protected SvmModel TrainSvmModel(SvmProblem problem)
{
_parameters.Check(problem);
var model = LibSvm.Svm.Train(problem, _parameters);
return(model);
}
public void TrainAndTest(IEnumerable <ItemRating> trainSet, IEnumerable <ItemRating> testSet)
{
var problem = new SvmProblem()
{
X = trainSet.Select(ir => FeatureBuilder.GetSvmNode(ir)).ToArray(),
Y = trainSet.Select(ir => LabelSelector(ir)).ToArray()
};
Parameters.Check(problem);
Console.WriteLine("Writing training samples...");
WriteSvmFile(problem, "train.libsvm");
Console.WriteLine("LibSvm training...");
LibSvm.SvmModel model = Svm.Train(problem, Parameters);
var predictedClasses = new List <float>();
Console.WriteLine("LibSvm testing...");
foreach (var ir in testSet)
{
ir.PredictedRating = (float)model.Predict(FeatureBuilder.GetSvmNode(ir));
predictedClasses.Add(ir.PredictedRating);
}
Console.WriteLine("Writing output...");
File.WriteAllLines("output.libsvm", predictedClasses.Select(i => i.ToString()));
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param, double redfactor)
{
int nfeatures = prob.x[0].Length;
int[] result = new int[nfeatures];
int[] survivingFeatures = ArrayUtils.ConsecutiveInts(nfeatures);
SvmProblem problem = prob.Copy();
int p = nfeatures - 1;
int[] indices = ArrayUtils.ConsecutiveInts(nfeatures);
while (survivingFeatures.Length > 0)
{
problem = problem.ExtractFeatures(survivingFeatures);
indices = ArrayUtils.SubArray(indices, survivingFeatures);
int nfeatures2 = survivingFeatures.Length;
double[] criteria = ComputeRankingCriteria(SvmMain.SvmTrain(problem, param)
.ComputeBinaryClassifierWeights(nfeatures2));
int[] order = ArrayUtils.Order(criteria);
int numFeaturesToRemove = Math.Max((int)Math.Round(nfeatures2 / redfactor), 1);
for (int i = 0; i < numFeaturesToRemove; ++i)
{
result[p--] = indices[order[i]];
}
survivingFeatures = ArrayUtils.SubArray(order, numFeaturesToRemove, nfeatures2);
}
return(result);
}
private void WriteSvmFile(SvmProblem problem, string fileName)
{
var samples = problem.Y.Zip(problem.X, (y, x) => string.Format("{0} {1}",
y,
x.Select(n => string.Format("{0}:{1}", n.Index, n.Value)).Aggregate((a, b) => a + " " + b)));
File.WriteAllLines(fileName, samples);
}
internal OneClassQ(SvmProblem prob, SvmParameter param)
: base(prob.Count, prob.x, param)
{
cache = new SvmCache(prob.Count, (long) (param.cacheSize*(1 << 20)));
qd = new double[prob.Count];
for (int i = 0; i < prob.Count; i++){
qd[i] = KernelFunctionEval(i, i);
}
}
internal OneClassQ(SvmProblem prob, SvmParameter param) : base(prob.Count, prob.x, param)
{
cache = new SvmCache(prob.Count, (long)(param.cacheSize * (1 << 20)));
qd = new double[prob.Count];
for (int i = 0; i < prob.Count; i++)
{
qd[i] = KernelFunctionEval(i, i);
}
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param)
{
int nfeatures = prob.x[0].Length;
double[] criteria = ComputeRankingCriteria(SvmMain.SvmTrain(prob, param).ComputeBinaryClassifierWeights(nfeatures));
int[] order = ArrayUtils.Order(criteria);
Array.Reverse(order);
return(order);
}
internal SvcQ(SvmProblem prob, SvmParameter param, short[] y1)
: base(prob.Count, prob.x, param)
{
y = (short[]) y1.Clone();
cache = new SvmCache(prob.Count, (long) (param.cacheSize*(1 << 20)));
qd = new double[prob.Count];
for (int i = 0; i < prob.Count; i++){
qd[i] = KernelFunctionEval(i, i);
}
}
