original_docstring: 
          Create X and Y for Logistic Regression Classifier.
          self.dict_projections: A dictionary of all nodes embeddings, where keys==nodes and values==embeddings
          :param true_edges: A list of true edges.
          :param false_edges: A list of false edges.
          :return: X_true/X_false - The feature matrix for logistic regression classifier, of true/false edge.
          The i'th row is the norm score calculated for each edge.
                  Y_true_edge/Y_false_edge - The edges labels, [1,0] for true/ [0,1] for false.
                  Also the edge of the label is concatenate to the label.
          
docstring: Create X and Y for Logistic Regression Classifier.
  self.dict_projections: A dictionary of all nodes embeddings, where keys==nodes and values==embeddings
docstring_tokens: Create X and Y for Logistic Regression Classifier . self . dict_projections : A dictionary of all nodes embeddings where keys == nodes and values == embeddings
code: def calculate_classifier_value(self, true_edges, false_edges):
          """
          Create X and Y for Logistic Regression Classifier.
          self.dict_projections: A dictionary of all nodes embeddings, where keys==nodes and values==embeddings
          :param true_edges: A list of true edges.
          :param false_edges: A list of false edges.
          :return: X_true/X_false - The feature matrix for logistic regression classifier, of true/false edge.
          The i'th row is the norm score calculated for each edge.
                  Y_true_edge/Y_false_edge - The edges labels, [1,0] for true/ [0,1] for false.
                  Also the edge of the label is concatenate to the label.
          """
          X_true, X_false = np.zeros(shape=(len(true_edges), 1)), np.zeros(shape=(len(false_edges), 1))
          Y_true_edge, Y_false_edge = np.zeros(shape=(len(true_edges), 4)).astype(int).astype(str), \
                                      np.zeros(shape=(len(false_edges), 4)).astype(int).astype(str)
          for i, edge in enumerate(true_edges):
              norm = self.edge_distance(edge)
              X_true[i, 0] = norm
              Y_true_edge[i, 2] = edge[0]
              Y_true_edge[i, 3] = edge[1]
              Y_true_edge[i, 0] = str(1)
          for i, edge in enumerate(false_edges):
              norm = self.edge_distance(edge)
              X_false[i, 0] = norm
              Y_false_edge[i, 2] = edge[0]
              Y_false_edge[i, 3] = edge[1]
              Y_false_edge[i, 1] = str(1)
          return X_true, X_false, Y_true_edge, Y_false_edge
code_tokens: def calculate_classifier_value ( self , true_edges , false_edges ) : X_true , X_false = np . zeros ( shape = ( len ( true_edges ) , 1 ) ) , np . zeros ( shape = ( len ( false_edges ) , 1 ) ) Y_true_edge , Y_false_edge = np . zeros ( shape = ( len ( true_edges ) , 4 ) ) . astype ( int ) . astype ( str ) , np . zeros ( shape = ( len ( false_edges ) , 4 ) ) . astype ( int ) . astype ( str ) for i , edge in enumerate ( true_edges ) : norm = self . edge_distance ( edge ) X_true [ i , 0 ] = norm Y_true_edge [ i , 2 ] = edge [ 0 ] Y_true_edge [ i , 3 ] = edge [ 1 ] Y_true_edge [ i , 0 ] = str ( 1 ) for i , edge in enumerate ( false_edges ) : norm = self . edge_distance ( edge ) X_false [ i , 0 ] = norm Y_false_edge [ i , 2 ] = edge [ 0 ] Y_false_edge [ i , 3 ] = edge [ 1 ] Y_false_edge [ i , 1 ] = str ( 1 ) return X_true , X_false , Y_true_edge , Y_false_edge
short_docstring: Create X and Y for Logistic Regression Classifier.
short_docstring_tokens: Create X and Y for Logistic Regression Classifier .
comment: """
          Create X and Y for Logistic Regression Classifier.
          self.dict_projections: A dictionary of all nodes embeddings, where keys==nodes and values==embeddings
          :param true_edges: A list of true edges.
          :param false_edges: A list of false edges.
          :return: X_true/X_false - The feature matrix for logistic regression classifier, of true/false edge.
          The i'th row is the norm score calculated for each edge.
                  Y_true_edge/Y_false_edge - The edges labels, [1,0] for true/ [0,1] for false.
                  Also the edge of the label is concatenate to the label.
          """