Java ArrayUtils.concatenate方法代码示例

import org.openimaj.util.array.ArrayUtils; //导入方法依赖的package包/类
/**
 * 	Returns an svm_problem for the given dataset. This function will return
 * 	a new SVM problem and will also side-affect the gamma member of the
 * 	param argument.
 *
 * 	@param trainingCorpus The corpus
 * 	@param param The SVM parameters
 * 	@param featureExtractor The feature extractor to use
 * 	@param positiveClass The name of the positive class in the dataset
 * 	@param negativeClasses The names of the negative classes in the dataset
 *	@return A new SVM problem.
 */
private svm_problem getSVMProblem( final List<? extends Annotated<OBJECT, ANNOTATION>> data,
	final svm_parameter param, final FeatureExtractor<? extends FeatureVector, OBJECT> extractor  )
{
	// Get all the nodes for the features
	final svm_node[][] positiveNodes = this.computeFeature(
			data, this.classMap.get( SVMAnnotator.POSITIVE_CLASS ) );
	final svm_node[][] negativeNodes = this.computeFeature(
			data, this.classMap.get( SVMAnnotator.NEGATIVE_CLASS ) );

	// Work out how long the problem is
	final int nSamples = positiveNodes.length + negativeNodes.length;

	// The array that determines whether a sample is positive or negative.
	final double[] flagArray = new double[nSamples];
	ArrayUtils.fill( flagArray, SVMAnnotator.POSITIVE_CLASS, 0, positiveNodes.length );
	ArrayUtils.fill( flagArray, SVMAnnotator.NEGATIVE_CLASS, positiveNodes.length, negativeNodes.length );

	// Concatenate the samples to a single array
	final svm_node[][] sampleArray = ArrayUtils.concatenate(
			positiveNodes, negativeNodes );

	// Create the svm problem to solve
	final svm_problem prob = new svm_problem();

	// Setup the problem
	prob.l = nSamples;
	prob.x = sampleArray;
	prob.y = flagArray;
	param.gamma = 1.0 / SVMAnnotator.getMaxIndex( sampleArray );

	return prob;
}
 

import org.openimaj.util.array.ArrayUtils; //导入方法依赖的package包/类
/**
 * @return the extracted feature (containing all statistics) from the last call to {@link #process(ConnectedComponent)}.
 */
public double[] getFeatureVectorArray() {
	return ArrayUtils.concatenate(
			colmodel.mean,
			colmodel.median,
			colmodel.mode,
			colmodel.range,
			colmodel.variance
	);
}
 

import org.openimaj.util.array.ArrayUtils; //导入方法依赖的package包/类
/**
 * Construct a histogram by concatenating the given histograms
 * 
 * @param hs
 *            histograms to concatenate
 */
public Histogram(DoubleFV... hs) {
	final double[][] hists = new double[hs.length][];
	for (int i = 0; i < hs.length; i++) {
		hists[i] = hs[i].values;
	}

	this.values = ArrayUtils.concatenate(hists);
}
 

import org.openimaj.util.array.ArrayUtils; //导入方法依赖的package包/类
/**
 * Create a new histogram by concatenating this one with the given ones.
 * 
 * @param hs
 *            histograms to concatenate
 * @return new histogram that is the concatenation of the argument
 *         histograms
 */
public Histogram combine(Histogram... hs) {
	final int hsLength = hs == null ? 0 : hs.length;
	final double[][] hists = new double[1 + hsLength][];
	hists[0] = this.values;

	for (int i = 0; i < hsLength; i++) {
		hists[i + 1] = hs[i].values;
	}

	return new Histogram(ArrayUtils.concatenate(hists));
}
 

import org.openimaj.util.array.ArrayUtils; //导入方法依赖的package包/类
@Override
public String toString() {
	final String[][] exptinfo = formatAsTable(getExptInfoTable());
	final String[][] timeInfo = formatAsTable(getTimingTable());
	final String[][] ivInfo = formatAsTable(getIndependentVariablesTable());
	final String[][] dvInfo = formatAsTable(getDependentVariablesTable());
	final String[][] biblInfo = formatAsTable(getBibliographyTable());

	final String[][] data = ArrayUtils.concatenate(exptinfo, timeInfo, ivInfo, dvInfo, biblInfo);
	final ASCIITableHeader[] header = { new ASCIITableHeader("Experiment Context", ASCIITable.ALIGN_LEFT) };

	return ASCIITable.getInstance().getTable(header, data);
}