Java MLDouble类代码示例

import com.jmatio.types.MLDouble; //导入依赖的package包/类
synchronized public void doImportGTfromMatlab() {
    JFileChooser chooser = new JFileChooser();
    chooser.setDialogTitle("Choose ground truth file");
    chooser.setFileSelectionMode(JFileChooser.FILES_ONLY);
    chooser.setMultiSelectionEnabled(false);
    if (chooser.showOpenDialog(chip.getAeViewer().getFilterFrame()) == JFileChooser.APPROVE_OPTION) {
        try {
            vxGTframe = ((MLDouble) (new MatFileReader(chooser.getSelectedFile().getPath())).getMLArray("vxGT")).getArray();
            vyGTframe = ((MLDouble) (new MatFileReader(chooser.getSelectedFile().getPath())).getMLArray("vyGT")).getArray();
            tsGTframe = ((MLDouble) (new MatFileReader(chooser.getSelectedFile().getPath())).getMLArray("ts")).getArray();
            importedGTfromMatlab = true;
            log.info("Imported ground truth file");
        } catch (IOException ex) {
            log.log(Level.SEVERE, null, ex);
        }
    }
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
public SampledData getSampledData() {
    // get generators active power
    MLDouble pGen = (MLDouble) matFileContent.get("PGEN");
    double[][] generatorsActivePower = null;
    if (pGen != null) {
        generatorsActivePower = pGen.getArray();
    }
    // get loads active power
    MLDouble pLoad = (MLDouble) matFileContent.get("PLOAD");
    double[][] loadsActivePower = null;
    if (pLoad != null) {
        loadsActivePower = pLoad.getArray();
    }
    // get loads reactive power
    MLDouble qLoad = (MLDouble) matFileContent.get("QLOAD");
    double[][] loadsReactivePower = null;
    if (qLoad != null) {
        loadsReactivePower = qLoad.getArray();
    }

    SampledData sampledData = new SampledData(generatorsActivePower, loadsActivePower, loadsReactivePower);
    return sampledData;
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
public void writeHistoricalData(ForecastErrorsHistoricalData forecastErrorsHistoricalData) throws IOException {
        Objects.requireNonNull(forecastErrorsHistoricalData, "forecast errors historical data is null");
//        LOGGER.debug("Preparing stochastic variables mat data");
//        MLStructure stochasticVariables = stochasticVariablesAsMLStructure(forecastErrorsHistoricalData.getStochasticVariables());
        LOGGER.debug("Preparing injections mat data");
        MLCell injections = histoDataHeadersAsMLChar(forecastErrorsHistoricalData.getForecastsData().columnKeyList());
        LOGGER.debug("Preparing injections countries mat data");
        MLCell injectionsCountries =  injectionCountriesAsMLChar(forecastErrorsHistoricalData.getStochasticVariables());
        LOGGER.debug("Preparing forecasts mat data");
        MLDouble forecastsData = histoDataAsMLDouble("forec_filt", forecastErrorsHistoricalData.getForecastsData());
        LOGGER.debug("Preparing snapshots mat data");
        MLDouble snapshotsData = histoDataAsMLDouble("snap_filt", forecastErrorsHistoricalData.getSnapshotsData());
        LOGGER.debug("Saving mat data into " + matFile.toString());
        List<MLArray> mlarray = new ArrayList<>();
//        mlarray.add((MLArray) stochasticVariables );
        mlarray.add((MLArray) injections);
        mlarray.add((MLArray) forecastsData);
        mlarray.add((MLArray) snapshotsData);
        mlarray.add((MLArray) injectionsCountries);
        MatFileWriter writer = new MatFileWriter();
        writer.write(matFile.toFile(), mlarray);
    }
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
private MLDouble histoDataAsMLDouble(String name, ArrayTable<Integer, String, Float> histoData) {
    int rowsSize = histoData.rowKeySet().size();
    int colsSize = histoData.columnKeySet().size();
    MLDouble mlDouble = new MLDouble(name, new int[] {rowsSize, colsSize});
    int i = 0;
    for (Integer rowKey : histoData.rowKeyList()) {
        int j = 0;
        for (String colkey : histoData.columnKeyList()) {
            Float v = histoData.get(rowKey, colkey);
            mlDouble.set(new Double(v), i, j);
            j++;
        }
        i++;
    }
    return mlDouble;
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
public static MLDouble readMDoubleFromCSVFile(Path inFilePath, String mName, int nrows, int ncols, char delimiter) throws NumberFormatException, IOException {
    MLDouble mlDouble = new MLDouble(mName, new int[] {nrows, ncols});
    CsvReader cvsReader = new CsvReader(inFilePath.toString());
    cvsReader.setDelimiter(delimiter);
    int i = 0;
    while (cvsReader.readRecord()) {
        String[] rows = cvsReader.getValues();
        int j = 0;
        for (String col : rows) {
            mlDouble.set(new Double(col), i, j);
            j++;
        }
        i++;
    }
    return mlDouble;
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
public static Matrix fromFile(File file, Object... parameters) throws IOException {
	String key = null;
	if (parameters != null && parameters.length > 0 && parameters[0] instanceof String) {
		key = (String) parameters[0];
	}
	MatFileReader reader = new MatFileReader();
	Map<String, MLArray> map = reader.read(file);
	if (key == null) {
		key = map.keySet().iterator().next();
	}
	MLArray array = map.get(key);
	if (array == null) {
		throw new RuntimeException("matrix with label [" + key + "] was not found in .mat file");
	} else if (array instanceof MLDouble) {
		return new MLDenseDoubleMatrix((MLDouble) array);
	} else {
		throw new RuntimeException("This type is not yet supported: " + array.getClass());
	}
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
public void writeMatlabFile1() throws Exception
{
    //1. First create example arrays
    double[] src = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
    MLDouble mlDouble = new MLDouble( "double_arr", src, 2 );
    MLChar mlChar = new MLChar( "char_arr", "I am dummy" );

