def run_time(self, Vt, Wt=None, Zt=None, Ht=None, niter=5):
self.time_analyzer.V = Vt
if Wt is None or Zt is None or Ht is None:
initW, initZ, initH = self.time_analyzer.initialize()
if Wt is None: Wt = initW
if Zt is None: Zt = initZ
if Ht is None: Ht = initH
for iter in xrange(niter):
logprob, WZH = self.time_analyzer.do_estep(Wt, Zt, Ht)
logger.info('Iteration t%d: logprob = %f', iter, logprob)
Wt, Zt, Ht = self.time_analyzer.do_mstep(iter)
assert Wt.ndim == 3
assert Zt.ndim == 1
assert Ht.ndim == 2
# Ht : (rank, rank*F*T)
# Ht_sum : (rank, F*T)
meta_Ht = self.sum_pieces(Ht)
Htt = meta_Ht.reshape(self.rank, self.f_steps, self.t_steps)
Hmax = np.max(Htt)
plt.clf()
plt.imshow(np.rollaxis(Htt[0:3]/Hmax*2, 0, 3), origin='lower', aspect='auto', interpolation='nearest')
plt.draw()
meta_logprob, meta_WZH = self.meta_time_analyzer.do_estep(Wt, Zt, meta_Ht)
logger.info('Meta t%d: logprob = %f', iter, logprob)
meta_Wt, meta_Zt, meta_Ht = self.meta_time_analyzer.do_mstep(0)
return Wt, Zt, Ht, meta_Ht
def run_freq(self, Vf, Wf=None, Zf=None, Hf=None, niter=5):
self.freq_analyzer.V = Vf
if Wf is None or Zf is None or Hf is None:
initW, initZ, initH = self.freq_analyzer.initialize()
if Wf is None: Wf = initW
if Zf is None: Zf = initZ
if Hf is None: Hf = initH
for iter in xrange(niter):
logprob, WZH = self.freq_analyzer.do_estep(Wf, Zf, Hf)
logger.info('Iteration f%d: logprob = %f', iter, logprob)
Wf, Zf, Hf = self.freq_analyzer.do_mstep(iter)
plt.clf()
plt.plot(Wf[...,0])
plt.draw()
# Hf : (rank, F, rank*T)
# Hf_sum : (rank, F, T)
meta_Hf = self.sum_pieces(Hf)
meta_logprob, meta_WZH = self.meta_freq_analyzer.do_estep(
Wf, Zf, meta_Hf)
logger.info('Meta f%d: logprob = %f', iter, logprob)
meta_Wf, meta_Zf, meta_Hf = self.meta_freq_analyzer.do_mstep(0)
return Wf, Zf, Hf, meta_Hf
def analyze(cls, V, rank, niter=100, convergence_thresh=1e-9,
printiter=50, plotiter=None, plotfilename=None,
initWf=None, initWt=None, initZ=None, initH=None,
updateW=True, updateZ=True, updateH=True, **kwargs):
norm = V.sum()
V /= norm
params = cls(V, rank, **kwargs)
iWf, iWt, iZ, iH = params.initialize()
Wf = iWf if initWf is None else initWf.copy()
Wt = iWt if initWt is None else initWt.copy()
Z = iZ if initZ is None else initZ.copy()
H = iH if initH is None else initH.copy()
params.Wf = Wf
params.Wt = Wt
params.Z = Z
params.H = H
oldlogprob = -np.inf
for n in xrange(niter):
logprob, WZH = params.do_estep(Wf, Wt, Z, H)
if n % printiter == 0:
logger.info('Iteration %d: logprob = %f', n, logprob)
if plotiter and n % plotiter == 0:
params.plot(V, Wf, Wt, Z, H, n)
if not plotfilename is None:
plt.savefig('%s_%04d.png' % (plotfilename, n))
if logprob < oldlogprob:
logger.debug('Warning: logprob decreased from %f to %f at '
'iteration %d!', oldlogprob, logprob, n)
#import pdb; pdb.set_trace()
elif n > 0 and logprob - oldlogprob < convergence_thresh:
logger.info('Converged at iteration %d', n)
break
oldlogprob = logprob
nWf, nWt, nZ, nH = params.do_mstep(n)
if updateW:
Wf = nWf
Wt = nWt
if updateZ: Z = nZ
if updateH: H = nH
params.Wf = Wf
params.Wt = Wt
params.Z = Z
params.H = H
if plotiter:
params.plot(V, Wf, Wt, Z, H, n)
if not plotfilename is None:
plt.savefig('%s_%04d.png' % (plotfilename, n))
logger.info('Iteration %d: final logprob = %f', n, logprob)
recon = norm * WZH
return Wf, Wt, Z, H, norm, recon, logprob
def _prune_undeeded_bases(self, Wf, Wt, Z, H, curriter):
"""Discards bases which do not contribute to the decomposition"""
threshold = 10 * EPS
zidx = np.argwhere(Z > threshold).flatten()
if len(zidx) < self.rank and curriter >= self.minpruneiter:
logger.info('Rank decreased from %d to %d during iteration %d',
self.rank, len(zidx), curriter)
self.rank = len(zidx)
Z = Z[zidx]
Wf = Wf[:,zidx]
Wt = Wt[zidx,:]
H = H[zidx,:]
self.VRWf = self.VRWf[:,zidx]
self.VRWt = self.VRWt[zidx,:]
self.VRH = self.VRH[:,zidx]
if isinstance(self.alphaWf, np.ndarray):
self.alphaWf = self.alphaWf[:,zidx]
if isinstance(self.alphaWt, np.ndarray):
self.alphaWt = self.alphaWt[zidx,:]
if isinstance(self.alphaH, np.ndarray):
self.alphaH = self.alphaH[zidx,:]
if isinstance(self.alphaZ, np.ndarray):
self.alphaZ = self.alphaZ[zidx]
return Wf, Wt, Z, H