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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Alan Saul
commit
a31fd31ccb
6 changed files with 73 additions and 67 deletions
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@ -142,7 +142,7 @@ class GP(GPBase):
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"""
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# normalize X values
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Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
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Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
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mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
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# now push through likelihood
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@ -21,12 +21,12 @@ class GPBase(Model):
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self.num_data, self.output_dim = self.likelihood.data.shape
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if normalize_X:
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self._Xmean = X.mean(0)[None, :]
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self._Xstd = X.std(0)[None, :]
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self.X = (X.copy() - self._Xmean) / self._Xstd
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self._Xoffset = X.mean(0)[None, :]
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self._Xscale = X.std(0)[None, :]
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self.X = (X.copy() - self._Xoffset) / self._Xscale
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else:
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self._Xmean = np.zeros((1, self.input_dim))
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self._Xstd = np.ones((1, self.input_dim))
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self._Xoffset = np.zeros((1, self.input_dim))
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self._Xscale = np.ones((1, self.input_dim))
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super(GPBase, self).__init__()
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# All leaf nodes should call self._set_params(self._get_params()) at
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@ -107,7 +107,7 @@ class GPBase(Model):
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if self.X.shape[1] == 1:
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Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
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m, _, lower, upper = self.predict(Xnew, which_parts=which_parts)
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@ -43,11 +43,11 @@ class SparseGP(GPBase):
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self.X_variance = X_variance
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if normalize_X:
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self.Z = (self.Z.copy() - self._Xmean) / self._Xstd
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self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
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# normalize X uncertainty also
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if self.has_uncertain_inputs:
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self.X_variance /= np.square(self._Xstd)
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self.X_variance /= np.square(self._Xscale)
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def _compute_kernel_matrices(self):
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# kernel computations, using BGPLVM notation
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@ -269,9 +269,9 @@ class SparseGP(GPBase):
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"""
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# normalize X values
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Xnew = (Xnew.copy() - self._Xmean) / self._Xstd
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Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
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if X_variance_new is not None:
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X_variance_new = X_variance_new / self._Xstd ** 2
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X_variance_new = X_variance_new / self._Xscale ** 2
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# here's the actual prediction by the GP model
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mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
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@ -292,13 +292,13 @@ class SparseGP(GPBase):
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GPBase.plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax)
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xstd + self._Xmean # NOTE self.X are the normalized values now
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xstd + self._Xmean
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xstd + self._Xmean
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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@ -162,6 +162,7 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
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Y[Y.flatten()==-1]=0
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m = GPy.models.FITCClassification(data['X'], Y,num_inducing=num_inducing)
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m.constrain_bounded('.*len',1.,1e3)
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m.ensure_default_constraints()
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m['.*len'] = 3.
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#m.update_likelihood_approximation()
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@ -304,72 +304,75 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
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m.plot_scales("MRD Scales")
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return m
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def brendan_faces():
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from GPy import kern
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data = GPy.util.datasets.brendan_faces()
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Q = 2
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Y = data['Y'][0:-1:10, :]
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# Y = data['Y']
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Yn = Y - Y.mean()
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Yn /= Yn.std()
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# # Commented out because dataset is missing
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# def brendan_faces():
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# from GPy import kern
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# data = GPy.util.datasets.brendan_faces()
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# Q = 2
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# Y = data['Y'][0:-1:10, :]
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# # Y = data['Y']
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# Yn = Y - Y.mean()
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# Yn /= Yn.std()
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m = GPy.models.GPLVM(Yn, Q)
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# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
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# m = GPy.models.GPLVM(Yn, Q)
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# # m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
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# optimize
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m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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# # optimize
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# m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=10000)
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# m.ensure_default_constraints()
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# m.optimize('scg', messages=1, max_f_eval=10000)
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ax = m.plot_latent(which_indices=(0, 1))
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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# ax = m.plot_latent(which_indices=(0, 1))
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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return m
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# return m
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def stick():
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data = GPy.util.datasets.stick()
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m = GPy.models.GPLVM(data['Y'], 2)
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# # Commented out because dataset is missing
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# def stick():
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# data = GPy.util.datasets.stick()
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# m = GPy.models.GPLVM(data['Y'], 2)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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m._set_params(m._get_params())
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# # optimize
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# m.ensure_default_constraints()
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# m.optimize(messages=1, max_f_eval=10000)
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# m._set_params(m._get_params())
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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# ax = m.plot_latent()
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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return m
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# return m
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def cmu_mocap(subject='35', motion=['01'], in_place=True):
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# # Commented out because dataset is missing
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# def cmu_mocap(subject='35', motion=['01'], in_place=True):
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data = GPy.util.datasets.cmu_mocap(subject, motion)
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Y = data['Y']
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if in_place:
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# Make figure move in place.
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data['Y'][:, 0:3] = 0.0
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m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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# data = GPy.util.datasets.cmu_mocap(subject, motion)
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# Y = data['Y']
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# if in_place:
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# # Make figure move in place.
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# data['Y'][:, 0:3] = 0.0
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# m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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# # optimize
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# m.ensure_default_constraints()
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# m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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plt.close('all')
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# ax = m.plot_latent()
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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return m
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# return m
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# def BGPLVM_oil():
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# data = GPy.util.datasets.oil()
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@ -143,5 +143,7 @@ def model_interaction():
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X = np.random.randn(20,1)
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Y = np.sin(X) + np.random.randn(*X.shape)*0.01 + 5.
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k = GPy.kern.rbf(1) + GPy.kern.bias(1)
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return GPy.models.GPRegression(X, Y, kernel=k)
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m = GPy.models.GPRegression(X, Y, kernel=k)
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m.ensure_default_constraints()
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return m
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