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__init__(self,
model)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature |
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pickBaseSample(self)
This is binary search using cumulative probabilities to pick a base
sample. The use of this routine makes Condensation O(NlogN) where N
is the number of samples. It is probably better to pick base
samples deterministically, since then the algorithm is O(N) and
probably marginally more efficient, but this routine is kept here
for conceptual simplicity and because it maps better to the
published literature. |
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predictNewBases(self)
This method computes all of the new (unweighted) sample
positions. For each sample, first a base is chosen, then the new
sample position is computed by sampling from the prediction density
p(x_t|x_t-1 = base). predict_sample_position is obviously
model-dependent and is found in Model, but it can be
replaced by any process model required. |
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calculateBaseWeights(self)
Once all the unweighted sample positions have been computed using
predict_new_bases, this routine computes the weights by evaluating
the observation density at each of the positions. Cumulative
probabilities are also computed at the same time, to permit an
efficient implementation of pick_base_sample using binary
search. evaluate_observation_density is obviously model-dependent
and is found in the Model class, but it can be replaced by any
observation model required. |
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updateAfterIterating(self,
iteration)
Go and output the estimate for this iteration (which is a
model-dependent routine found in Model) and then swap
over the arrays ready for the next iteration. |
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Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__sizeof__ ,
__str__ ,
__subclasshook__
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