internal SvcQ(SvmProblem prob, SvmParameter param, short[] y1) : base(prob.Count, prob.x, param)
{
y = (short[])y1.Clone();
cache = new SvmCache(prob.Count, (long)(param.cacheSize * (1 << 20)));
qd = new double[prob.Count];
for (int i = 0; i < prob.Count; i++)
{
qd[i] = KernelFunctionEval(i, i);
}
}
public IModel <double> Train(IEnumerable <Tuple <double[], double> > data)
{
var problem = new SvmProblem
{
Y = data.Select(p => p.Item2).ToArray(),
X = data.Select(p => p.Item1.ToSvmNodes()).ToArray()
};
var model = TrainSvmModel(problem);
return(new RegressionModel(model));
}
public IModel <bool> Train(IEnumerable <double[]> data)
{
var problem = new SvmProblem
{
Y = data.Select(p => 1.0).ToArray(),
X = data.Select(p => p.ToSvmNodes()).ToArray()
};
var model = TrainSvmModel(problem);
return(new OneClassModel(model));
}
public IModel <int> Train(IEnumerable <Tuple <double[], int> > data)
{
var problem = new SvmProblem
{
Y = data.Select(p => (double)p.Item2).ToArray(),
X = data.Select(p => p.Item1.ToSvmNodes()).ToArray()
};
var model = TrainSvmModel(problem);
return(new ClassificationModel(model));
}
private static SvmProblem[] CreateProblems(IList <BaseVector> x, IList <int[]> y, int ngroups, out bool[] invert)
{
if (ngroups == 2)
{
invert = new bool[1];
return(new[] { CreateProblem(x, y, 0, out invert[0]) });
}
SvmProblem[] result = new SvmProblem[ngroups];
invert = new bool[ngroups];
for (int i = 0; i < ngroups; i++)
{
result[i] = CreateProblem(x, y, i, out invert[i]);
}
return(result);
}
public static void Run()
{
Console.WriteLine("OneClassDemo");
var trainData = DemoHelper.GenerateClass(0, 0.5, 0.5, 100);
var parameters = new SvmParameter
{
SvmType = SvmType.ONE_CLASS,
KernelType = KernelType.Rbf,
Gamma = 0.5,
Nu = 0.5,
CacheSize = 128,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => 1.0).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.5, 0.5).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
var z = new Point(0.45, 0.45).ToSvmNodes();
var resz = model.Predict(z);
Console.WriteLine(resz);
}
public static void Run()
{
Console.WriteLine("EpsSVRDemo");
var rnd = new Random();
var trainData = DemoHelper.Range(-10.0, 10.01, 0.1).Select(val => new { X = val, Y = DemoHelper.Sinc(val) + (rnd.NextDouble() - 0.5) / 4 });
var parameters = new SvmParameter
{
SvmType = SvmType.EPSILON_SVR,
KernelType = KernelType.Rbf,
Gamma = 0.5,
CacheSize = 128,
C = 1,
Eps = 1e-3,
P = 0.1,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => p.Y).ToArray(),
X = trainData.Select(p => p.X.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
foreach (var item in DemoHelper.Range(-1.0, 1.01, 0.1))
{
var x = item.ToSvmNodes();
var yPred = model.Predict(x);
var yReal = DemoHelper.Sinc(item);
Console.WriteLine("x: {0}", item);
Console.WriteLine("y_real: {0}", yReal);
Console.WriteLine("y_pred: {0}", yPred);
Console.WriteLine();
}
}
internal SvrQ(SvmProblem prob, SvmParameter param)
: base(prob.Count, prob.x, param)
{
l = prob.Count;
cache = new SvmCache(l, (long) (param.cacheSize*(1 << 20)));
qd = new double[2*l];
sign = new short[2*l];
index = new int[2*l];
for (int k = 0; k < l; k++){
sign[k] = 1;
sign[k + l] = -1;
index[k] = k;
index[k + l] = k;
qd[k] = KernelFunctionEval(k, k);
qd[k + l] = qd[k];
}
buffer = new float[2][];
buffer[0] = new float[2*l];
buffer[1] = new float[2*l];
nextBuffer = 0;
}
