Java KernelDot類代碼示例

本文整理匯總了Java中com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot類的典型用法代碼示例。如果您正苦於以下問題：Java KernelDot類的具體用法？Java KernelDot怎麽用？Java KernelDot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。

KernelDot類屬於com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel包，在下文中一共展示了KernelDot類的9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: learn

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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());
	}
	this.svmExamples = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(exampleSet,
			label, getParameterAsBoolean(PARAMETER_SCALE));

	// kernel
	int cacheSize = getParameterAsInt(PARAMETER_KERNEL_CACHE);
	Kernel kernel = new KernelDot();
	kernel.init(svmExamples, cacheSize);

	// SVM
	SVMInterface svm = createSVM(label, kernel, svmExamples, exampleSet);
	svm.init(kernel, svmExamples);
	svm.train();

	LinearMySVMModel model = new LinearMySVMModel(exampleSet, svmExamples, kernel, KERNEL_DOT);
	this.svmExamples = null;
	return model;
}

開發者ID:transwarpio，項目名稱:rapidminer，代碼行數:24，代碼來源:LinearMySVMLearner.java

示例2: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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3: learn

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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());
    }
    this.svmExamples = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(exampleSet, label, getParameterAsBoolean(PARAMETER_SCALE));

    // kernel
    int cacheSize = getParameterAsInt(PARAMETER_KERNEL_CACHE);
    Kernel kernel = new KernelDot();
    kernel.init(svmExamples, cacheSize);

    // SVM
    SVMInterface svm = createSVM(label, kernel, svmExamples, exampleSet);
    svm.init(kernel, svmExamples);
    svm.train();

    LinearMySVMModel model = new LinearMySVMModel(exampleSet, svmExamples, kernel, KERNEL_DOT);
    this.svmExamples = null;
    return model;
}

開發者ID:rapidminer，項目名稱:rapidminer-5，代碼行數:23，代碼來源:LinearMySVMLearner.java

示例4: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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5: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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6: createKernel

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //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7: performPrediction

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //導入依賴的package包/類
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException {
	if (kernel instanceof KernelDot) {
		if (weights != null) {
			Map<Integer, MeanVariance> meanVariances = model.getMeanVariances();
			for (Example example : exampleSet) {
				double prediction = getBias();
				int a = 0;
				for (Attribute attribute : exampleSet.getAttributes()) {
					double value = example.getValue(attribute);
					MeanVariance meanVariance = meanVariances.get(a);
					if (meanVariance != null) {
						if (meanVariance.getVariance() == 0.0d) {
							value = 0.0d;
						} else {
							value = (value - meanVariance.getMean()) / Math.sqrt(meanVariance.getVariance());
						}
					}
					prediction += weights[a] * value;
					a++;
				}
				setPrediction(example, prediction);
			}
			return exampleSet;
		}
	}

	// only if not simple dot hyperplane (see above)...
	com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples toPredict = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(
			exampleSet, exampleSet.getAttributes().getPredictedLabel(), model.getMeanVariances());

	SVMInterface svm = createSVM();
	svm.init(kernel, model);
	svm.predict(toPredict);

	// set predictions from toPredict
	Iterator<Example> reader = exampleSet.iterator();
	int k = 0;
	while (reader.hasNext()) {
		setPrediction(reader.next(), toPredict.get_y(k++));
	}
	return exampleSet;
}

開發者ID:transwarpio，項目名稱:rapidminer，代碼行數:44，代碼來源:AbstractMySVMModel.java

示例8: performPrediction

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //導入依賴的package包/類
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException {
	if (kernel instanceof KernelDot) {
		if (weights != null) {
			Map<Integer, MeanVariance> meanVariances = model.getMeanVariances();
			OperatorProgress progress = null;
			if (getShowProgress() && getOperator() != null && getOperator().getProgress() != null) {
				progress = getOperator().getProgress();
				progress.setTotal(exampleSet.size());
			}
			int progressCounter = 0;
			Attribute[] regularAttributes = exampleSet.getAttributes().createRegularAttributeArray();
			for (Example example : exampleSet) {
				double prediction = getBias();
				int a = 0;
				for (Attribute attribute : regularAttributes) {
					double value = example.getValue(attribute);
					MeanVariance meanVariance = meanVariances.get(a);
					if (meanVariance != null) {
						if (meanVariance.getVariance() == 0.0d) {
							value = 0.0d;
						} else {
							value = (value - meanVariance.getMean()) / Math.sqrt(meanVariance.getVariance());
						}
					}
					prediction += weights[a] * value;
					a++;
				}
				setPrediction(example, prediction);

				if (progress != null && ++progressCounter % OPERATOR_PROGRESS_STEPS == 0) {
					progress.setCompleted(progressCounter);
				}
			}
			return exampleSet;
		}
	}

	// only if not simple dot hyperplane (see above)...
	com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples toPredict = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(
			exampleSet, exampleSet.getAttributes().getPredictedLabel(), model.getMeanVariances());

	SVMInterface svm = createSVM();
	svm.init(kernel, model);
	svm.predict(toPredict);

	// set predictions from toPredict
	Iterator<Example> reader = exampleSet.iterator();
	int k = 0;
	while (reader.hasNext()) {
		setPrediction(reader.next(), toPredict.get_y(k++));
	}
	return exampleSet;
}

開發者ID:rapidminer，項目名稱:rapidminer-studio，代碼行數:55，代碼來源:AbstractMySVMModel.java

示例9: performPrediction

import com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot; //導入依賴的package包/類
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabelAttribute) throws OperatorException {
	if (kernel instanceof KernelDot) {
		if (weights != null) {
			Map<Integer, MeanVariance> meanVariances = model.getMeanVariances();
			for (Example example : exampleSet) {
				double prediction = getBias();
				int a = 0;
				for (Attribute attribute : exampleSet.getAttributes()) {
					double value = example.getValue(attribute);
					MeanVariance meanVariance = meanVariances.get(a);
					if (meanVariance != null) {
						if (meanVariance.getVariance() == 0.0d)
							value = 0.0d;
						else
							value = (value - meanVariance.getMean()) / Math.sqrt(meanVariance.getVariance());
					}
					prediction += weights[a] * value;
					a++;
				}
				setPrediction(example, prediction);
			}
			return exampleSet;
		}
	}
	
	// only if not simple dot hyperplane (see above)...
	com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples toPredict = new com.rapidminer.operator.learner.functions.kernel.jmysvm.examples.SVMExamples(exampleSet, exampleSet.getAttributes().getPredictedLabel(), model.getMeanVariances());

	SVMInterface svm = createSVM();
	svm.init(kernel, model);
	svm.predict(toPredict);

	// set predictions from toPredict
	Iterator<Example> reader = exampleSet.iterator();
	int k = 0;
	while (reader.hasNext()) {
		setPrediction(reader.next(), toPredict.get_y(k++));
	}
	return exampleSet;
}

開發者ID:rapidminer，項目名稱:rapidminer-5，代碼行數:42，代碼來源:AbstractMySVMModel.java

注：本文中的com.rapidminer.operator.learner.functions.kernel.jmysvm.kernel.KernelDot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。