old_contents: # -*- coding: utf-8 -*-
  
  # The MIT License (MIT)
  
  # Copyright (c) 2014 Baxter S. Eaves Jr,
  # Copyright (c) 2015-2016 MIT Probabilistic Computing Project
  
  # Lead Developer: Feras Saad <fsaad@mit.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 gpmcc.utils import config as cu
  from gpmcc.utils import test as tu
  from gpmcc import state
  import numpy
  
  # This script generates acolumn of every data type and plots inference
  # in real time
  
  numpy.random.seed(10)
  
  # set up the data generation
  n_rows = 200
  view_weights = numpy.ones(1)
  cluster_weights = [ numpy.array([.33, .33, .34]) ]
  cctypes = ['normal', 'poisson', 'bernoulli', 'lognormal', 'exponential',
      'normal_uc', 'beta_uc', 'exponential_uc', 'geometric', 'vonmises']
  separation = [.95] * len(cctypes)
  cctypes, distargs = cu.parse_distargs(cctypes)
  
  T, Zv, Zc, dims = tu.gen_data_table(n_rows, view_weights, cluster_weights,
      cctypes, distargs, separation, return_dims=True)
  
  S = state.State(T.T, cctypes, distargs, seed=0)
  S.transition(N=31)
  S.plot()
  
new_contents: # -*- coding: utf-8 -*-
  
  # The MIT License (MIT)
  
  # Copyright (c) 2014 Baxter S. Eaves Jr,
  # Copyright (c) 2015-2016 MIT Probabilistic Computing Project
  
  # Lead Developer: Feras Saad <fsaad@mit.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 gpmcc.utils import config as cu
  from gpmcc.utils import test as tu
  from gpmcc import state
  import numpy
  
  # This script generates acolumn of every data type and plots inference
  # in real time
  
  numpy.random.seed(10)
  
  # set up the data generation
  n_rows = 200
  view_weights = numpy.ones(1)
  cluster_weights = [ numpy.array([.33, .33, .34]) ]
  cctypes = ['normal', 'poisson', 'bernoulli', 'lognormal', 'exponential',
      'normal_uc', 'beta_uc', 'exponential_uc', 'geometric', 'vonmises']
  separation = [.95] * len(cctypes)
  cctypes, distargs = cu.parse_distargs(cctypes)
  
  T, Zv, Zc = tu.gen_data_table(n_rows, view_weights, cluster_weights,
      cctypes, distargs, separation)
  
  S = state.State(T.T, cctypes, distargs, seed=0)
  S.transition(N=30)
  S.plot()
  
subject: Update example runner.