[50/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/arima_8sql__in.html
--
diff --git a/docs/v1.15.1/arima_8sql__in.html b/docs/v1.15.1/arima_8sql__in.html
new file mode 100644
index 000..506a153
--- /dev/null
+++ b/docs/v1.15.1/arima_8sql__in.html
@@ -0,0 +1,1070 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: arima.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('arima_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+arima.sql_in File Reference
+
+
+
+Arima function for forecasting of timeseries data.
+More...
+
+
+Functions
+voidarima_train
(text input_table, text output_table, text timestamp_column, text
timeseries_column, text grouping_columns, boolean include_mean, integer[]
non_seasonal_orders, text optimizer_params)
+
+voidarima_train
(text input_table, text output_table, text timestamp_column, text
timeseries_column, text grouping_columns, boolean include_mean, integer[]
non_seasonal_orders)
+
+voidarima_train
(text input_table, text output_table, text timestamp_column, text
timeseries_column, text grouping_columns, boolean include_mean)
+
+voidarima_train
(text input_table, text output_table, text timestamp_column, text
timeseries_column, text grouping_columns)
+
+voidarima_train
(text input_table, text output_table, text timestamp_column, text
timeseries_column)
+
+voidarima_forecast
(text model_table, text output_table, integer steps_ahead)
+
+textarima_train
(text message)
+
+textarima_train
()
+
+textarima_forecast
(text message)
+
+textarima_forecast
()
+
+float8 []__arima_residual
(integer distid, float8[] tvals, integer p, integer d, integer q, float8[]
phi, float8[] theta, float8 mean,
float8[] prez)
+
+float8 []__arima_diff
(float8[] tvals, integer d)
+
+float8 []__arima_adjust
(integer distid, float8[] curr_tvals, float8[] prev_tvals, integer p)
+
+float8 []__arima_lm_delta
(float8[] jj, float8[] jz, float8 u)
+
+__arima_lm_result__arima_lm
(integer distid, float8[] tvals, integer p, integer q, float8[] phi, float8[]
theta, float8 mean,
float8[] prez, float8[] prej)
+
+float8 []__arima_lm_result_sfunc
(float8[] state_data, float8[] jj, float8[] jz, float8 z2)
+
+float8 []__arima_lm_result_pfunc
(float8[] state1, float8[] state2)
+
+__arima_lm_sum_result__arima_lm_result_ffunc
(float8[] state_data)
+
+aggregate __arima_lm_sum_result__arima_lm_result_agg
(float8[], float8[], float8)
+
+float8 []__arima_lm_stat_sfunc
(float8[] state_data, integer distid, float8[] tvals, integer p, integer q,
float8[] phi, float8[] theta, float8 mean,
float8 delta)
+
+__arima_lm_stat_result__arima_lm_stat_ffunc
(float8[] state_data)
+
+aggregate __arima_lm_stat_result__arima_lm_stat_agg
(integer, float8[], integer, integer, float8[], float8[], float8,
float8)
+
+
+Detailed
Description
+DateAugust 2013
+Function Documentation
+
+__arima_adjust()
+
+
+
+
+
+ float8 [] __arima_adjust
+ (
+ integer
+ distid,
+
+
+
+
+ float8 []
+ curr_tvals,
+
+
+
+
+ float8 []
+ prev_tvals,
+
+
+
+
+
[27/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__correlation.html
--
diff --git a/docs/v1.15.1/group__grp__correlation.html
b/docs/v1.15.1/group__grp__correlation.html
new file mode 100644
index 000..35c2e7d
--- /dev/null
+++ b/docs/v1.15.1/group__grp__correlation.html
@@ -0,0 +1,391 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Covariance and Correlation
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__correlation.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Covariance and CorrelationStatistics Descriptive Statistics
+
+
+Contents
+
+Covariance and Correlation Functions
+
+Examples
+
+Literature
+
+Related Topics
+
+A correlation function is the degree and direction of association of
two variableshow well one random variable can be predicted from the
other. It is a normalized version of covariance. The Pearson correlation
coefficient is used here, which has a value between -1 and 1, where 1 implies
total positive linear correlation, 0 means no linear correlation, and -1 means
total negative linear correlation.
+This function generates an \(N\)x \(N\) cross correlation matrix for pairs
of numeric columns in a source_table. It is square symmetrical with
the \( (i,j) \)th element equal to the correlation coefficient between the
\(i\)th and the \(j\)th variable. The diagonal elements (correlations of
variables with themselves) are always equal to 1.0.
+We also provide a covariance function which is similar in nature to
correlation, and is a measure of the joint variability of two random
variables.
+Covariance and Correlation Functions
+The correlation function has the following syntax:
+correlation( source_table,
+ output_table,
+ target_cols,
+ verbose,
+ grouping_cols
+ )
+The covariance function has a similar syntax:
+covariance( source_table,
+output_table,
+target_cols,
+verbose,
+grouping_cols
+ )
+
+source_table
+TEXT. Name of the table containing the input data.
+
+
+output_table
+TEXT. Name of the table containing the cross
correlation matrix. The output table has N rows, where N is the number of
'target_cols' in the 'source_table' for which correlation or
covariance is being computed. It has the following columns:
+
+column_position An automatically generated sequential counter
indicating the order of the variable in the 'output_table'.
+
+variable Contains the row header for the variables of interest.
+
+grouping_cols Contains the grouping columns, if any.
+
+... The remainder of the table is the NxN correlation
matrix for the pairs of variables of interest.
+
+The output table is arranged as a lower-triangular matrix with the upper
triangle set to NULL and the diagonal elements set to 1.0. To obtain the result
from the 'output_table' order by 'column_position':
+SELECT * FROM output_table ORDER BY column_position;
+In addition to output table, a summary table named
output_table_summary is also created, which has the following columns:
+
+method'Correlation' or 'Covariance'
+
+source_tableVARCHAR. Data source table name.
+
+output_tableVARCHAR. Output table name.
+
[31/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/graph_legend.html
--
diff --git a/docs/v1.15.1/graph_legend.html b/docs/v1.15.1/graph_legend.html
new file mode 100644
index 000..1c4a320
--- /dev/null
+++ b/docs/v1.15.1/graph_legend.html
@@ -0,0 +1,161 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Graph Legend
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('graph_legend.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Graph Legend
+
+
+This page explains how to interpret the graphs that are generated by
doxygen.
+Consider the following example: /*! Invisible class because of truncation */class Invisible { };/*! Truncated class, inheritance relation is hidden */class Truncated : public Invisible { };/* Class not documented with doxygen
comments */class Undocumented { };/*! Class that is inherited using public
inheritance */class PublicBase : public Truncated {
};/*! A template class
*/templateclass T class Templ { };/*! Class that is inherited using
protected inheritance */class ProtectedBase {
};/*! Class that is
inherited using private inheritance */class
PrivateBase { };/*! Class that
is used by the Inherited class */class Used {
};/*! Super class that inherits
a number of other classes */class Inherited : public
PublicBase, protected
ProtectedBase, private
PrivateBase, public
Undocumented, public
Templint{
private:Used *m_usedClass;}; This will result in the
following graph:
+This browser is not able to show SVG: try
Firefox, Chrome, Safari, or Opera instead. The
boxes in the above graph have the following meaning:
+
+
+A filled gray box represents the struct or class for which the graph is
generated.
