mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-14 22:42:37 +02:00
LinearCF Psi Stat not working yet, strange bug in psi computations
This commit is contained in:
Max Zwiessele
parent
c502b66ea3
commit
42474f0044
8 changed files with 353 additions and 244 deletions
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@ -176,20 +176,34 @@ def bgplvm_simulation_matlab_compare():
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Y = sim_data['Y']
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S = sim_data['S']
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mu = sim_data['mu']
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M, [_, Q] = 20, mu.shape
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M, [_, Q] = 30, mu.shape
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Q = 2
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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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_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['{}_variance'.format(k.parts[0].name)] = .01
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m['noise'] = Y.var() / 100.
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lscstr = '{}'.format(k.parts[0].name)
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# m[lscstr] = .01
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m.unconstrain(lscstr); m.constrain_fixed(lscstr, 10)
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lscstr = 'X_variance'
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# m[lscstr] = .01
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m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
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# cstr = 'white'
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# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
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# cstr = 'noise'
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# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)
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return m
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def bgplvm_simulation(burnin='scg', plot_sim=False,
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@ -4,14 +4,14 @@ Created on 24 Apr 2013
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@author: maxz
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'''
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from GPy.inference.gradient_descent_update_rules import FletcherReeves
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import numpy
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from multiprocessing import Value
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from scipy.optimize.linesearch import line_search_wolfe1, line_search_wolfe2
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from multiprocessing.synchronize import Event
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from multiprocessing.queues import Queue
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from Queue import Empty
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import sys
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from multiprocessing import Value
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from multiprocessing.queues import Queue
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from multiprocessing.synchronize import Event
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from scipy.optimize.linesearch import line_search_wolfe1, line_search_wolfe2
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from threading import Thread
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import numpy
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import sys
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RUNNING = "running"
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CONVERGED = "converged"
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@ -20,10 +20,9 @@ MAX_F_EVAL = "maximum number of function calls reached"
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LINE_SEARCH = "line search failed"
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KBINTERRUPT = "interrupted"
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SENTINEL = None
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class _Async_Optimization(Thread):
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def __init__(self, f, df, x0, update_rule, runsignal,
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def __init__(self, f, df, x0, update_rule, runsignal, SENTINEL,
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report_every=10, messages=0, maxiter=5e3, max_f_eval=15e3,
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gtol=1e-6, outqueue=None, *args, **kw):
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"""
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@ -42,6 +41,7 @@ class _Async_Optimization(Thread):
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self.maxiter = maxiter
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self.max_f_eval = max_f_eval
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self.gtol = gtol
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self.SENTINEL = SENTINEL
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self.runsignal = runsignal
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# self.parent = parent
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# self.result = None
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@ -70,7 +70,7 @@ class _Async_Optimization(Thread):
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def callback_return(self, *a):
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self.callback(*a)
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self.outq.put(SENTINEL)
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self.outq.put(self.SENTINEL)
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self.runsignal.clear()
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def run(self, *args, **kwargs):
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@ -136,7 +136,7 @@ class _CGDAsync(_Async_Optimization):
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if gfi is not None:
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gi = gfi
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if fi_old > fi:
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if numpy.isnan(fi) or fi_old < fi:
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gi, ur, si = self.reset(xi, *a, **kw)
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else:
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xi += numpy.dot(alphai, si)
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@ -146,21 +146,22 @@ class _CGDAsync(_Async_Optimization):
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sys.stdout.write("iteration: {0:> 6g} f:{1:> 12e} |g|:{2:> 12e}".format(it, fi, numpy.dot(gi.T, gi)))
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if it % self.report_every == 0:
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self.callback(xi, fi, it, self.f_call.value, self.df_call.value, status)
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self.callback(xi, fi, gi, it, self.f_call.value, self.df_call.value, status)
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it += 1
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else:
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status = MAXITER
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# self.result = [xi, fi, it, self.f_call.value, self.df_call.value, status]
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self.callback_return(xi, fi, it, self.f_call.value, self.df_call.value, status)
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self.callback_return(xi, fi, gi, it, self.f_call.value, self.df_call.value, status)
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self.result = [xi, fi, gi, it, self.f_call.value, self.df_call.value, status]
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class Async_Optimize(object):
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callback = lambda *x: None
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runsignal = Event()
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SENTINEL = "SENTINEL"
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def async_callback_collect(self, q):
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while self.runsignal.is_set():
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try:
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for ret in iter(lambda: q.get(timeout=1), SENTINEL):
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for ret in iter(lambda: q.get(timeout=1), self.SENTINEL):
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self.callback(*ret)
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except Empty:
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pass
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@ -169,12 +170,12 @@ class Async_Optimize(object):
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messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
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report_every=10, *args, **kwargs):
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self.runsignal.set()
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outqueue = Queue(5)
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outqueue = Queue()
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if callback:
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self.callback = callback
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c = Thread(target=self.async_callback_collect, args=(outqueue,))
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c.start()
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p = _CGDAsync(f, df, x0, update_rule, self.runsignal,
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p = _CGDAsync(f, df, x0, update_rule, self.runsignal, self.SENTINEL,
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report_every=report_every, messages=messages, maxiter=maxiter,
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max_f_eval=max_f_eval, gtol=gtol, outqueue=outqueue, *args, **kwargs)
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p.run()
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@ -189,12 +190,14 @@ class Async_Optimize(object):
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while self.runsignal.is_set():
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try:
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p.join(1)
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c.join(1)
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# c.join(1)
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except KeyboardInterrupt:
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# print "^C"
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self.runsignal.clear()
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p.join()
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c.join()
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if c.is_alive():
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print "WARNING: callback still running, optimisation done!"
