# Path: examples/context.py

# 
# Path: examples/context.py



import matplotlib
import numpy as np
import pdb
import matplotlib.pylab as plt
    import GPy
    import GPy
from scipy import special
from .context import aep
from .context import config
    from operator import xor
    from scipy import special
    print ls
    inds = np.argsort(ls)
    plt.figure()
    mx, vx = lvm.get_posterior_x()
    plt.scatter(mx[:, inds[0]], mx[:, inds[1]], c=labels)
    zu = lvm.sgp_layer.zu
    # plt.plot(zu[:, inds[0]], zu[:, inds[1]], 'ko')
    # plt.show()
    plt.savefig('/tmp/gplvm_cluster.pdf')


def run_mnist():
    np.random.seed(42)

    # import dataset
    f = gzip.open('./tmp/data/mnist.pkl.gz', 'rb')
    (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f)
    f.close()

    Y = x_train[:100, :]
    labels = t_train[:100]

    Y[Y < 0.5] = -1
    Y[Y > 0.5] = 1

    # inference
    print "inference ..."
    M = 30
    D = 2
    # lvm = aep.SGPLVM(Y, D, M, lik='Gaussian')

# Path: examples/context.py

# 
# Path: examples/context.py



# Path: examples/gplvm_aep_examples.py
import matplotlib
import numpy as np
import pdb
import matplotlib.pylab as plt
    import GPy
    import GPy
from scipy import special
from .context import aep
from .context import config
    from operator import xor
    from scipy import special

    print ls
    inds = np.argsort(ls)
    plt.figure()
    mx, vx = lvm.get_posterior_x()
    plt.scatter(mx[:, inds[0]], mx[:, inds[1]], c=labels)
    zu = lvm.sgp_layer.zu
    # plt.plot(zu[:, inds[0]], zu[:, inds[1]], 'ko')
    # plt.show()
    plt.savefig('/tmp/gplvm_cluster.pdf')


def run_mnist():
    np.random.seed(42)

    # import dataset
    f = gzip.open('./tmp/data/mnist.pkl.gz', 'rb')
    (x_train, t_train), (x_valid, t_valid), (x_test, t_test) = cPickle.load(f)
    f.close()

    Y = x_train[:100, :]
    labels = t_train[:100]

    Y[Y < 0.5] = -1
    Y[Y > 0.5] = 1

    # inference
    print "inference ..."
    M = 30
    D = 2
    # lvm = aep.SGPLVM(Y, D, M, lik='Gaussian')

    lvm = aep.SGPLVM(Y, D, M, lik='Probit')