class_docstr: Removes the a learnt mean in a column-wise manner in an array.
  
  MWE of a Scikit-learn transformer, to be used for unit-tests of ensemble
  classes.
  
  Parameters
  ----------
  copy : bool (default = True)
      Whether to copy X before transforming.
  
  Examples
  --------
  Asserting :class:`Scale` passes the Scikit-learn estimator test
  
  >>> from sklearn.utils.estimator_checks import check_estimator
  >>> from mlens.utils.dummy import Scale
  >>> check_estimator(Scale)
  
  Scaling elements
  
  >>> from numpy import arange
  >>> from mlens.utils.dummy import Scale
  >>> X = arange(6).reshape(3, 2)
  >>> X[:, 1] *= 2
  >>> print('X:')
  >>> print('%r' % X)
  >>> print('Scaled:')
  >>> S = Scale().fit_transform(X)
  >>> print('%r' % S)
  X:
  array([[ 0,  2],
         [ 2,  6],
         [ 4, 10]])
  Scaled:
  array([[-2., -4.],
         [ 0.,  0.],
         [ 2.,  4.]])
class_docstr_tok: ["Removes","the","a","learnt","mean","in","a","column-wise","manner","in","an","array",".","MWE","of","a","Scikit-learn","transformer",",","to","be","used","for","unit-tests","of","ensemble","classes",".","Parameters","--","--","--","--","--","copy",":","bool","(","default","=","True",")","Whether","to","copy","X","before","transforming",".","Examples","--","--","--","--","Asserting",":","class",":","`","Scale","`","passes","the","Scikit-learn","estimator","test",">",">",">","from","sklearn.utils.estimator_checks","import","check_estimator",">",">",">","from","mlens.utils.dummy","import","Scale",">",">",">","check_estimator","(","Scale",")","Scaling","elements",">",">",">","from","numpy","import","arange",">",">",">","from","mlens.utils.dummy","import","Scale",">",">",">","X","=","arange","(","6",")",".reshape","(","3",",","2",")",">",">",">","X","[",":",",","1","]","*","=","2",">",">",">","print","(","'","X",":","'",")",">",">",">","print","(","'","%","r","'","%","X",")",">",">",">","print","(","'Scaled",":","'",")",">",">",">","S","=","Scale","(",")",".fit_transform","(","X",")",">",">",">","print","(","'","%","r","'","%","S",")","X",":","array","(","[","[","0",",","2","]",",","[","2",",","6","]",",","[","4",",","10","]","]",")","Scaled",":","array","(","[","[","-2.",",","-4",".","]",",","[","0.",",","0",".","]",",","[","2.",",","4",".","]","]",")"]
docstr: Estimate mean.
  
  Parameters
  ----------
  X : array-like
      training data to fit transformer on.
  
  y : array-like or None
      pass through for pipeline.
docstr_tok: ["Estimate","mean",".","Parameters","--","--","--","--","--","X",":","array-like","training","data","to","fit","transformer","on",".","y",":","array-like","or","None","pass","through","for","pipeline","."]
code: def fit(self, X, y=None):
      """Estimate mean.
  
      Parameters
      ----------
      X : array-like
          training data to fit transformer on.
  
      y : array-like or None
          pass through for pipeline.
      """
      X = check_array(X, accept_sparse=False)
      self.__is_fitted__ = True
      self.mean_ = X.mean(axis=0)
      return self
  
file_path: mlens/mlens/utils/dummy.py
imports: from __future__ import division, print_function
  import numpy
  from .exceptions import NotFittedError
  from ..externals.sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 2 for calling the Scale obj's underlying member method code with example usage: obj.fit(X, y) and returns: self