mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 12:32:40 +02:00
merge
This commit is contained in:
James Hensman
commit
65f408de9a
45 changed files with 1523 additions and 1347 deletions
|
|
@ -7,11 +7,6 @@ Gaussian Processes classification
|
|||
"""
|
||||
import GPy
|
||||
|
||||
try:
|
||||
import pylab as pb
|
||||
except:
|
||||
pass
|
||||
|
||||
default_seed = 10000
|
||||
|
||||
def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
|
||||
|
|
@ -19,7 +14,9 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
|
|||
Run a Gaussian process classification on the three phase oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
|
||||
|
||||
"""
|
||||
data = GPy.util.datasets.oil()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.oil()
|
||||
X = data['X']
|
||||
Xtest = data['Xtest']
|
||||
Y = data['Y'][:, 0:1]
|
||||
|
|
@ -54,7 +51,9 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
|
|||
|
||||
"""
|
||||
|
||||
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_linear_1d_classification(seed=seed)
|
||||
Y = data['Y'][:, 0:1]
|
||||
Y[Y.flatten() == -1] = 0
|
||||
|
||||
|
|
@ -71,7 +70,8 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
|
|||
|
||||
# Plot
|
||||
if plot:
|
||||
fig, axes = pb.subplots(2, 1)
|
||||
from matplotlib import pyplot as plt
|
||||
fig, axes = plt.subplots(2, 1)
|
||||
m.plot_f(ax=axes[0])
|
||||
m.plot(ax=axes[1])
|
||||
|
||||
|
|
@ -87,7 +87,9 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
|
|||
|
||||
"""
|
||||
|
||||
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_linear_1d_classification(seed=seed)
|
||||
Y = data['Y'][:, 0:1]
|
||||
Y[Y.flatten() == -1] = 0
|
||||
|
||||
|
|
@ -107,7 +109,8 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
|
|||
|
||||
# Plot
|
||||
if plot:
|
||||
fig, axes = pb.subplots(2, 1)
|
||||
from matplotlib import pyplot as plt
|
||||
fig, axes = plt.subplots(2, 1)
|
||||
m.plot_f(ax=axes[0])
|
||||
m.plot(ax=axes[1])
|
||||
|
||||
|
|
@ -123,7 +126,9 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
|
|||
|
||||
"""
|
||||
|
||||
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_linear_1d_classification(seed=seed)
|
||||
Y = data['Y'][:, 0:1]
|
||||
Y[Y.flatten() == -1] = 0
|
||||
|
||||
|
|
@ -137,7 +142,8 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
|
|||
|
||||
# Plot
|
||||
if plot:
|
||||
fig, axes = pb.subplots(2, 1)
|
||||
from matplotlib import pyplot as plt
|
||||
fig, axes = plt.subplots(2, 1)
|
||||
m.plot_f(ax=axes[0])
|
||||
m.plot(ax=axes[1])
|
||||
|
||||
|
|
@ -153,7 +159,9 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
|
|||
|
||||
"""
|
||||
|
||||
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_linear_1d_classification(seed=seed)
|
||||
Y = data['Y'][:, 0:1]
|
||||
Y[Y.flatten() == -1] = 0
|
||||
|
||||
|
|
@ -171,7 +179,8 @@ def toy_heaviside(seed=default_seed, optimize=True, plot=True):
|
|||
|
||||
# Plot
|
||||
if plot:
|
||||
fig, axes = pb.subplots(2, 1)
|
||||
from matplotlib import pyplot as plt
|
||||
fig, axes = plt.subplots(2, 1)
|
||||
m.plot_f(ax=axes[0])
|
||||
m.plot(ax=axes[1])
|
||||
|
||||
|
|
@ -190,7 +199,9 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
|
|||
:param kernel: kernel to use in the model
|
||||
:type kernel: a GPy kernel
|
||||
"""
|
||||
data = GPy.util.datasets.crescent_data(seed=seed)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.crescent_data(seed=seed)
|
||||
Y = data['Y']
|
||||
Y[Y.flatten()==-1] = 0
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
||||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
import numpy as _np
|
||||
|
||||
#default_seed = _np.random.seed(123344)
|
||||
|
||||
def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
|
||||
|
|
@ -68,7 +69,8 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
|
|||
|
||||
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
