parameters: (self, X)
return_statement: return logits
docstring: Predict confidence scores for samples.
          The confidence score for a sample is the signed distance of that
          sample to the hyperplane.
  
          Args:
              X : array_like or sparse matrix, shape (n_samples, n_features)
                  Samples.
  
          Returns:
              array, shape=(n_samples, n_classes): Confidence scores per (sample, class) combination.
docstring_summary: Predict confidence scores for samples.
          The confidence score for a sample is the signed distance of that
          sample to the hyperplane.
docstring_tokens: Predict confidence scores for samples . The confidence score for a sample is the signed distance of that sample to the hyperplane .
function: def decision_function(self, X):
          """
          Predict confidence scores for samples.
          The confidence score for a sample is the signed distance of that
          sample to the hyperplane.
  
          Args:
              X : array_like or sparse matrix, shape (n_samples, n_features)
                  Samples.
  
          Returns:
              array, shape=(n_samples, n_classes): Confidence scores per (sample, class) combination.
          """
  
          logits = logit(VW.predict(self, X=X))
          if logits.shape[1] == 2:
              logits = logits[:, 1]
          return logits
function_tokens: def decision_function ( self , X ) : logits = logit ( VW . predict ( self , X = X ) ) if logits . shape [ 1 ] == 2 : logits = logits [ : , 1 ] return logits