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anglican.inference

Inference abstractions

Inference abstractions
raw docstring

checkpointcljmultimethod

execution checkpoints, processed differently depending on the inference algorithm

execution checkpoints, processed differently
depending on the inference algorithm
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checkpoint-idclj

(checkpoint-id cpt state checkpoint-counts checkpoint-last-id)

returns checkpoint identifier and updated state; checkpoint-counts' andcheckpoint-last-id' should be keys in the state

returns checkpoint identifier and updated state;
`checkpoint-counts' and `checkpoint-last-id' should be
keys in the state
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collect-bycljdeprecated

alias to anglican.stat/collect-by, provided for backwards compatibility previous releases

alias to anglican.stat/collect-by, provided for backwards
compatibility previous releases
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drop-invalidclj

(drop-invalid samples)

drop zero-weight samples;

  • accepts a lazy sequence of samples;
  • returns a sequence that excludes zero-weight samples
drop zero-weight samples;
- accepts a lazy sequence of samples;
- returns a sequence that excludes zero-weight samples
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equalizeclj

(equalize samples)

equalizes samples;

  • accepts a lazy sequence of samples;
  • returns a sequence from the same distribution with equal weights
equalizes samples;
- accepts a lazy sequence of samples;
- returns a sequence from the same distribution
  with equal weights
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execclj

(exec algorithm prog value state)

executes the program, calling checkpoint handlers at the checkpoints and stopping when the handler returns a non-callable value

executes the program, calling checkpoint handlers
at the checkpoints and stopping when the handler
returns a non-callable value
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infercljmultimethod

main inference procedure, accepts algorithm, program, and options; returns a lazy sequence of states

main inference procedure, accepts algorithm, program, and
options; returns a lazy sequence of states
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log-marginalclj

(log-marginal samples)

calculates the empirical estimate of the log marginal likelihood (i.e. the log mean importance weight)

calculates the empirical estimate of the log marginal likelihood
(i.e. the log mean importance weight)
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prints a predict, accepts label, value, weight, and output format

prints a predict, accepts label, value, weight, and
output format
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(print-predicts format state)

print predicts as returned by a probabilistic program in the specified format

print predicts as returned by a probabilistic program
in the specified format
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randclj

(rand)
(rand n)

Returns a random floating point number between 0 (inclusive) and n (default 1) (exclusive)

Returns a random floating point number
between 0 (inclusive) and n (default 1) (exclusive)
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rand-intclj

(rand-int n)

Returns a random integer between 0 (inclusive) and n (exclusive)

Returns a random integer between 0 (inclusive)
and n (exclusive)
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rand-nthclj

(rand-nth coll)

Return a random element of the (sequential) collection. Will have the same performance characteristics as nth for the given collection.

Return a random element of the (sequential) collection.
Will have the same performance characteristics as nth
for the given collection.
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rand-rouletteclj

(rand-roulette weights)

random roulette selection, accepts unnormalized weights

random roulette selection,
accepts unnormalized weights
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stripdownclj

(stripdown state)

removes algorithm-specific entries from the state

removes algorithm-specific entries from the state
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warmupclj

(warmup prog)
(warmup prog value)

runs until the first checkpoint and returns a continuation that starts with that checkpoint

runs until the first checkpoint and returns
a continuation that starts with that checkpoint
raw docstring

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