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dimensionality reduction examples updated with optimize, plot and verbose
This commit is contained in:
Max Zwiessele
parent
d0a9995c38
commit
50e9034a6d
2 changed files with 216 additions and 261 deletions
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@ -66,5 +66,5 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
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pb.plot(mu[:, 0] , mu[:, 1], 'ko')
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def plot_latent(self, *args, **kwargs):
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input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
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pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')
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GPLVM.plot_latent(self, *args, **kwargs)
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#pb.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
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@ -1,99 +1,93 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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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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default_seed = _np.random.seed(123344)
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import numpy as np
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from matplotlib import pyplot as plt, cm
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import GPy
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from GPy.core.transformations import logexp
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from GPy.likelihoods.gaussian import Gaussian
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from GPy.models import BayesianGPLVM
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default_seed = np.random.seed(123344)
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def BGPLVM(seed=default_seed):
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N = 13
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def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0):
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"""
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model for testing purposes. Samples from a GP with rbf kernel and learns
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the samples with a new kernel. Normally not for optimization, just model cheking
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"""
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from GPy.likelihoods.gaussian import Gaussian
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import GPy
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num_inputs = 13
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num_inducing = 5
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Q = 6
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D = 25
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if plot:
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output_dim = 1
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input_dim = 2
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else:
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input_dim = 2
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output_dim = 25
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# generate GPLVM-like data
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X = np.random.rand(N, Q)
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lengthscales = np.random.rand(Q)
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k = (GPy.kern.rbf(Q, .5, lengthscales, ARD=True)
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+ GPy.kern.white(Q, 0.01))
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X = _np.random.rand(num_inputs, input_dim)
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lengthscales = _np.random.rand(input_dim)
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k = (GPy.kern.rbf(input_dim, .5, lengthscales, ARD=True)
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+ GPy.kern.white(input_dim, 0.01))
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N), K, D).T
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Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
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lik = Gaussian(Y, normalize=True)
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# k = GPy.kern.rbf_inv(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
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# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
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# k = GPy.kern.rbf(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.rbf(Q, .3, np.ones(Q) * .2, ARD=True)
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k = GPy.kern.rbf(Q, .5, np.ones(Q) * 2., ARD=True) + GPy.kern.linear(Q, np.ones(Q) * .2, ARD=True)
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# k = GPy.kern.rbf(Q, .5, 2., ARD=0) + GPy.kern.rbf(Q, .3, .2, ARD=0)
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k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
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# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.rbf(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
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# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
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# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
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m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing)
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m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
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m.lengthscales = lengthscales
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# m.constrain_positive('(rbf|bias|noise|white|S)')
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# m.constrain_fixed('S', 1)
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# pb.figure()
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# m.plot()
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# pb.title('PCA initialisation')
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# pb.figure()
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# m.optimize(messages = 1)
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# m.plot()
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# pb.title('After optimisation')
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# m.randomize()
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# m.checkgrad(verbose=1)
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if plot:
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import matplotlib.pyplot as pb
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m.plot()
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pb.title('PCA initialisation')
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if optimize:
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m.optimize('scg', messages=verbose)
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if plot:
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m.plot()
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pb.title('After optimisation')
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return m
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def GPLVM_oil_100(optimize=True):
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def gplvm_oil_100(optimize=1, verbose=1, plot=1):
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import GPy
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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(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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if optimize:
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m.optimize('scg', messages=1)
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# plot
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print(m)
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m.plot_latent(labels=m.data_labels)
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if optimize: m.optimize('scg', messages=verbose)
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if plot: m.plot_latent(labels=m.data_labels)
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return m
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def sparseGPLVM_oil(optimize=True, N=100, Q=6, num_inducing=15, max_iters=50):
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np.random.seed(0)
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def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15, max_iters=50):
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import GPy
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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Y = data['X'][:N]
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Y = Y - Y.mean(0)
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Y /= Y.std(0)
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# create simple GP model
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# Create the model
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q)
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m = GPy.models.SparseGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing)
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m.data_labels = data['Y'].argmax(axis=1)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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# optimize
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if optimize:
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m.optimize('scg', messages=1, max_iters=max_iters)
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# plot
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print(m)
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# m.plot_latent(labels=m.data_labels)
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if optimize: m.optimize('scg', messages=verbose, max_iters=max_iters)
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if plot:
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m.plot_latent(labels=m.data_labels)
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m.kern.plot_ARD()
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return m
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def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
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def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigma=.2):
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import GPy
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from GPy.util.datasets import swiss_roll_generated
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from GPy.core.transformations import logexp_clipped
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from GPy.models import BayesianGPLVM
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data = swiss_roll_generated(N=N, sigma=sigma)
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data = swiss_roll_generated(num_samples=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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@ -106,114 +100,98 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
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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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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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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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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D # @UnusedImport
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fig = plt.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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ax.set_title("BGPLVM init")
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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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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)[:num_inducing]
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Z = _np.random.permutation(X)[:num_inducing]
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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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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 = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, 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['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
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m['noise_variance'] = Y.var() / 100.