internal SvrQ(SvmProblem prob, SvmParameter param) : base(prob.Count, prob.x, param)
{
l = prob.Count;
cache = new SvmCache(l, (long)(param.cacheSize * (1 << 20)));
qd = new double[2 * l];
sign = new short[2 * l];
index = new int[2 * l];
for (int k = 0; k < l; k++)
{
sign[k] = 1;
sign[k + l] = -1;
index[k] = k;
index[k + l] = k;
qd[k] = KernelFunctionEval(k, k);
qd[k + l] = qd[k];
}
buffer = new float[2][];
buffer[0] = new float[2 * l];
buffer[1] = new float[2 * l];
nextBuffer = 0;
}
public static void Run()
{
Console.WriteLine("CSVMDemo");
var class1 = DemoHelper.GenerateClass(0, 0.1, 0.1, 50);
var class2 = DemoHelper.GenerateClass(1, 0.8, 0.8, 50);
var trainData = class1.Concat(class2);
var parameters = new SvmParameter
{
SvmType = SvmType.C_SVC,
KernelType = KernelType.Rbf,
Gamma = 0.5,
CacheSize = 128,
C = 1,
Eps = 1e-3,
Shrinking = true,
Probability = false
};
var problem = new SvmProblem
{
Y = trainData.Select(p => (double)p.Label).ToArray(),
X = trainData.Select(p => p.ToSvmNodes()).ToArray()
};
parameters.Check(problem);
var model = Svm.Train(problem, parameters);
var x = new Point(0.9, 0.9).ToSvmNodes();
var resx = model.Predict(x);
Console.WriteLine(resx);
var y = new Point(0.1, 0.1).ToSvmNodes();
var resy = model.Predict(y);
Console.WriteLine(resy);
}
public static string SvmCheckParameter(SvmProblem prob, SvmParameter param)
{
SvmType svmType = param.svmType;
// cache_size,eps,C,nu,p,shrinking
if (param.cacheSize <= 0){
return "cache_size <= 0";
}
if (param.eps <= 0){
return "eps <= 0";
}
if (svmType == SvmType.CSvc || svmType == SvmType.EpsilonSvr || svmType == SvmType.NuSvr){
if (param.c <= 0){
return "C <= 0";
}
}
if (svmType == SvmType.NuSvc || svmType == SvmType.OneClass || svmType == SvmType.NuSvr){
if (param.nu <= 0 || param.nu > 1){
return "nu <= 0 or nu > 1";
}
}
if (svmType == SvmType.EpsilonSvr){
if (param.p < 0){
return "p < 0";
}
}
if (param.probability && svmType == SvmType.OneClass){
return "one-class SVM probability output not supported yet";
}
// check whether nu-svc is feasible
if (svmType == SvmType.NuSvc){
int l = prob.Count;
int maxNrClass = 16;
int nrClass = 0;
int[] label = new int[maxNrClass];
int[] count = new int[maxNrClass];
int i;
for (i = 0; i < l; i++){
int thisLabel = (int) prob.y[i];
int j;
for (j = 0; j < nrClass; j++){
if (thisLabel == label[j]){
++count[j];
break;
}
}
if (j == nrClass){
if (nrClass == maxNrClass){
maxNrClass *= 2;
int[] newData = new int[maxNrClass];
Array.Copy(label, 0, newData, 0, label.Length);
label = newData;
newData = new int[maxNrClass];
Array.Copy(count, 0, newData, 0, count.Length);
count = newData;
}
label[nrClass] = thisLabel;
count[nrClass] = 1;
++nrClass;
}
}
for (i = 0; i < nrClass; i++){
int n1 = count[i];
for (int j = i + 1; j < nrClass; j++){
int n2 = count[j];
if (param.nu*(n1 + n2)/2 > Math.Min(n1, n2)){
return "specified nu is infeasible";
}
}
}
}
return null;
}
//
// Interface functions
//
public static SvmModel SvmTrain(SvmProblem prob, SvmParameter param)
{
SvmModel model = new SvmModel{param = param};
if (param.svmType == SvmType.OneClass || param.svmType == SvmType.EpsilonSvr || param.svmType == SvmType.NuSvr){
// regression or one-class-svm
model.nrClass = 2;
model.label = null;
model.nSv = null;
model.probA = null;
model.probB = null;
model.svCoef = new double[1][];