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return p.result
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class CGD(Async_Optimize):
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'''
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@ -215,7 +218,7 @@ class CGD(Async_Optimize):
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callback gets called every `report_every` iterations
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callback(xi, fi, iteration, function_calls, gradient_calls, status_message)
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callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)
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if df returns tuple (grad, natgrad) it will optimize according
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to natural gradient rules
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@ -233,7 +236,7 @@ class CGD(Async_Optimize):
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**calls**
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---------
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callback(x_opt, f_opt, iteration, function_calls, gradient_calls, status_message)
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callback(x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message)
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at end of optimization!
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"""
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@ -247,7 +250,7 @@ class CGD(Async_Optimize):
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Minimize f, calling callback every `report_every` iterations with following syntax:
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callback(xi, fi, iteration, function_calls, gradient_calls, status_message)
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callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)
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if df returns tuple (grad, natgrad) it will optimize according
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to natural gradient rules
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@ -260,7 +263,7 @@ class CGD(Async_Optimize):
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**returns**
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---------
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x_opt, f_opt, iteration, function_calls, gradient_calls, status_message
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x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message
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at end of optimization
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"""
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@ -5,6 +5,7 @@
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from kernpart import kernpart
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import numpy as np
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from ..util.linalg import tdot
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from GPy.util.linalg import mdot
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class linear(kernpart):
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"""
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@ -140,9 +141,25 @@ class linear(kernpart):
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returns N,M,M matrix
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"""
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self._psi_computations(Z, mu, S)
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#psi2 = self.ZZ*np.square(self.variances)*self.mu2_S[:, None, None, :]
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# psi2_old = self.ZZ * np.square(self.variances) * self.mu2_S[:, None, None, :]
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# target += psi2.sum(-1)
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target += np.tensordot(self.ZZ[None,:,:,:]*np.square(self.variances),self.mu2_S[:, None, None, :],((3),(3))).squeeze().T
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# slow way of doing it, but right
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psi2_real = np.zeros((mu.shape[0], Z.shape[0], Z.shape[0]))
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for n in range(mu.shape[0]):
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for m_prime in range(Z.shape[0]):
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for m in range(Z.shape[0]):
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tmp = self._Z[m:m + 1] * self.variances
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tmp = np.dot(tmp, (tdot(self._mu[n:n + 1].T) + np.diag(S[n:n + 1])))
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psi2_real[n, m, m_prime] = np.dot(tmp, (
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self._Z[m_prime:m_prime + 1] * self.variances).T)
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psi2_inner = mdot(self.ZA, self.inner, self.ZA.T)
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mu2_S = (self._mu[:, None] * self._mu[:, :, None]) + self._S[:, :, None]
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psi2 = (self.ZA[None, :, None, :] * mu2_S[:, None]).sum(-1)
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psi2 = (psi2[:, :, None] * self.ZA[None, None]).sum(-1)
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# psi2_tensor = np.tensordot(self.ZZ[None, :, :, :] * np.square(self.variances), self.mu2_S[:, None, None, :], ((3), (3))).squeeze().T
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# import ipdb;ipdb.set_trace()
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target += psi2_real
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def dpsi2_dtheta(self, dL_dpsi2, Z, mu, S, target):
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self._psi_computations(Z, mu, S)
