def finalizeSampling(self):
#from scikits.talkbox.tools.correlations import acorr
from pyhrf.stats import acorr
if 0 and self.smplHistory is not None:
logger.info('Compute autocorrelation of %s samples, shape=%s',
self.name, str(self.smplHistory.shape))
trajectory = self.smplHistory[self.samplerEngine.nbSweeps::2]
self.autocorrelation = acorr(trajectory)
sn = trajectory.shape[0] ** .5
t95 = 1.959963984540054 / sn
self.autocorrelation_test = np.zeros(self.autocorrelation.shape,
dtype=np.int32)
self.autocorrelation_test[np.where(self.autocorrelation > t95)] = 1
self.autocorrelation_test[
np.where(self.autocorrelation < -t95)] = -1
self.autocorrelation_thresh = t95
from scipy.stats import norm
self.autocorrelation_pvalue = np.zeros(self.autocorrelation.shape,
dtype=np.float32)
m_pos = np.where(self.autocorrelation > 0)
if len(m_pos[0]) > 0:
ac_pos = self.autocorrelation[m_pos]
self.autocorrelation_pvalue[m_pos] = norm.sf(ac_pos * sn)
m_neg = np.where(self.autocorrelation < 0)
if len(m_neg[0]) > 0:
ac_neg = self.autocorrelation[m_neg]
self.autocorrelation_pvalue[m_neg] = norm.cdf(ac_neg * sn)
logger.info('Compute posterior median for %s ', self.name)
self.median = np.median(self.smplHistory, axis=0)
self.check_final_value()
def finalizeSampling(self):
#from scikits.talkbox.tools.correlations import acorr
from pyhrf.stats import acorr
if 0 and self.smplHistory is not None:
logger.info('Compute autocorrelation of %s samples, shape=%s',
self.name, str(self.smplHistory.shape))
trajectory = self.smplHistory[self.samplerEngine.nbSweeps::2]
self.autocorrelation = acorr(trajectory)
sn = trajectory.shape[0]**.5
t95 = 1.959963984540054 / sn
self.autocorrelation_test = np.zeros(self.autocorrelation.shape,
dtype=np.int32)
self.autocorrelation_test[np.where(self.autocorrelation > t95)] = 1
self.autocorrelation_test[np.where(
self.autocorrelation < -t95)] = -1
self.autocorrelation_thresh = t95
from scipy.stats import norm
self.autocorrelation_pvalue = np.zeros(self.autocorrelation.shape,
dtype=np.float32)
m_pos = np.where(self.autocorrelation > 0)
if len(m_pos[0]) > 0:
ac_pos = self.autocorrelation[m_pos]
self.autocorrelation_pvalue[m_pos] = norm.sf(ac_pos * sn)
m_neg = np.where(self.autocorrelation < 0)
if len(m_neg[0]) > 0:
ac_neg = self.autocorrelation[m_neg]
self.autocorrelation_pvalue[m_neg] = norm.cdf(ac_neg * sn)
logger.info('Compute posterior median for %s ', self.name)
self.median = np.median(self.smplHistory, axis=0)
self.check_final_value()
def finalizeSampling(self):
# from scikits.talkbox.tools.correlations import acorr
from pyhrf.stats import acorr
if 0 and self.smplHistory is not None:
pyhrf.verbose(
3, "Compute autocorrelation of %s samples " ", shape=%s" % (self.name, str(self.smplHistory.shape))
)
trajectory = self.smplHistory[self.samplerEngine.nbSweeps :: 2]
# print 'trajectory:', trajectory.shape
self.autocorrelation = acorr(trajectory)
# print 'self.autocorrelation:', self.autocorrelation
sn = trajectory.shape[0] ** 0.5
t95 = 1.959963984540054 / sn
self.autocorrelation_test = np.zeros(self.autocorrelation.shape, dtype=np.int32)
# print 't95:', t95
self.autocorrelation_test[np.where(self.autocorrelation > t95)] = 1
self.autocorrelation_test[np.where(self.autocorrelation < -t95)] = -1
self.autocorrelation_thresh = t95
from scipy.stats import norm
self.autocorrelation_pvalue = np.zeros(self.autocorrelation.shape, dtype=np.float32)
# print 'self.autocorrelation ... :', self.autocorrelation
m_pos = np.where(self.autocorrelation > 0)
if len(m_pos[0]) > 0:
ac_pos = self.autocorrelation[m_pos]
self.autocorrelation_pvalue[m_pos] = norm.sf(ac_pos * sn)
# print 'ac_pos:', ac_pos.shape
# print ac_pos
# print '--- * sn'
# print ac_pos*sn
# print 'norm.sf:'
# print norm.sf(ac_pos * sn)
m_neg = np.where(self.autocorrelation < 0)
if len(m_neg[0]) > 0:
ac_neg = self.autocorrelation[m_neg]
# print 'ac_neg:', ac_neg.shape, (ac_neg*sn).mean()
self.autocorrelation_pvalue[m_neg] = norm.cdf(ac_neg * sn)
# print 'self.smplHistory:'
# print self.smplHistory
# print 'self.autocorrelation:', self.autocorrelation.shape
# print self.autocorrelation
pyhrf.verbose(3, "Compute posterior median for %s " % (self.name))
self.median = np.median(self.smplHistory, axis=0)
self.check_final_value()