if (param.probability && (param.svmType == SvmType.EpsilonSvr || param.svmType == SvmType.NuSvr)){
model.probA = new double[1];
model.probA[0] = SvmSvrProbability(prob, param);
}
DecisionFunction f = SvmTrainOne(prob, param, 0, 0);
model.rho = new double[1];
model.rho[0] = f.rho;
int nSv = 0;
int i;
for (i = 0; i < prob.Count; i++){
if (Math.Abs(f.alpha[i]) > 0){
++nSv;
}
}
model.l = nSv;
model.sv = new BaseVector[nSv];
model.svCoef[0] = new double[nSv];
int j = 0;
for (i = 0; i < prob.Count; i++){
if (Math.Abs(f.alpha[i]) > 0){
model.sv[j] = prob.x[i];
model.svCoef[0][j] = f.alpha[i];
++j;
}
}
} else{
// classification
int l = prob.Count;
int[] tmpNrClass = new int[1];
int[][] tmpLabel = new int[1][];
int[][] tmpStart = new int[1][];
int[][] tmpCount = new int[1][];
int[] perm = new int[l];
// group training data of the same class
SvmGroupClasses(prob, tmpNrClass, tmpLabel, tmpStart, tmpCount, perm);
int nrClass = tmpNrClass[0];
int[] label = tmpLabel[0];
int[] start = tmpStart[0];
int[] count = tmpCount[0];
if (nrClass == 1){
Info("WARNING: training data in only one class. See README for details.\n");
}
BaseVector[] x = new BaseVector[l];
int i;
for (i = 0; i < l; i++){
x[i] = prob.x[perm[i]];
}
// calculate weighted C
double[] weightedC = new double[nrClass];
for (i = 0; i < nrClass; i++){
weightedC[i] = param.c;
}
for (i = 0; i < param.nrWeight; i++){
int j;
for (j = 0; j < nrClass; j++){
if (param.weightLabel[i] == label[j]){
break;
}
}
if (j == nrClass){
Info("WARNING: class label " + param.weightLabel[i] + " specified in weight is not found\n");
} else{
weightedC[j] *= param.weight[i];
}
}
// train k*(k-1)/2 models
bool[] nonzero = new bool[l];
for (i = 0; i < l; i++){
nonzero[i] = false;
}
DecisionFunction[] f = new DecisionFunction[nrClass*(nrClass - 1)/2];
double[] probA = null, probB = null;
if (param.probability){
probA = new double[nrClass*(nrClass - 1)/2];
probB = new double[nrClass*(nrClass - 1)/2];
}
int p = 0;
for (i = 0; i < nrClass; i++){
for (int j = i + 1; j < nrClass; j++){
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
int c = ci + cj;
SvmProblem subProb = new SvmProblem{x = new BaseVector[c], y = new float[c]};
int k;
for (k = 0; k < ci; k++){
subProb.x[k] = x[si + k];
subProb.y[k] = +1;
}
for (k = 0; k < cj; k++){
subProb.x[ci + k] = x[sj + k];
subProb.y[ci + k] = -1;
}
if (param.probability){
double[] probAb = new double[2];
SvmBinarySvcProbability(subProb, param, weightedC[i], weightedC[j], probAb);
probA[p] = probAb[0];
probB[p] = probAb[1];
}
f[p] = SvmTrainOne(subProb, param, weightedC[i], weightedC[j]);
for (k = 0; k < ci; k++){
if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0){
nonzero[si + k] = true;
}
}
for (k = 0; k < cj; k++){
if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0){
nonzero[sj + k] = true;
}
}
++p;
}
}
// build output
model.nrClass = nrClass;
model.label = new int[nrClass];
for (i = 0; i < nrClass; i++){
model.label[i] = label[i];
}
model.rho = new double[nrClass*(nrClass - 1)/2];
for (i = 0; i < nrClass*(nrClass - 1)/2; i++){
model.rho[i] = f[i].rho;
}
if (param.probability){
model.probA = new double[nrClass*(nrClass - 1)/2];
model.probB = new double[nrClass*(nrClass - 1)/2];
for (i = 0; i < nrClass*(nrClass - 1)/2; i++){
model.probA[i] = probA[i];
model.probB[i] = probB[i];
}
} else{
model.probA = null;
model.probB = null;
}
int nnz = 0;
int[] nzCount = new int[nrClass];
