weka.filters.unsupervised.attribute.Standardize Java Examples
The following examples show how to use
weka.filters.unsupervised.attribute.Standardize.
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Example #1
Source File: PrincipalComponents.java From tsml with GNU General Public License v3.0 | 5 votes |
/** * Fill the correlation matrix */ private void fillCorrelation() throws Exception { m_correlation = new double[m_numAttribs][m_numAttribs]; double [] att1 = new double [m_numInstances]; double [] att2 = new double [m_numInstances]; double corr; for (int i = 0; i < m_numAttribs; i++) { for (int j = 0; j < m_numAttribs; j++) { for (int k = 0; k < m_numInstances; k++) { att1[k] = m_trainInstances.instance(k).value(i); att2[k] = m_trainInstances.instance(k).value(j); } if (i == j) { m_correlation[i][j] = 1.0; // store the standard deviation m_stdDevs[i] = Math.sqrt(Utils.variance(att1)); } else { corr = Utils.correlation(att1,att2,m_numInstances); m_correlation[i][j] = corr; m_correlation[j][i] = corr; } } } // now standardize the input data m_standardizeFilter = new Standardize(); m_standardizeFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter); }
Example #2
Source File: DataSetUtilsTest.java From AILibs with GNU Affero General Public License v3.0 | 5 votes |
public void updateIntermediateInstancesTest() throws Exception { DataSet data = DataSetUtils.getDataSetByID(FASHION_MNIST_ID); data = DataSetUtils.getStratifiedSplit(data, new Random(42), 0.1).get(0); WEKAFilter filter = new WEKAFilter(new Standardize()); DataSet result = filter.applyFilter(data, false); System.out.println(result.getInstances().get(0).value(0)); System.out.println(result.getIntermediateInstances().get(0).getDouble(0)); System.out.println(result.getInstances().numAttributes()); System.out.println(result.getIntermediateInstances().get(0).shapeInfoToString()); Assert.assertEquals(result.getInstances().get(0).value(0), result.getIntermediateInstances().get(0).getDouble(0), 0.01); }