本文整理了Java中libsvm.svm.svm_predict_values()
方法的一些代码示例,展示了svm.svm_predict_values()
的具体用法。这些代码示例主要来源于Github
/Stackoverflow
/Maven
等平台,是从一些精选项目中提取出来的代码,具有较强的参考意义,能在一定程度帮忙到你。svm.svm_predict_values()
方法的具体详情如下:
包路径:libsvm.svm
类名称:svm
方法名:svm_predict_values
暂无
代码示例来源:origin: tw.edu.ntu.csie/libsvm
public static double svm_predict(svm_model model, svm_node[] x)
{
int nr_class = model.nr_class;
double[] dec_values;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
dec_values = new double[1];
else
dec_values = new double[nr_class*(nr_class-1)/2];
double pred_result = svm_predict_values(model, x, dec_values);
return pred_result;
}
代码示例来源:origin: com.facebook.thirdparty/libsvm
public static double svm_predict(svm_model model, svm_node[] x)
{
int nr_class = model.nr_class;
double[] dec_values;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
dec_values = new double[1];
else
dec_values = new double[nr_class*(nr_class-1)/2];
double pred_result = svm_predict_values(model, x, dec_values);
return pred_result;
}
代码示例来源:origin: jzy3d/jzy3d-api
public static double svm_predict(svm_model model, svm_node[] x)
{
int nr_class = model.nr_class;
double[] dec_values;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
dec_values = new double[1];
else
dec_values = new double[nr_class*(nr_class-1)/2];
double pred_result = svm_predict_values(model, x, dec_values);
return pred_result;
}
代码示例来源:origin: com.facebook.thirdparty/libsvm
public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
{
if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA!=null && model.probB!=null)
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm_predict_values(model, x, dec_values);
double min_prob=1e-7;
double[][] pairwise_prob=new double[nr_class][nr_class];
int k=0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
pairwise_prob[j][i]=1-pairwise_prob[i][j];
k++;
}
multiclass_probability(nr_class,pairwise_prob,prob_estimates);
int prob_max_idx = 0;
for(i=1;i<nr_class;i++)
if(prob_estimates[i] > prob_estimates[prob_max_idx])
prob_max_idx = i;
return model.label[prob_max_idx];
}
else
return svm_predict(model, x);
}
代码示例来源:origin: jzy3d/jzy3d-api
public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
{
if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA!=null && model.probB!=null)
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm_predict_values(model, x, dec_values);
double min_prob=1e-7;
double[][] pairwise_prob=new double[nr_class][nr_class];
int k=0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
pairwise_prob[j][i]=1-pairwise_prob[i][j];
k++;
}
multiclass_probability(nr_class,pairwise_prob,prob_estimates);
int prob_max_idx = 0;
for(i=1;i<nr_class;i++)
if(prob_estimates[i] > prob_estimates[prob_max_idx])
prob_max_idx = i;
return model.label[prob_max_idx];
}
else
return svm_predict(model, x);
}
代码示例来源:origin: tw.edu.ntu.csie/libsvm
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm_predict_values(model, x, dec_values);
代码示例来源:origin: com.facebook.thirdparty/libsvm
svm_predict_values(submodel,prob.x[perm[j]],dec_value);
dec_values[perm[j]]=dec_value[0];
代码示例来源:origin: tw.edu.ntu.csie/libsvm
svm_predict_values(submodel,prob.x[perm[j]],dec_value);
dec_values[perm[j]]=dec_value[0];
代码示例来源:origin: jzy3d/jzy3d-api
svm_predict_values(submodel,prob.x[perm[j]],dec_value);
dec_values[perm[j]]=dec_value[0];
代码示例来源:origin: org.clulab/processors
final int nr_class = svm.svm_get_nr_class(model);
final double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm.svm_predict_values(model, x, dec_values);
代码示例来源:origin: org.maltparser/maltparser
final int nr_class = svm.svm_get_nr_class(model);
final double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm.svm_predict_values(model, x, dec_values);
代码示例来源:origin: jatecs/jatecs
public ClassificationResult classify(IIndex testIndex, int docID) {
ClassificationResult res = new ClassificationResult();
IIntIterator feats = testIndex.getContentDB().getDocumentFeatures(docID);
svm_node[] doc = new svm_node[testIndex.getContentDB().getDocumentFeaturesCount(docID)];
int i = 0;
int featID = 0;
while (feats.hasNext()) {
featID = feats.next();
svm_node node = new svm_node();
node.index = featID + 1;
node.value = testIndex.getWeightingDB().getDocumentFeatureWeight(docID, featID);
doc[i++] = node;
}
res.documentID = docID;
for (short catID = 0; catID < getCategoryCount(); catID++) {
svm_model model = _models[catID];
double[] values = new double[1];
double prediction = svm.svm_predict_values(model, doc, values);
res.categoryID.add(catID);
// If the classifier is completely un-confident (i.e. it has no positive examples for this category in the training set)
// the confidence value is set to the minimum negative value (negative confidence = negative decision)
if (values[0] == 0) {
prediction = -1;
values[0] = -Double.MIN_VALUE;
}
res.score.add(prediction * Math.abs(values[0]));
}
return res;
}
代码示例来源:origin: jdmp/java-data-mining-package
svm.svm_predict_values(model, x, prediction);
Matrix output = Matrix.Factory.linkToValue(prediction[0]);
return output;
代码示例来源:origin: elki-project/elki
x[d].value = vec.doubleValue(d);
svm.svm_predict_values(model, x, buf);
double score = -buf[0]; // / param.gamma; // Heuristic rescaling, sorry.
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