+
+A box with a black border denotes a documented struct or class.
+
+A box with a gray border denotes an undocumented struct or class.
+
+A box with a red border denotes a documented struct or class forwhich not all
inheritance/containment relations are shown. A graph is truncated if it does
not fit within the specified boundaries.
+
+The arrows have the following meaning:
+
+
+A dark blue arrow is used to visualize a public inheritance relation between
two classes.
+
+A dark green arrow is used for protected inheritance.
+
+A dark red arrow is used for private inheritance.
+
+A purple dashed arrow is used if a class is contained or used by another
class. The arrow is labelled with the variable(s) through which the pointed
class or struct is accessible.
+
+A yellow dashed arrow denotes a relation between a template instance and the
template class it was instantiated from. The arrow is labelled with the
template parameters of the instance.
+
+
+
+
+
+
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/graph_legend.md5
--
diff --git a/docs/v1.15.1/graph_legend.md5 b/docs/v1.15.1/graph_legend.md5
new file mode 100644
index 000..a06ed05
--- /dev/null
+++ b/docs/v1.15.1/graph_legend.md5
@@ -0,0 +1 @@
+387ff8eb65306fa251338d3c9bd7bfff
\ No
[24/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__deprecated.html
--
diff --git a/docs/v1.15.1/group__grp__deprecated.html
b/docs/v1.15.1/group__grp__deprecated.html
new file mode 100644
index 000..78699ee
--- /dev/null
+++ b/docs/v1.15.1/group__grp__deprecated.html
@@ -0,0 +1,149 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Deprecated Modules
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__deprecated.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Modules
+
+Deprecated Modules
+
+
+Detailed
Description
+Deprecated modules that will be removed in the next major version (2.0).
There are newer MADlib modules that have replaced these functions.
+
+
+Modules
+Create
Indicator Variables
+Provides utility functions helpful for data preparation
before modeling.
+
+Multinomial Logistic Regression
+Also called as softmax regression, models the relationship
between one or more independent variables and a categorical dependent variable.
+
+
+
+
+
+
+
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__deprecated.js
--
diff --git a/docs/v1.15.1/group__grp__deprecated.js
b/docs/v1.15.1/group__grp__deprecated.js
new file mode 100644
index 000..05ef03b
--- /dev/null
+++ b/docs/v1.15.1/group__grp__deprecated.js
@@ -0,0 +1,5 @@
+var group__grp__deprecated =
+[
+[ "Create Indicator Variables", "group__grp__indicator.html", null ],
+[ "Multinomial Logistic Regression", "group__grp__mlogreg.html", null ]
+];
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__desc__stats.html
--
diff --git a/docs/v1.15.1/group__grp__desc__stats.html
b/docs/v1.15.1/group__grp__desc__stats.html
new file mode 100644
index 000..3ca4434
--- /dev/null
+++ b/docs/v1.15.1/group__grp__desc__stats.html
@@ -0,0 +1,152 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Descriptive Statistics
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1',
[47/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/bfs_8sql__in.html
--
diff --git a/docs/v1.15.1/bfs_8sql__in.html b/docs/v1.15.1/bfs_8sql__in.html
new file mode 100644
index 000..a44e360
--- /dev/null
+++ b/docs/v1.15.1/bfs_8sql__in.html
@@ -0,0 +1,443 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: bfs.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('bfs_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+bfs.sql_in File Reference
+
+
+
+SQL functions for graph analytics.
+More...
+
+
+Functions
+voidgraph_bfs (text
vertex_table, text vertex_id, text edge_table, text edge_args, int
source_vertex, text out_table, int max_distance, boolean directed, text
grouping_cols)
+
+voidgraph_bfs (text
vertex_table, text vertex_id, text edge_table, text edge_args, int
source_vertex, text out_table, int max_distance, boolean directed)
+
+voidgraph_bfs (text
vertex_table, text vertex_id, text edge_table, text edge_args, int
source_vertex, text out_table, int max_distance)
+
+voidgraph_bfs (text
vertex_table, text vertex_id, text edge_table, text edge_args, int
source_vertex, text out_table)
+
+varchargraph_bfs
(varchar message)
+
+varchargraph_bfs
()
+
+
+Detailed
Description
+Licensed to the Apache Software Foundation (ASF)
under one or more contributor license agreements. See the NOTICE file
distributed with this work for additional information regarding copyright
ownership. The ASF licenses this file to you under the Apache License, Version
2.0 (the "License"); you may not use this file except in compliance with the
License. You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0;>http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
+DateJun 2017
+See alsoProvides a breadth first search
graph algorithm.
+Function Documentation
+
+graph_bfs()
[1/6]
+
+
+
+
+
+ void graph_bfs
+ (
+ text
+ vertex_table,
+
+
+
+
+ text
+ vertex_id,
+
+
+
+
+ text
+ edge_table,
+
+
+
+
+ text
+ edge_args,
+
+
+
+
+ int
+ source_vertex,
+
+
+
+
+ text
+ out_table,
+
+
+
+
+ int
+ max_distance,
+
+
+
+
+ boolean
+ directed,
+
+
+
+
+ text
+ grouping_cols
+
+
+
+ )
+
+
+
+
+
+
+
+
+graph_bfs()
[2/6]
+
+
+
+
+
+ void graph_bfs
+ (
+ text
+ vertex_table,
+
+
+
+
+ text
+
[20/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__kmeans.html
--
diff --git a/docs/v1.15.1/group__grp__kmeans.html
b/docs/v1.15.1/group__grp__kmeans.html
new file mode 100644
index 000..4fb7dea
--- /dev/null
+++ b/docs/v1.15.1/group__grp__kmeans.html
@@ -0,0 +1,492 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: k-Means Clustering
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__kmeans.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+k-Means ClusteringUnsupervised Learning Clustering
+
+
+Contents
+
+Training Function
+
+Output Format
+
+Cluster Assignment
+
+Examples
+
+Notes
+
+Technical Background
+
+Literature
+
+Related Topics
+
+Clustering refers to the problem of partitioning a set of objects
according to some problem-dependent measure of similarity. In the
k-means variant, given \( n \) points \( x_1, \dots, x_n \in \mathbb R^d \),
the goal is to position \( k \) centroids \( c_1, \dots, c_k \in \mathbb R^d \)
so that the sum of distances between each point and its closest
centroid is minimized. Each centroid represents a cluster that consists of all
points to which this centroid is closest.
