def convert_to_one_hot(integer_vector, dtype=None, max_labels=None, mode='stack', sparse=False):
 if (dtype is None):
  dtype = config.floatX
 if isinstance(integer_vector, list):
  integer_vector = np.array(integer_vector)
 assert (np.min(integer_vector) >= 0)
 assert (integer_vector.ndim <= 2)
 if (max_labels is None):
  max_labels = (max(integer_vector) + 1)
 return OneHotFormatter(max_labels, dtype=dtype).format(integer_vector, mode=mode, sparse=sparse)

def convert_to_one_hot ( integer_vector , dtype = None , max_labels = None , mode = 'stack' , sparse = False ) : if ( dtype is None ) : dtype = config . floatX if isinstance ( integer_vector , list ) : integer_vector = np . array ( integer_vector ) assert ( np . min ( integer_vector ) >= 0 ) assert ( integer_vector . ndim <= 2 ) if ( max_labels is None ) : max_labels = ( max ( integer_vector ) + 1 ) return OneHotFormatter ( max_labels , dtype = dtype ) . format ( integer_vector , mode = mode , sparse = sparse )
formats a given array of target labels into a one-hot vector .