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Merge remote-tracking branch 'upstream/devel' into devel

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Zhenwen Dai 2016-01-14 09:42:19 +00:00
commit 52c6fe599f
14 changed files with 106 additions and 99 deletions
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2
GPy/__version__.py
View file

@ -1 +1 @@
__version__ = "0.9.4"
__version__ = "0.9.5"

4
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -190,8 +190,8 @@ class VarDTC(LatentFunctionInference):
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
Bi, _ = dpotri(LB, lower=1)
symmetrify(Bi)
#Bi, _ = dpotri(LB, lower=1)
#symmetrify(Bi)
Bi = -dpotri(LB, lower=1)[0]
diag.add(Bi, 1)

2
GPy/kern/__init__.py
View file

@ -28,4 +28,4 @@ from .src.trunclinear import TruncLinear,TruncLinear_inf
from .src.splitKern import SplitKern,DEtime
from .src.splitKern import DEtime as DiffGenomeKern
from .src.spline import Spline
from .src.basis_funcs import LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel
from .src.basis_funcs import LogisticBasisFuncKernel, LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel

2
GPy/kern/src/ODE_UYC.py
View file

@ -18,7 +18,7 @@ class ODE_UYC(Kern):
self.lengthscale_U = Param('lengthscale_U', lengthscale_U, Logexp())
self.ubias = Param('ubias', ubias, Logexp())
self.add_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U, self.ubias)
self.link_parameters(self.variance_Y, self.variance_U, self.lengthscale_Y, self.lengthscale_U, self.ubias)
def K(self, X, X2=None):
# model : a * dy/dt + b * y = U

2
GPy/kern/src/ODE_st.py
View file

@ -38,7 +38,7 @@ class ODE_st(Kern):
self.b = Param('b', b, Logexp())
self.c = Param('c', c, Logexp())
self.add_parameters(self.a, self.b, self.c, self.variance_Yt, self.variance_Yx, self.lengthscale_Yt,self.lengthscale_Yx)
self.link_parameters(self.a, self.b, self.c, self.variance_Yt, self.variance_Yx, self.lengthscale_Yt,self.lengthscale_Yx)
def K(self, X, X2=None):

2
GPy/kern/src/ODE_t.py
View file

@ -17,7 +17,7 @@ class ODE_t(Kern):
self.a= Param('a', a, Logexp())
self.c = Param('c', c, Logexp())
self.ubias = Param('ubias', ubias, Logexp())
self.add_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
self.link_parameters(self.a, self.c, self.variance_Yt, self.lengthscale_Yt,self.ubias)
def K(self, X, X2=None):
"""Compute the covariance matrix between X and X2."""

34
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -50,6 +50,17 @@ def _wait_for_updates(view, updates):
# No updateable view:
pass
def _new_canvas(self, projection, kwargs, which_indices):
input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
if input_3 is None:
zlabel = None
else:
zlabel = 'latent dimension %i' % input_3
canvas, kwargs = pl().new_canvas(projection=projection, xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel=zlabel, **kwargs)
return canvas, projection, kwargs, sig_dims
def _plot_latent_scatter(canvas, X, visible_dims, labels, marker, num_samples, projection='2d', **kwargs):
from .. import Tango
@ -85,12 +96,8 @@ def plot_latent_scatter(self, labels=None,
:param str marker: markers to use - cycle if more labels then markers are given
:param kwargs: the kwargs for the scatter plots
"""
input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
canvas, kwargs = pl().new_canvas(projection=projection,
xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel='latent dimension %i' % input_3, **kwargs)
X, _, _ = get_x_y_var(self)
if labels is None:
labels = np.ones(self.num_data)
@ -101,8 +108,6 @@ def plot_latent_scatter(self, labels=None,
return pl().add_to_canvas(canvas, dict(scatter=scatters), legend=legend)
def plot_latent_inducing(self,
which_indices=None,
legend=False,
@ -122,17 +127,8 @@ def plot_latent_inducing(self,
:param str marker: markers to use - cycle if more labels then markers are given
:param kwargs: the kwargs for the scatter plots
"""
input_1, input_2, input_3 = sig_dims = self.get_most_significant_input_dimensions(which_indices)
if input_3 is None: zlabel=None
else: zlabel = 'latent dimension %i' % input_3
canvas, projection, kwargs, sig_dims = _new_canvas(self, projection, kwargs, which_indices)
if 'color' not in kwargs:
kwargs['color'] = 'white'
canvas, kwargs = pl().new_canvas(projection=projection,
xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel=zlabel, **kwargs)
Z = self.Z.values
labels = np.array(['inducing'] * Z.shape[0])
scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
@ -231,7 +227,7 @@ def plot_latent(self, labels=None, which_indices=None,
plot_limits=None,
updates=False,
kern=None, marker='<>^vsd',
num_samples=1000,
num_samples=1000, projection='2d',
scatter_kwargs=None, **imshow_kwargs):
"""
Plot the latent space of the GP on the inputs. This is the
@ -251,6 +247,8 @@ def plot_latent(self, labels=None, which_indices=None,
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param scatter_kwargs: the kwargs for the scatter plots
"""
if projection != '2d':
raise ValueError('Cannot plot latent in other then 2 dimensions, consider plot_scatter')
input_1, input_2 = which_indices = self.get_most_significant_input_dimensions(which_indices)[:2]
X = get_x_y_var(self)[0]
_, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, X, plot_limits, which_indices, None, resolution)