+Training
Function
+The k-means algorithm can be invoked in four ways, depending on the source
of the initial set of centroids:
+
+Use the random centroid seeding method.
+kmeans_random( rel_source,
+ expr_point,
+ k,
+ fn_dist,
+ agg_centroid,
+ max_num_iterations,
+ min_frac_reassigned
+ )
+
+Use the kmeans++ centroid seeding method.
+kmeanspp( rel_source,
+ expr_point,
+ k,
+ fn_dist,
+ agg_centroid,
+ max_num_iterations,
+ min_frac_reassigned,
+ seeding_sample_ratio
+)
+
+Supply an initial centroid set in a relation identified by the
rel_initial_centroids argument.
+kmeans( rel_source,
+expr_point,
+rel_initial_centroids,
+expr_centroid,
+fn_dist,
+agg_centroid,
+max_num_iterations,
+min_frac_reassigned
+ )
+
+Provide an initial centroid set as an array expression in the
initial_centroids argument.
+kmeans( rel_source,
+expr_point,
+initial_centroids,
+fn_dist,
+agg_centroid,
+max_num_iterations,
+min_frac_reassigned
+ )
+ Arguments
+rel_source
+TEXT. The name of the table containing the input data
points.
+Data points and predefined centroids (if used) are expected to be stored
row-wise, in a column of type SVEC (or any type convertible to
SVEC, like
FLOAT[] or INTEGER[]). Data points with non-finite
values (NULL, NaN, infinity) in any component are skipped during analysis.
+
+
+expr_point
+TEXT. The name of the column with point coordinates or
an array expression.
+
+
+k
+INTEGER. The number of centroids to calculate.
+
+
+fn_dist (optional)
+TEXT, default: squared_dist_norm2'. The name of the
function to use to calculate the distance from a data point to a centroid.
+The following distance functions can be used (computation of
barycenter/mean in parentheses):
+
+dist_norm1:
1-norm/Manhattan (element-wise median [Note that MADlib does
[33/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/encode__categorical_8sql__in.html
--
diff --git a/docs/v1.15.1/encode__categorical_8sql__in.html
b/docs/v1.15.1/encode__categorical_8sql__in.html
new file mode 100644
index 000..0cf7d1a
--- /dev/null
+++ b/docs/v1.15.1/encode__categorical_8sql__in.html
@@ -0,0 +1,749 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: encode_categorical.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('encode__categorical_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+encode_categorical.sql_in File Reference
+
+
+
+SQL functions for encoding categorical variables to numerical values.
+More...
+
+
+Functions
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top, varchar
value_to_drop, boolean encode_null, varchar output_type, boolean
output_dictionary, varchar distributed_by)
+Encode categorical columns
using either one-hot encoding or dummy coding. More...
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top, varchar
value_to_drop, boolean encode_null, varchar output_type, boolean
output_dictionary)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top, varchar
value_to_drop, boolean encode_null, varchar output_type)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top, varchar
value_to_drop, boolean encode_null)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top, varchar
value_to_drop)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id, varchar top)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude, varchar row_id)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar categorical_cols, varchar
categorical_cols_to_exclude)
+
+voidencode_categorical_variables
(varchar source_table, varchar output_table, varchar
categorical_cols)
+
+varcharencode_categorical_variables
(varchar message)
+
+varcharencode_categorical_variables
()
+
+
+Detailed
Description
+Licensed to the Apache Software Foundation (ASF)
under one or more contributor license agreements. See the NOTICE file
distributed with this work for additional information regarding copyright
ownership. The ASF licenses this file to you under the Apache License, Version
2.0 (the "License"); you may not use this file except in compliance with the
License. You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0;>http://www.apache.org/licenses/LICENSE-2.0
+Unless required by
[36/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_dc596537ad427a4d866006d1a3e1fe29.html
--
diff --git a/docs/v1.15.1/dir_dc596537ad427a4d866006d1a3e1fe29.html
b/docs/v1.15.1/dir_dc596537ad427a4d866006d1a3e1fe29.html
new file mode 100644
index 000..13e46e8
--- /dev/null
+++ b/docs/v1.15.1/dir_dc596537ad427a4d866006d1a3e1fe29.html
@@ -0,0 +1,190 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: modules Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dir_dc596537ad427a4d866006d1a3e1fe29.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+modules Directory Reference
+
+
+
+
+Directories
+directory assoc_rules
+
+directory bayes
+
+directory conjugate_gradient
+
+directory convex
+
+directory crf
+
+directory elastic_net
+
+directory glm
+
+directory graph
+
+directory kmeans
+
+directory knn
+
+directory lda
+
+directory linalg
+
+directory linear_systems
+
+directory pca
+
+directory pmml
+
+directory prob
+
+directory recursive_partitioning
+
+directory regress
+
+directory sample
+
+directory stats
+
+directory summary
+
+directory svm
+
+directory tsa
+
+directory utilities
+
+directory validation
+
+
+
+
+
+
+
+srcportspostgresmodules
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_df86748cb94fb6c2fa09e991cce090c0.html
--
diff --git a/docs/v1.15.1/dir_df86748cb94fb6c2fa09e991cce090c0.html
b/docs/v1.15.1/dir_df86748cb94fb6c2fa09e991cce090c0.html
new file mode 100644
index 000..ce046b3
--- /dev/null
+++ b/docs/v1.15.1/dir_df86748cb94fb6c2fa09e991cce090c0.html
@@ -0,0 +1,143 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: assoc_rules Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
[16/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__mfvsketch.html
--
diff --git a/docs/v1.15.1/group__grp__mfvsketch.html
b/docs/v1.15.1/group__grp__mfvsketch.html
new file mode 100644
index 000..aa76e83
--- /dev/null
+++ b/docs/v1.15.1/group__grp__mfvsketch.html
@@ -0,0 +1,183 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: MFV (Most Frequent Values)
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
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+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
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+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__mfvsketch.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+MFV (Most Frequent Values)Statistics Descriptive Statistics Cardinality
Estimators
+
+
+Contents
+
+Syntax
+
+Examples
+
+Literature
+
+Related Topics
+
+MFVSketch: Most Frequent Values variant of CountMin sketch,
implemented as a UDA.
+Produces an n-bucket histogram for a column where each bucket counts one of
the most frequent values in the column. The output is an array of doubles
{value, count} in descending order of frequency; counts are approximated via
CountMin sketches. Ties are handled arbitrarily.
+ The MFV frequent-value UDA comes in two
different versions:
+a faithful implementation that preserves the approximation guarantees of
Cormode/Muthukrishnan,
+mfvsketch_top_histogram( col_name,
+ n )
+
+and a "quick and dirty" version that can do parallel aggregation in
Greenplum at the expense of missing some of the most frequent values.
+mfvsketch_quick_histogram( col_name,
+ n )
+
+
+In PostgreSQL the two UDAs are identical. In Greenplum, the quick version
should produce good results unless the number of values requested is small, or
the distribution is flat.
