# 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 vfe
from .context import config
    from operator import xor
    from scipy import special
    # plt.show()
    plt.savefig('/tmp/gplvm_cluster_MM.pdf')


def run_cluster_MC():
    # create dataset
    print "creating dataset..."
    N = 100
    k1 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 10, 10, 0.1, 0.1]), ARD=True)
    k2 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 0.1, 10, 0.1, 10]), ARD=True)
    k3 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[0.1, 0.1, 10, 10, 10]), ARD=True)
    X = np.random.normal(0, 1, (N, 5))
    A = np.random.multivariate_normal(np.zeros(N), k1.K(X), 10).T
    B = np.random.multivariate_normal(np.zeros(N), k2.K(X), 10).T
    C = np.random.multivariate_normal(np.zeros(N), k3.K(X), 10).T

    Y = np.vstack((A, B, C))
    labels = np.hstack((np.zeros(A.shape[0]), np.ones(
        B.shape[0]), np.ones(C.shape[0]) * 2))

    # inference
    np.random.seed(42)
    print "inference ..."
    M = 30
    D = 5
    alpha = 0.5
    lvm = vfe.SGPLVM(Y, D, M, lik='Gaussian')

# Path: examples/context.py

# 
# Path: examples/context.py



# Path: examples/gplvm_vfe_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 vfe
from .context import config
    from operator import xor
    from scipy import special

    # plt.show()
    plt.savefig('/tmp/gplvm_cluster_MM.pdf')


def run_cluster_MC():
    # create dataset
    print "creating dataset..."
    N = 100
    k1 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 10, 10, 0.1, 0.1]), ARD=True)
    k2 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 0.1, 10, 0.1, 10]), ARD=True)
    k3 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[0.1, 0.1, 10, 10, 10]), ARD=True)
    X = np.random.normal(0, 1, (N, 5))
    A = np.random.multivariate_normal(np.zeros(N), k1.K(X), 10).T
    B = np.random.multivariate_normal(np.zeros(N), k2.K(X), 10).T
    C = np.random.multivariate_normal(np.zeros(N), k3.K(X), 10).T

    Y = np.vstack((A, B, C))
    labels = np.hstack((np.zeros(A.shape[0]), np.ones(
        B.shape[0]), np.ones(C.shape[0]) * 2))

    # inference
    np.random.seed(42)
    print "inference ..."
    M = 30
    D = 5
    alpha = 0.5
    lvm = vfe.SGPLVM(Y, D, M, lik='Gaussian')

    lvm.optimise(method='adam', adam_lr=0.05, maxiter=2000, prop_mode=config.PROP_MC)