apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 04:22:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Commit to pull.

Browse source
This commit is contained in:
Neil Lawrence 2015-05-08 11:44:23 +01:00
parent 48821a6b73
commit 0da25ee168
1 changed files with 7 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

9
GPy/examples/dimensionality_reduction.py
View file

@ -215,6 +215,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
return m
def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
"""Simulate some data drawn from a matern covariance and a periodic exponential for use in MRD demos."""
Q_signal = 4
import GPy
import numpy as np
@ -254,6 +255,7 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
return slist, [S1, S2, S3], Ylist
def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
"""Simulate some data drawn from sine and cosine for use in demos of MRD"""
_np.random.seed(1234)
x = _np.linspace(0, 4 * _np.pi, N)[:, None]
@ -402,7 +404,8 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim)
# Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
@ -585,6 +588,7 @@ def robot_wireless(optimize=True, verbose=True, plot=True):
return m
def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
"""Interactive visualisation of the Stick Man data from Ohio State University with the Bayesian GPLVM."""
from GPy.models import BayesianGPLVM
from matplotlib import pyplot as plt
import numpy as np
@ -613,7 +617,8 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
data_show = GPy.plotting.matplot_dep.visualize.stick_show(y, connect=data['connect'])
dim_select = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean[:1, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
fig.canvas.draw()
fig.canvas.show()
# Canvas.show doesn't work on OSX.
#fig.canvas.show()
raw_input('Press enter to finish')
return m