+NoteThis is a https://www.postgresql.org/docs/current/static/xaggr.html;>User Defined
Aggregate which returns the results when used in a query. Use "CREATE TABLE
AS ", with the UDA as subquery if the results are to be stored. This is unlike
the usual MADlib stored procedure interface which places the results in a table
instead of returning it.
+Examples
+
+Generate some data.
+CREATE TABLE data(class INT, a1 INT);
+INSERT INTO data SELECT 1,1 FROM generate_series(1,1);
+INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
+INSERT INTO data SELECT 1,3 FROM generate_series(1,1);
+INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
+INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
+
+Produce a histogram of 5 bins and return the most frequent value and
associated count in each bin.
+SELECT mfvsketch_top_histogram( a1, 5 )
+FROM data;
+ Result:
+mfvsketch_top_histogram
+-
+[0:4][0:1]={{2,15000},{1,1},{3,1},{5,1000},{6,1000}}
+(1 row)
+
+
+LiteratureThis method is not usually called an MFV sketch in
the literature; it is a natural extension of the CountMin sketch.
+Related
Topics
+File sketch.sql_in
documenting the SQL functions.
+Module CountMin
(Cormode-Muthukrishnan).
+
+
+
+
+
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__minibatch__preprocessing.html
[14/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
diff --git a/docs/v1.15.1/group__grp__ordinal.html
b/docs/v1.15.1/group__grp__ordinal.html
new file mode 100644
index 000..c4e9251
--- /dev/null
+++ b/docs/v1.15.1/group__grp__ordinal.html
@@ -0,0 +1,477 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Ordinal Regression
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__ordinal.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Ordinal RegressionSupervised Learning Regression Models
+
+
+Contents
+
+Training Function
+
+Prediction Function
+
+Examples
+
+Model Details
+
+Literature
+
+Related Topics
+
+In statistics, ordinal regression is a type of regression analysis
used for predicting an ordinal variable, i.e. a variable whose value exists on
an arbitrary scale where only the relative ordering between different values is
significant. The two most common types of ordinal regression models are ordered
logit, which applies to data that meet the proportional odds assumption, and
ordered probit.
+Training
FunctionThe ordinal regression training function has the following
syntax:
+ordinal(source_table,
+ model_table,
+ dependent_varname,
+ independent_varname,
+ cat_order,
+ link_func,
+ grouping_col,
+ optim_params,
+ verbose
+)
+
+Arguments
+source_table
+VARCHAR. Name of the table containing the training
data.
+
+
+model_table
+VARCHAR. Name of the generated table containing the
model.
+The model table produced by ordinal() contains the following columns:
+
+
+... Grouping columns, if provided in
input. This could be multiple columns depending on the
grouping_col input.
+
+
+
+coef_threshold FLOAT8[]. Vector of the
threshold coefficients in linear predictor. The threshold coefficients are the
intercepts specific to each categorical levels
+
+
+
+std_err_threshold FLOAT8[]. Vector of the
threshold standard errors of the threshold coefficients.
+
+
+
+z_stats_threshold FLOAT8[]. Vector of the
threshold z-statistics of the thresholdcoefficients.
+
+
+
+p_values_threshold FLOAT8[]. Vector of the
threshold p-values of the threshold coefficients.
+
+
+
+log_likelihood FLOAT8. The log-likelihood \(
l(\boldsymbol \beta) \). The value will be the same across categories within
the same group.
+
+
+
+coef_feature FLOAT8[]. Vector of the feature
coefficients in linear predictor. The feature coefficients are the coefficients
for the independent variables. They are the same across categories.
+
+
+
+std_err_feature FLOAT8[]. Vector of the
feature standard errors of the feature coefficients.
+
+
+
+z_stats_feature FLOAT8[]. Vector of the
feature z-statistics of the feature coefficients.
+
+
+
+p_values_feature FLOAT8[]. Vector of the
feature p-values of the feature coefficients.
+
+
+
+num_rows_processed BIGINT. Number of rows
processed.
+
+
+
+num_rows_skipped BIGINT. Number of rows
skipped due to missing values or failures.
+
+
+
+num_iterations INTEGER. Number of iterations actually completed.
This would be different from the nIterations argument if a
tolerance parameter is provided and the algorithm converges before
all
[13/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__pca.html
--
diff --git a/docs/v1.15.1/group__grp__pca.html
b/docs/v1.15.1/group__grp__pca.html
new file mode 100644
index 000..b90ca73
--- /dev/null
+++ b/docs/v1.15.1/group__grp__pca.html
@@ -0,0 +1,149 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Dimensionality Reduction
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__pca.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Modules
+
+Dimensionality ReductionUnsupervised Learning
+
+
+Detailed
Description
+Methods for reducing the number of variables in a dataset to obtain a set
of principle variables.
+
+
+Modules
+Principal
Component Analysis
+Produces a model that
transforms a number of (possibly) correlated variables into a (smaller) number
of uncorrelated variables called principal components.
+
+Principal
Component Projection
+Projects a higher
dimensional data point to a lower dimensional subspace spanned by principal
components learned through the PCA training procedure.
+
+
+
+
+
+
+
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__pca.js
--
diff --git a/docs/v1.15.1/group__grp__pca.js b/docs/v1.15.1/group__grp__pca.js
new file mode 100644
index 000..2863cf8
--- /dev/null
+++ b/docs/v1.15.1/group__grp__pca.js
@@ -0,0 +1,5 @@
+var group__grp__pca =
+[
+[ "Principal Component Analysis", "group__grp__pca__train.html", null ],
+[ "Principal Component Projection", "group__grp__pca__project.html", null ]
+];
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__pca__project.html
--
diff --git a/docs/v1.15.1/group__grp__pca__project.html
b/docs/v1.15.1/group__grp__pca__project.html
new file mode 100644
index 000..d2a418f
--- /dev/null
+++ b/docs/v1.15.1/group__grp__pca__project.html
@@ -0,0 +1,506 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Principal Component Projection
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/glm_8sql__in.html
--
diff --git a/docs/v1.15.1/glm_8sql__in.html b/docs/v1.15.1/glm_8sql__in.html
new file mode 100644
index 000..e639aaa
--- /dev/null
+++ b/docs/v1.15.1/glm_8sql__in.html
@@ -0,0 +1,1926 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: glm.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
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GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
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+
+
+
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+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
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GPL-v2 */
+$(document).ready(function(){initNavTree('glm_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+glm.sql_in File Reference
+
+
+
+SQL functions for GLM (Poisson)
+More...