    //2. write arrays to file
    ArrayList<MLArray> list = new ArrayList<MLArray>();
    list.add( mlDouble );
    list.add( mlChar );

    MatFileIncrementalWriter writer = new MatFileIncrementalWriter("mat_file.mat");
    writer.write(mlDouble);
    writer.write(mlChar);
    writer.close();
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
/** Create ML Structure with data for a channel
 *  @param index Index of channel in model
 *  @param name Channel name
 *  @param times Time stamps
 *  @param values Values
 *  @param severities Severities
 *  @return {@link MLStructure}
 */
private MLStructure createMLStruct(final int index, final String name,
        final List<Instant> times,
        final List<Double> values,
        final List<AlarmSeverity> severities)
{
    final MLStructure struct = new MLStructure("channel" + index, new int[] { 1, 1 });
    final int N = values.size();
    final int[] dims = new int[] { N, 1 };
    final MLCell time = new MLCell(null, dims);
    final MLDouble value = new MLDouble(null, dims);
    final MLCell severity = new MLCell(null, dims);
    for (int i=0; i<N; ++i)
    {
        setCellText(time, i, TimestampHelper.format(times.get(i)));
        value.set(values.get(i), i);
        setCellText(severity, i, severities.get(i).toString());
    }
    struct.setField("name", new MLChar(null, name));
    struct.setField("time", time);
    struct.setField("value", value);
    struct.setField("severity", severity);
    return struct;
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
/**
 * Write a CSV wordIndex to a {@link MLCell} writen to a .mat data file
 * 
 * @param path
 * @throws IOException
 */
public static void writeToMatlab(String path) throws IOException {
	final Path wordMatPath = new Path(path + "/words/wordIndex.mat");
	final FileSystem fs = HadoopToolsUtil.getFileSystem(wordMatPath);
	final LinkedHashMap<String, IndependentPair<Long, Long>> wordIndex = readWordCountLines(path);
	final MLCell wordCell = new MLCell("words", new int[] { wordIndex.size(), 2 });

	System.out.println("... reading words");
	for (final Entry<String, IndependentPair<Long, Long>> ent : wordIndex.entrySet()) {
		final String word = ent.getKey();
		final int wordCellIndex = (int) (long) ent.getValue().secondObject();
		final long count = ent.getValue().firstObject();
		wordCell.set(new MLChar(null, word), wordCellIndex, 0);
		wordCell.set(new MLDouble(null, new double[][] { new double[] { count } }), wordCellIndex, 1);
	}
	final ArrayList<MLArray> list = new ArrayList<MLArray>();
	list.add(wordCell);
	new MatFileWriter(Channels.newChannel(fs.create(wordMatPath)), list);
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
/**
 * Write a CSV timeIndex to a {@link MLCell} writen to a .mat data file
 * @param path
 * @throws IOException
 */
public static void writeToMatlab(String path) throws IOException {
	Path timeMatPath = new Path(path + "/times/timeIndex.mat");
	FileSystem fs = HadoopToolsUtil.getFileSystem(timeMatPath);
	LinkedHashMap<Long, IndependentPair<Long, Long>> timeIndex = readTimeCountLines(path);
	MLCell timeCell = new MLCell("times",new int[]{timeIndex.size(),2});
	