|
||||
import GPy
|
||||
data = GPy.util.datasets.oil_100()
|
||||
import pods
|
||||
data = pods.datasets.oil_100()
|
||||
Y = data['X']
|
||||
# create simple GP model
|
||||
kernel = GPy.kern.RBF(6, ARD=True) + GPy.kern.Bias(6)
|
||||
|
|
@ -80,8 +82,10 @@ def gplvm_oil_100(optimize=True, verbose=1, plot=True):
|
|||
|
||||
def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50):
|
||||
import GPy
|
||||
import pods
|
||||
|
||||
_np.random.seed(0)
|
||||
data = GPy.util.datasets.oil()
|
||||
data = pods.datasets.oil()
|
||||
Y = data['X'][:N]
|
||||
Y = Y - Y.mean(0)
|
||||
Y /= Y.std(0)
|
||||
|
|
@ -98,7 +102,7 @@ def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_induci
|
|||
|
||||
def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, Q=4, sigma=.2):
|
||||
import GPy
|
||||
from GPy.util.datasets import swiss_roll_generated
|
||||
from pods.datasets import swiss_roll_generated
|
||||
from GPy.models import BayesianGPLVM
|
||||
|
||||
data = swiss_roll_generated(num_samples=N, sigma=sigma)
|
||||
|
|
@ -157,9 +161,10 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
|
|||
import GPy
|
||||
from matplotlib import pyplot as plt
|
||||
import numpy as np
|
||||
import pods
|
||||
|
||||
_np.random.seed(0)
|
||||
data = GPy.util.datasets.oil()
|
||||
data = pods.datasets.oil()
|
||||
|
||||
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
Y = data['X'][:N]
|
||||
|
|
@ -182,9 +187,10 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
|
|||
def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
|
||||
import GPy
|
||||
from matplotlib import pyplot as plt
|
||||
import pods
|
||||
|
||||
_np.random.seed(0)
|
||||
data = GPy.util.datasets.oil()
|
||||
data = pods.datasets.oil()
|
||||
|
||||
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
Y = data['X'][:N]
|
||||
|
|
@ -208,12 +214,12 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
|
|||
Q_signal = 4
|
||||
import GPy
|
||||
import numpy as np
|
||||
np.random.seed(0)
|
||||
np.random.seed(3000)
|
||||
|
||||
k = GPy.kern.Matern32(Q_signal, 1., lengthscale=np.random.uniform(1,6,Q_signal), ARD=1)
|
||||
k = GPy.kern.Matern32(Q_signal, 10., lengthscale=1+(np.random.uniform(1,6,Q_signal)), ARD=1)
|
||||
t = np.c_[[np.linspace(-1,5,N) for _ in range(Q_signal)]].T
|
||||
K = k.K(t)
|
||||
s1, s2, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
|
||||
s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
|
||||
|
||||
Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
|
||||
|
||||
|
|
@ -360,23 +366,19 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
|
|||
):
|
||||
from GPy import kern
|
||||
from GPy.models import BayesianGPLVM
|
||||
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
|
||||
|
||||
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
|
||||
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
|
||||
Y = Ylist[0]
|
||||
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
|
||||
|
||||
inan = _np.random.binomial(1, .8, size=Y.shape).astype(bool) # 80% missing data
|
||||
inan = _np.random.binomial(1, .2, size=Y.shape).astype(bool) # 80% missing data
|
||||
Ymissing = Y.copy()
|
||||
Ymissing[inan] = _np.nan
|
||||
|
||||
m = BayesianGPLVM(Ymissing, Q, init="random", num_inducing=num_inducing,
|
||||
inference_method=VarDTCMissingData(inan=inan), kernel=k)
|
||||
kernel=k, missing_data=True)
|
||||
|
||||
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
|
||||
m.likelihood.variance = .01
|
||||
m.parameters_changed()
|
||||
m.Yreal = Y
|
||||
|
||||
if optimize:
|
||||
|
|
@ -445,8 +447,9 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
|
|||
|
||||
def brendan_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.brendan_faces()
|
||||
data = pods.datasets.brendan_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
Yn = Y - Y.mean()
|
