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async optimize working
This commit is contained in:
Max Zwiessele
parent
96a97ce790
commit
f3f6226287
4 changed files with 145 additions and 131 deletions
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@ -173,7 +173,7 @@ def bgplvm_simulation_matlab_compare():
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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.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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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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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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@ -3,16 +3,15 @@ Created on 24 Apr 2013
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@author: maxz
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'''
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from multiprocessing.process import Process
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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 Lock, Event
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from copy import deepcopy
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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 threading import Thread
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RUNNING = "running"
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CONVERGED = "converged"
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@ -21,7 +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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class _Async_Optimization(Process):
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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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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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@ -67,6 +68,11 @@ class _Async_Optimization(Process):
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pass
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# print "callback done"
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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.runsignal.clear()
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def run(self, *args, **kwargs):
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raise NotImplementedError("Overwrite this with optimization (for async use)")
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pass
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@ -91,7 +97,6 @@ class _CGDAsync(_Async_Optimization):
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it = 0
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while it < self.maxiter:
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print self.runsignal.is_set()
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if not self.runsignal.is_set():
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break
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@ -117,7 +122,7 @@ class _CGDAsync(_Async_Optimization):
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xi,
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si, gi,
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fi, fi_old)
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if alphai is not None:
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if alphai is not None and fi2 < fi:
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fi, fi_old = fi2, fi_old2
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else:
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alphai, _, _, fi, fi_old, gfi = \
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@ -130,30 +135,32 @@ class _CGDAsync(_Async_Optimization):
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break
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if gfi is not None:
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gi = gfi
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xi += numpy.dot(alphai, si)
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if self.messages:
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sys.stdout.write("\r")
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sys.stdout.flush()
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sys.stdout.write("iteration: {0:> 6g} f: {1:> 12F} g: {2:> 12F}".format(it, fi, 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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if 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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if self.messages:
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sys.stdout.write("\r")
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sys.stdout.flush()
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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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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(xi, fi, it, self.f_call.value, self.df_call.value, status)
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return
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self.callback_return(xi, fi, it, self.f_call.value, self.df_call.value, status)
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class Async_Optimize(object):
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callback = None
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SENTINEL = object()
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callback = lambda *x: None
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runsignal = Event()
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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), self.SENTINEL):
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for ret in iter(lambda: q.get(timeout=1), SENTINEL):
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self.callback(*ret)
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except Empty:
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pass
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@ -162,30 +169,32 @@ 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()
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outqueue = Queue(5)
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if callback:
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self.callback = callback
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collector = Process(target=self.async_callback_collect, args=(outqueue,))
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collector.start()
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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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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.start()
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return p
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p.run()
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return p, c
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def fmin(self, f, df, x0, callback=None, update_rule=FletcherReeves,
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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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p = self.fmin_async(f, df, x0, callback, update_rule, messages,
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p, c = self.fmin_async(f, df, x0, callback, update_rule, messages,
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maxiter, max_f_eval, gtol,
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report_every, *args, **kwargs)
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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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except KeyboardInterrupt:
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print "^C"
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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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class CGD(Async_Optimize):
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'''
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@ -30,22 +30,22 @@ class sparse_GP(GP):
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"""
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def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
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self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
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self.scale_factor = 100.0 # a scaling factor to help keep the algorithm stable