	System.out.println("... reading times");
	for (Entry<Long, IndependentPair<Long, Long>> ent : timeIndex.entrySet()) {
		long time = (long)ent.getKey();
		int timeCellIndex = (int)(long)ent.getValue().secondObject();
		long count = ent.getValue().firstObject();
		timeCell.set(new MLDouble(null, new double[][]{new double[]{time}}), timeCellIndex,0);
		timeCell.set(new MLDouble(null, new double[][]{new double[]{count}}), timeCellIndex,1);
	}
	ArrayList<MLArray> list = new ArrayList<MLArray>();
	list.add(timeCell);
	new MatFileWriter(Channels.newChannel(fs.create(timeMatPath)),list );
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
private void prepareFolds() {

		final MLArray setfolds = this.content.get("set_fold");
		if(setfolds==null) return;
		if (setfolds.isCell()) {
			this.folds = new ArrayList<Fold>();
			final MLCell foldcells = (MLCell) setfolds;
			final int nfolds = foldcells.getM();
			System.out.println(String.format("Found %d folds", nfolds));
			for (int i = 0; i < nfolds; i++) {
				final MLDouble training = (MLDouble) foldcells.get(i, 0);
				final MLDouble test = (MLDouble) foldcells.get(i, 1);
				final MLDouble validation = (MLDouble) foldcells.get(i, 2);
				final Fold f = new Fold(toIntArray(training), toIntArray(test),
						toIntArray(validation));
				folds.add(f);
			}
		} else {
			throw new RuntimeException(
					"Can't find set_folds in expected format");
		}
	}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
private void parseAnnotations(FileObject annotationFile) throws IOException {
	if (!annotationFile.exists()) {
		return;
	}

	final MatFileReader reader = new MatFileReader(annotationFile.getContent().getInputStream());

	final MLDouble boxes = (MLDouble) reader.getMLArray("box_coord");
	this.bounds = new Rectangle(
			(float) (double) boxes.getReal(2) - 1,
			(float) (double) boxes.getReal(0) - 1,
			(float) (boxes.getReal(3) - boxes.getReal(2)) - 1,
			(float) (boxes.getReal(1) - boxes.getReal(0)) - 1);

	final double[][] contourData = ((MLDouble) reader.getMLArray("obj_contour")).getArray();
	this.contour = new Polygon();
	for (int i = 0; i < contourData[0].length; i++) {
		contour.points.add(
				new Point2dImpl((float) contourData[0][i] + bounds.x - 1,
						(float) contourData[1][i] + bounds.y - 1)
				);
	}
	contour.close();
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
@Test
public void testMLStructureFieldNames() throws IOException
{
    //test column-packed vector
    double[] src = new double[] { 1.3, 2.0, 3.0, 4.0, 5.0, 6.0 };
    
    //create 3x2 double matrix
    //[ 1.0 4.0 ;
    //  2.0 5.0 ;
    //  3.0 6.0 ]
    MLDouble mlDouble = new MLDouble( null, src, 3 );
    MLChar mlChar = new MLChar( null, "I am dummy" );
    
    
    MLStructure mlStruct = new MLStructure("str", new int[] {1,1} );
    mlStruct.setField("f1", mlDouble);
    mlStruct.setField("f2", mlChar);
    
    Collection<String> fieldNames = mlStruct.getFieldNames();
    
    assertEquals( 2, fieldNames.size() );
    assertTrue( fieldNames.contains("f1") );
    assertTrue( fieldNames.contains("f2") );
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
/**
 * Regression bug
 * 
 * @throws Exception
 */
@Test 
public void testDoubleFromMatlabCreatedFile() throws Exception
{
    //array name
    String name = "arr";
    //file name in which array will be stored
    String fileName = "src/test/resources/matnativedouble.mat";

    //test column-packed vector
    double[] src = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
    MLDouble mlDouble = new MLDouble( name, src, 3 );
    
    //read array form file
    MatFileReader mfr = new MatFileReader( fileName );
    MLArray mlArrayRetrived = mfr.getMLArray( name );
    
    //test if MLArray objects are equal
    assertEquals("Test if value red from file equals value stored", mlDouble, mlArrayRetrived);
}
 

import com.jmatio.types.MLDouble; //导入依赖的package包/类
/**
 * Regression bug.
 
 * <pre><code>
 * Matlab code:
 * >> arr = [1.1, 4.4; 2.2, 5.5; 3.3, 6.6];
 * >> save('matnativedouble2', arr);
 * </code></pre>
 * 
 * @throws IOException
 */
@Test 
public void testDoubleFromMatlabCreatedFile2() throws IOException
{
    //array name
    String name = "arr";
    //file name in which array will be stored
    String fileName = "src/test/resources/matnativedouble2.mat";

    //test column-packed vector
    double[] src = new double[] { 1.1, 2.2, 3.3, 4.4, 5.5, 6.6 };
    MLDouble mlDouble = new MLDouble( name, src, 3 );
    
    //read array form file
    MatFileReader mfr = new MatFileReader( fileName );
    MLArray mlArrayRetrived = mfr.getMLArray( name );
    
    //test if MLArray objects are equal
    assertEquals("Test if value red from file equals value stored", mlDouble, mlArrayRetrived);
}