/** * Gets a value of the field described by name from current structe * in struc array. * * @param name * @return */ public MLArray getField(String name) { return getField(name, currentIndex); } /**
/** * Gets a value of the field described by name from current struct * in struct array or null if the field doesn't exist. * * @param name name * @return value of the field */ public MLArray getField(String name) { return getField(name, currentIndex); } /**
/** * Gets a value of the field described by name from current struct * in struct array or null if the field doesn't exist. * * @param name * @return */ public MLArray getField(String name) { return getField(name, currentIndex); } /**
/** * Gets a value of the field described by name from (m,n)'th structe * in struc array. * * @param name * @param m * @param n * @return */ public MLArray getField(String name, int m, int n) { return getField(name, getIndex(m,n) ); } /**
/** * Gets a value of the field described by name from (m,n)'th struct * in struct array or null if the field doesn't exist. * * @param name name * @param m m * @param n n * @return value of the field */ public MLArray getField(String name, int m, int n) { return getField(name, getIndex(m,n) ); } /**
/** * Gets a value of the field described by name from (m,n)'th struct * in struct array or null if the field doesn't exist. * * @param name * @param m * @param n * @return */ public MLArray getField(String name, int m, int n) { return getField(name, getIndex(m,n) ); } /**
public String contentToString() { StringBuffer sb = new StringBuffer(); sb.append(name + " = \n"); if ( getM()*getN() == 1 ) { for ( String key : keys ) { sb.append("\t" + key + " : " + getField(key) + "\n" ); } } else { sb.append("\n"); sb.append(getM() + "x" + getN() ); sb.append(" struct array with fields: \n"); for ( String key : keys) { sb.append("\t" + key + "\n"); } } return sb.toString(); }
public String contentToString() { StringBuffer sb = new StringBuffer(); sb.append(name + " = \n"); if ( getM()*getN() == 1 ) { for ( String key : keys ) { sb.append("\t" + key + " : " + getField(key) + "\n" ); } } else { sb.append("\n"); sb.append(getM() + "x" + getN() ); sb.append(" struct array with fields: \n"); for ( String key : keys) { sb.append("\t" + key + "\n"); } } return sb.toString(); }
public String contentToString() { StringBuffer sb = new StringBuffer(); sb.append(name + " = \n"); if ( getM()*getN() == 1 ) { for ( String key : keys ) { sb.append("\t" + key + " : " + getField(key) + "\n" ); } } else { sb.append("\n"); sb.append(getM() + "x" + getN() ); sb.append(" struct array with fields: \n"); for ( String key : keys) { sb.append("\t" + key + "\n"); } } return sb.toString(); }
private static MixtureOfGaussians loadMoG() throws IOException { final File f = new File(GMM_MATLAB_FILE); final MatFileReader reader = new MatFileReader(f); final MLStructure codebook = (MLStructure) reader.getContent().get("codebook"); final MLSingle mean = (MLSingle) codebook.getField("mean"); final MLSingle variance = (MLSingle) codebook.getField("variance"); final MLSingle coef = (MLSingle) codebook.getField("coef"); final int n_gaussians = mean.getN(); final int n_dims = mean.getM(); final MultivariateGaussian[] ret = new MultivariateGaussian[n_gaussians]; final double[] weights = new double[n_gaussians]; for (int i = 0; i < n_gaussians; i++) { weights[i] = coef.get(i, 0); final DiagonalMultivariateGaussian d = new DiagonalMultivariateGaussian(n_dims); for (int j = 0; j < n_dims; j++) { d.mean.set(0, j, mean.get(j, i)); d.variance[j] = variance.get(j, i); } ret[i] = d; } return new MixtureOfGaussians(ret, weights); }