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self.auto_scale_factor = False
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self.Z = Z
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self.M = Z.shape[0]
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self.likelihood = likelihood
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if X_variance is None:
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self.has_uncertain_inputs=False
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self.has_uncertain_inputs = False
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else:
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assert X_variance.shape==X.shape
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self.has_uncertain_inputs=True
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assert X_variance.shape == X.shape
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self.has_uncertain_inputs = True
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self.X_variance = X_variance
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GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X)
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#normalize X uncertainty also
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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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@ -54,155 +54,155 @@ class sparse_GP(GP):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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if self.has_uncertain_inputs:
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self.psi0 = self.kern.psi0(self.Z,self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z,self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
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self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
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else:
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self.psi0 = self.kern.Kdiag(self.X)
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self.psi1 = self.kern.K(self.Z,self.X)
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self.psi1 = self.kern.K(self.Z, self.X)
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self.psi2 = None
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def _computations(self):
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#TODO: find routine to multiply triangular matrices
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# TODO: find routine to multiply triangular matrices
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sf = self.scale_factor
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sf2 = sf**2
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sf2 = sf ** 2
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#The rather complex computations of psi2_beta_scaled
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# The rather complex computations of psi2_beta_scaled
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if self.likelihood.is_heteroscedastic:
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assert self.likelihood.D == 1 #TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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assert self.likelihood.D == 1 # TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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if self.has_uncertain_inputs:
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self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.flatten().reshape(self.N,1,1)/sf2)).sum(0)
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self.psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
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else:
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tmp = self.psi1*(np.sqrt(self.likelihood.precision.flatten().reshape(1,self.N))/sf)
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#self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)) / sf)
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# self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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self.psi2_beta_scaled = tdot(tmp)
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else:
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if self.has_uncertain_inputs:
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self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
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self.psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0)
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else:
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tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
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#self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
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# self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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self.psi2_beta_scaled = tdot(tmp)
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self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
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self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y
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self.V = (self.likelihood.precision / self.scale_factor) * self.likelihood.Y
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#Compute A = L^-1 psi2 beta L^-T
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#self. A = mdot(self.Lmi,self.psi2_beta_scaled,self.Lmi.T)
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm,self.psi2_beta_scaled.T,lower=1)[0]
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self.A = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1)[0]
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# Compute A = L^-1 psi2 beta L^-T
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# self. A = mdot(self.Lmi,self.psi2_beta_scaled,self.Lmi.T)
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm, self.psi2_beta_scaled.T, lower=1)[0]
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self.A = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1)[0]
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self.B = np.eye(self.M)/sf2 + self.A
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self.B = np.eye(self.M) / sf2 + self.A
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self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
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self.psi1V = np.dot(self.psi1, self.V)
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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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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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#self.Cpsi1V = np.dot(self.C,self.psi1V)
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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.E = tdot(self.Cpsi1V/sf)
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self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V, self.psi1V.T) # TODO: stabilize?
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self.E = tdot(self.Cpsi1V / sf)
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# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case
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self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T)
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self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V, self.V.T)
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if self.likelihood.is_heteroscedastic:
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if self.has_uncertain_inputs:
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#self.dL_dpsi2 = 0.5 * self.likelihood.precision[:,None,None] * self.D * self.Kmmi[None,:,:] # dB
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]/sf2 * self.D * self.C[None,:,:] # dC
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]* self.E[None,:,:] # dD
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self.dL_dpsi2 = 0.5*self.likelihood.precision[:,None,None]*(self.D*(self.Kmmi - self.C/sf2) -self.E)[None,:,:]
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# self.dL_dpsi2 = 0.5 * self.likelihood.precision[:,None,None] * self.D * self.Kmmi[None,:,:] # dB
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]/sf2 * self.D * self.C[None,:,:] # dC
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]* self.E[None,:,:] # dD
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self.dL_dpsi2 = 0.5 * self.likelihood.precision[:, None, None] * (self.D * (self.Kmmi - self.C / sf2) - self.E)[None, :, :]
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else:
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#self.dL_dpsi1 += mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
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#self.dL_dpsi1 += -mdot(self.C,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)/sf2) #dC
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#self.dL_dpsi1 += -mdot(self.E,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dD
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self.dL_dpsi1 += np.dot(self.Kmmi - self.C/sf2 -self.E,self.psi1*self.likelihood.precision.reshape(1,self.N))
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# self.dL_dpsi1 += mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
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# self.dL_dpsi1 += -mdot(self.C,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)/sf2) #dC
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# self.dL_dpsi1 += -mdot(self.E,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dD
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self.dL_dpsi1 += np.dot(self.Kmmi - self.C / sf2 - self.E, self.psi1 * self.likelihood.precision.reshape(1, self.N))
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self.dL_dpsi2 = None
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else:
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#self.dL_dpsi2 = 0.5 * self.likelihood.precision * self.D * self.Kmmi # dB
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision/sf2 * self.D * self.C # dC
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision * self.E # dD
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self.dL_dpsi2 = 0.5*self.likelihood.precision*(self.D*(self.Kmmi - self.C/sf2) -self.E)
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# self.dL_dpsi2 = 0.5 * self.likelihood.precision * self.D * self.Kmmi # dB
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision/sf2 * self.D * self.C # dC
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision * self.E # dD
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self.dL_dpsi2 = 0.5 * self.likelihood.precision * (self.D * (self.Kmmi - self.C / sf2) - self.E)
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if self.has_uncertain_inputs:
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#repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(self.dL_dpsi2[None,:,:],self.N,axis=0)
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# repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(self.dL_dpsi2[None, :, :], self.N, axis=0)
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else:
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self.dL_dpsi1 += 2.*np.dot(self.dL_dpsi2,self.psi1)
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self.dL_dpsi1 += 2.*np.dot(self.dL_dpsi2, self.psi1)
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self.dL_dpsi2 = None
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# Compute dL_dKmm
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#self.dL_dKmm_old = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
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#self.dL_dKmm += -0.5 * self.D * (- self.C/sf2 - 2.*mdot(self.C, self.psi2_beta_scaled, self.Kmmi) + self.Kmmi) # dC
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#self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1, self.Kmmi) + 0.5*self.E # dD
|
||||
tmp = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.B),lower=1,trans=1)[0]
|
||||
self.dL_dKmm = -0.5*self.D*sf2*linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1,trans=1)[0] #dA
|
||||
tmp = np.dot(self.D*self.C + self.E*sf2,self.psi2_beta_scaled) - self.Cpsi1VVpsi1
|
||||
tmp = linalg.lapack.flapack.dpotrs(self.Lm,np.asfortranarray(tmp.T),lower=1)[0].T
|
||||
self.dL_dKmm += 0.5*(self.D*self.C/sf2 + self.E) +tmp # d(C+D)
|
||||
# self.dL_dKmm_old = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
|
||||
# self.dL_dKmm += -0.5 * self.D * (- self.C/sf2 - 2.*mdot(self.C, self.psi2_beta_scaled, self.Kmmi) + self.Kmmi) # dC
|
||||
# self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1, self.Kmmi) + 0.5*self.E # dD
|
||||
tmp = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.B), lower=1, trans=1)[0]
|
||||
self.dL_dKmm = -0.5 * self.D * sf2 * linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1, trans=1)[0] # dA
|
||||
tmp = np.dot(self.D * self.C + self.E * sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1
|
||||
tmp = linalg.lapack.flapack.dpotrs(self.Lm, np.asfortranarray(tmp.T), lower=1)[0].T
|
||||
self.dL_dKmm += 0.5 * (self.D * self.C / sf2 + self.E) + tmp # d(C+D)
|
||||
|
||||
#the partial derivative vector for the likelihood
|
||||
if self.likelihood.Nparams ==0:
|
||||
#save computation here.
|
||||
# the partial derivative vector for the likelihood
|
||||
if self.likelihood.Nparams == 0:
|
||||
# save computation here.
|
||||
self.partial_for_likelihood = None
|
||||
elif self.likelihood.is_heteroscedastic:
|
||||
raise NotImplementedError, "heteroscedatic derivates not implemented"
|
||||
#self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA
|
||||
#self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB
|
||||
#self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC
|
||||
#self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
|
||||
# self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA
|
||||
# self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB
|
||||
# self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC
|
||||
# self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
|
||||
else:
|
||||
#likelihood is not heterscedatic
|
||||
self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * self.likelihood.trYYT*self.likelihood.precision**2
|
||||
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum()*self.likelihood.precision**2 - np.trace(self.A)*self.likelihood.precision*sf2)
|
||||
self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi,self.A)*self.likelihood.precision
|
||||
self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
|
||||
# likelihood is not heterscedatic
|
||||
self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
|
||||
self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
|
||||
self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi, self.A) * self.likelihood.precision
|
||||
self.partial_for_likelihood += self.likelihood.precision * (0.5 * trace_dot(self.psi2_beta_scaled, self.E * sf2) - np.trace(self.Cpsi1VVpsi1))
|
||||
|
||||
|
||||
|
||||
def log_likelihood(self):
|
||||
""" Compute the (lower bound on the) log marginal likelihood """
|
||||
sf2 = self.scale_factor**2
|
||||
sf2 = self.scale_factor ** 2
|
||||
if self.likelihood.is_heteroscedastic:
|
||||
A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
|
||||
B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
|
||||
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
|
||||
else:
|
||||
A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
|
||||
B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
|
||||
C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
|
||||
D = 0.5*np.trace(self.Cpsi1VVpsi1)
|
||||
return A+B+C+D
|
||||
A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
|
||||
B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
|
||||
C = -0.5 * self.D * (self.B_logdet + self.M * np.log(sf2))
|
||||
D = 0.5 * np.trace(self.Cpsi1VVpsi1)
|
||||
return A + B + C + D
|
||||
|
||||
def _set_params(self, p):
|
||||
self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
|
||||
self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
|
||||
self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
|
||||
self.Z = p[:self.M * self.Q].reshape(self.M, self.Q)
|
||||
self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
|
||||
self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
|
||||
self._compute_kernel_matrices()
|
||||
if self.auto_scale_factor:
|
||||
self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
#if self.auto_scale_factor:
|
||||
self.scale_factor = np.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
|
||||
# if self.auto_scale_factor:
|
||||
# if self.likelihood.is_heteroscedastic:
|
||||
# self.scale_factor = max(1,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
|
||||
# else:
|
||||
# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
|
||||
#self.scale_factor = 1.
|
||||
# self.scale_factor = 1.
|
||||
self._computations()
|
||||
|
||||
def _get_params(self):
|
||||
return np.hstack([self.Z.flatten(),GP._get_params(self)])
|
||||
return np.hstack([self.Z.flatten(), GP._get_params(self)])
|
||||
|
||||
def _get_param_names(self):
|
||||
return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
|
||||
return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], []) + GP._get_param_names(self)
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
"""
|
||||
|
|
@ -214,9 +214,9 @@ class sparse_GP(GP):
|
|||
if self.has_uncertain_inputs:
|
||||
raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
|
||||
else:
|
||||
self.likelihood.fit_DTC(self.Kmm,self.psi1)
|
||||
#self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1)
|
||||
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
|
|
@ -226,13 +226,13 @@ class sparse_GP(GP):
|
|||
"""
|
||||
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
|
||||
"""
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
|
||||
if self.has_uncertain_inputs:
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
|
||||
else:
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
|
||||
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
|
||||
|
||||
return dL_dtheta
|
||||
|
|
@ -243,22 +243,22 @@ class sparse_GP(GP):
|
|||
"""
|
||||
dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm, self.Z) # factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
|
||||
if self.has_uncertain_inputs:
|
||||
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1,self.Z,self.X, self.X_variance)
|
||||
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
|
||||
dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
|
||||
else:
|
||||
dL_dZ += self.kern.dK_dX(self.dL_dpsi1,self.Z,self.X)
|
||||
dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X)
|
||||
return dL_dZ
|
||||
|
||||
def _raw_predict(self, Xnew, which_parts='all', full_cov=False):
|
||||
"""Internal helper function for making predictions, does not account for normalization"""
|
||||
|
||||
Kx = self.kern.K(self.Z, Xnew)
|
||||
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
|
||||
mu = mdot(Kx.T, self.C / self.scale_factor, self.psi1V)
|
||||
if full_cov:
|
||||
Kxx = self.kern.K(Xnew,which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
|
||||
Kxx = self.kern.K(Xnew, which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C / self.scale_factor ** 2), Kx) # NOTE this won't work for plotting
|
||||
else:
|
||||
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
|
||||
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx * np.dot(self.Kmmi - self.C / self.scale_factor ** 2, Kx), 0)
|
||||
|
||||
return mu,var[:,None]
|
||||
return mu, var[:, None]
|
||||
|
|
|
|||
|
|
@ -47,10 +47,15 @@ if __name__ == "__main__":
|
|||
xopts = [x0.copy()]
|
||||
optplts, = ax.plot3D([x0[0]], [x0[1]], zs=f(x0), marker='o', color='r')
|
||||
|
||||
raw_input("enter to start optimize")
|
||||
|
||||
def callback(x, *a, **kw):
|
||||
xopts.append(x.copy())
|
||||
time.sleep(.3)
|
||||
# time.sleep(.3)
|
||||
optplts._verts3d = [numpy.array(xopts)[:, 0], numpy.array(xopts)[:, 1], [f(xs) for xs in xopts]]
|
||||
fig.canvas.draw()
|
||||
|
||||
res = opt.fmin(f, df, x0, callback, messages=True, report_every=1)
|
||||
res = opt.fmin(f, df, x0, callback, messages=True, maxiter=1000, report_every=1)
|
||||
|
||||
pylab.ion()
|
||||
pylab.show()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue