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https://issues.apache.org/jira/browse/SOLR-11602?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joel Bernstein updated SOLR-11602:
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Description:
Now that Streaming Expressions supports Monte Carlo simulations it would be
useful to also support Markov Chains. This ticket will add support for Markov
Chain simulations.
Here is the syntax:
{code}
let(state0=array(.3, .4, .3),
state1=array(.2, .1, .7),
state2=array(.6, .2, .2),
states=matrix(state0, state1, state2),
m=markovChain(states, 0),
s=sample(m, 500))
{code}
The Markov chain is initialized with an matrix who's rows represent the
different *states* of the system. The columns represent the probabilities of
changing from one state to another state.
For example if we are in state 1 represented by the array(.2,.1,.7), there is a
.7 percent probability that it transition to state 2.
was:Now that Streaming Expressions supports Monte Carlo simulations it would
be useful to also support Markov Chains. This ticket will add support for
Markov Chain simulations.
> Add Markov Chain Stream Evaluator
> ---------------------------------
>
> Key: SOLR-11602
> URL: https://issues.apache.org/jira/browse/SOLR-11602
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: Joel Bernstein
> Assignee: Joel Bernstein
> Fix For: 7.2
>
> Attachments: SOLR-11602.patch, SOLR-11602.patch
>
>
> Now that Streaming Expressions supports Monte Carlo simulations it would be
> useful to also support Markov Chains. This ticket will add support for Markov
> Chain simulations.
> Here is the syntax:
> {code}
> let(state0=array(.3, .4, .3),
> state1=array(.2, .1, .7),
> state2=array(.6, .2, .2),
> states=matrix(state0, state1, state2),
> m=markovChain(states, 0),
> s=sample(m, 500))
> {code}
> The Markov chain is initialized with an matrix who's rows represent the
> different *states* of the system. The columns represent the probabilities of
> changing from one state to another state.
> For example if we are in state 1 represented by the array(.2,.1,.7), there is
> a .7 percent probability that it transition to state 2.
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