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@ -156,13 +173,18 @@ class linear(kernpart):
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"""Think N,M,M,Q """
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self._psi_computations(Z, mu, S)
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tmp = self.ZZ * np.square(self.variances) # M,M,Q
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# import ipdb;ipdb.set_trace()
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target_mu += (dL_dpsi2[:, :, :, None] * tmp * 2.*mu[:, None, None, :]).sum(1).sum(1)
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target_S += (dL_dpsi2[:,:,:,None]*tmp).sum(1).sum(1)
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target_S += (dL_dpsi2[:, :, :, None] * tmp).sum(1).sum(1) * S.shape[0]
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def dpsi2_dZ(self, dL_dpsi2, Z, mu, S, target):
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self._psi_computations(Z, mu, S)
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mu2_S = np.sum(self.mu2_S,0)# Q,
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target += (dL_dpsi2[:,:,:,None] * (self.mu2_S[:,None,None,:]*(Z*np.square(self.variances)[None,:])[None,None,:,:])).sum(0).sum(1)
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# mu2_S = np.sum(self.mu2_S, 0) # Q,
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# import ipdb;ipdb.set_trace()
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# prod = (np.eye(Z.shape[0])[:, None, :, None] * (np.dot(self.ZA, self.inner) * self.variances)[None, :, None])
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# psi2_dZ = prod.swapaxes(0, 1) + prod
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psi2_dZ_old = (dL_dpsi2[:, :, :, None] * (self.mu2_S[:, None, None, :] * (Z * np.square(self.variances)[None, :])[None, None, :, :])).sum(0).sum(1)
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target += psi2_dZ_old # .sum(0).sum(1)
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# TODO: tensordot would gain some time here
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#---------------------------------------#
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@ -187,6 +209,8 @@ class linear(kernpart):
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self.ZZ = np.empty((Z.shape[0], Z.shape[0], Z.shape[1]), order='F')
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[tdot(Z[:, i:i + 1], self.ZZ[:, :, i].T) for i in xrange(Z.shape[1])]
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self._Z = Z.copy()
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self.ZA = Z * self.variances
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if not (np.all(mu == self._mu) and np.all(S == self._S)):
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self.mu2_S = np.square(mu) + S
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self.inner = tdot(mu.T) + (np.diag(S.sum(0)))
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self._mu, self._S = mu.copy(), S.copy()
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@ -308,6 +308,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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Slatentgrads = ax3.quiver(xlatent, S, Ulatent, Sg, color=colors,
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units=quiver_units, scale_units=quiver_scale_units,
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scale=quiver_scale)
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ax3.set_ylim(0, 1.)
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xZ = np.tile(np.arange(0, Z.shape[0])[:, None], Z.shape[1])
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UZ = np.zeros_like(Z)
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@ -427,11 +428,11 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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cbarkmmdl.update_normal(imkmmdl)
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ax2.relim()
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ax3.relim()
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# ax3.relim()
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ax4.relim()
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ax5.relim()
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ax2.autoscale()
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ax3.autoscale()
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# ax3.autoscale()
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ax4.autoscale()
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ax5.autoscale()
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@ -102,13 +102,14 @@ class sparse_GP(GP):
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.Bi),lower=1,trans=1)[0]
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self.C = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1,trans=1)[0]
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#back substutue C into psi1V
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tmp,info1 = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.psi1V),lower=1,trans=0)
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tmp,info2 = linalg.lapack.flapack.dpotrs(self.LB,tmp,lower=1)
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self.Cpsi1V,info3 = linalg.lapack.flapack.dtrtrs(self.Lm,tmp,lower=1,trans=1)
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#self.Cpsi1V = np.dot(self.C,self.psi1V)
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# back substitute C into psi1V
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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tmp, _ = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, _ = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
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self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V, self.psi1V.T) # TODO: stabilize?