70
GPy/plotting/matplot_dep/plot_definitions.py
View file

@ -1,21 +1,21 @@
#===============================================================================
# Copyright (c) 2015, Max Zwiessele
# All rights reserved.
#
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
#
# * Neither the name of GPy.plotting.matplot_dep.plot_definitions nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
@ -41,14 +41,14 @@ class MatplotlibPlots(AbstractPlottingLibrary):
def __init__(self):
super(MatplotlibPlots, self).__init__()
self._defaults = defaults.__dict__
def figure(self, rows=1, cols=1, **kwargs):
fig = plt.figure(**kwargs)
fig.rows = rows
fig.cols = cols
return fig
def new_canvas(self, figure=None, col=1, row=1, projection='2d', xlabel=None, ylabel=None, zlabel=None, title=None, xlim=None, ylim=None, zlim=None, **kwargs):
def new_canvas(self, figure=None, row=1, col=1, projection='2d', xlabel=None, ylabel=None, zlabel=None, title=None, xlim=None, ylim=None, zlim=None, **kwargs):
if projection == '3d':
from mpl_toolkits.mplot3d import Axes3D
elif projection == '2d':
@ -64,10 +64,10 @@ class MatplotlibPlots(AbstractPlottingLibrary):
fig = self.figure(figsize=kwargs.pop('figsize'))
else:
fig = self.figure()
#if hasattr(fig, 'rows') and hasattr(fig, 'cols'):
ax = fig.add_subplot(fig.rows, fig.cols, (col,row), projection=projection)
if xlim is not None: ax.set_xlim(xlim)
if ylim is not None: ax.set_ylim(ylim)
if xlabel is not None: ax.set_xlabel(xlabel)
@ -77,7 +77,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
if zlim is not None: ax.set_zlim(zlim)
if zlabel is not None: ax.set_zlabel(zlabel)
return ax, kwargs
def add_to_canvas(self, ax, plots, legend=False, title=None, **kwargs):
ax.autoscale_view()
fontdict=dict(family='sans-serif', weight='light', size=9)
@ -88,18 +88,18 @@ class MatplotlibPlots(AbstractPlottingLibrary):
legend_ontop(ax, ncol=legend, fontdict=fontdict)
if title is not None: ax.figure.suptitle(title)
return ax
def show_canvas(self, ax, tight_layout=False, **kwargs):
if tight_layout:
ax.figure.tight_layout()
ax.figure.canvas.draw()
return ax.figure
def scatter(self, ax, X, Y, Z=None, color=Tango.colorsHex['mediumBlue'], label=None, marker='o', **kwargs):
if Z is not None:
return ax.scatter(X, Y, c=color, zs=Z, label=label, marker=marker, **kwargs)
return ax.scatter(X, Y, c=color, label=label, marker=marker, **kwargs)
def plot(self, ax, X, Y, Z=None, color=None, label=None, **kwargs):
if Z is not None:
return ax.plot(X, Y, color=color, zs=Z, label=label, **kwargs)
@ -122,23 +122,23 @@ class MatplotlibPlots(AbstractPlottingLibrary):
if 'align' not in kwargs:
kwargs['align'] = 'center'
return ax.bar(left=x, height=height, width=width,
bottom=bottom, label=label, color=color,
bottom=bottom, label=label, color=color,
**kwargs)
def xerrorbar(self, ax, X, Y, error, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
if not('linestyle' in kwargs or 'ls' in kwargs):
kwargs['ls'] = 'none'
#if Z is not None:
# return ax.errorbar(X, Y, Z, xerr=error, ecolor=color, label=label, **kwargs)
return ax.errorbar(X, Y, xerr=error, ecolor=color, label=label, **kwargs)
def yerrorbar(self, ax, X, Y, error, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
