2019-02-21 19:00:09 +04:00
<!doctype html>
< html xmlns = "http://www.w3.org/1999/xhtml" >
< head >
< meta http-equiv = "X-UA-Compatible" content = "IE=Edge" / >
< meta http-equiv = "Content-Type" content = "text/html; charset=utf-8" / > < script type = "text/javascript" >
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-55120145-3']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
< / script >
2019-05-07 23:24:59 +04:00
< title > pyFTS.distributed package — pyFTS 1.6 documentation< / title >
2019-02-21 19:00:09 +04:00
< link rel = "stylesheet" href = "_static/bizstyle.css" type = "text/css" / >
< link rel = "stylesheet" href = "_static/pygments.css" type = "text/css" / >
< script type = "text/javascript" src = "_static/documentation_options.js" > < / script >
< script type = "text/javascript" src = "_static/jquery.js" > < / script >
< script type = "text/javascript" src = "_static/underscore.js" > < / script >
< script type = "text/javascript" src = "_static/doctools.js" > < / script >
< script type = "text/javascript" src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML" > < / script >
< script type = "text/javascript" src = "_static/bizstyle.js" > < / script >
< link rel = "index" title = "Index" href = "genindex.html" / >
< link rel = "search" title = "Search" href = "search.html" / >
< link rel = "next" title = "pyFTS.hyperparam package" href = "pyFTS.hyperparam.html" / >
< link rel = "prev" title = "pyFTS.data package" href = "pyFTS.data.html" / >
< meta name = "viewport" content = "width=device-width,initial-scale=1.0" >
<!-- [if lt IE 9]>
< script type = "text/javascript" src = "_static/css3-mediaqueries.js" > < / script >
<![endif]-->
< / head > < body >
< div class = "related" role = "navigation" aria-label = "related navigation" >
< h3 > Navigation< / h3 >
< ul >
< li class = "right" style = "margin-right: 10px" >
< a href = "genindex.html" title = "General Index"
accesskey="I">index< / a > < / li >
< li class = "right" >
< a href = "py-modindex.html" title = "Python Module Index"
>modules< / a > |< / li >
< li class = "right" >
< a href = "pyFTS.hyperparam.html" title = "pyFTS.hyperparam package"
accesskey="N">next< / a > |< / li >
< li class = "right" >
< a href = "pyFTS.data.html" title = "pyFTS.data package"
accesskey="P">previous< / a > |< / li >
2019-05-07 23:24:59 +04:00
< li class = "nav-item nav-item-0" > < a href = "index.html" > pyFTS 1.6 documentation< / a > » < / li >
2019-02-21 19:00:09 +04:00
< li class = "nav-item nav-item-1" > < a href = "modules.html" > pyFTS< / a > » < / li >
< li class = "nav-item nav-item-2" > < a href = "pyFTS.html" accesskey = "U" > pyFTS package< / a > » < / li >
< / ul >
< / div >
< div class = "sphinxsidebar" role = "navigation" aria-label = "main navigation" >
< div class = "sphinxsidebarwrapper" >
< p class = "logo" > < a href = "index.html" >
< img class = "logo" src = "_static/logo_heading2.png" alt = "Logo" / >
< / a > < / p >
< h3 > < a href = "index.html" > Table Of Contents< / a > < / h3 >
< ul >
< li > < a class = "reference internal" href = "#" > pyFTS.distributed package< / a > < ul >
< li > < a class = "reference internal" href = "#module-pyFTS.distributed" > Module contents< / a > < / li >
< li > < a class = "reference internal" href = "#submodules" > Submodules< / a > < / li >
< li > < a class = "reference internal" href = "#pyfts-distributed-dispy-module" > pyFTS.distributed.dispy module< / a > < / li >
< li > < a class = "reference internal" href = "#module-pyFTS.distributed.spark" > pyFTS.distributed.spark module< / a > < / li >
< / ul >
< / li >
< / ul >
< h4 > Previous topic< / h4 >
< p class = "topless" > < a href = "pyFTS.data.html"
title="previous chapter">pyFTS.data package< / a > < / p >
< h4 > Next topic< / h4 >
< p class = "topless" > < a href = "pyFTS.hyperparam.html"
title="next chapter">pyFTS.hyperparam package< / a > < / p >
< div role = "note" aria-label = "source link" >
< h3 > This Page< / h3 >
< ul class = "this-page-menu" >
< li > < a href = "_sources/pyFTS.distributed.rst.txt"
rel="nofollow">Show Source< / a > < / li >
< / ul >
< / div >
< div id = "searchbox" style = "display: none" role = "search" >
< h3 > Quick search< / h3 >
< div class = "searchformwrapper" >
< form class = "search" action = "search.html" method = "get" >
< input type = "text" name = "q" / >
< input type = "submit" value = "Go" / >
< input type = "hidden" name = "check_keywords" value = "yes" / >
< input type = "hidden" name = "area" value = "default" / >
< / form >
< / div >
< / div >
< script type = "text/javascript" > $ ( '#searchbox' ) . show ( 0 ) ; < / script >
< / div >
< / div >
< div class = "document" >
< div class = "documentwrapper" >
< div class = "bodywrapper" >
< div class = "body" role = "main" >
< div class = "section" id = "pyfts-distributed-package" >
< h1 > pyFTS.distributed package< a class = "headerlink" href = "#pyfts-distributed-package" title = "Permalink to this headline" > ¶< / a > < / h1 >
< div class = "section" id = "module-pyFTS.distributed" >
< span id = "module-contents" > < / span > < h2 > Module contents< a class = "headerlink" href = "#module-pyFTS.distributed" title = "Permalink to this headline" > ¶< / a > < / h2 >
< / div >
< div class = "section" id = "submodules" >
< h2 > Submodules< a class = "headerlink" href = "#submodules" title = "Permalink to this headline" > ¶< / a > < / h2 >
< / div >
< div class = "section" id = "pyfts-distributed-dispy-module" >
< h2 > pyFTS.distributed.dispy module< a class = "headerlink" href = "#pyfts-distributed-dispy-module" title = "Permalink to this headline" > ¶< / a > < / h2 >
< / div >
< div class = "section" id = "module-pyFTS.distributed.spark" >