model.nSv = new int[nrClass];
for (i = 0; i < nrClass; i++){
int nSv = 0;
for (int j = 0; j < count[i]; j++){
if (nonzero[start[i] + j]){
++nSv;
++nnz;
}
}
model.nSv[i] = nSv;
nzCount[i] = nSv;
}
Info("Total nSV = " + nnz + "\n");
model.l = nnz;
model.sv = new BaseVector[nnz];
p = 0;
for (i = 0; i < l; i++){
if (nonzero[i]){
model.sv[p++] = x[i];
}
}
int[] nzStart = new int[nrClass];
nzStart[0] = 0;
for (i = 1; i < nrClass; i++){
nzStart[i] = nzStart[i - 1] + nzCount[i - 1];
}
model.svCoef = new double[nrClass - 1][];
for (i = 0; i < nrClass - 1; i++){
model.svCoef[i] = new double[nnz];
}
p = 0;
for (i = 0; i < nrClass; i++){
for (int j = i + 1; j < nrClass; j++){
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nzStart[i];
int k;
for (k = 0; k < ci; k++){
if (nonzero[si + k]){
model.svCoef[j - 1][q++] = f[p].alpha[k];
}
}
q = nzStart[j];
for (k = 0; k < cj; k++){
if (nonzero[sj + k]){
model.svCoef[i][q++] = f[p].alpha[ci + k];
}
}
++p;
}
}
}
return model;
}
// Cross-validation decision values for probability estimates
internal static void SvmBinarySvcProbability(SvmProblem prob, SvmParameter param, double cp, double cn,
IList<double> probAb)
{
int i;
const int nrFold = 5;
int[] perm = new int[prob.Count];
double[] decValues = new double[prob.Count];
// random shuffle
for (i = 0; i < prob.Count; i++){
perm[i] = i;
}
for (i = 0; i < prob.Count; i++){
int j = i + rand.Next(prob.Count - i);
do{
int _ = perm[i];
perm[i] = perm[j];
perm[j] = _;
} while (false);
}
for (i = 0; i < nrFold; i++){
int begin = i*prob.Count/nrFold;
int end = (i + 1)*prob.Count/nrFold;
int j;
int count = prob.Count - (end - begin);
SvmProblem subprob = new SvmProblem{x = new BaseVector[count], y = new float[count]};
int k = 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 < prob.Count; j++){
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
int pCount = 0, nCount = 0;
for (j = 0; j < k; j++){
if (subprob.y[j] > 0){
pCount++;
} else{
nCount++;
}
}
if (pCount == 0 && nCount == 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = 0;
}
} else if (pCount > 0 && nCount == 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = 1;
}
} else if (pCount == 0 && nCount > 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = -1;
}
} else{
SvmParameter subparam = (SvmParameter) param.Clone();
subparam.probability = false;
subparam.c = 1.0;
subparam.nrWeight = 2;
subparam.weightLabel = new int[2];
subparam.weight = new double[2];
subparam.weightLabel[0] = +1;
subparam.weightLabel[1] = -1;
subparam.weight[0] = cp;
subparam.weight[1] = cn;
SvmModel submodel = SvmTrain(subprob, subparam);
for (j = begin; j < end; j++){
double[] decValue = new double[1];
SvmPredictValues(submodel, prob.x[perm[j]], decValue);
decValues[perm[j]] = decValue[0];
// ensure +1 -1 order; reason not using CV subroutine
decValues[perm[j]] *= submodel.label[0];
}
}
}
SigmoidTrain(prob.Count, decValues, prob.y, probAb);
}
// Return parameter of a Laplace distribution
internal static double SvmSvrProbability(SvmProblem prob, SvmParameter param)
{
int i;
const int nrFold = 5;
double[] ymv = new double[prob.Count];
double mae = 0;
SvmParameter newparam = (SvmParameter) param.Clone();
newparam.probability = false;
SvmCrossValidation(prob, newparam, nrFold, ymv);
for (i = 0; i < prob.Count; i++){
ymv[i] = prob.y[i] - ymv[i];