if not('linestyle' in kwargs or 'ls' in kwargs):
kwargs['ls'] = 'none'
#if Z is not None:
# return ax.errorbar(X, Y, Z, yerr=error, ecolor=color, label=label, **kwargs)
return ax.errorbar(X, Y, yerr=error, ecolor=color, label=label, **kwargs)
def imshow(self, ax, X, extent=None, label=None, vmin=None, vmax=None, **imshow_kwargs):
if 'origin' not in imshow_kwargs:
imshow_kwargs['origin'] = 'lower'
@ -178,7 +178,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
if 'origin' not in imshow_kwargs:
imshow_kwargs['origin'] = 'lower'
return ImAnnotateController(ax, plot_function, extent, resolution=resolution, imshow_kwargs=imshow_kwargs or {}, **annotation_kwargs)
def contour(self, ax, X, Y, C, levels=20, label=None, **kwargs):
return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), label=label, **kwargs)
@ -191,13 +191,13 @@ class MatplotlibPlots(AbstractPlottingLibrary):
def fill_gradient(self, canvas, X, percentiles, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
ax = canvas
plots = []
if 'edgecolors' not in kwargs:
kwargs['edgecolors'] = 'none'
if 'facecolors' in kwargs:
color = kwargs.pop('facecolors')
if 'array' in kwargs:
array = kwargs.pop('array')
else:
@ -231,8 +231,8 @@ class MatplotlibPlots(AbstractPlottingLibrary):
# pass
a, b = tee(iterable)
next(b, None)
return zip(a, b)
return zip(a, b)
polycol = []
for y1, y2 in pairwise(percentiles):
import matplotlib.mlab as mlab
@ -244,51 +244,51 @@ class MatplotlibPlots(AbstractPlottingLibrary):
x = ma.masked_invalid(ax.convert_xunits(X))
y1 = ma.masked_invalid(ax.convert_yunits(y1))
y2 = ma.masked_invalid(ax.convert_yunits(y2))
if y1.ndim == 0:
y1 = np.ones_like(x) * y1
if y2.ndim == 0:
y2 = np.ones_like(x) * y2
if where is None:
where = np.ones(len(x), np.bool)
else:
where = np.asarray(where, np.bool)
if not (x.shape == y1.shape == y2.shape == where.shape):
raise ValueError("Argument dimensions are incompatible")
from functools import reduce
mask = reduce(ma.mask_or, [ma.getmask(a) for a in (x, y1, y2)])
if mask is not ma.nomask:
where &= ~mask
polys = []
for ind0, ind1 in mlab.contiguous_regions(where):
xslice = x[ind0:ind1]
y1slice = y1[ind0:ind1]
y2slice = y2[ind0:ind1]
if not len(xslice):
continue
N = len(xslice)
p = np.zeros((2 * N + 2, 2), np.float)
# the purpose of the next two lines is for when y2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the y1 sample points do
start = xslice[0], y2slice[0]
end = xslice[-1], y2slice[-1]
p[0] = start
p[N + 1] = end
p[1:N + 1, 0] = xslice
p[1:N + 1, 1] = y1slice
p[N + 2:, 0] = xslice[::-1]
p[N + 2:, 1] = y2slice[::-1]
polys.append(p)
polycol.extend(polys)
from matplotlib.collections import PolyCollection

2
GPy/plotting/plotly_dep/defaults.py
View file

@ -72,5 +72,5 @@ ard = dict(linewidth=1.2, barmode='stack')
latent = dict(colorscale='Greys', reversescale=True, zsmooth='best')
gradient = dict(colorscale='RdBu', opacity=.7)
magnification = dict(colorscale='Greys', zsmooth='best', reversescale=True)
latent_scatter = dict(marker_kwargs=dict(size='15', opacity=.7))
latent_scatter = dict(marker_kwargs=dict(size='5', opacity=.7))
# annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7)