< span id = "pyfts-distributed-spark-module" > < / span > < h2 > pyFTS.distributed.spark module< a class = "headerlink" href = "#module-pyFTS.distributed.spark" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.create_multivariate_model" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > create_multivariate_model< / code > < span class = "sig-paren" > (< / span > < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.create_multivariate_model" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > From the dictionary of parameters, create a multivariate FTS model< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < strong > parameters< / strong > – < / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > multivariate FTS model< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.create_spark_conf" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > create_spark_conf< / code > < span class = "sig-paren" > (< / span > < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.create_spark_conf" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Configure the Spark master node< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < strong > kwargs< / strong > – < / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.create_univariate_model" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > create_univariate_model< / code > < span class = "sig-paren" > (< / span > < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.create_univariate_model" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > From the dictionary of parameters, create an univariate FTS model< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < strong > parameters< / strong > – < / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > univariate FTS model< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.distributed_predict" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > distributed_predict< / code > < span class = "sig-paren" > (< / span > < em > data< / em > , < em > model< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.distributed_predict" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > The main method for distributed forecasting with FTS models using Spark clusters.< / p >
< p > It takes a trained FTS model and the test data, connect with the Spark cluster,
proceed the distributed forecasting and return the merged forecasted values.< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
2019-02-21 19:00:09 +04:00
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
2020-01-24 07:42:51 +04:00
< li > < strong > model< / strong > – < / li >
< li > < strong > data< / strong > – < / li >
< li > < strong > url< / strong > – < / li >
2019-02-21 19:00:09 +04:00
< li > < strong > app< / strong > – < / li >
< / ul >
< / td >
< / tr >
2020-01-24 07:42:51 +04:00
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > forecasted values< / p >
2019-02-21 19:00:09 +04:00
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.distributed_train" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > distributed_train< / code > < span class = "sig-paren" > (< / span > < em > model< / em > , < em > data< / em > , < em > **kwargs< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.distributed_train" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > The main method for distributed training of FTS models using Spark clusters.< / p >
< p > It takes an empty model and the train data, connect with the Spark cluster, proceed the
distributed training and return the learned model.< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
2019-02-21 19:00:09 +04:00
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
2020-01-24 07:42:51 +04:00
< li > < strong > model< / strong > – < / li >
< li > < strong > data< / strong > – < / li >
< li > < strong > url< / strong > – < / li >
< li > < strong > app< / strong > – < / li >
2019-02-21 19:00:09 +04:00
< / ul >
< / td >
< / tr >
2020-01-24 07:42:51 +04:00
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > trained model< / p >
2019-02-21 19:00:09 +04:00
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.get_clustered_partitioner" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > get_clustered_partitioner< / code > < span class = "sig-paren" > (< / span > < em > explanatory_variables< / em > , < em > target_variable< / em > , < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.get_clustered_partitioner" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Return the UoD partitioner from the ‘ ’
clustered multivariate FTS.< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > explanatory_variables< / strong > – < / li >
< li > < strong > target_variable< / strong > – < / li >
< / ul >
< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > Partitioner object< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.get_partitioner" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > get_partitioner< / code > < span class = "sig-paren" > (< / span > < em > shared_partitioner< / em > , < em > type='common'< / em > , < em > variables=[]< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.get_partitioner" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Return the UoD partitioner from the ‘ ’ < / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
2019-02-21 19:00:09 +04:00
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
2020-01-24 07:42:51 +04:00
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > shared_partitioner< / strong > – < / li >
< li > < strong > type< / strong > – < / li >
< li > < strong > variables< / strong > – < / li >
< / ul >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
2020-01-24 07:42:51 +04:00
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > Partitioner object< / p >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.get_variables" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > get_variables< / code > < span class = "sig-paren" > (< / span > < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.get_variables" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > From the dictionary of parameters, return a tuple with the list of explanatory and target variables< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < strong > parameters< / strong > – < / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > a tuple with the list of explanatory and target variables< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.share_parameters" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > share_parameters< / code > < span class = "sig-paren" > (< / span > < em > model< / em > , < em > context< / em > , < em > data< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.share_parameters" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Create a shared variable with a dictionary of the model parameters and hyperparameters< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > model< / strong > – < / li >
< li > < strong > context< / strong > – < / li >
< li > < strong > data< / strong > – < / li >
< / ul >
< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > the shared variable with the dictionary of parameters< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.slave_forecast_multivariate" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > slave_forecast_multivariate< / code > < span class = "sig-paren" > (< / span > < em > data< / em > , < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.slave_forecast_multivariate" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Receive test data, create a multivariate FTS model from the parameters and return the forecasted values< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > data< / strong > – < / li >
< li > < strong > parameters< / strong > – < / li >
< / ul >
< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > forecasted values from the data input< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.slave_forecast_univariate" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > slave_forecast_univariate< / code > < span class = "sig-paren" > (< / span > < em > data< / em > , < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.slave_forecast_univariate" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Receive test data, create an univariate FTS model from the parameters and return the forecasted values< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
2019-02-21 19:00:09 +04:00
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
2020-01-24 07:42:51 +04:00
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > data< / strong > – < / li >
< li > < strong > parameters< / strong > – < / li >
< / ul >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
2020-01-24 07:42:51 +04:00
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > forecasted values from the data input< / p >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.slave_train_multivariate" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > slave_train_multivariate< / code > < span class = "sig-paren" > (< / span > < em > data< / em > , < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.slave_train_multivariate" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Receive train data, train a multivariate FTS model and return the learned rules< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > data< / strong > – < / li >
< li > < strong > parameters< / strong > – < / li >
< / ul >
< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > Key/value list of the learned rules< / p >
< / td >
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
2019-02-21 19:00:09 +04:00
< dl class = "function" >
< dt id = "pyFTS.distributed.spark.slave_train_univariate" >
2019-06-06 18:04:20 +04:00
< code class = "descclassname" > pyFTS.distributed.spark.< / code > < code class = "descname" > slave_train_univariate< / code > < span class = "sig-paren" > (< / span > < em > data< / em > , < em > **parameters< / em > < span class = "sig-paren" > )< / span > < a class = "headerlink" href = "#pyFTS.distributed.spark.slave_train_univariate" title = "Permalink to this definition" > ¶< / a > < / dt >
2020-01-24 07:42:51 +04:00
< dd > < p > Receive train data, train an univariate FTS model and return the learned rules< / p >
< table class = "docutils field-list" frame = "void" rules = "none" >
2019-02-21 19:00:09 +04:00
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
2020-01-24 07:42:51 +04:00
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > data< / strong > – < / li >
< li > < strong > parameters< / strong > – < / li >
< / ul >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
2020-01-24 07:42:51 +04:00
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first last" > Key/value list of the learned rules< / p >
< / td >
2019-02-21 19:00:09 +04:00
< / tr >
< / tbody >
< / table >
< / dd > < / dl >
< / div >
< / div >
< / div >
< / div >
< / div >
< div class = "clearer" > < / div >
< / div >
< div class = "related" role = "navigation" aria-label = "related navigation" >
< h3 > Navigation< / h3 >
< ul >
< li class = "right" style = "margin-right: 10px" >
< a href = "genindex.html" title = "General Index"
>index< / a > < / li >
< li class = "right" >
< a href = "py-modindex.html" title = "Python Module Index"
>modules< / a > |< / li >
< li class = "right" >
< a href = "pyFTS.hyperparam.html" title = "pyFTS.hyperparam package"
>next< / a > |< / li >
< li class = "right" >
< a href = "pyFTS.data.html" title = "pyFTS.data package"
>previous< / a > |< / li >
2019-05-07 23:24:59 +04:00
< li class = "nav-item nav-item-0" > < a href = "index.html" > pyFTS 1.6 documentation< / a > » < / li >
2019-02-21 19:00:09 +04:00
< li class = "nav-item nav-item-1" > < a href = "modules.html" > pyFTS< / a > » < / li >
< li class = "nav-item nav-item-2" > < a href = "pyFTS.html" > pyFTS package< / a > » < / li >
< / ul >
< / div >
< div class = "footer" role = "contentinfo" >
© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
Created using < a href = "http://sphinx-doc.org/" > Sphinx< / a > 1.7.2.
< / div >
< / body >
< / html >