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self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T)
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self.E = tdot(self.Cpsi1V/sf)
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@ -5,7 +5,7 @@ Created on 26 Apr 2013
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'''
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import unittest
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import numpy
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from GPy.inference.conjugate_gradient_descent import CGD
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from GPy.inference.conjugate_gradient_descent import CGD, RUNNING
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import pylab
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import time
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from scipy.optimize.optimize import rosen, rosen_der
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@ -14,17 +14,62 @@ from scipy.optimize.optimize import rosen, rosen_der
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class Test(unittest.TestCase):
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def testMinimizeSquare(self):
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f = lambda x: x ** 2 + 2 * x - 2
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if __name__ == "__main__":
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# import sys;sys.argv = ['', 'Test.testMinimizeSquare']
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# unittest.main()
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N = 2
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A = numpy.random.rand(N) * numpy.eye(N)
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b = numpy.random.rand(N)
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# f = lambda x: numpy.dot(x.T.dot(A), x) + numpy.dot(x.T, b)
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# df = lambda x: numpy.dot(A, x) - b
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b = numpy.random.rand(N) * 0
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f = lambda x: numpy.dot(x.T.dot(A), x) - numpy.dot(x.T, b)
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df = lambda x: numpy.dot(A, x) - b
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opt = CGD()
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restarts = 10
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for _ in range(restarts):
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try:
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x0 = numpy.random.randn(N) * .5
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res = opt.fmin(f, df, x0, messages=0,
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maxiter=1000, gtol=1e-10)
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assert numpy.allclose(res[0], 0, atol=1e-3)
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break
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except:
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# RESTART
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pass
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else:
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raise AssertionError("Test failed for {} restarts".format(restarts))
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def testRosen(self):
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N = 2
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f = rosen
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df = rosen_der
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x0 = numpy.random.randn(N) * .5
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opt = CGD()
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restarts = 10
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for _ in range(restarts):
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try:
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x0 = numpy.random.randn(N) * .5
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res = opt.fmin(f, df, x0, messages=0,
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maxiter=1000, gtol=1e-10)
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assert numpy.allclose(res[0], 1, atol=1e-5)
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break
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except:
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# RESTART
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pass
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else:
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raise AssertionError("Test failed for {} restarts".format(restarts))
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||||
|
||||
if __name__ == "__main__":
|
||||
# import sys;sys.argv = ['',
|
||||
# 'Test.testMinimizeSquare',
|
||||
# 'Test.testRosen',
|
||||
# ]
|
||||
# unittest.main()
|
||||
|
||||
N = 2
|
||||
A = numpy.random.rand(N) * numpy.eye(N)
|
||||
b = numpy.random.rand(N) * 0
|
||||
# f = lambda x: numpy.dot(x.T.dot(A), x) - numpy.dot(x.T, b)
|
||||
# df = lambda x: numpy.dot(A, x) - b
|
||||
f = rosen
|
||||
df = rosen_der
|
||||
x0 = numpy.random.randn(N) * .5
|
||||
|
|
@ -48,14 +93,21 @@ if __name__ == "__main__":
|
|||
optplts, = ax.plot3D([x0[0]], [x0[1]], zs=f(x0), marker='o', color='r')
|
||||
|
||||
raw_input("enter to start optimize")
|
||||
res = [0]
|
||||
|
||||
def callback(x, *a, **kw):
|
||||
xopts.append(x.copy())
|
||||
def callback(*r):
|
||||
xopts.append(r[0].copy())
|
||||
# time.sleep(.3)
|
||||
