584 x 100 のデータ セットがあり、各データには 584 個の特徴ベクトル (合計 100 個のトレーニング ベクトル) があります。Java で Libsvm を実装しました。((1).trainX のサイズは 584 x 100、(2).biny はクラス 1 に +1、クラス 2 に -1 を持つ配列、(3). LinearSVMNormVector はモデルの結果の w (重みベクトル) です。 ）。以下は私のコードです -
// scale train data between 0 and 1
double[][] trainX_scale = new double[trainX.length][trainX[0].length];
for (int i = 0; i < trainX.length; i++) {
double min = Double.MAX_VALUE;
double max = Double.MIN_VALUE;
for (int inner = 0; inner < trainX[i].length; inner++) {
if (trainX[i][inner] < min)
min = trainX[i][inner];
if (trainX[i][inner] > max)
max = trainX[i][inner];
}
double difference = max - min;
for (int inner = 0; inner < trainX[i].length; inner++) {
trainX_scale[i][inner] = (trainX[i][inner] - min)/ difference;
}
}
// prepare the svm node
svm_node[][] SVM_node_Train = new svm_node[trainX[0].length][trainX.length];
for (int p = 0; p < trainX[0].length; p++) {
for (int q = 0; q < trainX.length; q++) {
SVM_node_Train[p][q] = new svm_node();
SVM_node_Train[p][q].index = q;
SVM_node_Train[p][q].value = trainX_scale[q][p];
}
}
double[] biny_SVM = new double[biny.length];// for svm compatible
for (int p = 0; p < biny.length; p++) {
biny_SVM[p] = biny[p];
}
svm_problem SVM_Prob = new svm_problem();
SVM_Prob.l = trainX[0].length;
SVM_Prob.x = SVM_node_Train;
SVM_Prob.y = biny_SVM;
svm_parameter SVM_Param = new svm_parameter();
SVM_Param.svm_type = 0;
SVM_Param.kernel_type = 2;
SVM_Param.cache_size = 100;
SVM_Param.eps = 0.0000001;
SVM_Param.C = 1.0;
SVM_Param.gamma = 0.5;
svm_model SVM_Model = new svm_model();
SVM_Model.param = SVM_Param;
SVM_Model.l = trainX[0].length;
SVM_Model.nr_class = 2;
SVM_Model.SV = SVM_node_Train;
//SVM_Model.label = biny;
// String check =svm.svm_check_parameter(SVM_Prob, SVM_Param); //
// System.out.println(check);
double[] target = new double[biny.length];// for svm compatible
Arrays.fill(target, 0.0);
svm.svm_cross_validation(SVM_Prob, SVM_Param, 2, target);
// train the classifier
svm_model test_model = svm.svm_train(SVM_Prob, SVM_Param);
/********** get the training results of libsvm **********/
//double[][] weights1 = test_model.sv_coef;
double Bias = test_model.rho[0];
double NumberOfSupportVectors = svm.svm_get_nr_sv(test_model);
double [] SupportVectorIDs = new int[NumberOfSupportVectors];
svm.svm_get_sv_indices(test_model, SupportVectorIDs);
svm_node[][] SV= test_model.SV;
double [][]SupportVectors=new double [SV.length][SV[0].length];
for(int ii=0;ii<SV.length;ii++){
for(int jj=0;jj<SV[0].length;jj++){
SupportVectors[ii][jj]=SV[ii][jj].value;
}
}
double[] SupportVectorWeights=test_model.sv_coef[0];
double[] LinearSVMNormVector = new double [SupportVectors[0].length];
for (int ii=0;ii<msvm[0].SupportVectors[0].length;ii++){
for (int jj=0;jj<SupportVectors.length;jj++){
LinearSVMNormVector[ii] += (SupportVectors[jj][ii] * SupportVectorWeights[jj]);
}
}
このコードでは、svm_train の結果は次のようになります。
optimization finished, #iter = 25
nu = 0.9999999995725399
obj = -24.999999987969172, rho = 1.1534070678518276E-10
nSV = 50, nBSV = 26
Total nSV = 50
*
optimization finished, #iter = 25
nu = 0.9999999998014489
obj = -24.999999994976864, rho = -4.654032538963752E-10
nSV = 50, nBSV = 28
Total nSV = 50
*
optimization finished, #iter = 50
nu = 0.9999999994269334
obj = -49.999999961945335, rho = -4.303699855872079E-10
nSV = 100, nBSV = 56
Total nSV = 100
サポート ベクターの数が 100 の場合、有界サポート ベクターの数は 56 にできますか? 私は少し混乱しています この分類子が機能しない理由を誰か教えてもらえますか?
ありがとう！