29
GPy/plotting/plotly_dep/plot_definitions.py
View file

@ -130,14 +130,15 @@ class PlotlyPlots(AbstractPlottingLibrary):
except:
#not matplotlib marker
pass
marker_kwargs = marker_kwargs or {}
marker_kwargs.setdefault('symbol', marker)
if Z is not None:
return Scatter3d(x=X, y=Y, z=Z, mode='markers',
showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}),
return Scatter3d(x=X, y=Y, z=Z, mode='markers',
showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs),
name=label, **kwargs)
return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}),
return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None,
marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}),
name=label, **kwargs)
def plot(self, ax, X, Y, Z=None, color=None, label=None, line_kwargs=None, **kwargs):
@ -169,10 +170,10 @@ class PlotlyPlots(AbstractPlottingLibrary):
elif X.shape[1] == 2:
marker_kwargs.setdefault('symbol', 'diamond')
opacity = kwargs.pop('opacity', .8)
return Scatter3d(x=X[:, 0], y=X[:, 1], z=np.zeros(X.shape[0]),
return Scatter3d(x=X[:, 0], y=X[:, 1], z=np.zeros(X.shape[0]),
mode='markers',
projection=dict(z=dict(show=True, opacity=opacity)),
marker=Marker(color=color, **marker_kwargs or {}),
projection=dict(z=dict(show=True, opacity=opacity)),
marker=Marker(color=color, **marker_kwargs or {}),
opacity=0,
name=label,
showlegend=label is not None, **kwargs)
@ -284,11 +285,11 @@ class PlotlyPlots(AbstractPlottingLibrary):
if color.startswith('#'):
colarray = Tango.hex2rgb(color)
opacity = .9
else:
else:
colarray = map(float(color.strip(')').split('(')[1]))
if len(colarray) == 4:
colarray, opacity = colarray[:3] ,colarray[3]
alpha = opacity*(1.-np.abs(np.linspace(-1,1,len(percentiles)-1)))
def pairwise(iterable):
@ -302,11 +303,11 @@ class PlotlyPlots(AbstractPlottingLibrary):
for i, y1, a in zip(range(len(percentiles)), percentiles, alpha):
fcolor = 'rgba({}, {}, {}, {alpha})'.format(*colarray, alpha=a)
if i == len(percentiles)/2:
polycol.append(Scatter(x=X, y=y1, fillcolor=fcolor, showlegend=True,
name=label, line=Line(width=0, smoothing=0), mode='none', fill='tonextx',
polycol.append(Scatter(x=X, y=y1, fillcolor=fcolor, showlegend=True,
name=label, line=Line(width=0, smoothing=0), mode='none', fill='tonextx',
legendgroup='density', hoverinfo='none', **kwargs))
else:
polycol.append(Scatter(x=X, y=y1, fillcolor=fcolor, showlegend=False,
name=None, line=Line(width=1, smoothing=0, color=fcolor), mode='none', fill='tonextx',
polycol.append(Scatter(x=X, y=y1, fillcolor=fcolor, showlegend=False,
name=None, line=Line(width=1, smoothing=0, color=fcolor), mode='none', fill='tonextx',
legendgroup='density', hoverinfo='none', **kwargs))
return polycol