optplts._verts3d = [numpy.array(xopts)[:, 0], numpy.array(xopts)[:, 1], [f(xs) for xs in xopts]]
|
||||
fig.canvas.draw()
|
||||
if r[-1] != RUNNING:
|
||||
res[0] = r
|
||||
|
||||
p, c = opt.fmin_async(f, df, x0.copy(), callback, messages=True, maxiter=1000,
|
||||
report_every=20, gtol=1e-12)
|
||||
|
||||
res = opt.fmin(f, df, x0, callback, messages=True, maxiter=1000, report_every=1)
|
||||
|
||||
pylab.ion()
|
||||
pylab.show()
|
||||
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -9,21 +9,30 @@ import numpy as np
|
|||
import pylab
|
||||
|
||||
__test__ = False
|
||||
np.random.seed(0)
|
||||
|
||||
def ard(p):
|
||||
try:
|
||||
if p.ARD:
|
||||
return "ARD"
|
||||
except:
|
||||
pass
|
||||
return ""
|
||||
|
||||
class Test(unittest.TestCase):
|
||||
D = 9
|
||||
M = 5
|
||||
Nsamples = 3e6
|
||||
M = 3
|
||||
Nsamples = 6e6
|
||||
|
||||
def setUp(self):
|
||||
self.kerns = (
|
||||
GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
|
||||
GPy.kern.linear(self.D), GPy.kern.linear(self.D, ARD=True),
|
||||
# GPy.kern.rbf(self.D), GPy.kern.rbf(self.D, ARD=True),
|
||||
GPy.kern.linear(self.D, ARD=False), GPy.kern.linear(self.D, ARD=True),
|
||||
GPy.kern.linear(self.D) + GPy.kern.bias(self.D),
|
||||
GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
|
||||
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D),
|
||||
GPy.kern.linear(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
|
||||
GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
|
||||
GPy.kern.bias(self.D), GPy.kern.white(self.D),
|
||||
# GPy.kern.rbf(self.D) + GPy.kern.bias(self.D) + GPy.kern.white(self.D),
|
||||
# GPy.kern.bias(self.D), GPy.kern.white(self.D),
|
||||
)
|
||||
self.q_x_mean = np.random.randn(self.D)
|
||||
self.q_x_variance = np.exp(np.random.randn(self.D))
|
||||
|
|
@ -66,18 +75,21 @@ class Test(unittest.TestCase):
|
|||
K_ += K
|
||||
diffs.append(((psi2 - (K_ / (i + 1))) ** 2).mean())
|
||||
K_ /= self.Nsamples / Nsamples
|
||||
msg = "psi2: {}".format("+".join([p.name + ard(p) for p in kern.parts]))
|
||||
try:
|
||||
# pylab.figure("+".join([p.name for p in kern.parts]) + "psi2")
|
||||
# pylab.plot(diffs)
|
||||
pylab.figure(msg)
|
||||
pylab.plot(diffs)
|
||||
self.assertTrue(np.allclose(psi2.squeeze(), K_,
|
||||
rtol=1e-1, atol=.1),
|
||||
msg="{}: not matching".format("+".join([p.name for p in kern.parts])))
|
||||
msg=msg + ": not matching")
|
||||
except:
|
||||
print "{}: not matching".format(kern.parts[0].name)
|
||||
import ipdb;ipdb.set_trace()
|
||||
kern.psi2(self.Z, self.q_x_mean, self.q_x_variance)
|
||||
print msg + ": not matching"
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys;sys.argv = ['',
|
||||
'Test.test_psi0',
|
||||
'Test.test_psi1',
|
||||
# 'Test.test_psi0',
|
||||
# 'Test.test_psi1',
|
||||
'Test.test_psi2']
|
||||
unittest.main()
|
||||
|
|
|
|||
|
|
@ -106,18 +106,18 @@ if __name__ == "__main__":
|
|||
import sys
|
||||
interactive = 'i' in sys.argv
|
||||
if interactive:
|
||||
N, M, Q, D = 30, 5, 4, 30
|
||||
X = numpy.random.rand(N, Q)
|
||||
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
|
||||
K = k.K(X)
|
||||
Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
|
||||
Y -= Y.mean(axis=0)
|
||||
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
|
||||
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
|
||||
m.ensure_default_constraints()
|
||||
m.randomize()
|
||||
# self.assertTrue(m.checkgrad())
|
||||
|
||||
# N, M, Q, D = 30, 5, 4, 30
|
||||
# X = numpy.random.rand(N, Q)
|
||||
# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
|
||||
# K = k.K(X)
|
||||
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
|
||||
# Y -= Y.mean(axis=0)
|
||||
# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
|
||||
# m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
|
||||
# m.ensure_default_constraints()
|
||||
# m.randomize()
|
||||
# # self.assertTrue(m.checkgrad())
|
||||
numpy.random.seed(0)
|
||||
Q = 5
|
||||
N = 50
|
||||
M = 10
|
||||
|
|
@ -126,11 +126,11 @@ if __name__ == "__main__":
|
|||
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
|
||||
Z = numpy.random.permutation(X)[:M]
|
||||
Y = X.dot(numpy.random.randn(Q, D))
|
||||
kernel = GPy.kern.bias(Q)
|
||||
|
||||
kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
|
||||
GPy.kern.linear(Q) + GPy.kern.bias(Q),
|
||||
GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
|
||||
# kernel = GPy.kern.bias(Q)
|
||||
#
|
||||
# kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
|
||||
# GPy.kern.linear(Q) + GPy.kern.bias(Q),
|
||||
# GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
|
||||
|
||||
# for k in kernels:
|
||||
# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
|
||||
|
|
@ -143,11 +143,13 @@ if __name__ == "__main__":
|
|||
# M=M, kernel=kernel)
|
||||
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
|
||||
# M=M, kernel=kernel)
|
||||
m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
|
||||
M=M, kernel=GPy.kern.rbf(Q))
|
||||
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
|
||||
# M=M, kernel=GPy.kern.rbf(Q))
|
||||
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
|
||||
M=M, kernel=GPy.kern.linear(Q) + GPy.kern.bias(Q))
|
||||
m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
|
||||
M=M, kernel=GPy.kern.rbf(Q) + GPy.kern.bias(Q))
|
||||
M=M, kernel=GPy.kern.linear(Q))
|
||||
m3.ensure_default_constraints()
|
||||
# + GPy.kern.bias(Q))
|
||||
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
|
||||
# M=M, kernel=GPy.kern.rbf(Q) + GPy.kern.bias(Q))
|
||||
else:
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue