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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Teo de Campos
commit
6b47d28e7e
17 changed files with 329 additions and 289 deletions
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@ -79,7 +79,6 @@ def toy_linear_1d_classification(seed=default_seed):
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data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y == -1] = 0
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# Kernel object
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kernel = GPy.kern.rbf(1)
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@ -96,7 +95,7 @@ def toy_linear_1d_classification(seed=default_seed):
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m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize()
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#m.EPEM() #FIXME
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#m.pseudo_EM() #FIXME
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# Plot
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pb.subplot(211)
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@ -109,14 +108,13 @@ def toy_linear_1d_classification(seed=default_seed):
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def sparse_toy_linear_1d_classification(seed=default_seed):
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"""
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Simple 1D classification example
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Sparse 1D classification example
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:param seed : seed value for data generation (default is 4).
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:type seed: int
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"""
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data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
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Y = data['Y'][:, 0:1]
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Y[Y == -1] = 0
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# Kernel object
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kernel = GPy.kern.rbf(1) + GPy.kern.white(1)
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@ -168,7 +166,6 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
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sample = np.random.randint(0,data['X'].shape[0],inducing)
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Z = data['X'][sample,:]
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#Z = (np.random.random_sample(2*inducing)*(data['X'].max()-data['X'].min())+data['X'].min()).reshape(inducing,-1)
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# create sparse GP EP model
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m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z)
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@ -2,13 +2,11 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import pylab as pb
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from matplotlib import pyplot as plt, pyplot
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from matplotlib import pyplot as plt
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import GPy
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from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
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from GPy.util.datasets import simulation_BGPLVM
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from GPy.core.transformations import square, logexp_clipped
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from GPy.util.datasets import swiss_roll_generated
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default_seed = np.random.seed(123344)
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@ -47,10 +45,11 @@ def BGPLVM(seed=default_seed):
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def GPLVM_oil_100(optimize=True):
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data = GPy.util.datasets.oil_100()
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Y = data['X']
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# create simple GP model
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kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
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m = GPy.models.GPLVM(data['X'], 6, kernel=kernel)
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m = GPy.models.GPLVM(Y, 6, kernel=kernel)
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m.data_labels = data['Y'].argmax(axis=1)
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# optimize
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@ -63,27 +62,88 @@ def GPLVM_oil_100(optimize=True):
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m.plot_latent(labels=m.data_labels)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=20, max_f_eval=300, plot=False):
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def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
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from GPy.util.datasets import swiss_roll
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from GPy.core.transformations import logexp_clipped
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data = swiss_roll_generated(N=N, sigma=sigma)
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Y = data['Y']
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Y -= Y.mean()
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Y /= Y.std()
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t = data['t']
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c = data['colors']
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try:
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from sklearn.manifold.isomap import Isomap
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iso = Isomap().fit(Y)
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X = iso.embedding_
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if Q > 2:
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X = np.hstack((X, np.random.randn(N, Q - 2)))
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except ImportError:
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X = np.random.randn(N, Q)
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if plot:
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from mpl_toolkits import mplot3d
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import pylab
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fig = pylab.figure("Swiss Roll Data")
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ax = fig.add_subplot(121, projection='3d')
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ax.scatter(*Y.T, c=c)
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ax.set_title("Swiss Roll")
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ax = fig.add_subplot(122)
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ax.scatter(*X.T[:2], c=c)
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ax.set_title("Initialization")
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var = .5
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S = (var * np.ones_like(X) + np.clip(np.random.randn(N, Q) * var ** 2,
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- (1 - var),
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(1 - var))) + .001
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Z = np.random.permutation(X)[:M]
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
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m.data_colors = c
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m.data_t = t
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m.constrain('variance|length', logexp_clipped())
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m['lengthscale'] = 1. # X.var(0).max() / X.var(0)
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m['noise'] = Y.var() / 100.
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m.ensure_default_constraints()
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if optimize:
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m.optimize('scg', messages=1)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k):
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data = GPy.util.datasets.oil()
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from GPy.core.transformations import logexp_clipped
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np.random.seed(0)
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# create simple GP model
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M)
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Yn = Y - Y.mean(0)
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Yn /= Yn.std(0)
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m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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m.constrain('variance', logexp_clipped())
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m.constrain('length', logexp_clipped())
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m['lengt'] = 100.
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# m.constrain('variance', logexp_clipped())
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# m.constrain('length', logexp_clipped())
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m['lengt'] = m.X.var(0).max() / m.X.var(0)
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m['noise'] = Yn.var() / 100.
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m.ensure_default_constraints()
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# optimize
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if optimize:
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m.unconstrain('noise'); m.constrain_fixed('noise', Y.var() / 100.)
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m.optimize('scg', messages=1, max_f_eval=150)
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m.unconstrain('noise')
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m.constrain('noise', logexp_clipped())
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# m.unconstrain('noise'); m.constrain_fixed('noise')
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# m.optimize('scg', messages=1, max_f_eval=200)
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# m.unconstrain('noise')
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# m.constrain('noise', logexp_clipped())
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m.optimize('scg', messages=1, max_f_eval=max_f_eval)
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if plot:
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@ -115,6 +175,8 @@ def oil_100():
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# m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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x = np.linspace(0, 4 * np.pi, N)[:, None]
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s1 = np.vectorize(lambda x: np.sin(x))
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@ -178,6 +240,7 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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return slist, [S1, S2, S3], Ylist
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def bgplvm_simulation_matlab_compare():
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from GPy.util.datasets import simulation_BGPLVM
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sim_data = simulation_BGPLVM()
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Y = sim_data['Y']
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S = sim_data['S']
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@ -213,6 +276,8 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
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max_burnin=100, true_X=False,
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do_opt=True,
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max_f_eval=1000):
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from GPy.core.transformations import logexp_clipped
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D1, D2, D3, N, M, Q = 15, 8, 8, 350, 3, 6
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
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@ -317,6 +382,8 @@ def mrd_simulation(plot_sim=False):
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from GPy.models import mrd
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from GPy import kern
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from GPy.core.transformations import logexp_clipped
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reload(mrd); reload(kern)
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# k = kern.rbf(2, ARD=True) + kern.bias(2) + kern.white(2)
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