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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
James Hensman 2013-06-04 18:25:56 +01:00
commit e53b97e09f
7 changed files with 41 additions and 35 deletions
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1
GPy/core/priors.py
View file

@ -136,6 +136,7 @@ def gamma_from_EV(E, V):
warnings.warn("use Gamma.from_EV to create Gamma Prior", FutureWarning)
return Gamma.from_EV(E, V)
class Gamma(Prior):
"""
Implementation of the Gamma probability function, coupled with random variables.

2
GPy/inference/SCG.py
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@ -63,7 +63,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
success = True # Force calculation of directional derivs.
nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter.
betamin = 1.0e-15 # Lower bound on scale.
betamin = 1.0e-60 # Lower bound on scale.
betamax = 1.0e100 # Upper bound on scale.
status = "Not converged"

8
GPy/inference/SGD.py
View file

@ -192,7 +192,7 @@ class opt_SGD(Optimizer):
if self.model.N == 0 or Y.std() == 0.0:
return 0, step, self.model.N
self.model.likelihood._bias = Y.mean()
self.model.likelihood._offset = Y.mean()
self.model.likelihood._scale = Y.std()
self.model.likelihood.set_data(Y)
# self.model.likelihood.V = self.model.likelihood.Y*self.model.likelihood.precision
@ -219,9 +219,9 @@ class opt_SGD(Optimizer):
self.restore_constraints(ci)
self.model.grads[j] = fp
# restore likelihood _bias and _scale, otherwise when we call set_data(y) on
# restore likelihood _offset and _scale, otherwise when we call set_data(y) on
# the next feature, it will get normalized with the mean and std of this one.
self.model.likelihood._bias = 0
self.model.likelihood._offset = 0
self.model.likelihood._scale = 1
return f, step, self.model.N
@ -266,7 +266,7 @@ class opt_SGD(Optimizer):
self.model.likelihood.YYT = 0
self.model.likelihood.trYYT = 0
self.model.likelihood._bias = 0.0
self.model.likelihood._offset = 0.0
self.model.likelihood._scale = 1.0
N, Q = self.model.X.shape

5
GPy/kern/kern.py
View file

@ -46,10 +46,11 @@ class kern(parameterised):
parameterised.__init__(self)
def plot_ARD(self, ax=None):
def plot_ARD(self, fignum=None, ax=None):
"""If an ARD kernel is present, it bar-plots the ARD parameters"""
if ax is None:
ax = pb.gca()
fig = pb.figure(fignum)
ax = fig.add_subplot(111)
for p in self.parts:
if hasattr(p, 'ARD') and p.ARD:
ax.set_title('ARD parameters, %s kernel' % p.name)

8
GPy/likelihoods/Gaussian.py
View file

@ -19,12 +19,12 @@ class Gaussian(likelihood):
# normalization
if normalize:
self._bias = data.mean(0)[None, :]
self._offset = data.mean(0)[None, :]
self._scale = data.std(0)[None, :]
# Don't scale outputs which have zero variance to zero.
self._scale[np.nonzero(self._scale == 0.)] = 1.0e-3
else:
self._bias = np.zeros((1, self.D))
self._offset = np.zeros((1, self.D))
self._scale = np.ones((1, self.D))
self.set_data(data)
@ -36,7 +36,7 @@ class Gaussian(likelihood):
self.data = data
self.N, D = data.shape
assert D == self.D
self.Y = (self.data - self._bias) / self._scale
self.Y = (self.data - self._offset) / self._scale
if D > self.N:
self.YYT = np.dot(self.Y, self.Y.T)
self.trYYT = np.trace(self.YYT)
@ -66,7 +66,7 @@ class Gaussian(likelihood):
"""
Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
"""
mean = mu * self._scale + self._bias
mean = mu * self._scale + self._offset
if full_cov:
if self.D > 1:
raise NotImplementedError, "TODO"

