abcpmc Package¶
sampler
Module¶
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class
abcpmc.sampler.
Sampler
(N, Y, postfn, dist, threads=1, pool=None)[source]¶ ABC population monte carlo sampler
Parameters: - N – number of particles
- Y – observed data set
- postfn – model function (a callable), which creates a new dataset x for a given theta
- dist – distance function rho(X, Y) (a callable)
- threads – (optional) number of threads. If >1 and no pool is given <threads> multiprocesses will be started
- pool – (optional) a pool instance which has a <map> function
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particle_proposal_cls
¶ alias of
ParticleProposal
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sample
(prior, eps_proposal, pool=None)[source]¶ Launches the sampling process. Yields the intermediate results per iteration.
Parameters: - prior – instance of a prior definition (or an other callable) see
sampler.GaussianPrior
- eps_proposal – an instance of a threshold proposal (or an other callable) see
sampler.ConstEps
- pool – (optional) a PoolSpec instance,if not None the initial rejection sampling
will be skipped and the pool is used for the further sampling
Yields pool: yields a namedtuple representing the values of one iteration - prior – instance of a prior definition (or an other callable) see
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class
abcpmc.sampler.
GaussianPrior
(mu, sigma)[source]¶ Normal gaussian prior
Parameters: - mu – scalar or vector of means
- sigma – scalar variance or covariance matrix
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class
abcpmc.sampler.
TophatPrior
(min, max)[source]¶ Tophat prior
Parameters: - min – scalar or array of min values
- max – scalar or array of max values
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class
abcpmc.sampler.
ParticleProposal
(sampler, eps, pool, kwargs)[source]¶ Creates new particles using twice the weighted covariance matrix (Beaumont et al. 2009)
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class
abcpmc.sampler.
OLCMParticleProposal
(sampler, eps, pool, kwargs)[source]¶ Bases:
abcpmc.sampler.ParticleProposal
Creates new particles using an optimal loacl covariance matrix (Fillipi et al. 2012)
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class
abcpmc.sampler.
KNNParticleProposal
(sampler, eps, pool, kwargs)[source]¶ Bases:
abcpmc.sampler.ParticleProposal
Creates new particles using a covariance matrix from the K-nearest neighbours (Fillipi et al. 2012) Set k as key-word arguement in abcpmc.Sampler.particle_proposal_kwargs
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abcpmc.sampler.
weighted_cov
(values, weights)[source]¶ Computes a weighted covariance matrix
Parameters: - values – the array of values
- weights – array of weights for each entry of the values
Returns sigma: the weighted covariance matrix
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abcpmc.sampler.
weighted_avg_and_std
(values, weights, axis=None)[source]¶ Return the weighted avg and standard deviation.
Parameters: - values – Array with the values
- weights – Array with the same shape as values containing the weights
- axis – (optional) the axis to be used for the computation
Returns avg, sigma: weighted average and standard deviation
threshold
Module¶
Various different threshold implementations
Created on Jan 19, 2015
author: jakeret
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class
abcpmc.threshold.
ConstEps
(T, eps)[source]¶ Bases:
abcpmc.threshold.EpsProposal
Constant threshold. Can be used to apply alpha-percentile threshold decrease :param eps: epsilon value
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class
abcpmc.threshold.
ExponentialConstEps
(max, min, T1, T2)[source]¶ Bases:
abcpmc.threshold.EpsProposal
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class
abcpmc.threshold.
ExponentialEps
(T, max, min)[source]¶ Bases:
abcpmc.threshold.EpsProposal
Exponentially decreasing threshold
Parameters: - max – epsilon at t=0
- min – epsilon at t=T
- T – number of iterations
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class
abcpmc.threshold.
LinearConstEps
(max, min, T1, T2)[source]¶ Bases:
abcpmc.threshold.EpsProposal
Linearly decreasing threshold until T1, then constant until T2
Parameters: - max – epsilon at t=0
- min – epsilon at t=T
- T1 – number of iterations for decrease
- T2 – number of iterations for constant behavior
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class
abcpmc.threshold.
LinearEps
(T, max, min)[source]¶ Bases:
abcpmc.threshold.EpsProposal
Linearly decreasing threshold
Parameters: - max – epsilon at t=0
- min – epsilon at t=T
- T – number of iterations
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class
abcpmc.threshold.
ListEps
(T, eps_vals)[source]¶ Bases:
abcpmc.threshold.EpsProposal