Random initialization for NUTS #1744
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pymc3/examples/gelman_bioassay.py
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| @@ -1,5 +1,5 @@ | |||
| import pymc3 as pm | |||
| from numpy import ones, array | |||
| from pymc3 import * | |||
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Sorry, was testing and forgot to take it out.
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Generalized to use with any step method |
pymc3/distributions/discrete.py
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| @@ -389,7 +389,7 @@ def _random(self, lower, upper, size=None): | |||
| # as array-like. | |||
| samples = stats.uniform.rvs(lower, upper - lower - np.finfo(float).eps, | |||
| size=size) | |||
| return np.floor(samples).astype('int32') | |||
| return np.floor(samples).astype('int64') | |||
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Do we need int64 here? This causes problems with float32 interaction.
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I doubt it. I think I was trying to diagnose a bug here. I will remove it.
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dtype='int64' is the default for the Discrete class. Should that be changed?
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I will never understand |
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Sometimes it's better to make a new branch from master and cherry-pick the commits you want (can squash them before to make that faster). |
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Yes, I need to do that more. I am lazy. |
pymc3/sampling.py
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| @@ -1,4 +1,4 @@ | |||
| from collections import defaultdict | |||
| rom collections import defaultdict | |||
Use ADVI to initialize NUTS for marginalizes GMM example
Fix multivariate metropolis proposal
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Same rebase issue here. Closing. |
Added a
"random"initialization option for NUTS that simply draws from the prior. Seems to work pretty well in some cases. Might be a good idea for initializing other samplers as well.