test_class.declarator: PRECISION = 1e-6
test_class.modifier: private static final
test_class.original_string: private static final double PRECISION = 1e-6;
test_class.type: double
test_class.var_name: PRECISION
test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/regressions/logistic/LogisticRegressionModelTest.java
test_class.identifier: LogisticRegressionModelTest
test_case.body: @Test(expected = CardinalityException.class)
      public void testPredictOnAnObservationWithWrongCardinality() {
          Vector weights = new DenseVector(new double[] {2.0, 3.0});
  
          LogisticRegressionModel mdl = new LogisticRegressionModel(weights, 1.0);
  
          Vector observation = new DenseVector(new double[] {1.0});
  
          mdl.predict(observation);
      }
test_case.class_method_signature: LogisticRegressionModelTest.testPredictOnAnObservationWithWrongCardinality()
test_case.full_signature: @Test(expected = CardinalityException.class) public void testPredictOnAnObservationWithWrongCardinality()
test_case.identifier: testPredictOnAnObservationWithWrongCardinality
test_case.invocations: predict
test_case.modifiers: @Test(expected = CardinalityException.class) public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testPredictOnAnObservationWithWrongCardinality()
focal_class.declarator: serialVersionUID = -133984600091550776L
focal_class.modifier: private static final
focal_class.original_string: private static final long serialVersionUID = -133984600091550776L;
focal_class.type: long
focal_class.var_name: serialVersionUID
focal_class.declarator: weights
focal_class.modifier: private
focal_class.original_string: private Vector weights;
focal_class.type: Vector
focal_class.var_name: weights
focal_class.declarator: intercept
focal_class.modifier: private
focal_class.original_string: private double intercept;
focal_class.type: double
focal_class.var_name: intercept
focal_class.declarator: isKeepingRawLabels = false
focal_class.modifier: private
focal_class.original_string: private boolean isKeepingRawLabels = false;
focal_class.type: boolean
focal_class.var_name: isKeepingRawLabels
focal_class.declarator: threshold = 0.5
focal_class.modifier: private
focal_class.original_string: private double threshold = 0.5;
focal_class.type: double
focal_class.var_name: threshold
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/regressions/logistic/LogisticRegressionModel.java
focal_class.identifier: LogisticRegressionModel
focal_class.interfaces: implements IgniteModel<Vector, Double>, Exportable<LogisticRegressionModel>, Serializable
focal_class.class_method_signature: LogisticRegressionModel.LogisticRegressionModel(Vector weights, double intercept)
focal_class.full_signature: public  LogisticRegressionModel(Vector weights, double intercept)
focal_class.identifier: LogisticRegressionModel
focal_class.modifiers: public
focal_class.parameters: (Vector weights, double intercept)
focal_class.signature:  LogisticRegressionModel(Vector weights, double intercept)
focal_class.class_method_signature: LogisticRegressionModel.withRawLabels(boolean isKeepingRawLabels)
focal_class.full_signature: public LogisticRegressionModel withRawLabels(boolean isKeepingRawLabels)
focal_class.identifier: withRawLabels
focal_class.modifiers: public
focal_class.parameters: (boolean isKeepingRawLabels)
focal_class.return: LogisticRegressionModel
focal_class.signature: LogisticRegressionModel withRawLabels(boolean isKeepingRawLabels)
focal_class.class_method_signature: LogisticRegressionModel.withThreshold(double threshold)
focal_class.full_signature: public LogisticRegressionModel withThreshold(double threshold)
focal_class.identifier: withThreshold
focal_class.modifiers: public
focal_class.parameters: (double threshold)
focal_class.return: LogisticRegressionModel
focal_class.signature: LogisticRegressionModel withThreshold(double threshold)
focal_class.class_method_signature: LogisticRegressionModel.withWeights(Vector weights)
focal_class.full_signature: public LogisticRegressionModel withWeights(Vector weights)
focal_class.identifier: withWeights
focal_class.modifiers: public
focal_class.parameters: (Vector weights)
focal_class.return: LogisticRegressionModel
focal_class.signature: LogisticRegressionModel withWeights(Vector weights)
focal_class.class_method_signature: LogisticRegressionModel.withIntercept(double intercept)
focal_class.full_signature: public LogisticRegressionModel withIntercept(double intercept)