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m['bias_variance'] = 0.05
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if optimize:
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m.optimize('scg', messages=1)
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m.optimize('scg', messages=verbose, max_iters=2e3)
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if plot:
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fig = plt.figure('fitted')
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ax = fig.add_subplot(111)
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s = m.input_sensitivity().argsort()[::-1][:2]
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ax.scatter(*m.X.T[s], c=c)
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return m
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def BGPLVM_oil(optimize=True, N=200, Q=7, num_inducing=40, max_iters=1000, plot=False, **k):
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np.random.seed(0)
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def bgplvm_oil(optimize=1, verbose=1, plot=1, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
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import GPy
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from GPy.likelihoods import Gaussian
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from matplotlib import pyplot as plt
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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# create simple GP model
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kernel = GPy.kern.rbf_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2))
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kernel = GPy.kern.rbf_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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Yn = Gaussian(Y, normalize=True)
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# Yn = Y - Y.mean(0)
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# Yn /= Yn.std(0)
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m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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# m.constrain('variance|leng', logexp_clipped())
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# m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
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m['noise'] = Yn.Y.var() / 100.
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# optimize
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if optimize:
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m.optimize('scg', messages=1, max_iters=max_iters, gtol=.05)
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m.optimize('scg', messages=verbose, max_iters=max_iters, gtol=.05)
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if plot:
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y = m.likelihood.Y[0, :]
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes)
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close(fig)
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return m
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def oil_100():
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data = GPy.util.datasets.oil_100()
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m = GPy.models.GPLVM(data['X'], 2)
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# optimize
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m.optimize(messages=1, max_iters=2)
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# plot
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print(m)
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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, num_inducing, 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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s2 = np.vectorize(lambda x: np.cos(x))
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s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
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sS = np.vectorize(lambda x: np.sin(2 * x))
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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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s2 = _np.vectorize(lambda x: _np.cos(x))
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s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
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sS = _np.vectorize(lambda x: _np.sin(2 * x))
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s1 = s1(x)
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s2 = s2(x)
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s3 = s3(x)
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sS = sS(x)
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S1 = np.hstack([s1, sS])
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S2 = np.hstack([s2, s3, sS])
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S3 = np.hstack([s3, sS])
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S1 = _np.hstack([s1, sS])
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S2 = _np.hstack([s2, s3, sS])
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S3 = _np.hstack([s3, sS])
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Y1 = S1.dot(np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
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Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
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Y1 = S1.dot(_np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(_np.random.randn(S2.shape[1], D2))
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Y3 = S3.dot(_np.random.randn(S3.shape[1], D3))
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Y1 += .3 * np.random.randn(*Y1.shape)
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Y2 += .2 * np.random.randn(*Y2.shape)
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Y3 += .25 * np.random.randn(*Y3.shape)
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Y1 += .3 * _np.random.randn(*Y1.shape)
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Y2 += .2 * _np.random.randn(*Y2.shape)
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Y3 += .25 * _np.random.randn(*Y3.shape)
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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@ -245,88 +223,74 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, 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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mu = sim_data['mu']
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num_inducing, [_, Q] = 3, mu.shape
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# def bgplvm_simulation_matlab_compare():
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# from GPy.util.datasets import simulation_BGPLVM
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# from GPy import kern
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# from GPy.models import BayesianGPLVM
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#
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# sim_data = simulation_BGPLVM()
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# Y = sim_data['Y']
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# mu = sim_data['mu']
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# num_inducing, [_, Q] = 3, mu.shape
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#
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# k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
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# m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
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# _debug=False)
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# m.auto_scale_factor = True
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# m['noise'] = Y.var() / 100.
