def getAutocorrelation(self,
paramVec,
maxOff=None,
weight_units=True,
normalized=True):
"""
Gets auto-correlation of an array of parameter values (e.g. for correlated samples from MCMC)
By default uses weight units (i.e. standard units for separate samples from original chain).
If samples are made from multiple chains, neglects edge effects.
:param paramVec: an array of parameter values, or the int index of the parameter in stored samples to use
:param maxOff: maximum autocorrelation distance to return
:param weight_units: False to get result in sample point (row) units; weight_units=False gives standard definition for raw chains
:param normalized: Set to False to get covariance (note even if normalized, corr[0]<>1 in general unless weights are unity).
:return: zero-based array giving auto-correlations
"""
if maxOff is None: maxOff = self.n - 1
d = self.mean_diff(paramVec) * self.weights
corr = autoConvolve(d, n=maxOff + 1, normalize=True)
if normalized: corr /= self.var(paramVec)
if weight_units:
return corr * d.size / self.get_norm()
else:
return corr
def getAutocorrelation(self, paramVec, maxOff=None, weight_units=True, normalized=True):
"""
Gets auto-correlation of an array of parameter values (e.g. for correlated samples from MCMC)
By default uses weight units (i.e. standard units for separate samples from original chain).
If samples are made from multiple chains, neglects edge effects.
:param paramVec: an array of parameter values, or the int index of the parameter in stored samples to use
:param maxOff: maximum autocorrelation distance to return
:param weight_units: False to get result in sample point (row) units; weight_units=False gives standard definition for raw chains
:param normalized: Set to False to get covariance (note even if normalized, corr[0]<>1 in general unless weights are unity).
:return: zero-based array giving auto-correlations
"""
if maxOff is None: maxOff = self.n - 1
d = self.mean_diff(paramVec) * self.weights
corr = autoConvolve(d, n=maxOff + 1, normalize=True)
if normalized: corr /= self.var(paramVec)
if weight_units:
return corr * d.size / self.get_norm()
else:
return corr