mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-01 07:46:22 +02:00
Minor changes to naming of signitures
This commit is contained in:
Alan Saul
parent
98074e1e6c
commit
9e6cc7ea6e
1 changed files with 29 additions and 29 deletions
|
|
@ -3,23 +3,23 @@
|
|||
import numpy as _np
|
||||
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
|
||||
"""
|
||||
from GPy.likelihoods.gaussian import Gaussian
|
||||
import GPy
|
||||
|
||||
|
||||
num_inputs = 13
|
||||
num_inducing = 5
|
||||
if plot:
|
||||
if plot:
|
||||
output_dim = 1
|
||||
input_dim = 2
|
||||
else:
|
||||
else:
|
||||
input_dim = 2
|
||||
output_dim = 25
|
||||
|
||||
|
||||
# generate GPLVM-like data
|
||||
X = _np.random.rand(num_inputs, input_dim)
|
||||
lengthscales = _np.random.rand(input_dim)
|
||||
|
|
@ -43,7 +43,7 @@ def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0):
|
|||
import matplotlib.pyplot as pb
|
||||
m.plot()
|
||||
pb.title('PCA initialisation')
|
||||
|
||||
|
||||
if optimize:
|
||||
m.optimize('scg', messages=verbose)
|
||||
if plot:
|
||||
|
|
@ -52,7 +52,7 @@ def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0):
|
|||
|
||||
return m
|
||||
|
||||
def gplvm_oil_100(optimize=1, verbose=1, plot=1):
|
||||
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
|
||||
import GPy
|
||||
data = GPy.util.datasets.oil_100()
|
||||
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)
|
||||
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
|
||||
_np.random.seed(0)
|
||||
data = GPy.util.datasets.oil()
|
||||
|
|
@ -77,12 +77,12 @@ def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15,
|
|||
m.data_labels = data['Y'][:N].argmax(axis=1)
|
||||
|
||||
if optimize: m.optimize('scg', messages=verbose, max_iters=max_iters)
|
||||
if plot:
|
||||
if plot:
|
||||
m.plot_latent(labels=m.data_labels)
|
||||
m.kern.plot_ARD()
|
||||
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
|
||||
from GPy.util.datasets import swiss_roll_generated
|
||||
from GPy.models import BayesianGPLVM
|
||||
|
|
@ -131,16 +131,16 @@ def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigm
|
|||
|
||||
if optimize:
|
||||
m.optimize('scg', messages=verbose, max_iters=2e3)
|
||||
|
||||
|
||||
if plot:
|
||||
fig = plt.figure('fitted')
|
||||
ax = fig.add_subplot(111)
|
||||
s = m.input_sensitivity().argsort()[::-1][:2]
|
||||
ax.scatter(*m.X.T[s], c=c)
|
||||
|
||||
|
||||
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
|
||||
from GPy.likelihoods import Gaussian
|
||||
from matplotlib import pyplot as plt
|
||||
|
|
@ -164,7 +164,7 @@ def bgplvm_oil(optimize=1, verbose=1, plot=1, N=200, Q=7, num_inducing=40, max_i
|
|||
m.plot_latent(ax=latent_axes)
|
||||
data_show = GPy.util.visualize.vector_show(y)
|
||||
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], # @UnusedVariable
|
||||
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
|
||||
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
|
||||
raw_input('Press enter to finish')
|
||||
plt.close(fig)
|
||||
return m
|
||||
|
|
@ -227,12 +227,12 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
|
|||
# from GPy.util.datasets import simulation_BGPLVM
|
||||
# from GPy import kern
|
||||
# from GPy.models import BayesianGPLVM
|
||||
#
|
||||
#
|
||||
# sim_data = simulation_BGPLVM()
|
||||
# Y = sim_data['Y']
|
||||
# mu = sim_data['mu']
|
||||
# num_inducing, [_, Q] = 3, mu.shape
|
||||
#
|
||||
#
|
||||
# k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
|
||||
# m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
|
||||
# _debug=False)
|
||||
|
|
@ -241,8 +241,8 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
|
|||
# m['linear_variance'] = .01
|
||||
# return m
|
||||
|
||||
def bgplvm_simulation(optimize=1, verbose=1,
|
||||
plot=1, plot_sim=False,
|
||||
def bgplvm_simulation(optimize=True, verbose=1,
|
||||
plot=True, plot_sim=False,
|
||||
max_iters=2e4,
|
||||
):
|
||||
from GPy import kern
|
||||
|
|
@ -268,7 +268,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
|
|||
from GPy import kern
|
||||
from GPy.models import MRD
|
||||
from GPy.likelihoods import Gaussian
|
||||
|
||||
|
||||
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
|
||||
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
|
||||
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
|
||||
|
|
@ -290,7 +290,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
|
|||
|
||||
def brendan_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.brendan_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
|
|
@ -315,7 +315,7 @@ def brendan_faces(optimize=True, verbose=True, plot=True):
|
|||
|
||||
def olivetti_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.olivetti_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
|
|
@ -350,7 +350,7 @@ def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=Tru
|
|||
def stick(kernel=None, optimize=True, verbose=True, plot=True):
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
# optimize
|
||||
m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
|
||||
|
|
@ -362,13 +362,13 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
|
||||
return m
|
||||
|
||||
def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
# optimize
|
||||
mapping = GPy.mappings.Linear(data['Y'].shape[1], 2)
|
||||
|
|
@ -387,7 +387,7 @@ def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
# optimize
|
||||
back_kernel=GPy.kern.rbf(data['Y'].shape[1], lengthscale=5.)
|
||||
|
|
@ -407,7 +407,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
def robot_wireless(optimize=True, verbose=True, plot=True):
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.robot_wireless()
|
||||
# optimize
|
||||
m = GPy.models.GPLVM(data['Y'], 2)
|
||||
|
|
@ -422,7 +422,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
|
|||
from GPy.models import BayesianGPLVM
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
Q = 6
|
||||
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
|
||||
|
|
@ -445,7 +445,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
|
|||
|
||||
def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
|
||||
|
||||
data = GPy.util.datasets.cmu_mocap(subject, motion)
|
||||
if in_place:
|
||||
# Make figure move in place.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue