parameters: (X, y, sample_weight, random_state=None)
return_statement: return dataset, intercept_decay
docstring: Create ``Dataset`` abstraction for sparse and dense inputs.
  
      This also returns the ``intercept_decay`` which is different
      for sparse datasets.
  
      Parameters
      ----------
      X : array_like, shape (n_samples, n_features)
          Training data
  
      y : array_like, shape (n_samples, )
          Target values.
  
      sample_weight : numpy array of shape (n_samples,)
          The weight of each sample
  
      random_state : int, RandomState instance or None (default)
          Determines random number generation for dataset shuffling and noise.
          Pass an int for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      Returns
      -------
      dataset
          The ``Dataset`` abstraction
      intercept_decay
          The intercept decay
docstring_summary: Create ``Dataset`` abstraction for sparse and dense inputs.
docstring_tokens: Create Dataset abstraction for sparse and dense inputs .
function: def make_dataset(X, y, sample_weight, random_state=None):
      """Create ``Dataset`` abstraction for sparse and dense inputs.
  
      This also returns the ``intercept_decay`` which is different
      for sparse datasets.
  
      Parameters
      ----------
      X : array_like, shape (n_samples, n_features)
          Training data
  
      y : array_like, shape (n_samples, )
          Target values.
  
      sample_weight : numpy array of shape (n_samples,)
          The weight of each sample
  
      random_state : int, RandomState instance or None (default)
          Determines random number generation for dataset shuffling and noise.
          Pass an int for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      Returns
      -------
      dataset
          The ``Dataset`` abstraction
      intercept_decay
          The intercept decay
      """
  
      rng = check_random_state(random_state)
      # seed should never be 0 in SequentialDataset64
      seed = rng.randint(1, np.iinfo(np.int32).max)
  
      if X.dtype == np.float32:
          CSRData = CSRDataset32
          ArrayData = ArrayDataset32
      else:
          CSRData = CSRDataset64
          ArrayData = ArrayDataset64
  
      if sp.issparse(X):
          dataset = CSRData(X.data, X.indptr, X.indices, y, sample_weight,
                            seed=seed)
          intercept_decay = SPARSE_INTERCEPT_DECAY
      else:
          X = np.ascontiguousarray(X)
          dataset = ArrayData(X, y, sample_weight, seed=seed)
          intercept_decay = 1.0
  
      return dataset, intercept_decay
function_tokens: def make_dataset ( X , y , sample_weight , random_state = None ) : rng = check_random_state ( random_state ) # seed should never be 0 in SequentialDataset64 seed = rng . randint ( 1 , np . iinfo ( np . int32 ) . max ) if X . dtype == np . float32 : CSRData = CSRDataset32 ArrayData = ArrayDataset32 else : CSRData = CSRDataset64 ArrayData = ArrayDataset64 if sp . issparse ( X ) : dataset = CSRData ( X . data , X . indptr , X . indices , y , sample_weight , seed = seed ) intercept_decay = SPARSE_INTERCEPT_DECAY else : X = np . ascontiguousarray ( X ) dataset = ArrayData ( X , y , sample_weight , seed = seed ) intercept_decay = 1.0 return dataset , intercept_decay