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# m['linear_variance'] = .01
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# return m
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from GPy.models import mrd
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from GPy import kern
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reload(mrd); reload(kern)
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k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
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# X=mu,
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# X_variance=S,
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_debug=False)
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m.auto_scale_factor = True
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
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return m
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def bgplvm_simulation(optimize='scg',
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plot=True,
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def bgplvm_simulation(optimize=1, verbose=1,
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plot=1, plot_sim=False,
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max_iters=2e4,
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plot_sim=False):
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# from GPy.core.transformations import logexp_clipped
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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)
|
||||
|
||||
from GPy.models import mrd
|
||||
):
|
||||
from GPy import kern
|
||||
reload(mrd); reload(kern)
|
||||
from GPy.models import BayesianGPLVM
|
||||
|
||||
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
|
||||
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
|
||||
Y = Ylist[0]
|
||||
|
||||
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
|
||||
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['noise'] = Y.var() / 100.
|
||||
|
||||
if optimize:
|
||||
print "Optimizing model:"
|
||||
m.optimize(optimize, max_iters=max_iters,
|
||||
messages=True, gtol=.05)
|
||||
m.optimize('scg', messages=verbose, max_iters=max_iters,
|
||||
gtol=.05)
|
||||
if plot:
|
||||
m.plot_X_1d("BGPLVM Latent Space 1D")
|
||||
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
|
||||
return m
|
||||
|
||||
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
|
||||
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
|
||||
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
|
||||
|
||||
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
|
||||
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
|
||||
|
||||
from GPy.models import mrd
|
||||
from GPy import kern
|
||||
|
||||
reload(mrd); reload(kern)
|
||||
|
||||
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
|
||||
m = mrd.MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
|
||||
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
|
||||
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
|
||||
m.ensure_default_constraints()
|
||||
|
||||
for i, bgplvm in enumerate(m.bgplvms):
|
||||
m['{}_noise'.format(i)] = bgplvm.likelihood.Y.var() / 500.
|
||||
|
||||
|
||||
# DEBUG
|
||||
# np.seterr("raise")
|
||||
|
||||
if optimize:
|
||||
print "Optimizing Model:"
|
||||
m.optimize(messages=1, max_iters=8e3, gtol=.1)
|
||||
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)
|
||||
if plot:
|
||||
m.plot_X_1d("MRD Latent Space 1D")
|
||||
m.plot_scales("MRD Scales")
|
||||
return m
|
||||
|
||||
def brendan_faces():
|
||||
from GPy import kern
|
||||
def brendan_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
|
||||
data = GPy.util.datasets.brendan_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
|
|
@ -338,18 +302,20 @@ def brendan_faces():
|
|||
# optimize
|
||||
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
|
||||
|
||||
m.optimize('scg', messages=1, max_iters=1000)
|
||||
if optimize: m.optimize('scg', messages=verbose, max_iters=1000)
|
||||
|
||||
ax = m.plot_latent(which_indices=(0, 1))
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
if plot:
|
||||
ax = m.plot_latent(which_indices=(0, 1))
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
|
||||
GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
def olivetti_faces():
|
||||
from GPy import kern
|
||||
def olivetti_faces(optimize=True, verbose=True, plot=True):
|
||||
import GPy
|
||||
|
||||
data = GPy.util.datasets.olivetti_faces()
|
||||
Q = 2
|
||||
Y = data['Y']
|
||||
|
|
@ -357,153 +323,142 @@ def olivetti_faces():
|
|||
Yn /= Yn.std()
|
||||
|
||||
m = GPy.models.GPLVM(Yn, Q)
|
||||
m.optimize('scg', messages=1, max_iters=1000)
|
||||
|
||||
ax = m.plot_latent(which_indices=(0, 1))
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
if optimize: m.optimize('scg', messages=verbose, max_iters=1000)
|
||||
if plot:
|
||||
ax = m.plot_latent(which_indices=(0, 1))
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
|
||||
GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
def stick_play(range=None, frame_rate=15):
|
||||
|
||||
def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True):
|
||||
import GPy
|
||||
data = GPy.util.datasets.osu_run1()
|
||||
# optimize
|
||||
if range == None:
|
||||
Y = data['Y'].copy()
|
||||
else:
|
||||
Y = data['Y'][range[0]:range[1], :].copy()
|
||||
y = Y[0, :]
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
GPy.util.visualize.data_play(Y, data_show, frame_rate)
|
||||
if plot:
|
||||
y = Y[0, :]
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
GPy.util.visualize.data_play(Y, data_show, frame_rate)
|
||||
return Y
|
||||
|
||||
def stick(kernel=None):
|
||||
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)
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
if GPy.util.visualize.visual_available:
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
if plot and GPy.util.visualize.visual_available:
|
||||
plt.clf
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
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):
|
||||
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)
|
||||
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
if GPy.util.visualize.visual_available:
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
if plot and GPy.util.visualize.visual_available:
|
||||
plt.clf
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
def bcgplvm_stick(kernel=None):
|
||||
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.)