focal_class.identifier: withIntercept
focal_class.modifiers: public
focal_class.parameters: (double intercept)
focal_class.return: LogisticRegressionModel
focal_class.signature: LogisticRegressionModel withIntercept(double intercept)
focal_class.class_method_signature: LogisticRegressionModel.isKeepingRawLabels()
focal_class.full_signature: public boolean isKeepingRawLabels()
focal_class.identifier: isKeepingRawLabels
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: boolean
focal_class.signature: boolean isKeepingRawLabels()
focal_class.class_method_signature: LogisticRegressionModel.threshold()
focal_class.full_signature: public double threshold()
focal_class.identifier: threshold
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: double
focal_class.signature: double threshold()
focal_class.class_method_signature: LogisticRegressionModel.weights()
focal_class.full_signature: public Vector weights()
focal_class.identifier: weights
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: Vector
focal_class.signature: Vector weights()
focal_class.class_method_signature: LogisticRegressionModel.intercept()
focal_class.full_signature: public double intercept()
focal_class.identifier: intercept
focal_class.modifiers: public
focal_class.parameters: ()
focal_class.return: double
focal_class.signature: double intercept()
focal_class.class_method_signature: LogisticRegressionModel.predict(Vector input)
focal_class.full_signature: @Override public Double predict(Vector input)
focal_class.identifier: predict
focal_class.modifiers: @Override public
focal_class.parameters: (Vector input)
focal_class.return: Double
focal_class.signature: Double predict(Vector input)
focal_class.class_method_signature: LogisticRegressionModel.sigmoid(double z)
focal_class.full_signature: private static double sigmoid(double z)
focal_class.identifier: sigmoid
focal_class.modifiers: private static
focal_class.parameters: (double z)
focal_class.return: double
focal_class.signature: double sigmoid(double z)
focal_class.class_method_signature: LogisticRegressionModel.saveModel(Exporter<LogisticRegressionModel, P> exporter, P path)
focal_class.full_signature: @Override public void saveModel(Exporter<LogisticRegressionModel, P> exporter, P path)
focal_class.identifier: saveModel
focal_class.modifiers: @Override public
focal_class.parameters: (Exporter<LogisticRegressionModel, P> exporter, P path)
focal_class.return: void
focal_class.signature: void saveModel(Exporter<LogisticRegressionModel, P> exporter, P path)
focal_class.class_method_signature: LogisticRegressionModel.equals(Object o)
focal_class.full_signature: @Override public boolean equals(Object o)
focal_class.identifier: equals
focal_class.modifiers: @Override public
focal_class.parameters: (Object o)
focal_class.return: boolean
focal_class.signature: boolean equals(Object o)
focal_class.class_method_signature: LogisticRegressionModel.hashCode()
focal_class.full_signature: @Override public int hashCode()
focal_class.identifier: hashCode
focal_class.modifiers: @Override public
focal_class.parameters: ()
focal_class.return: int
focal_class.signature: int hashCode()
focal_class.class_method_signature: LogisticRegressionModel.toString()
focal_class.full_signature: @Override public String toString()
focal_class.identifier: toString
focal_class.modifiers: @Override public
focal_class.parameters: ()
focal_class.return: String
focal_class.signature: String toString()
focal_class.class_method_signature: LogisticRegressionModel.toString(boolean pretty)
focal_class.full_signature: @Override public String toString(boolean pretty)
focal_class.identifier: toString
focal_class.modifiers: @Override public
focal_class.parameters: (boolean pretty)
focal_class.return: String
focal_class.signature: String toString(boolean pretty)
focal_method.body: @Override public Double predict(Vector input) {
  
          final double res = sigmoid(input.dot(weights) + intercept);
  
          if (isKeepingRawLabels)
              return res;
          else
              return res - threshold > 0 ? 1.0 : 0;
      }
focal_method.class_method_signature: LogisticRegressionModel.predict(Vector input)
focal_method.full_signature: @Override public Double predict(Vector input)
focal_method.identifier: predict
focal_method.invocations: sigmoid dot
focal_method.modifiers: @Override public
focal_method.parameters: (Vector input)
focal_method.return: Double
focal_method.signature: Double predict(Vector input)