def adaptation(self, ti):
kadp = np.ones(self.nadp)
kadp[0] = STD_RATIO1
kadp[1:3] = STD_RATIO2
kadp[3:5] = STD_RATIO3
sumX = 0
sumX2 = 0
for iadp in range(self.nadp):
nCompo = self.gmdistr.get_params()['n_components']
tmp_gmdistr = GMM(n_components=nCompo, covariance_type='full')
tmp_gmdistr.weights_ = self.gmdistr.weights_
tmp_gmdistr.means_ = self.gmdistr.means_
tmp_gmdistr.covars_ = self.gmdistr.covars_ * kadp[iadp]**2
subSmp = MainSmp(self.nsmp, tmp_gmdistr, self.rv_array,
self.r_array, self.sl_array, self.gd_array,
self.gcov_array)
i = 0
while i <= MAX_NUM_UPDATE:
try:
smps = subSmp.sample()
f = subSmp.priorPDF(smps)
g = 1 - subSmp.condAvailability(smps, ti)
hv, responsibilities = subSmp.score_samples(smps)
gf2hv = g * f / hv
self.updateParameter(smps, responsibilities, gf2hv)
i += 1
break
except ValueError:
i += 1
continue
sumX += np.sum(gf2hv)
sumX2 += np.sum(gf2hv**2)
npre = self.nadp * self.nsmp
pfpre = 1. / npre * sumX
Spf2pre = 1. / (npre * (npre - 1)) * (sumX2 - 2 * sumX * pfpre +
npre * pfpre**2)
return pfpre, Spf2pre
def getKopt(self, k_array, ti):
pf = np.zeros(k_array.shape)
for k in k_array:
nCompo = self.gmdistr.get_params()['n_components']
tmp_gmdistr = GMM(n_components=nCompo,covariance_type='full')
tmp_gmdistr.weights_ = self.gmdistr.weights_
tmp_gmdistr.means_ = self.gmdistr.means_
tmp_gmdistr.covars_ = self.gmdistr.covars_ * k**2
subSmp = MainSmp(self.nsmp, tmp_gmdistr, self.rv_array, self.r_array, self.sl_array, self.gd_array, self.gcov_array)
smps = subSmp.sample()
indx = np.where(k_array==k)[0][0]
pf[indx], dummy = subSmp.getPf(smps, ti)
if indx>0 and pf[indx] < KRATIO_CRITERIA * np.mean(pf[:indx]):
k_indx = indx-1
break
else:
k_indx = -1
return k_array[k_indx]
def getKopt(self, k_array, ti):
pf = np.zeros(k_array.shape)
for k in k_array:
nCompo = self.gmdistr.get_params()['n_components']
tmp_gmdistr = GMM(n_components=nCompo, covariance_type='full')
tmp_gmdistr.weights_ = self.gmdistr.weights_
tmp_gmdistr.means_ = self.gmdistr.means_
tmp_gmdistr.covars_ = self.gmdistr.covars_ * k**2
subSmp = MainSmp(self.nsmp, tmp_gmdistr, self.r_array,
self.sl_array, self.gd_array)
smps = subSmp.sample()
indx = np.where(k_array == k)[0][0]
pf[indx], dummy = subSmp.getPf(smps, ti)
if indx > 0 and pf[indx] < KRATIO_CRITERIA * np.mean(pf[:indx]):
k_indx = indx - 1
break
else:
k_indx = -1
return k_array[k_indx]
def adaptation(self, ti):
kadp = np.ones(self.nadp)
kadp[0] = STD_RATIO1
kadp[1:3] = STD_RATIO2
kadp[3:5] = STD_RATIO3
sumX = 0; sumX2 = 0;
for iadp in range(self.nadp):
nCompo = self.gmdistr.get_params()['n_components']
tmp_gmdistr = GMM(n_components=nCompo,covariance_type='full')
tmp_gmdistr.weights_ = self.gmdistr.weights_
tmp_gmdistr.means_ = self.gmdistr.means_
tmp_gmdistr.covars_ = self.gmdistr.covars_ * kadp[iadp]**2
subSmp = MainSmp(self.nsmp, tmp_gmdistr, self.rv_array, self.r_array, self.sl_array, self.gd_array, self.gcov_array)
i = 0
while i<=MAX_NUM_UPDATE:
try:
smps = subSmp.sample()
f = subSmp.priorPDF(smps)
g = 1 - subSmp.condAvailability(smps, ti)
hv, responsibilities = subSmp.score_samples(smps)
gf2hv = g*f/hv
self.updateParameter(smps, responsibilities, gf2hv)
i += 1
break
except ValueError:
i += 1
continue
sumX += np.sum(gf2hv)
sumX2 += np.sum(gf2hv**2)
npre = self.nadp * self.nsmp
pfpre = 1./npre * sumX
Spf2pre = 1./(npre*(npre-1)) * (sumX2 - 2*sumX*pfpre + npre * pfpre**2)
return pfpre, Spf2pre