|
||||
mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
|
||||
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
if GPy.util.visualize.visual_available:
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
if plot and GPy.util.visualize.visual_available:
|
||||
plt.clf
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
def robot_wireless():
|
||||
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)
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
m._set_params(m._get_params())
|
||||
plt.clf
|
||||
ax = m.plot_latent()
|
||||
if plot:
|
||||
m.plot_latent()
|
||||
|
||||
return m
|
||||
|
||||
def stick_bgplvm(model=None):
|
||||
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))
|
||||
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
|
||||
m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
|
||||
# optimize
|
||||
m.ensure_default_constraints()
|
||||
m.optimize('scg', messages=1, max_iters=200, xtol=1e-300, ftol=1e-300)
|
||||
if optimize: m.optimize('scg', messages=verbose, max_iters=200, xtol=1e-300, ftol=1e-300)
|
||||
m._set_params(m._get_params())
|
||||
plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
plt.sca(latent_axes)
|
||||
m.plot_latent()
|
||||
y = m.likelihood.Y[0, :].copy()
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
|
||||
raw_input('Press enter to finish')
|
||||
if plot:
|
||||
plt.clf, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
plt.sca(latent_axes)
|
||||
m.plot_latent()
|
||||
y = m.likelihood.Y[0, :].copy()
|
||||
data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
|
||||
raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
|
||||
def cmu_mocap(subject='35', motion=['01'], in_place=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)
|
||||
Y = data['Y']
|
||||
if in_place:
|
||||
# Make figure move in place.
|
||||
data['Y'][:, 0:3] = 0.0
|
||||
m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
|
||||
|
||||
# optimize
|
||||
m.optimize(messages=1, max_f_eval=10000)
|
||||
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
lvm_visualizer.close()
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
if plot:
|
||||
ax = m.plot_latent()
|
||||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
|
||||
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
raw_input('Press enter to finish')
|
||||
lvm_visualizer.close()
|
||||
|
||||
return m
|
||||
|
||||
# def BGPLVM_oil():
|
||||
# data = GPy.util.datasets.oil()
|
||||
# Y, X = data['Y'], data['X']
|
||||
# X -= X.mean(axis=0)
|
||||
# X /= X.std(axis=0)
|
||||
#
|
||||
# Q = 10
|
||||
# num_inducing = 30
|
||||
#
|
||||
# kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
|
||||
# m = GPy.models.BayesianGPLVM(X, Q, kernel=kernel, num_inducing=num_inducing)
|
||||
# # m.scale_factor = 100.0
|
||||
# m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
|
||||
# from sklearn import cluster
|
||||
# km = cluster.KMeans(num_inducing, verbose=10)
|
||||
# Z = km.fit(m.X).cluster_centers_
|
||||
# # Z = GPy.util.misc.kmm_init(m.X, num_inducing)
|
||||
# m.set('iip', Z)
|
||||
# m.set('bias', 1e-4)
|
||||
# # optimize
|
||||
#
|
||||
# import pdb; pdb.set_trace()
|
||||
# m.optimize('tnc', messages=1)
|
||||
# print m
|
||||
# m.plot_latent(labels=data['Y'].argmax(axis=1))
|
||||
# return m
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue