text: # -*- coding: utf-8 -*-
  # Spearmint
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  # Spearmint is a software package to perform Bayesian optimization
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  # Practical Bayesian Optimization of Machine Learning Algorithms
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  # Kevin Swersky, Jasper Snoek and Ryan Prescott Adams
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  # Preprint, arXiv:1402.0929, http://arxiv.org/abs/1402.0929, 2013
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  # Assistive Technology Jasper Snoek, PhD Thesis, University of
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  from __future__ import print_function
  from builtins import str
  from builtins import range
  import sys
  import numpy        as np
  import numpy.random as npr
  
  from .mcmc             import slice_sample
  # from .mcmc             import slice_sample_simple as slice_sample
  from .abstract_sampler import AbstractSampler
  from ..utils           import param as hyperparameter_utils
  
  
  class SliceSampler(AbstractSampler):
      """generate samples from a model using slice sampling
      
      Parameters
      ----------
      *params_to_sample : args of type Params
          The parameters that we are to be sampled.
      **sampler_options
      
      Attributes
      ----------
      params : list of Params objects
          The atribute `value` of each element in the list is updated
          upon calling `self.sample()`.  
      """
      def logprob(self, x, model):
          """compute the log probability of observations x
          
          This includes the model likelihood as well as any prior
          probability of the parameters
          
          Returns
          -------
          lp : float
              the log probability
          """
          # set values of the parameers in self.params to be x
          hyperparameter_utils.set_params_from_array(self.params, x)
          
          lp = 0.0
          # sum the log probabilities of the parameter priors
          for param in self.params:
              lp += param.prior_logprob()
  
              if np.isnan(lp): # Positive infinity should be ok, right?
                  print('Param diagnostics:')
                  param.print_diagnostics()
                  print('Prior logprob: %f' % param.prior_logprob())
                  raise Exception("Prior returned %f logprob" % lp)
  
          if not np.isfinite(lp):
              return lp
          
          # include the log probability from the model
          lp += model.log_likelihood()
  
          if np.isnan(lp):
              raise Exception("Likelihood returned %f logprob" % lp)
  
          return lp
  
      def sample(self, model):
          """generate a new sample of parameters for the model
          
          Notes
          -----
          The parameters are stored as self.params which is a list of Params objects.  
          The values of the parameters are updated on each call.  Pesumably the value of 
          the parameter affects the model (this is not required, but it would be a bit 
          pointless othewise) 
          
          """
          # turn self.params into a 1d numpy array
          params_array = hyperparameter_utils.params_to_array(self.params)
          for i in range(self.thinning + 1):
              # get a new value for the parameter array via slice sampling
              params_array, current_ll = slice_sample(params_array, self.logprob, model, **self.sampler_options)
              hyperparameter_utils.set_params_from_array(self.params, params_array) # Can this be untabbed safely?
          self.current_ll = current_ll # for diagnostics
  
  
  if __name__ == '__main__':
  
      sys.path.append('..')
      from utils import priors
  
      import matplotlib.pyplot as plt
  
  
      n = 10000
  
      # Test on 1D Gaussian
      x_samples = np.zeros(n)
      x = np.zeros(1)
  
      gsn = priors.Gaussian(mu = -1, sigma = 4)
  
      for i in range(n):
          if i % 1000 == 0:
              print('Sample %d/%d' % (i,n))
  
          x, cur_ll = slice_sample(x, gsn. logprob)
          x_samples[i] = x.copy()
  
      print('1D Gaussian actual mean: %f, mean of samples: %f' % (-1, np.mean(x_samples)))
      print('1D Gaussian actual sigma: %f, std of samples: %f' % (4, np.std(x_samples)))
  
  
      plt.figure(1)
      plt.clf()
      plt.hist(x_samples, 40)
      plt.savefig('slice_sampler_test.pdf')
  
      # Test on 2D Gaussian
      mu = np.array([-2, 5])
      a = npr.rand(2,2)
      cov = np.dot(a,a.T)
  
      mvn = priors.MultivariateNormal(mu = mu, cov = cov)
      x_samples = np.zeros((2,n))
      x = np.zeros(2)
  
      for i in range(n):
          if i % 1000 == 0:
              print('Sample %d/%d' % (i,n))
  
          x, cur_ll = slice_sample(x, mvn.logprob)
          x_samples[:,i] = x.copy()
  
      mu_samp = np.mean(x_samples,axis=1)
      print('2D Gaussian:')
      print('Actual mean:     [%f,%f]' % (mu[0], mu[1]))
      print('Mean of samples: [%f,%f]' % (mu_samp[0], mu_samp[1]))
      print('Actual Cov:')
      print(str(cov))
      print('Cov of samples')
      print(str(np.cov(x_samples)))
  
      # plt.figure(1)
      # plt.clf()
      # plt.hist(x_samples, 40)
      # plt.savefig('slice_sampler_test.pdf')
  
  
  