# Path: examples/context.py

# 
# Path: examples/context.py



import matplotlib
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import os
import sys
import pdb
from .context import aep
from .context import config
        y[1:model.N], 
        'r+', alpha=0.5)

    # mx, vx = model.get_posterior_x()
    # ax.plot(mx[0:model.N-1], mx[1:model.N], 'og', alpha=0.3)
    my, vy_noiseless, vy = model.get_posterior_y()
    ax.plot(my[0:model.N-1], my[1:model.N], 'or', alpha=0.2)
    
    ax.set_xlabel(r'$x_{t-1}$')
    ax.set_ylabel(r'$x_{t}$')
    ax.set_xlim([-4, 6])
    ax.set_ylim([-7, 7])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_'+plot_title+'.pdf')


def test_kink_linear_MM():
    # generate a dataset from the kink function above
    T = 200
    process_noise = 0.2
    obs_noise = 0.1
    (xtrue, x, y) = kink(T, process_noise, obs_noise)
    y_train = np.reshape(y, [y.shape[0], 1])

    # init hypers
    Dlatent = 1
    Dobs = 1
    M = 15
    alpha = 0.5
    # create AEP model

# Path: examples/context.py

# 
# Path: examples/context.py



# Path: examples/gpssm_aep_examples.py
import matplotlib
import numpy as np
import scipy.stats
import matplotlib.pylab as plt
import os
import sys
import pdb
from .context import aep
from .context import config

        y[1:model.N], 
        'r+', alpha=0.5)

    # mx, vx = model.get_posterior_x()
    # ax.plot(mx[0:model.N-1], mx[1:model.N], 'og', alpha=0.3)
    my, vy_noiseless, vy = model.get_posterior_y()
    ax.plot(my[0:model.N-1], my[1:model.N], 'or', alpha=0.2)
    
    ax.set_xlabel(r'$x_{t-1}$')
    ax.set_ylabel(r'$x_{t}$')
    ax.set_xlim([-4, 6])
    ax.set_ylim([-7, 7])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_'+plot_title+'.pdf')


def test_kink_linear_MM():
    # generate a dataset from the kink function above
    T = 200
    process_noise = 0.2
    obs_noise = 0.1
    (xtrue, x, y) = kink(T, process_noise, obs_noise)
    y_train = np.reshape(y, [y.shape[0], 1])

    # init hypers
    Dlatent = 1
    Dobs = 1
    M = 15
    alpha = 0.5
    # create AEP model

    model_aep = aep.SGPSSM(y_train, Dlatent, M, 