[
https://issues.apache.org/jira/browse/SOLR-10651?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joel Bernstein updated SOLR-10651:
----------------------------------
Description:
This is a ticket for organizing the new statistical programming features of
Streaming Expressions. It's also a place for the community to discuss what
functions are needed to support statistical programming.
Basic Syntax:
{code}
let(a = timeseries(...),
b = timeseries(...),
c = col(a, count(*)),
d = col(b, count(*)),
r = regress(c, d),
tuple (p = predict(r, 50)))
{code}
The expression above is doing the following:
1) The let expression is setting variables (a, b, c, d, r).
2) Variables *a* and *b* are the output of timeseries() Streaming Expressions.
These will be stored in memory as list of Tuples with time series result.
3) Variables *c* and *d* are set using the *col* evaluator. The col evaluator
extracts a column of numbers from a list of tuples. In the example it
extracting the count\(*\) field from the two time series result sets.
4) Variable *r* is the output from the *regress* evaluator. The regress
evaluator performs a simple regress analysis on two columns of numbers.
5) Once the variables are set, a single Streaming Expression is run by the
*let* expression. In the example the *tuple* expression is run. The tuple
expression outputs a single Tuple with name/value pairs. Any Streaming
Expression can be run by the *let* expression so this can be a complex program.
The streaming expression run by *let* has access to all the variables defined
earlier.
6) The tuple expression in the example has one name / value pair. The name *p*
is set to the output of the *predict* evaluator. The predict evaluator is
predicting the value 50 based on the regression result stored in variable *r*.
7) The output of this expression will be a single tuple with value of the
predict function in the *p* field.
was:
This is a ticket for organizing the new statistical programming features of
Streaming Expressions. It's also a place for the community to discuss what
functions are needed to support statistical programming.
Basic Syntax:
{code}
let(a = timeseries(...),
b = timeseries(...),
c = col(a, count(*)),
d = col(b, count(*)),
r = regress(c, d),
tuple (p = predict(r, 50)))
{code}
The expression above is doing the following:
1) The let expression is setting variables (a, b, c, d, r).
2) Variables *a* and *b* are the output of timeseries() Streaming Expressions.
These will be stored in memory as list of Tuples with time series result.
3) Variables *c* and *d* a set using the *col* evaluator. The col evaluator
extracts a column of numbers from a list of tuples. In the example it
extracting the count\(*\) field from the two time series result sets.
4) Variable *r* is the output from the *regress* evaluator. The regress
evaluator performs a simple regress analysis on two columns of numbers.
5) Once the variables are set, a single Streaming Expression is run by the
*let* expression. In the example the *tuple* expression is run. The tuple
expression outputs a single Tuple with name/value pairs. Any Streaming
Expression can be run by the *let* expression so this can be a complex program.
The streaming expression run by *let* has access to all the variables defined
earlier.
6) The tuple expression in the example has one name / value pair. The name *p*
is set to the output of the *predict* evaluator. The predict evaluator is
predicting the value 50 based on the regression result stored in variable *r*.
7) The output of this expression will be a single tuple with value of the
predict function in the *p* field.
> Streaming Expressions statistical functions library
> ---------------------------------------------------
>
> Key: SOLR-10651
> URL: https://issues.apache.org/jira/browse/SOLR-10651
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: Joel Bernstein
> Fix For: master (7.0)
>
>
> This is a ticket for organizing the new statistical programming features of
> Streaming Expressions. It's also a place for the community to discuss what
> functions are needed to support statistical programming.
> Basic Syntax:
> {code}
> let(a = timeseries(...),
> b = timeseries(...),
> c = col(a, count(*)),
> d = col(b, count(*)),
> r = regress(c, d),
> tuple (p = predict(r, 50)))
> {code}
> The expression above is doing the following:
> 1) The let expression is setting variables (a, b, c, d, r).
> 2) Variables *a* and *b* are the output of timeseries() Streaming
> Expressions. These will be stored in memory as list of Tuples with time
> series result.
> 3) Variables *c* and *d* are set using the *col* evaluator. The col evaluator
> extracts a column of numbers from a list of tuples. In the example it
> extracting the count\(*\) field from the two time series result sets.
> 4) Variable *r* is the output from the *regress* evaluator. The regress
> evaluator performs a simple regress analysis on two columns of numbers.
> 5) Once the variables are set, a single Streaming Expression is run by the
> *let* expression. In the example the *tuple* expression is run. The tuple
> expression outputs a single Tuple with name/value pairs. Any Streaming
> Expression can be run by the *let* expression so this can be a complex
> program. The streaming expression run by *let* has access to all the
> variables defined earlier.
> 6) The tuple expression in the example has one name / value pair. The name
> *p* is set to the output of the *predict* evaluator. The predict evaluator is
> predicting the value 50 based on the regression result stored in variable *r*.
> 7) The output of this expression will be a single tuple with value of the
> predict function in the *p* field.
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