private static MixtureOfGaussians loadMoG() throws IOException { final File f = new File(GMM_MATLAB_FILE); final MatFileReader reader = new MatFileReader(f); final MLStructure codebook = (MLStructure) reader.getContent().get("codebook"); final MLSingle mean = (MLSingle) codebook.getField("mean"); final MLSingle variance = (MLSingle) codebook.getField("variance"); final MLSingle coef = (MLSingle) codebook.getField("coef"); final int n_gaussians = mean.getN(); final int n_dims = mean.getM(); final MultivariateGaussian[] ret = new MultivariateGaussian[n_gaussians]; final double[] weights = new double[n_gaussians]; for (int i = 0; i < n_gaussians; i++) { weights[i] = coef.get(i, 0); final DiagonalMultivariateGaussian d = new DiagonalMultivariateGaussian(n_dims); for (int j = 0; j < n_dims; j++) { d.mean.set(0, j, mean.get(j, i)); d.variance[j] = variance.get(j, i); } ret[i] = d; } return new MixtureOfGaussians(ret, weights); }
public static MixtureOfGaussians loadMoG(File f) throws IOException { final MatFileReader reader = new MatFileReader(f); final MLStructure codebook = (MLStructure) reader.getContent().get("codebook"); final MLSingle mean = (MLSingle) codebook.getField("mean"); final MLSingle variance = (MLSingle) codebook.getField("variance"); final MLSingle coef = (MLSingle) codebook.getField("coef"); final int n_gaussians = mean.getN(); final int n_dims = mean.getM(); final MultivariateGaussian[] ret = new MultivariateGaussian[n_gaussians]; final double[] weights = new double[n_gaussians]; for (int i = 0; i < n_gaussians; i++) { weights[i] = coef.get(i, 0); final DiagonalMultivariateGaussian d = new DiagonalMultivariateGaussian(n_dims); for (int j = 0; j < n_dims; j++) { d.mean.set(0, j, mean.get(j, i)); d.variance[j] = variance.get(j, i); } ret[i] = d; } return new MixtureOfGaussians(ret, weights); }
public static MixtureOfGaussians loadMoG(File f) throws IOException { final MatFileReader reader = new MatFileReader(f); final MLStructure codebook = (MLStructure) reader.getContent().get("codebook"); final MLSingle mean = (MLSingle) codebook.getField("mean"); final MLSingle variance = (MLSingle) codebook.getField("variance"); final MLSingle coef = (MLSingle) codebook.getField("coef"); final int n_gaussians = mean.getN(); final int n_dims = mean.getM(); final MultivariateGaussian[] ret = new MultivariateGaussian[n_gaussians]; final double[] weights = new double[n_gaussians]; for (int i = 0; i < n_gaussians; i++) { weights[i] = coef.get(i, 0); final DiagonalMultivariateGaussian d = new DiagonalMultivariateGaussian(n_dims); for (int j = 0; j < n_dims; j++) { d.mean.set(0, j, mean.get(j, i)); d.variance[j] = variance.get(j, i); } ret[i] = d; } return new MixtureOfGaussians(ret, weights); }
case MLArray.mxSTRUCT_CLASS: { MLStructure struct = cast(current, MLStructure.class); MLArray field = struct.getField(name, prevM > 0 ? prevM : 0, prevN > 0 ? prevN : 0); if (field == null) { throw new RuntimeException("no such field: " + name);
case MLArray.mxSTRUCT_CLASS: { MLStructure struct = cast(current, MLStructure.class); MLArray field = struct.getField(name, prevM > 0 ? prevM : 0, prevN > 0 ? prevN : 0); if (field == null) { throw new RuntimeException("no such field: " + name);
@Test public void testUTF() throws IOException { // read array form file MatFileReader mfr = new MatFileReader(getTestFile("utf.mat")); Map<String, MLArray> map = mfr.getContent(); MLStructure val = (MLStructure) map.get("val"); // extract each utf MLChar utf8 = (MLChar) val.getField("utf8"); MLChar utf16 = (MLChar) val.getField("utf16"); MLChar utf32 = (MLChar) val.getField("utf32"); // assert the content String expected = "\uD841\uDF0E"; Assert.assertEquals(expected, utf8.getString(0)); Assert.assertEquals(expected, utf16.getString(0)); Assert.assertEquals(expected, utf32.getString(0)); }
assertThat(((MLDouble) settings.getField("set")).get(0), equalTo(1.0)); assertThat((settings.getField("inputs")), instanceOf(MLDouble.class)); assertThat(((MLDouble) settings.getField("count")).get(0), equalTo(1000.0)); assertThat(((MLDouble) settings.getField("range")).get(0), equalTo(100.0)); assertThat(((MLDouble) settings.getField("except")).get(0), equalTo(0.0));