+
+
+Functions
+bytea8__glm_merge_states
(bytea8 state1, bytea8 state2)
+
+bytea8__glm_final
(bytea8 state)
+
+bytea8__glm_poisson_log_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_poisson_log_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_poisson_identity_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_poisson_identity_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_poisson_sqrt_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_poisson_sqrt_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gaussian_identity_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gaussian_identity_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gaussian_log_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gaussian_log_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gaussian_inverse_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gaussian_inverse_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gamma_log_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gamma_log_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gamma_inverse_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gamma_inverse_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_gamma_identity_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_gamma_identity_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_binomial_probit_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_binomial_probit_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_inverse_gaussian_identity_transition
(bytea8, float8, float8[], bytea8)
+
+bytea8__glm_binomial_logit_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_binomial_logit_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+__glm_result_type__glm_result_z_stats
(bytea8 state)
+
+aggregate bytea8__glm_inverse_gaussian_identity_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_inverse_gaussian_log_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_inverse_gaussian_log_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_inverse_gaussian_inverse_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate bytea8__glm_inverse_gaussian_inverse_agg
(float8 y, float8[] x, bytea8 previous_state)
+
+bytea8__glm_inverse_gaussian_sqr_inverse_transition
(bytea8, float8, float8[], bytea8)
+
+aggregate
[39/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_42e4eb27424bda9fbbfa95509de09bad.html
--
diff --git a/docs/v1.15.1/dir_42e4eb27424bda9fbbfa95509de09bad.html
b/docs/v1.15.1/dir_42e4eb27424bda9fbbfa95509de09bad.html
new file mode 100644
index 000..8792753
--- /dev/null
+++ b/docs/v1.15.1/dir_42e4eb27424bda9fbbfa95509de09bad.html
@@ -0,0 +1,143 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: conjugate_gradient Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dir_42e4eb27424bda9fbbfa95509de09bad.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+conjugate_gradient Directory Reference
+
+
+
+
+Files
+file conjugate_gradient.sql_in
+SQL function computing
Conjugate Gradient.
+
+
+
+
+
+
+
+srcportspostgresmodulesconjugate_gradient
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_4f54709f5fc5d0f64da91428555e2469.html
--
diff --git a/docs/v1.15.1/dir_4f54709f5fc5d0f64da91428555e2469.html
b/docs/v1.15.1/dir_4f54709f5fc5d0f64da91428555e2469.html
new file mode 100644
index 000..96fbb09
--- /dev/null
+++ b/docs/v1.15.1/dir_4f54709f5fc5d0f64da91428555e2469.html
@@ -0,0 +1,142 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: src Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dir_4f54709f5fc5d0f64da91428555e2469.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+src Directory
[37/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_9f5e6edf0db58b0627c46b41d27f.html
--
diff --git a/docs/v1.15.1/dir_9f5e6edf0db58b0627c46b41d27f.html
b/docs/v1.15.1/dir_9f5e6edf0db58b0627c46b41d27f.html
new file mode 100644
index 000..21585e8
--- /dev/null
+++ b/docs/v1.15.1/dir_9f5e6edf0db58b0627c46b41d27f.html
@@ -0,0 +1,143 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: validation Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dir_9f5e6edf0db58b0627c46b41d27f.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+validation Directory Reference
+
+
+
+
+Files
+file cross_validation.sql_in
+SQL functions for cross
validation.
+
+
+
+
+
+
+
+srcportspostgresmodulesvalidation
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dir_a4a48839224ef8488facbffa8a397967.html
--
diff --git a/docs/v1.15.1/dir_a4a48839224ef8488facbffa8a397967.html
b/docs/v1.15.1/dir_a4a48839224ef8488facbffa8a397967.html
new file mode 100644
index 000..d9bea52
--- /dev/null
+++ b/docs/v1.15.1/dir_a4a48839224ef8488facbffa8a397967.html
@@ -0,0 +1,142 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: postgres Directory Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dir_a4a48839224ef8488facbffa8a397967.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+postgres Directory Reference
+
+
+
+
[34/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/elastic__net_8sql__in.html
--
diff --git a/docs/v1.15.1/elastic__net_8sql__in.html
b/docs/v1.15.1/elastic__net_8sql__in.html
new file mode 100644
index 000..9ab2d42
--- /dev/null
+++ b/docs/v1.15.1/elastic__net_8sql__in.html
@@ -0,0 +1,2483 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: elastic_net.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('elastic__net_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+elastic_net.sql_in File Reference
+
+
+
+SQL functions for elastic net regularization.
+More...
+
+
+Functions
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_dep_var, text col_ind_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardize, text
grouping_col, text optimizer, text optimizer_params, text excluded, integer
max_iter, float8 tolerance)
+Interface for elastic net.
More...
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardization,
text grouping_columns, text optimizer, text optimizer_params, text excluded,
integer max_iter)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardization,
text grouping_columns, text optimizer, text optimizer_params, text
excluded)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardization,
text grouping_columns, text optimizer, text optimizer_params)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardization,
text grouping_columns, text optimizer)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean standardization,
text grouping_columns)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value, boolean
standardization)
+
+voidelastic_net_train
(text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text
regress_family, float8 alpha, float8 lambda_value)
+
+textelastic_net_train
()
+Help function, to print out
the supported families. More...
+
+textelastic_net_train
(text family_or_optimizer)
+Help function, to print out
the supported optimizer for a family or print out the parameter list for an
optimizer. More...
+
+voidelastic_net_predict
(text tbl_model, text tbl_new_source, text col_id, text tbl_predict)
+Prediction and put the
result in a table can be used together with General-CV. More...
+
+float8elastic_net_predict
(text regress_family, float8[] coefficients, float8 intercept, float8[]
ind_var)
+Prediction use learned
coefficients for a given example. More...
+
+float8elastic_net_gaussian_predict
(float8[] coefficients, float8 intercept, float8[] ind_var)
+Prediction for
[22/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__glm.html
--
diff --git a/docs/v1.15.1/group__grp__glm.html
b/docs/v1.15.1/group__grp__glm.html
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index 000..8bab53e
--- /dev/null
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@@ -0,0 +1,585 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Generalized Linear Models
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__glm.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Generalized Linear ModelsSupervised Learning Regression Models
+
+
+Contents
+
+Training Function
+
+Prediction Function
+
+Examples
+
+Related Topics
+
+Generalized linear models extends ordinary linear regression by
allowing the response variable to follow a more general set of distributions
(rather than simply Gaussian distributions), and for a general family of
functions of the response variable (the link function) to vary linearly with
the predicted values (rather than assuming that the response itself must vary
linearly).
+For example, data of counts would typically be modeled with a Poisson
distribution and a log link, while binary outcomes would typically be modeled
with a Bernoulli distribution (or binomial distribution, depending on exactly
how the problem is phrased) and a log-odds (or logit) link function.
+Currently, the implemented distribution families are
+
+Distribution Family Link Functions
+
+Binomial logit, probit
+
+Gamma inverse, identity, log
+
+Gaussian identity, inverse, log
+
+Inverse Gaussian inverse of square, inverse, identity, log
+
+Poisson log, identity, square-root
+
+
+Training FunctionGLM training function has the following
format:
+glm(source_table,
+model_table,
+dependent_varname,
+independent_varname,
+family_params,
+grouping_col,
+optim_params,
+verbose
+)
+ Arguments
+source_table
+TEXT. The name of the table containing the training
data.
