Java KernelPolynomial類代碼示例

本文整理匯總了Java中com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial類的典型用法代碼示例。如果您正苦於以下問題：Java KernelPolynomial類的具體用法？Java KernelPolynomial怎麽用？Java KernelPolynomial使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。

KernelPolynomial類屬於com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel包，在下文中一共展示了KernelPolynomial類的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
/**
 * 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();
	}
}

開發者ID:transwarpio，項目名稱:rapidminer，代碼行數:28，代碼來源:AbstractMySVMLearner.java

示例2: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
/**
 * 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();
	}
}

開發者ID:rapidminer，項目名稱:rapidminer-5，代碼行數:28，代碼來源:AbstractMySVMLearner.java

示例3: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
/**
 * 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();
	}
}

開發者ID:transwarpio，項目名稱:rapidminer，代碼行數:17，代碼來源:SVClustering.java

示例4: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
/**
 * 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();
	}
}

開發者ID:rapidminer，項目名稱:rapidminer-5，代碼行數:16，代碼來源:SVClustering.java

示例5: learn

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
@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);
}

開發者ID:transwarpio，項目名稱:rapidminer，代碼行數:48，代碼來源:AbstractMySVMLearner.java

示例6: learn

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial; //導入依賴的package包/類
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);
}

開發者ID:rapidminer，項目名稱:rapidminer-5，代碼行數:46，代碼來源:AbstractMySVMLearner.java

注：本文中的com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelPolynomial類示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。