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xvalidation.go
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xvalidation.go
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/*
** Copyright 2014 Edward Walker
**
** Licensed under the Apache License, Version 2.0 (the "License");
** you may not use this file except in compliance with the License.
** You may obtain a copy of the License at
**
** http ://www.apache.org/licenses/LICENSE-2.0
**
** Unless required by applicable law or agreed to in writing, software
** distributed under the License is distributed on an "AS IS" BASIS,
** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
** See the License for the specific language governing permissions and
** limitations under the License.
**
** Description: Cross validation API
** @author: Ed Walker
*/
package libSvm
import (
"fmt"
"math/rand"
"time"
)
/**
* This function conducts cross validation. Data are separated to
nrFold folds. Under given parameters, sequentially each fold is
validated using the model from training the remaining. Predicted
labels (of all prob's instances) in the validation process are
stored in the slice called target.
*/
func CrossValidation(prob *Problem, param *Parameter, nrFold int) (target []float64) {
var l int = prob.l
target = make([]float64, l) // slice to return
if nrFold > l {
nrFold = l
fmt.Printf("WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)\n")
}
foldStart := make([]int, nrFold+1)
perm := make([]int, l)
random := rand.New(rand.NewSource(time.Now().UTC().UnixNano()))
// stratified cv may not give leave-one-out rate
// Each class to l folds -> some folds may have zero elements
if (param.SvmType == C_SVC || param.SvmType == NU_SVC) && nrFold < l {
nrClass, _, start, count, localPerm := groupClasses(prob) // group SV with the same labels together
perm = localPerm
// random shuffle and then data grouped by fold using the array perm
foldCount := make([]int, nrFold)
index := make([]int, l)
for i := 0; i < l; i++ {
index[i] = perm[i]
}
for c := 0; c < nrClass; c++ {
for i := 0; i < count[c]; i++ {
j := i + random.Intn(count[c]-i)
//j := i + randIntn(count[c]-i)
index[start[c]+j], index[start[c]+i] = index[start[c]+i], index[start[c]+j]
}
}
for i := 0; i < nrFold; i++ {
foldCount[i] = 0
for c := 0; c < nrClass; c++ {
foldCount[i] += (i+1)*count[c]/nrFold - i*count[c]/nrFold
}
}
foldStart[0] = 0
for i := 1; i <= nrFold; i++ {
foldStart[i] = foldStart[i-1] + foldCount[i-1]
}
for c := 0; c < nrClass; c++ {
for i := 0; i < nrFold; i++ {
begin := start[c] + i*count[c]/nrFold
end := start[c] + (i+1)*count[c]/nrFold
for j := begin; j < end; j++ {
perm[foldStart[i]] = index[j]
foldStart[i]++
}
}
}
foldStart[0] = 0
for i := 1; i <= nrFold; i++ {
foldStart[i] = foldStart[i-1] + foldCount[i-1]
}
} else {
for i := 0; i < l; i++ {
perm[i] = i
}
for i := 0; i < l; i++ {
j := i + random.Intn(l-i)
perm[i], perm[j] = perm[j], perm[i]
}
for i := 0; i <= nrFold; i++ {
foldStart[i] = i * l / nrFold
}
}
for i := 0; i < nrFold; i++ {
begin := foldStart[i]
end := foldStart[i+1]
var subProb Problem
subProb.xSpace = prob.xSpace // inherit problem space
subProb.l = l - (end - begin)
subProb.x = make([]int, subProb.l)
subProb.y = make([]float64, subProb.l)
var k int = 0
for j := 0; j < begin; j++ {
subProb.x[k] = prob.x[perm[j]]
subProb.y[k] = prob.y[perm[j]]
k++
}
for j := end; j < l; j++ {
subProb.x[k] = prob.x[perm[j]]
subProb.y[k] = prob.y[perm[j]]
k++
}
subModel := NewModel(param)
subModel.Train(&subProb)
if param.Probability &&
(param.SvmType == C_SVC || param.SvmType == NU_SVC) {
for j := begin; j < end; j++ {
idx := prob.x[perm[j]]
x := SnodeToMap(prob.xSpace[idx:])
target[perm[j]], _ = subModel.PredictProbability(x)
}
} else {
for j := begin; j < end; j++ {
idx := prob.x[perm[j]]
x := SnodeToMap(prob.xSpace[idx:])
target[perm[j]] = subModel.Predict(x)
}
}
}
return
}