mae += Math.Abs(ymv[i]);
}
mae /= prob.Count;
double std = Math.Sqrt(2*mae*mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.Count; i++){
if (Math.Abs(ymv[i]) > 5*std){
count = count + 1;
} else{
mae += Math.Abs(ymv[i]);
}
}
mae /= (prob.Count - count);
Info(
"Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" +
mae + "\n");
return mae;
}
private static void SolveCSvc(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si,
double cp, double cn)
{
int l = prob.Count;
double[] minusOnes = new double[l];
short[] y = new short[l];
int i;
for (i = 0; i < l; i++){
alpha[i] = 0;
minusOnes[i] = -1;
if (prob.y[i] > 0){
y[i] = +1;
} else{
y[i] = -1;
}
}
SvmSolver s = new SvmSolver();
s.Solve(l, new SvcQ(prob, param, y), minusOnes, y, alpha, cp, cn, param.eps, si, param.shrinking);
double sumAlpha = 0;
for (i = 0; i < l; i++){
sumAlpha += alpha[i];
}
if (cp == cn){
Info("nu = " + sumAlpha/(cp*prob.Count) + "\n");
}
for (i = 0; i < l; i++){
alpha[i] *= y[i];
}
}
private static DecisionFunction SvmTrainOne(SvmProblem prob, SvmParameter param, double cp, double cn)
{
double[] alpha = new double[prob.Count];
SvmSolver.SolutionInfo si = new SvmSolver.SolutionInfo();
switch (param.svmType){
case SvmType.CSvc:
SolveCSvc(prob, param, alpha, si, cp, cn);
break;
case SvmType.NuSvc:
SolveNuSvc(prob, param, alpha, si);
break;
case SvmType.OneClass:
SolveOneClass(prob, param, alpha, si);
break;
case SvmType.EpsilonSvr:
SolveEpsilonSvr(prob, param, alpha, si);
break;
case SvmType.NuSvr:
SolveNuSvr(prob, param, alpha, si);
break;
}
Info("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSv = 0;
int nBsv = 0;
for (int i = 0; i < prob.Count; i++){
if (Math.Abs(alpha[i]) > 0){
++nSv;
if (prob.y[i] > 0){
if (Math.Abs(alpha[i]) >= si.upperBoundP){
++nBsv;
}
} else{
if (Math.Abs(alpha[i]) >= si.upperBoundN){
++nBsv;
}
}
}
}
Info("nSV = " + nSv + ", nBSV = " + nBsv + "\n");
DecisionFunction f = new DecisionFunction{alpha = alpha, rho = si.rho};
return f;
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param)
{
int nfeatures = prob.x[0].Length;
double[] criteria = ComputeRankingCriteria(SvmMain.SvmTrain(prob, param).ComputeBinaryClassifierWeights(nfeatures));
int[] order = ArrayUtils.Order(criteria);
Array.Reverse(order);
return order;
}
private static SvmProblem[] CreateProblems(IList<BaseVector> x, IList<int[]> y, int ngroups, out bool[] invert)
{
if (ngroups == 2){
invert = new bool[1];
return new[]{CreateProblem(x, y, 0, out invert[0])};
}
SvmProblem[] result = new SvmProblem[ngroups];
invert = new bool[ngroups];
for (int i = 0; i < ngroups; i++){
result[i] = CreateProblem(x, y, i, out invert[i]);
}
return result;
}
private static void SolveNuSvc(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si)
{
int i;
int l = prob.Count;
double nu = param.nu;
short[] y = new short[l];
for (i = 0; i < l; i++){
if (prob.y[i] > 0){
y[i] = +1;
} else{
y[i] = -1;
}
}
double sumPos = nu*l/2;
double sumNeg = nu*l/2;
for (i = 0; i < l; i++){
if (y[i] == +1){
alpha[i] = Math.Min(1.0, sumPos);
sumPos -= alpha[i];
} else{
alpha[i] = Math.Min(1.0, sumNeg);
sumNeg -= alpha[i];
}
}
double[] zeros = new double[l];
for (i = 0; i < l; i++){
zeros[i] = 0;
}
SvmSolverNu s = new SvmSolverNu();
s.Solve(l, new SvcQ(prob, param, y), zeros, y, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
double r = si.r;
Info("C = " + 1/r + "\n");
