# Path: geepee/config.py
# PROP_MC = 'MC'
# 
# PROP_MM = 'MM'


import matplotlib
import sys
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import os, sys
import geepee.aep_models as aep
import geepee.vfe_models as vfe
import geepee.ep_models as ep
import pdb
from geepee.config import PROP_MC, PROP_MM
    blue = 1. - red
    color = np.array([red, green, blue]).T
    for k in np.argsort(ranks):
        ax.plot(ttest, my[:, k, 0], '-', color=color*ranks[k], alpha=0.5)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), my[:, :, 0], '-x', color='r', alpha=0.3)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), x_samples[:, :, 0], 'x', color='m', alpha=0.3)
    ax.plot(ttest, y_test, 'ro')
    ax.set_xlim([T_train-5, T_train + T])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.pdf')
    # plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.png')


# generate a dataset from the kink function above
T = 198
Ttest = 10
process_noise = 0.2
obs_noise = 0.1
(xtrue, x, y) = kink(T+Ttest, process_noise, obs_noise)
y_train = y[:T]
y_test = y[T:]
y_train = np.reshape(y_train, [y_train.shape[0], 1])

Dlatent = 1
Dobs = 1
M = 15

# create VFE model
np.random.seed(42)
model_vfe = vfe.SGPSSM(y_train, Dlatent, M, 

# Path: geepee/config.py
# PROP_MC = 'MC'
# 
# PROP_MM = 'MM'


# Path: exps/gpssm/kink_exp.py
import matplotlib
import sys
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import os, sys
import geepee.aep_models as aep
import geepee.vfe_models as vfe
import geepee.ep_models as ep
import pdb
from geepee.config import PROP_MC, PROP_MM

    blue = 1. - red
    color = np.array([red, green, blue]).T
    for k in np.argsort(ranks):
        ax.plot(ttest, my[:, k, 0], '-', color=color*ranks[k], alpha=0.5)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), my[:, :, 0], '-x', color='r', alpha=0.3)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), x_samples[:, :, 0], 'x', color='m', alpha=0.3)
    ax.plot(ttest, y_test, 'ro')
    ax.set_xlim([T_train-5, T_train + T])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.pdf')
    # plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.png')


# generate a dataset from the kink function above
T = 198
Ttest = 10
process_noise = 0.2
obs_noise = 0.1
(xtrue, x, y) = kink(T+Ttest, process_noise, obs_noise)
y_train = y[:T]
y_test = y[T:]
y_train = np.reshape(y_train, [y_train.shape[0], 1])

Dlatent = 1
Dobs = 1
M = 15

# create VFE model
np.random.seed(42)
model_vfe = vfe.SGPSSM(y_train, Dlatent, M, 

    lik='Gaussian', prior_mean=0, prior_var=1)