+
+
+model_table
+TEXT. Name of the generated table containing the
model.
+The model table produced by glm contains the following columns:
+
+
+... Text. Grouping columns, if
provided in input. This could be multiple columns depending on the
grouping_col input.
+
+
+
+coef FLOAT8. Vector of the coefficients in
linear predictor.
+
+
+
+log_likelihood FLOAT8. The log-likelihood \(
l(\boldsymbol \beta) \). We use the maximum likelihood estimate of dispersion
parameter to calculate the log-likelihood while R and Python use deviance
estimate and Pearson estimate respectively.
+
+
+
+std_err FLOAT8[]. Vector of the standard error
of the coefficients.
+
+
+
+z_stats or t_stats FLOAT8[]. Vector of the
z-statistics (in Poisson distribtuion and Binomial distribution) or the
t-statistics (in all other distributions) of the coefficients.
+
+
+
+p_values FLOAT8[]. Vector of the p-values of
the coefficients.
+
+
+
+dispersion FLOAT8. The dispersion value
(Pearson estimate). When family=poisson or family=binomial, the dispersion is
always 1.
+
+
+
+num_rows_processed BIGINT. Numbers of rows
processed.
+
+
+
+num_rows_skipped BIGINT. Numbers of rows
skipped due to missing values or failures.
+
+
+
+num_iterations INTEGER. The number of iterations actually
completed. This would
[17/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
diff --git a/docs/v1.15.1/group__grp__marginal.html
b/docs/v1.15.1/group__grp__marginal.html
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--- /dev/null
+++ b/docs/v1.15.1/group__grp__marginal.html
@@ -0,0 +1,440 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Marginal Effects
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__marginal.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Marginal EffectsSupervised Learning Regression Models
+
+
+Contents
+
+Marginal Effects with Interaction Terms
+
+Examples
+
+Notes
+
+Technical Background
+
+Literature
+
+Related Topics
+
+A marginal effect (ME) or partial effect measures the effect on the
conditional mean of \( y \) for a change in one of the regressors, say \(X_k\).
In the linear regression model, the ME equals the relevant slope coefficient,
greatly simplifying analysis. For nonlinear models, specialized algorithms are
required for calculating ME. The marginal effect computed is the average of the
marginal effect at every data point present in the source table.
+MADlib provides marginal effects regression functions for linear, logistic
and multinomial logistic regressions.
+WarningThe margins_logregr()
and margins_mlogregr()
functions have been deprecated in favor of the margins() function.
+Marginal Effects with Interaction Terms
+margins( model_table,
+ output_table,
+ x_design,
+ source_table,
+ marginal_vars
+ )
+ Arguments
+model_table
+VARCHAR. The name of the model table, which is the output of logregr_train() or mlogregr_train().
+output_table
+VARCHAR. The name of the result table. The output table has the following
columns.
+
+variables INTEGER[]. The indices of the basis variables.
+
+margins DOUBLE PRECISION[]. The marginal effects.
+
+std_err DOUBLE PRECISION[]. An array of the standard errors,
computed using the delta method.
+
+z_stats DOUBLE PRECISION[]. An array of the z-stats of the
marginal effects.
+
+p_values DOUBLE PRECISION[]. An array of the Wald p-values of the
marginal effects.
+
+
+x_design (optional)
+VARCHAR, default: NULL. The design of independent
variables, necessary only if interaction term or indicator (categorical) terms
are present. This parameter is necessary since the independent variables in the
underlying regression is not parsed to extract the relationship between
variables.
+Example: The independent_varname in the regression method can be
specified in either of the following ways:
+ âarray[1, color_blue, color_green, gender_female, gpa, gpa^2,
gender_female*gpa, gender_female*gpa^2, weight]â
+ âxâ
+
+In the second version, the column x is an array containing data
identical to that expressed in the first version, computed in a prior data
preparation step. Supply an x_design argument to the margins() function in the following
way:
+ â1, i.color_blue.color, i.color_green.color, i.gender_female,
gpa, gpa^2, gender_female*gpa, gender_female*gpa^2, weightâ
+
+The variable names ('gpa', 'weight', ...), referred to here as
identifiers, should be unique for each basis variable and need not be
the same as the
[12/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__pivot.html
--
diff --git a/docs/v1.15.1/group__grp__pivot.html
b/docs/v1.15.1/group__grp__pivot.html
new file mode 100644
index 000..1001b80
--- /dev/null
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@@ -0,0 +1,668 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Pivot
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__pivot.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+PivotData Types and
Transformations
+
+
+Contents
+
+Pivoting
+
+Notes
+
+Examples
+
+Literature
+
+The goal of the MADlib pivot
function is to provide a data summarization tool that can do basic OLAP type
operations on data stored in one table and output the summarized data to a
second table.
+
+pivot(
+source_table,
+output_table,
+index,
+pivot_cols,
+pivot_values,
+aggregate_func,
+fill_value,
+keep_null,
+output_col_dictionary,
+output_type
+)
+ Arguments
+source_table
+VARCHAR. Name of the source table (or view) containing
data to pivot.
+
+
+output_table
+VARCHAR. Name of output table that contains the pivoted
data. The output table contains all the columns present in the 'index'
column list, plus additional columns for each distinct value in
'pivot_cols'.
+NoteThe names of the columns in the
output table are auto-generated. Please see the examples section below to see
how this works in practice. The convention used is to concatenate the following
strings and separate each by an underscore '_' :
+name of the value column 'pivot_values'
+aggregate function
+name of the pivot column 'pivot_cols'
+values in the pivot column
+
+
+
+index
+VARCHAR. Comma-separated columns that will form the
index of the output pivot table. By index we mean the values to group by; these
are the rows in the output pivot table.
+
+
+pivot_cols
+VARCHAR. Comma-separated columns that will form the
columns of the output pivot table.
+
+
+pivot_values
+VARCHAR. Comma-separated columns that contain the
values to be summarized in the output pivot table.
+
+
+aggregate_func (optional)
+VARCHAR. default: 'AVG'. A comma-separated list of
aggregates to be applied to values. These can be PostgreSQL built-in aggregates
[1] or UDAs. It is possible to assign a set of aggregates per value column.
Please refer to the examples 12-14 below for syntax details.
+NoteOnly aggregates with strict
transition functions are permitted here. A strict transition function means
rows with null values are ignored; the function is not called and the previous
state value is retained. If you need some other behavior for null inputs, this
should be done prior to calling the pivot function. Aggregates with strict
transition functions are described in [2,3].
+
+fill_value (optional)
+VARCHAR. default: NULL. If specified, determines how to
fill NULL values resulting from pivot operation. This is a global parameter
(not applied per aggregate) and is applied post-aggregation to the output
table.
