vector, based on a vector input, using the KNN algorithm.
// See http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
func (KNN *KNNClassifier) PredictOne(vector []float64) string {
rows := KNN.TrainingData.Rows
rownumbers := make(map[int]float64)
labels := make([]string, 0)
maxmap := make(map[string]int)
convertedVector := util.FloatsToMatrix(vector)
// Check what distance function we are using
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
}
}
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
}
}
}
sorted := util.SortIntMap(rownumbers)
values := sorted[:KNN.NearestNeighbours]
for _, elem := range values {
label := KNN.TrainingData.GetClass(elem)
labels = append(labels, label)
if _, ok := maxmap[label]; ok {
maxmap[label] += 1
} else {
maxmap[label] = 1
}
}
sortedlabels := util.SortStringMap(maxmap)
label := sortedlabels[0]
return label
}
//Returns an average of the K nearest labels/variables, based on a vector input.
func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
// Get the number of rows
rows, _ := KNN.Data.Dims()
rownumbers := make(map[int]float64)
labels := make([]float64, 0)
// Check what distance function we are using
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
}
}
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
}
}
}
sorted := util.SortIntMap(rownumbers)
values := sorted[:K]
var sum float64
for _, elem := range values {
value := KNN.Values[elem]
labels = append(labels, value)
sum += value
}
average := sum / float64(K)
return average
}
func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
// Get the number of rows
rows, _ := KNN.Data.Dims()
rownumbers := make(map[int]float64)
labels := make([]float64, 0)
// Check what distance function we are using
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
for i := 0; i < rows; i++ {
row := KNN.Data.RowView(i)
distance := distanceFunc.Distance(utilities.VectorToMatrix(row), vector)
rownumbers[i] = distance
}
sorted := utilities.SortIntMap(rownumbers)
values := sorted[:K]
var sum float64
for _, elem := range values {
value := KNN.Values[elem]
labels = append(labels, value)
sum += value
}
average := sum / float64(K)
return average
}
// Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Check what distance function we are using
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
// Check Compatibility
allAttrs := base.CheckCompatible(what, KNN.TrainingData)
if allAttrs == nil {
// Don't have the same Attributes
return nil
}
// Remove the Attributes which aren't numeric
allNumericAttrs := make([]base.Attribute, 0)
for _, a := range allAttrs {
if fAttr, ok := a.(*base.FloatAttribute); ok {
allNumericAttrs = append(allNumericAttrs, fAttr)
}
}
// Generate return vector
ret := base.GeneratePredictionVector(what)
// Resolve Attribute specifications for both
whatAttrSpecs := base.ResolveAttributes(what, allNumericAttrs)
trainAttrSpecs := base.ResolveAttributes(KNN.TrainingData, allNumericAttrs)
// Reserve storage for most the most similar items
distances := make(map[int]float64)
// Reserve storage for voting map
maxmap := make(map[string]int)
// Reserve storage for row computations
trainRowBuf := make([]float64, len(allNumericAttrs))
predRowBuf := make([]float64, len(allNumericAttrs))
// Iterate over all outer rows
what.MapOverRows(whatAttrSpecs, func(predRow [][]byte, predRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
}
predMat := utilities.FloatsToMatrix(predRowBuf)
// Find the closest match in the training data
KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
trainRowBuf[i] = base.UnpackBytesToFloat(trainRow[i])
}
// Compute the distance
trainMat := utilities.FloatsToMatrix(trainRowBuf)
distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
return true, nil
})
sorted := utilities.SortIntMap(distances)
values := sorted[:KNN.NearestNeighbours]
// Reset maxMap
for a := range maxmap {
maxmap[a] = 0
}
// Refresh maxMap
for _, elem := range values {
label := base.GetClass(KNN.TrainingData, elem)
if _, ok := maxmap[label]; ok {
maxmap[label]++
} else {
maxmap[label] = 1
}
}
// Sort the maxMap
var maxClass string
maxVal := -1
for a := range maxmap {
if maxmap[a] > maxVal {
maxVal = maxmap[a]
maxClass = a
}
}
base.SetClass(ret, predRowNo, maxClass)
return true, nil
})
return ret
}
// Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Check what distance function we are using
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
// Check Compatibility
allAttrs := base.CheckCompatible(what, KNN.TrainingData)
if allAttrs == nil {
// Don't have the same Attributes
return nil
}
// Use optimised version if permitted
if KNN.AllowOptimisations {
if KNN.DistanceFunc == "euclidean" {
if KNN.canUseOptimisations(what) {
return KNN.optimisedEuclideanPredict(what.(*base.DenseInstances))
}
}
}
fmt.Println("Optimisations are switched off")
// Remove the Attributes which aren't numeric
allNumericAttrs := make([]base.Attribute, 0)
for _, a := range allAttrs {
if fAttr, ok := a.(*base.FloatAttribute); ok {
allNumericAttrs = append(allNumericAttrs, fAttr)
}
}
// Generate return vector
ret := base.GeneratePredictionVector(what)
// Resolve Attribute specifications for both
whatAttrSpecs := base.ResolveAttributes(what, allNumericAttrs)
trainAttrSpecs := base.ResolveAttributes(KNN.TrainingData, allNumericAttrs)
// Reserve storage for most the most similar items
distances := make(map[int]float64)
// Reserve storage for voting map
maxmap := make(map[string]int)
// Reserve storage for row computations
trainRowBuf := make([]float64, len(allNumericAttrs))
predRowBuf := make([]float64, len(allNumericAttrs))
_, maxRow := what.Size()
curRow := 0
// Iterate over all outer rows
what.MapOverRows(whatAttrSpecs, func(predRow [][]byte, predRowNo int) (bool, error) {
if (curRow%1) == 0 && curRow > 0 {
fmt.Printf("KNN: %.2f %% done\n", float64(curRow)*100.0/float64(maxRow))
}
curRow++
// Read the float values out
for i, _ := range allNumericAttrs {
predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
}
predMat := utilities.FloatsToMatrix(predRowBuf)
// Find the closest match in the training data
KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
trainRowBuf[i] = base.UnpackBytesToFloat(trainRow[i])
}
// Compute the distance
trainMat := utilities.FloatsToMatrix(trainRowBuf)
distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
return true, nil
})
sorted := utilities.SortIntMap(distances)
values := sorted[:KNN.NearestNeighbours]
maxClass := KNN.vote(maxmap, values)
base.SetClass(ret, predRowNo, maxClass)
return true, nil
})
return ret
}