16
GPy/models/Bayesian_GPLVM.py
View file

@ -218,20 +218,20 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
return means, covars
def plot_X_1d(self, fig=None, axes=None, fig_num="LVM mu S 1d", colors=None):
def plot_X_1d(self, fignum=None, ax=None, colors=None):
"""
Plot latent space X in 1D:
-if fig is given, create Q subplots in fig and plot in these
-if axes is given plot Q 1D latent space plots of X into each `axis`
-if neither fig nor axes is given create a figure with fig_num and plot in there
-if ax is given plot Q 1D latent space plots of X into each `axis`
-if neither fig nor ax is given create a figure with fignum and plot in there
colors:
colors of different latent space dimensions Q
"""
import pylab
if fig is None and axes is None:
fig = pylab.figure(num=fig_num, figsize=(8, min(12, (2 * self.X.shape[1]))))
if ax is None:
fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.X.shape[1]))))
if colors is None:
colors = pylab.gca()._get_lines.color_cycle
pylab.clf()
@ -240,10 +240,12 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
plots = []
x = np.arange(self.X.shape[0])
for i in range(self.X.shape[1]):
if axes is None:
if ax is None:
ax = fig.add_subplot(self.X.shape[1], 1, i + 1)
elif isinstance(ax, (tuple, list)):
ax = ax[i]
else:
ax = axes[i]
raise ValueError("Need one ax per latent dimnesion Q")
ax.plot(self.X, c='k', alpha=.3)
plots.extend(ax.plot(x, self.X.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
ax.fill_between(x,

36
GPy/models/mrd.py
View file

@ -256,17 +256,19 @@ class MRD(model):
self.Z = Z
return Z
def _handle_plotting(self, fig_num, axes, plotf):
if axes is None:
fig = pylab.figure(num=fig_num, figsize=(4 * len(self.bgplvms), 3))
def _handle_plotting(self, fignum, ax, plotf):
if ax is None:
fig = pylab.figure(num=fignum, figsize=(4 * len(self.bgplvms), 3))
for i, g in enumerate(self.bgplvms):
if axes is None:
if ax is None:
ax = fig.add_subplot(1, len(self.bgplvms), i + 1)
elif isinstance(ax, (tuple, list)):
ax = ax[i]
else:
ax = axes[i]
raise ValueError("Need one ax per latent dimension Q")
plotf(i, g, ax)
pylab.draw()
if axes is None:
if ax is None:
fig.tight_layout()
return fig
else:
@ -275,20 +277,20 @@ class MRD(model):
def plot_X_1d(self):
return self.gref.plot_X_1d()
def plot_X(self, fig_num="MRD Predictions", axes=None):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: ax.imshow(g.X))
def plot_X(self, fignum="MRD Predictions", ax=None):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
return fig
def plot_predict(self, fig_num="MRD Predictions", axes=None, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs))
def plot_predict(self, fignum="MRD Predictions", ax=None, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs))
return fig
def plot_scales(self, fig_num="MRD Scales", axes=None, *args, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
def plot_scales(self, fignum="MRD Scales", ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
return fig
def plot_latent(self, fig_num="MRD Latent Spaces", axes=None, *args, **kwargs):
fig = self._handle_plotting(fig_num, axes, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
def plot_latent(self, fignum="MRD Latent Spaces", ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
return fig
def _debug_plot(self):
@ -296,11 +298,11 @@ class MRD(model):
fig = pylab.figure("MRD DEBUG PLOT", figsize=(4 * len(self.bgplvms), 9))
fig.clf()
axes = [fig.add_subplot(3, len(self.bgplvms), i + 1) for i in range(len(self.bgplvms))]
self.plot_X(axes=axes)
self.plot_X(ax=axes)
axes = [fig.add_subplot(3, len(self.bgplvms), i + len(self.bgplvms) + 1) for i in range(len(self.bgplvms))]
self.plot_latent(axes=axes)
self.plot_latent(ax=axes)
axes = [fig.add_subplot(3, len(self.bgplvms), i + 2 * len(self.bgplvms) + 1) for i in range(len(self.bgplvms))]
self.plot_scales(axes=axes)
self.plot_scales(ax=axes)
pylab.draw()
fig.tight_layout()