new_contents: # -*- coding: utf-8 -*-
  # Copyright (C) 2008, Luís Pedro Coelho <lpc@cmu.edu>
  # 
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  #  of this software and associated documentation files (the "Software"), to deal
  #  in the Software without restriction, including without limitation the rights
  #  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  #  copies of the Software, and to permit persons to whom the Software is
  #  furnished to do so, subject to the following conditions:
  # 
  # The above copyright notice and this permission notice shall be included in
  #  all copies or substantial portions of the Software.
  # 
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  #  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  #  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  #  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  #  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  #  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  #  THE SOFTWARE.
  
  from __future__ import division
  import numpy
  
  import milk.supervised.normalise
  
  def test_interval_normalise():
      I=milk.supervised.normalise.interval_normalise()
      numpy.random.seed(1234)
      features=numpy.random.rand(20,100)
      L=numpy.zeros(100)
      I.train(features,L)
      assert numpy.all(numpy.abs( ( I(features).max(0)-I(features).min(0) ) - 2) < 1e-4)
  
  def test_zscore_normalise():
      I=milk.supervised.normalise.zscore_normalise()
      numpy.random.seed(1234)
      features=numpy.random.rand(20,100)
      L=numpy.zeros(100)
      I.train(features,L)
      assert numpy.all( I(features).mean(0)**2 < 1e-7 )
      assert numpy.all( I(features).std(0) - 1 < 1e-3 )
  
  # vim: set ts=4 sts=4 sw=4 expandtab smartindent:
  
subject: Test normalisation functions