+
+
+keep_null (optional)
+BOOLEAN. default: FALSE. If TRUE, then pivot columns
are created corresponding to NULL categories. If FALSE, then no pivot columns
will be created for NULL categories.
+
+
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--
diff --git a/docs/v1.15.1/group__grp__random__forest.html
b/docs/v1.15.1/group__grp__random__forest.html
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@@ -0,0 +1,1157 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Random Forest
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__random__forest.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Random ForestSupervised Learning Tree Methods
+
+
+Contents
+
+Training Function
+
+Prediction Function
+
+Tree Display
+
+Importance Display
+
+Examples
+
+Literature
+
+Related Topics
+
+Random forest builds an ensemble of classifiers, each of which is a
tree model constructed using bootstrapped samples from the input data. The
results of these models are then combined to yield a single prediction, which,
at the expense of some loss in interpretation, can be highly accurate. Refer to
Breiman et al. [1][2][3] for details on the implementation used here.
+Also refer to the decision tree
user documentation since many parameters and examples are similar to random
forest.
+Training
FunctionRandom forest training function has the following format:
+forest_train(training_table_name,
+ output_table_name,
+ id_col_name,
+ dependent_variable,
+ list_of_features,
+ list_of_features_to_exclude,
+ grouping_cols,
+ num_trees,
+ num_random_features,
+ importance,
+ num_permutations,
+ max_tree_depth,
+ min_split,
+ min_bucket,
+ num_splits,
+ null_handling_params,
+ verbose,
+ sample_ratio
+ )
+
+Arguments
+training_table_name
+text. Name of the table containing the training
data.
+
+
+output_table_name
+TEXT. Name of the generated table containing the model.
If a table with the same name already exists, an error will be returned. A
summary table named output_table_name_summary and a grouping
table named output_table_name_group are also created. These
are described later on this page.
+
+
+id_col_name
+TEXT. Name of the column containing id information in
the training data. This is a mandatory argument and is used for prediction and
other purposes. The values are expected to be unique for each row.
+
+
+dependent_variable
+TEXT. Name of the column that contains the output
(response) for training. Boolean, integer and text types are considered to be
classification outputs, while double precision values are considered to be
regression outputs. The response variable for a classification tree can be
multinomial, but the time and space complexity of the training function
increases linearly as the number of response classes increases.
+
+
+list_of_features
+TEXT. Comma-separated string of column names or
expressions to use as predictors. Can also be a '*' implying all columns are to
be used as predictors (except for the ones included in the next argument that
lists exclusions). The types of the features can be mixed: boolean, integer,
and text columns
madlib-site git commit: Remove stray softlink from v1.14 docs dir.
Repository: madlib-site Updated Branches: refs/heads/asf-site af0e5f141 -> 9a5ca766b Remove stray softlink from v1.14 docs dir. Project: http://git-wip-us.apache.org/repos/asf/madlib-site/repo Commit: http://git-wip-us.apache.org/repos/asf/madlib-site/commit/9a5ca766 Tree: http://git-wip-us.apache.org/repos/asf/madlib-site/tree/9a5ca766 Diff: http://git-wip-us.apache.org/repos/asf/madlib-site/diff/9a5ca766 Branch: refs/heads/asf-site Commit: 9a5ca766b493191c0f0198f0da45791b7a675488 Parents: af0e5f1 Author: Nikhil Kak Authored: Mon Oct 15 11:50:25 2018 -0700 Committer: Nikhil Kak Committed: Mon Oct 15 11:50:25 2018 -0700 -- docs/v1.14/v1.15 | 1 - 1 file changed, 1 deletion(-) -- http://git-wip-us.apache.org/repos/asf/madlib-site/blob/9a5ca766/docs/v1.14/v1.15 -- diff --git a/docs/v1.14/v1.15 b/docs/v1.14/v1.15 deleted file mode 12 index 40f104c..000 --- a/docs/v1.14/v1.15 +++ /dev/null @@ -1 +0,0 @@ -v1.15 \ No newline at end of file
[48/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/balance__sample_8sql__in.html
--
diff --git a/docs/v1.15.1/balance__sample_8sql__in.html
b/docs/v1.15.1/balance__sample_8sql__in.html
new file mode 100644
index 000..0529d63
--- /dev/null
+++ b/docs/v1.15.1/balance__sample_8sql__in.html
@@ -0,0 +1,497 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: balance_sample.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('balance__sample_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+balance_sample.sql_in File Reference
+
+
+
+SQL functions for balanced data sets sampling.
+More...
+
+
+Functions
+voidbalance_sample
(text source_table, text output_table, text class_col, varchar class_sizes,
integer output_table_size, text grouping_cols, boolean with_replacement,
boolean keep_null)
+
+voidbalance_sample
(text source_table, text output_table, text class_col, varchar class_sizes,
integer output_table_size, text grouping_cols, boolean
with_replacement)
+
+voidbalance_sample
(text source_table, text output_table, text class_col, varchar class_sizes,
integer output_table_size, text grouping_cols)
+
+voidbalance_sample
(text source_table, text output_table, text class_col, varchar class_sizes,
integer output_table_size)
+
+voidbalance_sample
(text source_table, text output_table, text class_col, varchar
class_sizes)
+
+voidbalance_sample
(text source_table, text output_table, text class_col)
+
+varcharbalance_sample
(varchar message)
+
+varcharbalance_sample
()
+
+
+Detailed
Description
+Licensed to the Apache Software Foundation (ASF)
under one or more contributor license agreements. See the NOTICE file
distributed with this work for additional information regarding copyright
ownership. The ASF licenses this file to you under the Apache License, Version
2.0 (the "License"); you may not use this file except in compliance with the
License. You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0;>http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
+Date12/14/2017
+See alsoGiven a table, balanced sampling
returns a sampled data set with specified proportions for each class (defaults
to uniform sampling).
+Function Documentation
+
+balance_sample()
[1/8]
+
+
+
+
+
+ void balance_sample
+ (
+ text
+ source_table,
+
+
+
+
+ text
+ output_table,
+
+
+
+
+ text
+ class_col,
+
+
+
+
+ varchar
+ class_sizes,
+
+
+
+
+ integer
+ output_table_size,
+
+
+
+
+ text
+ grouping_cols,
+
+
+
+
+ boolean
+ with_replacement,
+
+
[43/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/create__indicators_8sql__in.html
--
diff --git a/docs/v1.15.1/create__indicators_8sql__in.html
b/docs/v1.15.1/create__indicators_8sql__in.html
new file mode 100644
index 000..e677041
--- /dev/null
+++ b/docs/v1.15.1/create__indicators_8sql__in.html
@@ -0,0 +1,340 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: create_indicators.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('create__indicators_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+create_indicators.sql_in File Reference
+
+
+
+SQL functions for dummy coding categorical variables.
