Python hstack Examples

Example #1

    def get_hyperparameters(self):
        theta = self.covariance.get_hyperparameters()
        theta = hstack((theta, self.likelihood.get_hyperparameters()))

        return theta

Example #2

File: GaussianProcess.py Project: karlnapf/kameleon-mcmc

    def get_hyperparameters(self):
        theta=self.covariance.get_hyperparameters()
        theta=hstack((theta, self.likelihood.get_hyperparameters()))

        return theta

Example #3

from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from matplotlib.pyplot import plot
from numpy.lib.twodim_base import eye
from numpy.ma.core import mean, std, ones, shape
from numpy.ma.extras import vstack, hstack
import os
import sys

if __name__ == '__main__':
    # sample data
    data_circle, labels_circle = GPData.sample_circle_data(n=40, seed_init=1)
    data_rect, labels_rect = GPData.sample_rectangle_data(n=60, seed_init=1)

    # combine
    data = vstack((data_circle, data_rect))
    labels = hstack((labels_circle, labels_rect))
    dim = shape(data)[1]

    # normalise data
    data -= mean(data, 0)
    data /= std(data, 0)

    # plot
    idx_a = labels > 0
    idx_b = labels < 0
    plot(data[idx_a, 0], data[idx_a, 1], "ro")
    plot(data[idx_b, 0], data[idx_b, 1], "bo")

    # prior on theta and posterior target estimate
    theta_prior = Gaussian(mu=0 * ones(dim), Sigma=eye(dim) * 5)
    target=PseudoMarginalHyperparameterDistribution(data, labels, \

Example #4

File: circle_rect_hybrid_gaussian_ard.py Project: karlnapf/kameleon-mcmc

from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from matplotlib.pyplot import plot
from numpy.lib.twodim_base import eye
from numpy.ma.core import mean, std, ones, shape
from numpy.ma.extras import vstack, hstack
import os
import sys
    
if __name__ == '__main__':
    # sample data
    data_circle, labels_circle=GPData.sample_circle_data(n=40, seed_init=1)
    data_rect, labels_rect=GPData.sample_rectangle_data(n=60, seed_init=1)
    
    # combine
    data=vstack((data_circle, data_rect))
    labels=hstack((labels_circle, labels_rect))
    dim=shape(data)[1]
    
    # normalise data
    data-=mean(data, 0)
    data/=std(data,0)

    # plot
    idx_a=labels>0
    idx_b=labels<0
    plot(data[idx_a,0], data[idx_a,1],"ro")
    plot(data[idx_b,0], data[idx_b,1],"bo")
    
    # prior on theta and posterior target estimate
    theta_prior=Gaussian(mu=0*ones(dim), Sigma=eye(dim)*5)
    target=PseudoMarginalHyperparameterDistribution(data, labels, \