old_contents: from kernel_exp_family.estimators.lite.gaussian import KernelExpLiteGaussianAdaptive
  from kernel_exp_family.estimators.parameter_search_bo import plot_bayesopt_model_1d
  from kernel_exp_family.examples.tools import visualise_fit_2d
  import matplotlib.pyplot as plt
  import numpy as np
  
  
  if __name__ == '__main__':
      """
      This simple demo demonstrates how to automatically tune parameters for the lite
      estimator, without user interaction.
      Note that this procedure is "hot-started", i.e. the second fit call uses the
      previously learned parameters to start learning parameters from.
      """
      N = 200
      D = 2
      
      # fit model to samples from a standard Gaussian
      X = np.random.randn(N, D)
      
      # fully automatic parameter tuning in every fit call
      est = KernelExpLiteGaussianAdaptive(sigma=1., lmbda=.001, D=D, N=N,
                                          # these paramters are all optional, to controll Bayesian opt.
                                          n_initial=3, n_iter=3, minimum_size_learning=100,
                                          # these depend on how much data changes between the "fit" calls
                                          n_initial_relearn=3, n_iter_relearn=3,
                                          # this can be used to adjust search spaces or include more parameters
                                          # by default, only sigma is optimised
                                          param_bounds={'sigma': [-3, 3]}
                                          )
      
      # automatically sets parameters
      est.fit(X)
      
      # only for illustration purpose
      plt.figure()
      plot_bayesopt_model_1d(est.bo)
      plt.title("Original fit")
      
      visualise_fit_2d(est, X)
      plt.suptitle("Original fit")
      
      # now change data, with different length scale
      X = np.random.randn(N, D) * .1
      
      # re-learns parameters, but starts from previous ones
      est.fit(X)
      
      visualise_fit_2d(est, X)
      plt.suptitle("New fit")
      
      # only for illustration purpose
      plt.figure()
      plot_bayesopt_model_1d(est.bo)
      plt.title("New fit")
      
      
      plt.show()
  
new_contents: from kernel_exp_family.estimators.lite.gaussian import KernelExpLiteGaussianAdaptive
  from kernel_exp_family.estimators.parameter_search_bo import plot_bayesopt_model_1d
  from kernel_exp_family.examples.tools import visualise_fit_2d
  import matplotlib.pyplot as plt
  import numpy as np
  
  
  if __name__ == '__main__':
      """
      This simple demo demonstrates how to automatically tune parameters for the lite
      estimator, without user interaction.
      Note that this procedure is "hot-started", i.e. the second fit call uses the
      previously learned parameters to start learning parameters from.
      """
      N = 200
      D = 2
      
      # fit model to samples from a standard Gaussian
      X = np.random.randn(N, D)
      
      # fully automatic parameter tuning in every fit call
      est = KernelExpLiteGaussianAdaptive(sigma=1., lmbda=.001, D=D, N=N,
                                          # these paramters are all optional, to controll Bayesian opt.
                                          num_initial_evaluations=3, n_iter=3, minimum_size_learning=100,
                                          # these depend on how much data changes between the "fit" calls
                                          n_initial_relearn=3, n_iter_relearn=3,
                                          # this can be used to adjust search spaces or include more parameters
                                          # by default, only sigma is optimised
                                          param_bounds={'sigma': [-3, 3]}
                                          )
      
      # automatically sets parameters
      est.fit(X)
      
      # only for illustration purpose
      plt.figure()
      plot_bayesopt_model_1d(est.bo)
      plt.title("Original fit")
      
      visualise_fit_2d(est, X)
      plt.suptitle("Original fit")
      
      # now change data, with different length scale
      X = np.random.randn(N, D) * .1
      
      # re-learns parameters, but starts from previous ones
      est.fit(X)
      
      visualise_fit_2d(est, X)
      plt.suptitle("New fit")
      
      # only for illustration purpose
      plt.figure()
      plot_bayesopt_model_1d(est.bo)
      plt.title("New fit")
      
      
      plt.show()
  
subject: fix example after name change of parameter