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Minor changes to naming of signitures

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Alan Saul 2013-11-29 14:45:44 +00:00
parent 98074e1e6c
commit 9e6cc7ea6e
1 changed files with 29 additions and 29 deletions
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14
GPy/examples/dimensionality_reduction.py
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@ -3,7 +3,7 @@
import numpy as _np import numpy as _np
default_seed = _np.random.seed(123344) default_seed = _np.random.seed(123344)
def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0): def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
""" """
model for testing purposes. Samples from a GP with rbf kernel and learns model for testing purposes. Samples from a GP with rbf kernel and learns
the samples with a new kernel. Normally not for optimization, just model cheking the samples with a new kernel. Normally not for optimization, just model cheking
@ -52,7 +52,7 @@ def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0):
return m return m
def gplvm_oil_100(optimize=1, verbose=1, plot=1): def gplvm_oil_100(optimize=True, verbose=1, plot=True):
import GPy import GPy
data = GPy.util.datasets.oil_100() data = GPy.util.datasets.oil_100()
Y = data['X'] Y = data['X']
@ -64,7 +64,7 @@ def gplvm_oil_100(optimize=1, verbose=1, plot=1):
if plot: m.plot_latent(labels=m.data_labels) if plot: m.plot_latent(labels=m.data_labels)
return m return m
def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15, max_iters=50): def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50):
import GPy import GPy
_np.random.seed(0) _np.random.seed(0)
data = GPy.util.datasets.oil() data = GPy.util.datasets.oil()
@ -82,7 +82,7 @@ def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15,
m.kern.plot_ARD() m.kern.plot_ARD()
return m return m
def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigma=.2): def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4, sigma=.2):
import GPy import GPy
from GPy.util.datasets import swiss_roll_generated from GPy.util.datasets import swiss_roll_generated
from GPy.models import BayesianGPLVM from GPy.models import BayesianGPLVM
@ -140,7 +140,7 @@ def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigm
return m return m
def bgplvm_oil(optimize=1, verbose=1, plot=1, N=200, Q=7, num_inducing=40, max_iters=1000, **k): def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
import GPy import GPy
from GPy.likelihoods import Gaussian from GPy.likelihoods import Gaussian
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
@ -241,8 +241,8 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
# m['linear_variance'] = .01 # m['linear_variance'] = .01
# return m # return m
def bgplvm_simulation(optimize=1, verbose=1, def bgplvm_simulation(optimize=True, verbose=1,
plot=1, plot_sim=False, plot=True, plot_sim=False,
max_iters=2e4, max_iters=2e4,
): ):
from GPy import kern from GPy import kern