for (i = 0; i < l; i++){
alpha[i] *= y[i]/r;
}
si.rho /= r;
si.obj /= (r*r);
si.upperBoundP = 1/r;
si.upperBoundN = 1/r;
}
private static void SolveNuSvr(SvmProblem prob, SvmParameter param, IList<double> alpha, SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double c = param.c;
double[] alpha2 = new double[2*l];
double[] linearTerm = new double[2*l];
short[] y = new short[2*l];
int i;
double sum = c*param.nu*l/2;
for (i = 0; i < l; i++){
alpha2[i] = alpha2[i + l] = Math.Min(sum, c);
sum -= alpha2[i];
linearTerm[i] = -prob.y[i];
y[i] = 1;
linearTerm[i + l] = prob.y[i];
y[i + l] = -1;
}
SvmSolverNu s = new SvmSolverNu();
s.Solve(2*l, new SvrQ(prob, param), linearTerm, y, alpha2, c, c, param.eps, si, param.shrinking);
Info("epsilon = " + (-si.r) + "\n");
for (i = 0; i < l; i++){
alpha[i] = alpha2[i] - alpha2[i + l];
}
}
private static void SolveOneClass(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double[] zeros = new double[l];
short[] ones = new short[l];
int i;
int n = (int) (param.nu*prob.Count); // # of alpha's at upper bound
for (i = 0; i < n; i++){
alpha[i] = 1;
}
if (n < prob.Count){
alpha[n] = param.nu*prob.Count - n;
}
for (i = n + 1; i < l; i++){
alpha[i] = 0;
}
for (i = 0; i < l; i++){
zeros[i] = 0;
ones[i] = 1;
}
SvmSolver s = new SvmSolver();
s.Solve(l, new OneClassQ(prob, param), zeros, ones, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
}
// Stratified cross validation
public static void SvmCrossValidation(SvmProblem prob, SvmParameter param, int nrFold, double[] target)
{
int i;
int[] foldStart = new int[nrFold + 1];
int l = prob.Count;
int[] perm = new int[l];
// stratified cv may not give leave-one-out rate
// Each class to l folds -> some folds may have zero elements
if ((param.svmType == SvmType.CSvc || param.svmType == SvmType.NuSvc) && nrFold < l){
int[] tmpNrClass = new int[1];
int[][] tmpLabel = new int[1][];
int[][] tmpStart = new int[1][];
int[][] tmpCount = new int[1][];
SvmGroupClasses(prob, tmpNrClass, tmpLabel, tmpStart, tmpCount, perm);
int nrClass = tmpNrClass[0];
int[] start = tmpStart[0];
int[] count = tmpCount[0];
// random shuffle and then data grouped by fold using the array perm
int[] foldCount = new int[nrFold];
int[] index = new int[l];
for (i = 0; i < l; i++){
index[i] = perm[i];
}
for (int c = 0; c < nrClass; c++){
for (i = 0; i < count[c]; i++){
int j = i + rand.Next(count[c] - i);
do{
int _ = index[start[c] + j];
index[start[c] + j] = index[start[c] + i];
index[start[c] + i] = _;
} while (false);
}
}
for (i = 0; i < nrFold; i++){
foldCount[i] = 0;
for (int 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 (int c = 0; c < nrClass; c++){
for (i = 0; i < nrFold; i++){
int begin = start[c] + i*count[c]/nrFold;
int end = start[c] + (i + 1)*count[c]/nrFold;
for (int 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++){
int j = i + rand.Next(l - i);
do{
int _ = perm[i];
perm[i] = perm[j];
perm[j] = _;
} while (false);
}
for (i = 0; i <= nrFold; i++){
foldStart[i] = i*l/nrFold;
}
}
for (i = 0; i < nrFold; i++){
int begin = foldStart[i];
int end = foldStart[i + 1];
int j;
int count = l - (end - begin);
SvmProblem subprob = new SvmProblem{x = new BaseVector[count], y = new float[count]};
int k = 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;
}
SvmModel submodel = SvmTrain(subprob, param);