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54
GPy/testing/plotting_tests.py
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@ -27,13 +27,21 @@
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#===============================================================================
#===============================================================================
# SKIPPING PLOTTING BECAUSE IT BEHAVES DIFFERENTLY ON DIFFERENT
# SYSTEMS, AND WILL MISBEHAVE
from nose import SkipTest
raise SkipTest("Skipping Matplotlib testing")
#===============================================================================
import matplotlib
from unittest.case import TestCase
matplotlib.use('agg')
import numpy as np
import GPy, os
from nose import SkipTest
from GPy.util.config import config
from GPy.plotting import change_plotting_library, plotting_library
@ -41,7 +49,7 @@ from GPy.plotting import change_plotting_library, plotting_library
class ConfigTest(TestCase):
def tearDown(self):
change_plotting_library('matplotlib')
def test_change_plotting(self):
self.assertRaises(ValueError, change_plotting_library, 'not+in9names')
change_plotting_library('none')
@ -115,12 +123,12 @@ def test_figure():
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ax, _ = pl().new_canvas(num=1)
def test_func(x):
return x[:, 0].reshape(3,3)
pl().imshow_interact(ax, test_func, extent=(-1,1,-1,1), resolution=3)
ax, _ = pl().new_canvas()
def test_func_2(x):
y = x[:, 0].reshape(3,3)
@ -129,21 +137,21 @@ def test_figure():
pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3)
pl().annotation_heatmap_interact(ax, test_func_2, extent=(-1,1,-1,1), resolution=3, imshow_kwargs=dict(interpolation='nearest'))
ax, _ = pl().new_canvas(figsize=(4,3))
x = np.linspace(0,1,100)
y = [0,1,2]
array = np.array([.4,.5])
cmap = matplotlib.colors.LinearSegmentedColormap.from_list('WhToColor', ('r', 'b'), N=array.size)
pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
pl().fill_gradient(ax, x, y, facecolors=['r', 'g'], array=array, cmap=cmap)
ax, _ = pl().new_canvas(num=4, figsize=(4,3), projection='3d', xlabel='x', ylabel='y', zlabel='z', title='awsome title', xlim=(-1,1), ylim=(-1,1), zlim=(-3,3))
z = 2-np.abs(np.linspace(-2,2,(100)))+1
x, y = z*np.sin(np.linspace(-2*np.pi,2*np.pi,(100))), z*np.cos(np.linspace(-np.pi,np.pi,(100)))
pl().plot(ax, x, y, z, linewidth=2)
for do_test in _image_comparison(
baseline_images=['coverage_{}'.format(sub) for sub in ["imshow_interact",'annotation_interact','gradient','3d_plot',]],
extensions=extensions):
@ -194,9 +202,9 @@ def test_plot():
m.plot_errorbars_trainset()
m.plot_samples()
m.plot_data_error()
for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
'density',
'out_error',
for do_test in _image_comparison(baseline_images=['gp_{}'.format(sub) for sub in ["data", "mean", 'conf',
'density',
'out_error',
'samples', 'in_error']], extensions=extensions):
yield (do_test, )
@ -216,9 +224,9 @@ def test_twod():
m.plot_inducing()
#m.plot_errorbars_trainset()
m.plot_data_error()
for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
'inducing',
#'out_error',
for do_test in _image_comparison(baseline_images=['gp_2d_{}'.format(sub) for sub in ["data", "mean",
'inducing',
#'out_error',
'in_error',
]], extensions=extensions):
yield (do_test, )
@ -242,7 +250,7 @@ def test_threed():
m.plot_mean(projection='3d')
m.plot_inducing(projection='3d')
#m.plot_errorbars_trainset(projection='3d')
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
for do_test in _image_comparison(baseline_images=['gp_3d_{}'.format(sub) for sub in ["data", "mean", 'inducing',
#'error',
#"samples", "samples_lik"
]], extensions=extensions):
@ -316,7 +324,7 @@ def test_gplvm():
matplotlib.rcParams[u'figure.figsize'] = (4,3)
matplotlib.rcParams[u'text.usetex'] = False
Q = 3
# Define dataset
# Define dataset
N = 10
k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
@ -325,10 +333,10 @@ def test_gplvm():
A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
Y = np.vstack((A,B,C))
labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = GPLVM(Y, Q, init="PCA", kernel=k)
m.kern.lengthscale[:] = [1./.3, 1./.1, 1./.7]
@ -341,7 +349,7 @@ def test_gplvm():
np.random.seed(111)
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=10, data_labels=labels)
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']],
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']],
extensions=extensions,
tol=12):
yield (do_test, )
@ -355,7 +363,7 @@ def test_bayesian_gplvm():
matplotlib.rcParams[u'figure.figsize'] = (4,3)
matplotlib.rcParams[u'text.usetex'] = False
Q = 3
# Define dataset
# Define dataset
N = 10
k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
@ -364,10 +372,10 @@ def test_bayesian_gplvm():
A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
Y = np.vstack((A,B,C))
labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", kernel=k)
m.kern.lengthscale[:] = [1./.3, 1./.1, 1./.7]

2
setup.cfg
View file

@ -1,5 +1,5 @@
[bumpversion]
current_version = 0.9.4
current_version = 0.9.5
tag = True
commit = True