def set_up(self):
# load data using kameleon-mcmc code
logger.info("Loading data")
X, y = GPData.get_glass_data()
# normalise and whiten dataset, as done in kameleon-mcmc code
logger.info("Whitening data")
X -= np.mean(X, 0)
L = np.linalg.cholesky(np.cov(X.T))
X = sp.linalg.solve_triangular(L, X.T, lower=True).T
# build target, as in kameleon-mcmc code
self.gp_posterior = PseudoMarginalHyperparameters(X,
y,
self.n_importance,
self.prior,
self.ridge,
num_shogun_threads=1)
if len(sys.argv) != 3:
print "usage:", str(sys.argv[0]).split(os.sep)[-1], "<experiment_dir_base> <number_of_experiments>"
print "example:"
print "python " + str(sys.argv[0]).split(os.sep)[-1] + " /nfs/nhome/live/ucabhst/kameleon_experiments/ 3"
exit()
experiment_dir_base = str(sys.argv[1])
n = int(str(sys.argv[2]))
# loop over parameters here
experiment_dir = experiment_dir_base + str(os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
print "running experiments", n, "times at base", experiment_dir
# load data
data,labels=GPData.get_glass_data()
# normalise and whiten dataset
data-=mean(data, 0)
L=cholesky(cov(data.T))
data=solve_triangular(L, data.T, lower=True).T
dim=shape(data)[1]
# prior on theta and posterior target estimate
theta_prior=Gaussian(mu=0*ones(dim), Sigma=eye(dim)*5)
distribution=PseudoMarginalHyperparameterDistribution(data, labels, \
n_importance=100, prior=theta_prior, \
ridge=1e-3)
sigma = 23.0
print "using sigma", sigma
if len(sys.argv) != 3:
print "usage:", str(sys.argv[0]).split(os.sep)[-1], "<experiment_dir_base> <number_of_experiments>"
print "example:"
print "python " + str(sys.argv[0]).split(os.sep)[-1] + " /nfs/nhome/live/ucabhst/kameleon_experiments/ 3"
exit()
experiment_dir_base = str(sys.argv[1])
n = int(str(sys.argv[2]))
# loop over parameters here
experiment_dir = experiment_dir_base + str(os.path.abspath(sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
print "running experiments", n, "times at base", experiment_dir
# load data
data,labels=GPData.get_mushroom_data()
# throw away some data
n=500
idx=permutation(len(data))
idx=idx[:n]
data=data[idx]
labels=labels[idx]
dim=shape(data)[1]
# prior on theta and posterior target estimate
theta_prior=Gaussian(mu=0*ones(dim), Sigma=eye(dim)*5)
distribution=PseudoMarginalHyperparameterDistributionDiffusion(data, labels, \
n_importance=100, prior=theta_prior, \
ridge=1e-3)
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from numpy.lib.twodim_base import eye
from numpy.linalg.linalg import cholesky
from numpy.ma.core import mean, ones, shape, asarray, zeros
from numpy.ma.extras import cov
from numpy.random import permutation, seed
from scipy.linalg.basic import solve_triangular
import os
import sys
if __name__ == '__main__':
# load data
data, labels = GPData.get_glass_data()
# throw away some data
n = 250
seed(1)
idx = permutation(len(data))
idx = idx[:n]
data = data[idx]
labels = labels[idx]
# normalise and whiten dataset
data -= mean(data, 0)
L = cholesky(cov(data.T))
data = solve_triangular(L, data.T, lower=True).T
dim = shape(data)[1]
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from numpy.lib.twodim_base import eye
from numpy.linalg.linalg import cholesky
from numpy.ma.core import mean, ones, shape, asarray, std, zeros
from numpy.ma.extras import cov
from numpy.random import permutation, seed
from scipy.linalg.basic import solve_triangular
import os
import sys
if __name__ == '__main__':
# load data
data,labels=GPData.get_madelon_data()
# throw away some data
n=750
seed(1)
idx=permutation(len(data))
idx=idx[:n]
data=data[idx]
labels=labels[idx]
# normalise dataset
data-=mean(data, 0)
data/=std(data,0)
dim=shape(data)[1]
# prior on theta and posterior target estimate
from kameleon_mcmc.mcmc.output.PlottingOutput import PlottingOutput
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
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")
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from numpy.lib.twodim_base import eye
from numpy.linalg.linalg import cholesky
from numpy.ma.core import mean, ones, shape, asarray
from numpy.ma.extras import cov
from numpy.random import permutation, seed
from scipy.linalg.basic import solve_triangular
import os
import sys
if __name__ == '__main__':
# load data
data, labels = GPData.get_pima_data()
# throw away some data
n = 250
seed(1)
idx = permutation(len(data))
idx = idx[:n]
data = data[idx]
labels = labels[idx]
# normalise and whiten dataset
data -= mean(data, 0)
L = cholesky(cov(data.T))
data = solve_triangular(L, data.T, lower=True).T
dim = shape(data)[1]
print "example:"
print "python " + str(sys.argv[0]).split(
os.sep)[-1] + " /nfs/nhome/live/ucabhst/kameleon_experiments/ 3"
exit()
experiment_dir_base = str(sys.argv[1])
n = int(str(sys.argv[2]))
# loop over parameters here
experiment_dir = experiment_dir_base + str(os.path.abspath(
sys.argv[0])).split(os.sep)[-1].split(".")[0] + os.sep
print "running experiments", n, "times at base", experiment_dir
# load data
data, labels = GPData.get_mushroom_data()
# throw away some data
n = 500
idx = permutation(len(data))
idx = idx[:n]
data = data[idx]
labels = labels[idx]
dim = shape(data)[1]
# prior on theta and posterior target estimate
theta_prior = Gaussian(mu=0 * ones(dim), Sigma=eye(dim) * 5)
distribution=PseudoMarginalHyperparameterDistributionDiffusion(data, labels, \
n_importance=100, prior=theta_prior, \
ridge=1e-3)
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
from kameleon_mcmc.mcmc.samplers.StandardMetropolis import StandardMetropolis
from numpy.lib.twodim_base import eye
from numpy.linalg.linalg import cholesky
from numpy.ma.core import mean, ones, shape, asarray
from numpy.ma.extras import cov
from numpy.random import permutation, seed
from scipy.linalg.basic import solve_triangular
import os
import sys
if __name__ == '__main__':
# load data
data,labels=GPData.get_pima_data()
# throw away some data
n=250
seed(1)
idx=permutation(len(data))
idx=idx[:n]
data=data[idx]
labels=labels[idx]
# normalise and whiten dataset
data-=mean(data, 0)
L=cholesky(cov(data.T))
data=solve_triangular(L, data.T, lower=True).T
dim=shape(data)[1]
from kameleon_mcmc.mcmc.output.PlottingOutput import PlottingOutput
from kameleon_mcmc.mcmc.output.StatisticsOutput import StatisticsOutput
from kameleon_mcmc.mcmc.samplers.AdaptiveMetropolisLearnScale import \
AdaptiveMetropolisLearnScale
from kameleon_mcmc.mcmc.samplers.KameleonWindowLearnScale import KameleonWindowLearnScale
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")
