Comments (4)
Just to clarify, is this still true or does the sample object's probabilities
attribute take care of this?
From my glance at calculateP (very early stages of learning to read code written in parallel), it seems like this is no longer an issue.
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This is still true. The standard ansatz is used in all of the prob
methods in bet.calculateP.calculateP
as the line:
P[Itemp] = discretization._output_probability_set._probabilities[i]/Itemp_sum
and :
P_local[Itemp] = discretization._output_probability_set._probabilities[i]*discretization._input_sample_set._volumes_local[Itemp]/Itemp_sum``
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I think this could be done by using the emulated input sample set appropriately.
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I actually accomplished this by setting probabilities
to 1/num_samples if empty, and then using that vector in place of volumes in the main loop of calcP.prob.
@lcgraham This feature is coming down the line very soon, I think I can make it it's own PR. It will be aligned with setting an "initial" distribution which we "update". If unspecified, uniform is assumed.
Check out the animation in #161 to see it in action.
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Related Issues (20)
- TODO: fix parallel loading of sample sets HOT 10
- revisit PendingDeprecationWarning HOT 1
- Mismatch of docs and definition of postTools.sample_prob HOT 4
- TODO: Feature Scaling HOT 1
- How we do installs HOT 4
- TODO: un-lock scipy HOT 6
- TODO: clean up some tests
- why no pickle? HOT 10
- discussion of decorators
- sampler class options HOT 2
- document when predicted distribution is erased
- nose deprecation HOT 9
- data-consistent methods HOT 5
- Deprecate adaptive sampling? HOT 3
- no mpi4py + mpirun for plotting HOT 2
- May 19 Meeting HOT 9
- Improve Fitting Random Variables
- Parallel Tests HOT 19
- New features for estimating observed and predicted distributions (other than KDE) HOT 4
- Create better (and simpler) plotting routines for density based approach
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