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manual merging
This commit is contained in:
James Hensman
commit
3cb445886d
3 changed files with 101 additions and 16 deletions
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@ -130,9 +130,9 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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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 += .2 * np.random.randn(*Y1.shape)
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Y2 += .2 * np.random.randn(*Y2.shape)
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Y3 += .2 * np.random.randn(*Y3.shape)
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Y1 += .1 * np.random.randn(*Y1.shape)
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Y2 += .1 * np.random.randn(*Y2.shape)
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Y3 += .1 * 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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@ -173,14 +173,15 @@ def bgplvm_simulation_matlab_compare():
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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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k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
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# X=mu,
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# X_variance=S,
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# X=mu,
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# X_variance=S,
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_debug=True)
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m.ensure_default_constraints()
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m.auto_scale_factor = True
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m['noise'] = .01 # Y.var() / 100.
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m['linear_variance'] = .01
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m['{}_variance'.format(k.parts[0].name)] = .01
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return m
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def bgplvm_simulation(burnin='scg', plot_sim=False,
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@ -47,7 +47,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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self._debug = _debug
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if self._debug:
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self.fcall = 0
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self.f_call = 0
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self._count = itertools.count()
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self._savedklll = []
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self._savedparams = []
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@ -94,7 +94,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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except (LinAlgError, FloatingPointError, ZeroDivisionError):
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print "\rWARNING: Caught LinAlgError, continueing without setting "
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if self._debug:
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self._savederrors.append(self.fcall)
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self._savederrors.append(self.f_call)
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# if save_count > 10:
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# raise
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# self._set_params(self.oldps[-1], save_old=False, save_count=save_count + 1)
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@ -242,9 +242,9 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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ax1.text(.5, .5, "Optimization", alpha=.3, transform=ax1.transAxes,
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ha='center', va='center')
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kllls = np.array(self._savedklll)
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LL, = ax1.plot(kllls[:, 0], kllls[:, 1] - kllls[:, 2], label=r'$\log p(\mathbf{Y})$', mew=1.5)
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KL, = ax1.plot(kllls[:, 0], kllls[:, 2], label=r'$\mathcal{KL}(p||q)$', mew=1.5)
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L, = ax1.plot(kllls[:, 0], kllls[:, 1], label=r'$L$', mew=1.5) # \mathds{E}_{q(\mathbf{X})}[p(\mathbf{Y|X})\frac{p(\mathbf{X})}{q(\mathbf{X})}]
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LL, = ax1.plot(kllls[:, 0], kllls[:, 1] - kllls[:, 2], '-', label=r'$\log p(\mathbf{Y})$', mew=1.5)
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KL, = ax1.plot(kllls[:, 0], kllls[:, 2], '-', label=r'$\mathcal{KL}(p||q)$', mew=1.5)
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L, = ax1.plot(kllls[:, 0], kllls[:, 1], '-', label=r'$L$', mew=1.5) # \mathds{E}_{q(\mathbf{X})}[p(\mathbf{Y|X})\frac{p(\mathbf{X})}{q(\mathbf{X})}]
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param_dict = dict(self._savedparams)
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gradient_dict = dict(self._savedgradients)
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@ -361,10 +361,11 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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indicatorKL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 2], 'o', c=KL.get_color())
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indicatorLL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 1] - kllls[self.showing, 2], 'o', c=LL.get_color())
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indicatorL, = ax1.plot(kllls[self.showing, 0], kllls[self.showing, 1], 'o', c=L.get_color())
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for err in self._savederrors:
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ax1.plot(kllls[err, 0], kllls[err, 2], "*", c=KL.get_color())
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ax1.plot(kllls[err, 0], kllls[err, 1] - kllls[err, 2], "*", c=LL.get_color())
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ax1.plot(kllls[err, 0], kllls[err, 1], "*", c=L.get_color())
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# for err in self._savederrors:
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# if err < kllls.shape[0]:
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# ax1.scatter(kllls[err, 0], kllls[err, 2], s=50, marker=(5, 2), c=KL.get_color())
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# ax1.scatter(kllls[err, 0], kllls[err, 1] - kllls[err, 2], s=50, marker=(5, 2), c=LL.get_color())
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# ax1.scatter(kllls[err, 0], kllls[err, 1], s=50, marker=(5, 2), c=L.get_color())
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# try:
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# for f in figs:
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83
GPy/testing/kern_psi_stat_tests.py
Normal file
83
GPy/testing/kern_psi_stat_tests.py
Normal file
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@ -0,0 +1,83 @@
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'''
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Created on 26 Apr 2013
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@author: maxz
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'''
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import unittest
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import GPy
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import numpy as np
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import pylab
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__test__ = False
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class Test(unittest.TestCase):
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D = 9
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M = 5
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Nsamples = 3e6
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def setUp(self):
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self.kerns = (
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GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
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GPy.kern.linear(self.D), GPy.kern.linear(self.D, ARD=True),
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GPy.kern.linear(self.D) + GPy.kern.bias(self.D),
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GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
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GPy.kern.linear(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
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GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
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GPy.kern.bias(self.D), GPy.kern.white(self.D),
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)
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self.q_x_mean = np.random.randn(self.D)
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self.q_x_variance = np.exp(np.random.randn(self.D))
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self.q_x_samples = np.random.randn(self.Nsamples, self.D) * np.sqrt(self.q_x_variance) + self.q_x_mean
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self.Z = np.random.randn(self.M, self.D)
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self.q_x_mean.shape = (1, self.D)
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self.q_x_variance.shape = (1, self.D)
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def test_psi0(self):
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for kern in self.kerns:
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psi0 = kern.psi0(self.Z, self.q_x_mean, self.q_x_variance)
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Kdiag = kern.Kdiag(self.q_x_samples)
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self.assertAlmostEqual(psi0, np.mean(Kdiag), 1)
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# print kern.parts[0].name, np.allclose(psi0, np.mean(Kdiag))
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def test_psi1(self):
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for kern in self.kerns:
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Nsamples = 100
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psi1 = kern.psi1(self.Z, self.q_x_mean, self.q_x_variance)
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K_ = np.zeros((self.N, self.M))
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diffs = []
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for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
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K = kern.K(q_x_sample_stripe, self.Z)
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K_ += K
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diffs.append(((psi1 - (K_ / (i + 1))) ** 2).mean())
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K_ /= self.Nsamples / Nsamples
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# pylab.figure("+".join([p.name for p in kern.parts]) + "psi1")
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# pylab.plot(diffs)
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self.assertTrue(np.allclose(psi1.flatten() , K.mean(0), rtol=1e-1))
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def test_psi2(self):
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for kern in self.kerns:
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Nsamples = 100
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psi2 = kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
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K_ = np.zeros((self.M, self.M))
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diffs = []
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for i, q_x_sample_stripe in enumerate(np.array_split(self.q_x_samples, self.Nsamples / Nsamples)):
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K = kern.K(q_x_sample_stripe, self.Z)
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K = (K[:, :, None] * K[:, None, :]).mean(0)
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K_ += K
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diffs.append(((psi2 - (K_ / (i + 1))) ** 2).mean())
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K_ /= self.Nsamples / Nsamples
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try:
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# pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
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# pylab.plot(diffs)
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self.assertTrue(np.allclose(psi2.squeeze(), K_,
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rtol=1e-1, atol=.1),
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msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
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except:
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print "{}: not matching".format(kern.parts[0].name)
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if __name__ == "__main__":
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import sys;sys.argv = ['',
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'Test.test_psi0',
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'Test.test_psi1',
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'Test.test_psi2']
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unittest.main()
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