class_docstr: Initialize weights as sparse matrix.
  
  Parameters
  ----------
  sparsity : float
      Exact fraction of non-zero values per column. Larger values give less
      sparsity.
  std : float
      Non-zero weights are sampled from N(0, std).
class_docstr_tok: ["Initialize","weights","as","sparse","matrix",".","Parameters","--","--","--","--","--","sparsity",":","float","Exact","fraction","of","non-zero","values","per","column",".","Larger","values","give","less","sparsity",".","std",":","float","Non-zero","weights","are","sampled","from","N","(","0",",","std",")","."]
code: def __init__(self, sparsity=0.1, std=0.01):
      self.sparsity = sparsity
      self.std = std
  
imports: import numpy
  from .utils import floatX
  from .random import get_rng
label_desc: Use image node_id 1 to create a new Sparse object from inherited base classes: Initializer with example: obj = Sparse(sparsity, std)