if (param.probability && (param.svmType == SvmType.CSvc || param.svmType == SvmType.NuSvc)){
double[] probEstimates = new double[submodel.nrClass];
for (j = begin; j < end; j++){
target[perm[j]] = SvmPredictProbability(submodel, prob.x[perm[j]], probEstimates);
}
} else{
for (j = begin; j < end; j++){
target[perm[j]] = SvmPredict(submodel, prob.x[perm[j]]);
}
}
}
}
private static void SolveEpsilonSvr(SvmProblem prob, SvmParameter param, IList<double> alpha,
SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double[] alpha2 = new double[2*l];
double[] linearTerm = new double[2*l];
short[] y = new short[2*l];
int i;
for (i = 0; i < l; i++){
alpha2[i] = 0;
linearTerm[i] = param.p - prob.y[i];
y[i] = 1;
alpha2[i + l] = 0;
linearTerm[i + l] = param.p + prob.y[i];
y[i + l] = -1;
}
SvmSolver s = new SvmSolver();
s.Solve(2*l, new SvrQ(prob, param), linearTerm, y, alpha2, param.c, param.c, param.eps, si, param.shrinking);
double sumAlpha = 0;
for (i = 0; i < l; i++){
alpha[i] = alpha2[i] - alpha2[i + l];
sumAlpha += Math.Abs(alpha[i]);
}
Info("nu = " + sumAlpha/(param.c*l) + "\n");
}
// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
// perm, length l, must be allocated before calling this subroutine
private static void SvmGroupClasses(SvmProblem prob, IList<int> nrClassRet, IList<int[]> labelRet,
IList<int[]> startRet, IList<int[]> countRet, IList<int> perm)
{
int l = prob.Count;
int maxNrClass = 16;
int nrClass = 0;
int[] label = new int[maxNrClass];
int[] count = new int[maxNrClass];
int[] dataLabel = new int[l];
int i;
for (i = 0; i < l; i++){
int thisLabel = (int) (prob.y[i]);
int j;
for (j = 0; j < nrClass; j++){
if (thisLabel == label[j]){
++count[j];
break;
}
}
dataLabel[i] = j;
if (j == nrClass){
if (nrClass == maxNrClass){
maxNrClass *= 2;
int[] newData = new int[maxNrClass];
Array.Copy(label, 0, newData, 0, label.Length);
label = newData;
newData = new int[maxNrClass];
Array.Copy(count, 0, newData, 0, count.Length);
count = newData;
}
label[nrClass] = thisLabel;
count[nrClass] = 1;
++nrClass;
}
}
int[] start = new int[nrClass];
start[0] = 0;
for (i = 1; i < nrClass; i++){
start[i] = start[i - 1] + count[i - 1];
}
for (i = 0; i < l; i++){
perm[start[dataLabel[i]]] = i;
++start[dataLabel[i]];
}
start[0] = 0;
for (i = 1; i < nrClass; i++){
start[i] = start[i - 1] + count[i - 1];
}
nrClassRet[0] = nrClass;
labelRet[0] = label;
startRet[0] = start;
countRet[0] = count;
}
private static int[] RankBinary(SvmProblem prob, SvmParameter param, double redfactor)
{
int nfeatures = prob.x[0].Length;
int[] result = new int[nfeatures];
int[] survivingFeatures = ArrayUtils.ConsecutiveInts(nfeatures);
SvmProblem problem = prob.Copy();
int p = nfeatures - 1;
int[] indices = ArrayUtils.ConsecutiveInts(nfeatures);
while (survivingFeatures.Length > 0){
problem = problem.ExtractFeatures(survivingFeatures);
indices = ArrayUtils.SubArray(indices, survivingFeatures);
int nfeatures2 = survivingFeatures.Length;
double[] criteria =
ComputeRankingCriteria(SvmMain.SvmTrain(problem, param).ComputeBinaryClassifierWeights(nfeatures2));
int[] order = ArrayUtils.Order(criteria);
int numFeaturesToRemove = Math.Max((int) Math.Round(nfeatures2/redfactor), 1);
for (int i = 0; i < numFeaturesToRemove; ++i){
result[p--] = indices[order[i]];
}
survivingFeatures = ArrayUtils.SubArray(order, numFeaturesToRemove, nfeatures2);
}
return result;
}
