本文整理汇总了Java中com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial类的典型用法代码示例。如果您正苦于以下问题：Java KernelPolynomial类的具体用法？Java KernelPolynomial怎么用？Java KernelPolynomial使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
KernelPolynomial类属于com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel包，在下文中一共展示了KernelPolynomial类的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: createKernel
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
/**
* Creates a new kernel of the given type. The kernel type has to be one out of KERNEL_DOT,
* KERNEL_RADIAL, KERNEL_POLYNOMIAL, KERNEL_NEURAL, KERNEL_EPANECHNIKOV,
* KERNEL_GAUSSIAN_COMBINATION, or KERNEL_MULTIQUADRIC.
*/
public static Kernel createKernel(int kernelType) {
switch (kernelType) {
case KERNEL_DOT:
return new KernelDot();
case KERNEL_RADIAL:
return new KernelRadial();
case KERNEL_POLYNOMIAL:
return new KernelPolynomial();
case KERNEL_NEURAL:
return new KernelNeural();
case KERNEL_ANOVA:
return new KernelAnova();
case KERNEL_EPANECHNIKOV:
return new KernelEpanechnikov();
case KERNEL_GAUSSIAN_COMBINATION:
return new KernelGaussianCombination();
case KERNEL_MULTIQUADRIC:
return new KernelMultiquadric();
default:
return new KernelDot();
}
}
示例2: createKernel
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
/**
* Creates a new kernel of the given type. The kernel type has to be one out
* of KERNEL_DOT, KERNEL_RADIAL, KERNEL_POLYNOMIAL, KERNEL_NEURAL,
* KERNEL_EPANECHNIKOV, KERNEL_GAUSSIAN_COMBINATION, or KERNEL_MULTIQUADRIC.
*/
public static Kernel createKernel(int kernelType) {
switch (kernelType) {
case KERNEL_DOT:
return new KernelDot();
case KERNEL_RADIAL:
return new KernelRadial();
case KERNEL_POLYNOMIAL:
return new KernelPolynomial();
case KERNEL_NEURAL:
return new KernelNeural();
case KERNEL_ANOVA:
return new KernelAnova();
case KERNEL_EPANECHNIKOV:
return new KernelEpanechnikov();
case KERNEL_GAUSSIAN_COMBINATION:
return new KernelGaussianCombination();
case KERNEL_MULTIQUADRIC:
return new KernelMultiquadric();
default:
return new KernelDot();
}
}
示例3: createKernel
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
/**
* Creates a new kernel of the given type. The kernel type has to be one out of KERNEL_DOT,
* KERNEL_RADIAL, KERNEL_POLYNOMIAL, or KERNEL_NEURAL.
*/
public static Kernel createKernel(int kernelType) {
switch (kernelType) {
case KERNEL_RADIAL:
return new KernelRadial();
case KERNEL_POLYNOMIAL:
return new KernelPolynomial();
case KERNEL_NEURAL:
return new KernelNeural();
default:
return new KernelDot();
}
}
示例4: createKernel
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
/**
* Creates a new kernel of the given type. The kernel type has to be one out of KERNEL_DOT, KERNEL_RADIAL, KERNEL_POLYNOMIAL, or KERNEL_NEURAL.
*/
public static Kernel createKernel(int kernelType) {
switch (kernelType) {
case KERNEL_RADIAL:
return new KernelRadial();
case KERNEL_POLYNOMIAL:
return new KernelPolynomial();
case KERNEL_NEURAL:
return new KernelNeural();
default:
return new KernelDot();
}
}
示例5: learn
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
if (label.isNominal() && label.getMapping().size() != 2) {
throw new UserError(this, 114, getName(), label.getName());
}
// check if example set contains missing values, if so fail because
// this operator produces garbage with them
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this, Attributes.LABEL_NAME);
this.svmExamples = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(exampleSet,
label, getParameterAsBoolean(PARAMETER_SCALE));
// kernel
int cacheSize = getParameterAsInt(PARAMETER_KERNEL_CACHE);
int kernelType = getParameterAsInt(PARAMETER_KERNEL_TYPE);
kernel = createKernel(kernelType);
if (kernelType == KERNEL_RADIAL) {
((KernelRadial) kernel).setGamma(getParameterAsDouble(PARAMETER_KERNEL_GAMMA));
} else if (kernelType == KERNEL_POLYNOMIAL) {
((KernelPolynomial) kernel).setDegree(getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
} else if (kernelType == KERNEL_NEURAL) {
((KernelNeural) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_A),
getParameterAsDouble(PARAMETER_KERNEL_B));
} else if (kernelType == KERNEL_ANOVA) {
((KernelAnova) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_GAMMA),
getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
} else if (kernelType == KERNEL_EPANECHNIKOV) {
((KernelEpanechnikov) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
} else if (kernelType == KERNEL_GAUSSIAN_COMBINATION) {
((KernelGaussianCombination) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_SIGMA2), getParameterAsDouble(PARAMETER_KERNEL_SIGMA3));
} else if (kernelType == KERNEL_MULTIQUADRIC) {
((KernelMultiquadric) kernel).setParameters(getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_SHIFT));
}
kernel.init(svmExamples, cacheSize);
// SVM
svm = createSVM(label, kernel, svmExamples, exampleSet);
svm.init(kernel, svmExamples);
svm.train();
return createSVMModel(exampleSet, svmExamples, kernel, kernelType);
}
示例6: learn
import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //导入依赖的package包/类
public Model learn(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
if ((label.isNominal()) && (label.getMapping().size() != 2)) {
throw new UserError(this, 114, getName(), label.getName());
}
this.svmExamples = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(exampleSet, label, getParameterAsBoolean(PARAMETER_SCALE));
// kernel
int cacheSize = getParameterAsInt(PARAMETER_KERNEL_CACHE);
int kernelType = getParameterAsInt(PARAMETER_KERNEL_TYPE);
kernel = createKernel(kernelType);
if (kernelType == KERNEL_RADIAL)
((KernelRadial) kernel).setGamma(getParameterAsDouble(PARAMETER_KERNEL_GAMMA));
else if (kernelType == KERNEL_POLYNOMIAL)
((KernelPolynomial) kernel).setDegree(getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
else if (kernelType == KERNEL_NEURAL)
((KernelNeural) kernel).setParameters(
getParameterAsDouble(PARAMETER_KERNEL_A),
getParameterAsDouble(PARAMETER_KERNEL_B));
else if (kernelType == KERNEL_ANOVA)
((KernelAnova) kernel).setParameters(
getParameterAsDouble(PARAMETER_KERNEL_GAMMA),
getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
else if (kernelType == KERNEL_EPANECHNIKOV)
((KernelEpanechnikov) kernel).setParameters(
getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_DEGREE));
else if (kernelType == KERNEL_GAUSSIAN_COMBINATION)
((KernelGaussianCombination) kernel).setParameters(
getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_SIGMA2),
getParameterAsDouble(PARAMETER_KERNEL_SIGMA3));
else if (kernelType == KERNEL_MULTIQUADRIC)
((KernelMultiquadric) kernel).setParameters(
getParameterAsDouble(PARAMETER_KERNEL_SIGMA1),
getParameterAsDouble(PARAMETER_KERNEL_SHIFT));
kernel.init(svmExamples, cacheSize);
// SVM
svm = createSVM(label, kernel, svmExamples, exampleSet);
svm.init(kernel, svmExamples);
svm.train();
return createSVMModel(exampleSet, svmExamples, kernel, kernelType);
}