||||
|
|
@ -469,8 +472,9 @@ def brendan_faces(optimize=True, verbose=True, plot=True):
|
|||
|
||||
def olivetti_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.olivetti_faces()
|
||||
data = pods.datasets.olivetti_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
Yn = Y - Y.mean()
|
||||
|
|
@ -490,7 +494,9 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
|
|||
|
||||
def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True):
|
||||
import GPy
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
import pods
|
||||
|
||||
data = pods.datasets.osu_run1()
|
||||
# optimize
|
||||
if range == None:
|
||||
Y = data['Y'].copy()
|
||||
|
|
@ -505,8 +511,9 @@ 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
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
data = pods.datasets.osu_run1()
|
||||
# optimize
|
||||
m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
|
||||
if optimize: m.optimize('bfgs', messages=verbose, max_f_eval=10000)
|
||||
|
|
@ -524,8 +531,9 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
||||
from matplotlib import pyplot as plt
|
||||
import GPy
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
data = pods.datasets.osu_run1()
|
||||
# optimize
|
||||
mapping = GPy.mappings.Linear(data['Y'].shape[1], 2)
|
||||
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
|
||||
|
|
@ -543,8 +551,9 @@ 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
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
data = pods.datasets.osu_run1()
|
||||
# optimize
|
||||
back_kernel=GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
|
||||
mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
|
||||
|
|
@ -563,8 +572,9 @@ 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
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.robot_wireless()
|
||||
data = pods.datasets.robot_wireless()
|
||||
# optimize
|
||||
m = GPy.models.BayesianGPLVM(data['Y'], 4, num_inducing=25)
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
|
|
@ -578,8 +588,9 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
|
|||
from matplotlib import pyplot as plt
|
||||
import numpy as np
|
||||
import GPy
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
data = pods.datasets.osu_run1()
|
||||
Q = 6
|
||||
kernel = GPy.kern.RBF(Q, lengthscale=np.repeat(.5, Q), ARD=True)
|
||||
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
|
||||
|
|
@ -609,8 +620,9 @@ 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
|
||||
import pods
|
||||
|
||||
data = GPy.util.datasets.cmu_mocap(subject, motion)
|
||||
data = pods.datasets.cmu_mocap(subject, motion)
|
||||
if in_place:
|
||||
# Make figure move in place.
|
||||
data['Y'][:, 0:3] = 0.0
|
||||
|
|
|
|||
|
|
@ -13,7 +13,9 @@ import GPy
|
|||
|
||||
def olympic_marathon_men(optimize=True, plot=True):
|
||||
"""Run a standard Gaussian process regression on the Olympic marathon data."""
|
||||
data = GPy.util.datasets.olympic_marathon_men()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.olympic_marathon_men()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['X'], data['Y'])
|
||||
|
|
@ -82,7 +84,9 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
|
|||
from the Mount Epomeo runs. Requires gpxpy to be installed on your system
|
||||
to load in the data.
|
||||
"""
|
||||
data = GPy.util.datasets.epomeo_gpx()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.epomeo_gpx()
|
||||
num_data_list = []
|
||||
for Xpart in data['X']:
|
||||
num_data_list.append(Xpart.shape[0])
|
||||
|
|
@ -107,9 +111,9 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
|
|||
k = k1**k2
|
||||
|
||||
m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)
|
||||
m.constrain_fixed('.*rbf_var', 1.)
|
||||
m.constrain_fixed('iip')
|
||||
m.constrain_bounded('noise_variance', 1e-3, 1e-1)
|
||||
m.constrain_fixed('.*variance', 1.)
