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pep8'ed transformations module

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Max Zwiessele 2013-09-04 09:30:47 +01:00
parent 2558418b08
commit acb06941b4
3 changed files with 27 additions and 27 deletions
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12
GPy/examples/dimensionality_reduction.py
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@ -5,7 +5,7 @@ import numpy as np
from matplotlib import pyplot as plt, cm
import GPy
from GPy.core.transformations import logexp
from GPy.core.transformations import Logexp
from GPy.models.bayesian_gplvm import BayesianGPLVM
from GPy.likelihoods.gaussian import Gaussian
@ -88,7 +88,7 @@ def sparseGPLVM_oil(optimize=True, N=100, Q=6, num_inducing=15, max_iters=50):
def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp_clipped
from GPy.core.transformations import LogexpClipped
data = swiss_roll_generated(N=N, sigma=sigma)
Y = data['Y']
@ -155,7 +155,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=7, num_inducing=40, max_iters=1000, plot=
m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
# m.constrain('variance|leng', logexp_clipped())
# m.constrain('variance|leng', LogexpClipped())
# m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.Y.var() / 100.
@ -272,7 +272,7 @@ def bgplvm_simulation(optimize='scg',
plot=True,
max_iters=2e4,
plot_sim=False):
# from GPy.core.transformations import logexp_clipped
# from GPy.core.transformations import LogexpClipped
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
@ -285,7 +285,7 @@ def bgplvm_simulation(optimize='scg',
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
# m.constrain('variance|noise', logexp_clipped())
# m.constrain('variance|noise', LogexpClipped())
m['noise'] = Y.var() / 100.
if optimize:
@ -340,7 +340,7 @@ def brendan_faces():
# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
# optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
m.constrain('rbf|noise|white', GPy.core.transformations.LogexpClipped())
m.optimize('scg', messages=1, max_f_eval=10000)