test_class.declarator: log = LoggerFactory.getLogger(DPGMMTest.class)
test_class.modifier: private static final
test_class.original_string: private static final Logger log = LoggerFactory.getLogger(DPGMMTest.class);
test_class.type: Logger
test_class.var_name: log
test_class.file: contrib/src/test/java/macrobase/analysis/stats/mixture/DPGMMTest.java
test_class.identifier: DPGMMTest
test_case.body: @Test
      /**
       * Tests Bayesian Dirichlet Process Gaussian Mixture on a three well separated clusters.
       */
      public void univariateToyBimodalTest() throws Exception {
          MacroBaseConf conf = new MacroBaseConf()
                  .set(MacroBaseConf.RANDOM_SEED, 4)
                  .set(MacroBaseConf.TRANSFORM_TYPE, "VARIATIONAL_DPMM")
                  .set(GMMConf.MAX_ITERATIONS_TO_CONVERGE, 15)
                  .set(GMMConf.DPM_TRUNCATING_PARAMETER, 8)
                  .set(GMMConf.DPM_CONCENTRATION_PARAMETER, 2)
                  .set(MacroBaseConf.DATA_LOADER_TYPE, "CSV_LOADER")
                  .set(MacroBaseConf.CSV_COMPRESSION, CSVIngester.Compression.UNCOMPRESSED)
                  .set(MacroBaseConf.CSV_INPUT_FILE, "src/test/resources/data/toy2gaussians.csv")
                  .set(MacroBaseConf.METRICS, "XX")
                  .set(MacroBaseConf.ATTRIBUTES, "");
          List<Datum> data = conf.constructIngester().getStream().drain();
          assertEquals(18, data.size());
  
          DPGMM variationalDPGM = new DPGMM(conf);
          variationalDPGM.train(data);
          log.debug("{}",  variationalDPGM.getClusterCenters(), variationalDPGM.getClusterProportions());
      }
test_case.class_method_signature: DPGMMTest.univariateToyBimodalTest()
test_case.full_signature: @Test /** * Tests Bayesian Dirichlet Process Gaussian Mixture on a three well separated clusters. */ public void univariateToyBimodalTest()
test_case.identifier: univariateToyBimodalTest
test_case.invocations: set set set set set set set set set set drain getStream constructIngester assertEquals size train debug getClusterCenters getClusterProportions
test_case.modifiers: @Test /** * Tests Bayesian Dirichlet Process Gaussian Mixture on a three well separated clusters. */ public
test_case.parameters: ()
test_case.return: void
test_case.signature: void univariateToyBimodalTest()
focal_class.declarator: log = LoggerFactory.getLogger(DPGMM.class)
focal_class.modifier: private static final
focal_class.original_string: private static final Logger log = LoggerFactory.getLogger(DPGMM.class);
focal_class.type: Logger
focal_class.var_name: log
focal_class.declarator: mixingComponents
focal_class.modifier: protected
focal_class.original_string: protected DPComponents mixingComponents;
focal_class.type: DPComponents
focal_class.var_name: mixingComponents
focal_class.declarator: T
focal_class.modifier: protected final
focal_class.original_string: protected final int T;
focal_class.type: int
focal_class.var_name: T
focal_class.file: contrib/src/main/java/macrobase/analysis/stats/mixture/DPGMM.java
focal_class.identifier: DPGMM
focal_class.class_method_signature: DPGMM.DPGMM(MacroBaseConf conf)
focal_class.full_signature: public  DPGMM(MacroBaseConf conf)
focal_class.identifier: DPGMM
focal_class.modifiers: public
focal_class.parameters: (MacroBaseConf conf)
focal_class.signature:  DPGMM(MacroBaseConf conf)
focal_class.class_method_signature: DPGMM.trainTest(List<Datum> trainData, List<Datum> testData)
focal_class.full_signature: public void trainTest(List<Datum> trainData, List<Datum> testData)
focal_class.identifier: trainTest
focal_class.modifiers: public
focal_class.parameters: (List<Datum> trainData, List<Datum> testData)
focal_class.return: void
focal_class.signature: void trainTest(List<Datum> trainData, List<Datum> testData)
focal_class.class_method_signature: DPGMM.getClusterProportions()
focal_class.full_signature: @Override public double[] getClusterProportions()
focal_class.identifier: getClusterProportions
focal_class.modifiers: @Override public
focal_class.parameters: ()
focal_class.return: double[]
focal_class.signature: double[] getClusterProportions()
focal_class.class_method_signature: DPGMM.getNormClusterContrib()
focal_class.full_signature: @Override protected double[] getNormClusterContrib()
focal_class.identifier: getNormClusterContrib
focal_class.modifiers: @Override protected
focal_class.parameters: ()
focal_class.return: double[]
focal_class.signature: double[] getNormClusterContrib()
focal_class.superclass: extends VarGMM
focal_method.body: @Override
      public double[] getClusterProportions() {
          return mixingComponents.getNormalizedClusterProportions();
      }
focal_method.class_method_signature: DPGMM.getClusterProportions()
focal_method.full_signature: @Override public double[] getClusterProportions()
focal_method.identifier: getClusterProportions
focal_method.invocations: getNormalizedClusterProportions
focal_method.modifiers: @Override public
focal_method.parameters: ()
focal_method.return: double[]
focal_method.signature: double[] getClusterProportions()