|
||||
m.inducing_inputs.constrain_fixed()
|
||||
m.Gaussian_noise.variance.constrain_bounded(1e-3, 1e-1)
|
||||
m.optimize(max_iters=max_iters,messages=True)
|
||||
|
||||
return m
|
||||
|
|
@ -125,7 +129,9 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
|
|||
length_scales = np.linspace(0.1, 60., resolution)
|
||||
log_SNRs = np.linspace(-3., 4., resolution)
|
||||
|
||||
data = GPy.util.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
|
||||
# data['Y'] = data['Y'][0::2, :]
|
||||
# data['X'] = data['X'][0::2, :]
|
||||
|
||||
|
|
@ -205,13 +211,15 @@ def _contour_data(data, length_scales, log_SNRs, kernel_call=GPy.kern.RBF):
|
|||
|
||||
def olympic_100m_men(optimize=True, plot=True):
|
||||
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
|
||||
data = GPy.util.datasets.olympic_100m_men()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.olympic_100m_men()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['X'], data['Y'])
|
||||
|
||||
# set the lengthscale to be something sensible (defaults to 1)
|
||||
m['rbf_lengthscale'] = 10
|
||||
m.rbf.lengthscale = 10
|
||||
|
||||
if optimize:
|
||||
m.optimize('bfgs', max_iters=200)
|
||||
|
|
@ -222,7 +230,9 @@ def olympic_100m_men(optimize=True, plot=True):
|
|||
|
||||
def toy_rbf_1d(optimize=True, plot=True):
|
||||
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
|
||||
data = GPy.util.datasets.toy_rbf_1d()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_rbf_1d()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['X'], data['Y'])
|
||||
|
|
@ -236,7 +246,9 @@ def toy_rbf_1d(optimize=True, plot=True):
|
|||
|
||||
def toy_rbf_1d_50(optimize=True, plot=True):
|
||||
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
|
||||
data = GPy.util.datasets.toy_rbf_1d_50()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.toy_rbf_1d_50()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['X'], data['Y'])
|
||||
|
|
@ -303,12 +315,11 @@ def toy_ARD(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize
|
|||
# m.set_prior('.*lengthscale',len_prior)
|
||||
|
||||
if optimize:
|
||||
m.optimize(optimizer='scg', max_iters=max_iters, messages=1)
|
||||
m.optimize(optimizer='scg', max_iters=max_iters)
|
||||
|
||||
if plot:
|
||||
m.kern.plot_ARD()
|
||||
|
||||
print m
|
||||
return m
|
||||
|
||||
def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, optimize=True, plot=True):
|
||||
|
|
@ -343,24 +354,25 @@ def toy_ARD_sparse(max_iters=1000, kernel_type='linear', num_samples=300, D=4, o
|
|||
# m.set_prior('.*lengthscale',len_prior)
|
||||
|
||||
if optimize:
|
||||
m.optimize(optimizer='scg', max_iters=max_iters, messages=1)
|
||||
m.optimize(optimizer='scg', max_iters=max_iters)
|
||||
|
||||
if plot:
|
||||
m.kern.plot_ARD()
|
||||
|
||||
print m
|
||||
return m
|
||||
|
||||
def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
|
||||
"""Predict the location of a robot given wirelss signal strength readings."""
|
||||
data = GPy.util.datasets.robot_wireless()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.robot_wireless()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['Y'], data['X'], kernel=kernel)
|
||||
|
||||
# optimize
|
||||
if optimize:
|
||||
m.optimize(messages=True, max_iters=max_iters)
|
||||
m.optimize(max_iters=max_iters)
|
||||
|
||||
Xpredict = m.predict(data['Ytest'])[0]
|
||||
if plot:
|
||||
|
|
@ -372,13 +384,14 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
|
|||
|
||||
sse = ((data['Xtest'] - Xpredict)**2).sum()
|
||||
|
||||
print m
|
||||
print('Sum of squares error on test data: ' + str(sse))
|
||||
return m
|
||||
|
||||
def silhouette(max_iters=100, optimize=True, plot=True):
|
||||
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
|
||||
data = GPy.util.datasets.silhouette()
|
||||
try:import pods
|
||||
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets'
|
||||
data = pods.datasets.silhouette()
|
||||
|
||||
# create simple GP Model
|
||||
m = GPy.models.GPRegression(data['X'], data['Y'])
|
||||
|
|
@ -390,7 +403,7 @@ def silhouette(max_iters=100, optimize=True, plot=True):
|
|||
print m
|
||||
return m
|
||||
|
||||
def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=True):
|
||||
def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False):
|
||||
"""Run a 1D example of a sparse GP regression."""
|
||||
# sample inputs and outputs
|
||||
X = np.random.uniform(-3., 3., (num_samples, 1))
|
||||
|
|
@ -401,10 +414,10 @@ def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, opti
|
|||
m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)
|
||||
|
||||
if checkgrad:
|
||||
m.checkgrad(verbose=1)
|
||||
m.checkgrad()
|
||||
|
||||
if optimize:
|
||||
m.optimize('tnc', messages=1, max_iters=max_iters)
|
||||
m.optimize('tnc', max_iters=max_iters)
|
||||
|
||||
if plot:
|
||||
m.plot()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue