test_class.declarator: log = LoggerFactory.getLogger(MultivariateTDistributionTest.class)
test_class.modifier: private static final
test_class.original_string: private static final Logger log = LoggerFactory.getLogger(MultivariateTDistributionTest.class);
test_class.type: Logger
test_class.var_name: log
test_class.file: contrib/src/test/java/macrobase/analysis/stats/distribution/MultivariateTDistributionTest.java
test_class.identifier: MultivariateTDistributionTest
test_case.body: @Test
      public void valueTest() {
          double[][] means  = {
                  {5.8, 1.3},
                  {0.25, 10.96},
          };
          double[][][] covariances = {
                  {{0.18, 0.03}, {0.03, 0.27}},
                  {{0.55, 0.018}, {0.018, 0.21}},
          };
          double[] dofs = {
                  292.1,
                  10.7,
          };
  
          double[][][] points = {
                  {{5.8, 1.3}, {5.2, 2.3}},
                  {{0.25, 10.96}, {0, 10}, {1, 11}},
          };
  
          double[][] pdfs = {
                  {0.7287204, 0.02772672},
                  {0.4689636, 0.05175506, 0.2624979},
          };
  
          for (int s=0; s< means.length; s ++) {
              MultivariateTDistribution mvtd = new MultivariateTDistribution(new ArrayRealVector(means[s]), new BlockRealMatrix(covariances[s]), dofs[s]);
              for (int i =0; i< points[s].length; i++) {
                  assertEquals(pdfs[s][i], mvtd.density(new ArrayRealVector(points[s][i])), 1e-7);
              }
          }
      }
test_case.class_method_signature: MultivariateTDistributionTest.valueTest()
test_case.full_signature: @Test public void valueTest()
test_case.identifier: valueTest
test_case.invocations: assertEquals density
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void valueTest()
focal_class.declarator: log = LoggerFactory.getLogger(MultivariateTDistribution.class)
focal_class.modifier: private static final
focal_class.original_string: private static final Logger log = LoggerFactory.getLogger(MultivariateTDistribution.class);
focal_class.type: Logger
focal_class.var_name: log
focal_class.declarator: mean
focal_class.modifier: private
focal_class.original_string: private RealVector mean;
focal_class.type: RealVector
focal_class.var_name: mean
focal_class.declarator: precisionMatrix
focal_class.modifier: private
focal_class.original_string: private RealMatrix precisionMatrix;
focal_class.type: RealMatrix
focal_class.var_name: precisionMatrix
focal_class.declarator: dof
focal_class.modifier: private
focal_class.original_string: private double dof;
focal_class.type: double
focal_class.var_name: dof
focal_class.declarator: D
focal_class.modifier: private
focal_class.original_string: private int D;
focal_class.type: int
focal_class.var_name: D
focal_class.declarator: multiplier
focal_class.modifier: private
focal_class.original_string: private double multiplier;
focal_class.type: double
focal_class.var_name: multiplier
focal_class.file: contrib/src/main/java/macrobase/analysis/stats/distribution/MultivariateTDistribution.java
focal_class.identifier: MultivariateTDistribution
focal_class.interfaces: implements MultivariateDistribution
focal_class.class_method_signature: MultivariateTDistribution.MultivariateTDistribution(RealVector mean, RealMatrix covarianceMatrix, double degreesOfFreedom)
focal_class.full_signature: public  MultivariateTDistribution(RealVector mean, RealMatrix covarianceMatrix, double degreesOfFreedom)
focal_class.identifier: MultivariateTDistribution
focal_class.modifiers: public
focal_class.parameters: (RealVector mean, RealMatrix covarianceMatrix, double degreesOfFreedom)
focal_class.signature:  MultivariateTDistribution(RealVector mean, RealMatrix covarianceMatrix, double degreesOfFreedom)
focal_class.class_method_signature: MultivariateTDistribution.density(RealVector vector)
focal_class.full_signature: public double density(RealVector vector)
focal_class.identifier: density
focal_class.modifiers: public
focal_class.parameters: (RealVector vector)
focal_class.return: double
focal_class.signature: double density(RealVector vector)
focal_method.body: public double density(RealVector vector) {
          if (dof == 0) {
              return 0;
          }
          RealVector _diff = vector.subtract(mean);
          double prob = 1. / dof * _diff.dotProduct(precisionMatrix.operate(_diff));
          return multiplier * Math.pow(1 + prob, -(dof + D) / 2);
      }
focal_method.class_method_signature: MultivariateTDistribution.density(RealVector vector)
focal_method.full_signature: public double density(RealVector vector)
focal_method.identifier: density
focal_method.invocations: subtract dotProduct operate pow
focal_method.modifiers: public
focal_method.parameters: (RealVector vector)
focal_method.return: double
focal_method.signature: double density(RealVector vector)