text: # -*- coding: utf-8 -*-
  # Spearmint
  #
  # Academic and Non-Commercial Research Use Software License and Terms
  # of Use
  #
  # Spearmint is a software package to perform Bayesian optimization
  # according to specific algorithms (the “Software”).  The Software is
  # designed to automatically run experiments (thus the code name
  # 'spearmint') in a manner that iteratively adjusts a number of
  # parameters so as to minimize some objective in as few runs as
  # possible.
  #
  # The Software was developed by Ryan P. Adams, Michael Gelbart, and
  # Jasper Snoek at Harvard University, Kevin Swersky at the
  # University of Toronto (“Toronto”), and Hugo Larochelle at the
  # Université de Sherbrooke (“Sherbrooke”), which assigned its rights
  # in the Software to Socpra Sciences et Génie
  # S.E.C. (“Socpra”). Pursuant to an inter-institutional agreement
  # between the parties, it is distributed for free academic and
  # non-commercial research use by the President and Fellows of Harvard
  # College (“Harvard”).
  #
  # Using the Software indicates your agreement to be bound by the terms
  # of this Software Use Agreement (“Agreement”). Absent your agreement
  # to the terms below, you (the “End User”) have no rights to hold or
  # use the Software whatsoever.
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  # 5.  FEEDBACK. In order to improve the Software, comments from End
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  # 7.  PUBLICATION & ATTRIBUTION. End User has the right to publish,
  # present, or share results from the use of the Software.  In
  # accordance with customary academic practice, End User will
  # acknowledge Harvard, Toronto and Sherbrooke/Socpra as the providers
  # of the Software and may cite the relevant reference(s) from the
  # following list of publications:
  #
  # Practical Bayesian Optimization of Machine Learning Algorithms
  # Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams
  # Neural Information Processing Systems, 2012
  #
  # Multi-Task Bayesian Optimization
  # Kevin Swersky, Jasper Snoek and Ryan Prescott Adams
  # Advances in Neural Information Processing Systems, 2013
  #
  # Input Warping for Bayesian Optimization of Non-stationary Functions
  # Jasper Snoek, Kevin Swersky, Richard Zemel and Ryan Prescott Adams
  # Preprint, arXiv:1402.0929, http://arxiv.org/abs/1402.0929, 2013
  #
  # Bayesian Optimization and Semiparametric Models with Applications to
  # Assistive Technology Jasper Snoek, PhD Thesis, University of
  # Toronto, 2013
  #
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  # THAT THE SOFTWARE WILL OPERATE WITHOUT ERROR OR INTERRUPTION.
  #
  # 9.  LIMITATIONS OF LIABILITY AND REMEDIES. USE OF THE SOFTWARE IS AT
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  # ITS EXCLUSIVE REMEDY IS TO STOP USING IT.  IN NO EVENT SHALL
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  # the laws of the Commonwealth of Massachusetts regardless of
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  # to enter into this License and Terms of Use on behalf of itself and
  # its Institution.
  
  from __future__ import division
  from __future__ import print_function
  from builtins import range
  from past.utils import old_div
  import numpy        as np
  import numpy.random as npr
  
  from spearmint.kernels import Matern52, Subset
  
  def test_grad():
      npr.seed(1)
  
      eps  = 1e-5
      N    = 10
      M    = 5
      D    = 5
      inds = [0,2,4]
  
      kernel = Subset(D, Matern52(len(inds)), inds)
  
      data1 = npr.randn(N,D)
      data2 = npr.randn(M,D)
  
      loss  = np.sum(kernel.cross_cov(data1, data2))
      dloss = kernel.cross_cov_grad_data(data1, data2).sum(0)
      
      dloss_est = np.zeros(dloss.shape)
      for i in range(M):
          for j in range(D):
              data2[i,j] += eps
              loss_1 = np.sum(kernel.cross_cov(data1, data2))
              data2[i,j] -= 2*eps
              loss_2 = np.sum(kernel.cross_cov(data1, data2))
              data2[i,j] += eps
              dloss_est[i,j] = (old_div((loss_1 - loss_2), (2*eps)))
  
      print('Subset kernel grad using indices %s:' % inds)
      print(dloss)
  
      assert np.linalg.norm(dloss - dloss_est) < 1e-6
  
  
  
  