+More...
+
+
+Functions
+voidcreate_indicator_variables
(text source_table, text out_table, text categorical_cols, boolean keep_null,
text distributed_by)
+Create new table containing
dummy coded variables for categorical variables. More...
+
+voidcreate_indicator_variables
(text source_table, text out_table, text categorical_cols, boolean
keep_null)
+Create new table containing
dummy coded variables for categorical variables. More...
+
+voidcreate_indicator_variables
(text source_table, text out_table, text categorical_cols)
+
+varcharcreate_indicator_variables
(varchar message)
+
+varcharcreate_indicator_variables
()
+
+
+Detailed
Description
+Licensed to the Apache Software Foundation (ASF)
under one or more contributor license agreements. See the NOTICE file
distributed with this work for additional information regarding copyright
ownership. The ASF licenses this file to you under the Apache License, Version
2.0 (the "License"); you may not use this file except in compliance with the
License. You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0;>http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
+DateJune 2014
+See alsoCalculates dummy-coded indicator
variables for categorical variables
+Function Documentation
+
+create_indicator_variables()
[1/5]
+
+
+
+
+
+ void create_indicator_variables
+ (
+ text
+ source_table,
+
+
+
+
+ text
+ out_table,
+
+
+
+
+ text
+ categorical_cols,
+
+
+
+
+ boolean
+ keep_null,
+
+
+
+
+ text
+ distributed_by
+
+
+
+ )
+
+
+
+
+Parameters
+
+source_tableName of table containing
categorical variable
+out_tableName of table to output dummy
variables
+categorical_colsComma-separated list of
column names to dummy code
+keep_nullBoolean to determine the
behavior for rows with NULL value
+distributed_byComma-separated list of
column names to use for distribution of output
+
+
+
+ReturnsVoid
+
+
+
+
[41/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/decision__tree_8sql__in.html
--
diff --git a/docs/v1.15.1/decision__tree_8sql__in.html
b/docs/v1.15.1/decision__tree_8sql__in.html
new file mode 100644
index 000..6047c9a
--- /dev/null
+++ b/docs/v1.15.1/decision__tree_8sql__in.html
@@ -0,0 +1,2764 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: decision_tree.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('decision__tree_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+decision_tree.sql_in File Reference
+
+
+
+
+Functions
+voidtree_train
(text training_table_name, text output_table_name, text id_col_name, text
dependent_variable, text list_of_features, text list_of_features_to_exclude,
text split_criterion, text grouping_cols, text weights, integer max_depth,
integer min_split, integer min_bucket, integer n_bins, text pruning_params,
text null_handling_params, boolean verbose_mode)
+Training of decision tree.
More...
+
+void__build_tree
(boolean is_classification, text split_criterion, text training_table_name,
text output_table_name, text id_col_name, text dependent_variable, boolean
dep_is_bool, text list_of_features, varchar[] cat_features, varchar[]
ordered_cat_features, varchar[] boolean_cats, varchar[] con_features, text
grouping_cols, text weights, integer max_depth, integer min_split, integer
min_bucket, integer n_bins, text cp_table, smallint max_n_surr, text msg_level,
text null_proxy, integer n_folds)
+
+texttree_train
(text message)
+
+texttree_train
()
+
+bytea8_dst_compute_con_splits_transition
(bytea8 state, float8[] con_features, integer n_per_seg, smallint
num_splits)
+
+bytea8_dst_compute_con_splits_final
(bytea8 state)
+
+aggregate bytea8_dst_compute_con_splits
(float8[], integer, smallint)
+
+integer []_dst_compute_entropy_transition
(integer[] state, integer encoded_dep_var, integer num_dep_var)
+
+integer []_dst_compute_entropy_merge
(integer[] state1, integer[] state2)
+
+float8_dst_compute_entropy_final
(integer[] state)
+
+aggregate float8_dst_compute_entropy
(integer, integer)
+
+integer []_map_catlevel_to_int
(text[] cat_values_in_text, text[] cat_levels_in_text, integer[] cat_n_levels,
boolean null_as_category)
+
+bytea8_initialize_decision_tree
(boolean is_regression_tree, text impurity_function, smallint
num_response_labels, smallint max_n_surr)
+
+bytea8_compute_leaf_stats_transition
(bytea8 state, bytea8 tree_state, integer[] cat_features, float8[]
con_features, float8 response, float8 weight, integer[] cat_levels, bytea8
con_splits, smallint n_response_labels, boolean weights_as_rows)
+
+bytea8_compute_leaf_stats_merge
(bytea8 state1, bytea8 state2)
+
+aggregate bytea8_compute_leaf_stats
(bytea8, integer[], float8[], float8, float8, integer[], bytea8, smallint,
boolean)
+
+_tree_result_type_dt_apply
(bytea8 tree, bytea8 state, bytea8 con_splits, smallint min_split, smallint
min_bucket, smallint max_depth, boolean subsample, integer
num_random_features)
+
+bytea8_compute_surr_stats_transition
(bytea8 state, bytea8 tree_state, integer[] cat_features, float8[]
con_features, integer[] cat_levels, bytea8 con_splits, integer
dup_count)
+
+aggregate
[49/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/array__ops_8sql__in.html
--
diff --git a/docs/v1.15.1/array__ops_8sql__in.html
b/docs/v1.15.1/array__ops_8sql__in.html
new file mode 100644
index 000..34e0821
--- /dev/null
+++ b/docs/v1.15.1/array__ops_8sql__in.html
@@ -0,0 +1,1274 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: array_ops.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('array__ops_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+array_ops.sql_in File Reference
+
+
+
+implementation of array operations in SQL
+More...
+
+
+Functions
+anyarrayarray_add
(anyarray x, anyarray y)
+Adds two arrays. It
requires that all the values are NON-NULL. Return type is the same as the input
type. More...
+
+aggregate anyarraysum
(anyarray)
+Aggregate, element-wise sum
of arrays. It requires that all the values are NON-NULL. Return type is the
same as the input type. More...
+
+anyarrayarray_sub
(anyarray x, anyarray y)
+Subtracts two arrays. It
requires that all the values are NON-NULL. Return type is the same as the input
type. More...
+
+anyarrayarray_mult
(anyarray x, anyarray y)
+Element-wise product of two
arrays. It requires that all the values are NON-NULL. Return type is the same
as the input type. More...
+
+anyarrayarray_div
(anyarray x, anyarray y)
+Element-wise division of
two arrays. It requires that all the values are NON-NULL. Return type is the
same as the input type. More...
+
+float8array_dot
(anyarray x, anyarray y)
+Dot-product of two arrays.
It requires that all the values are NON-NULL. Return type is the same as the
input type. More...
+
+boolarray_contains
(anyarray x, anyarray y)
+Checks whether one array
contains the other. This function returns TRUE if each non-zero element in the
right array equals to the element with the same index in the left array. More...
+
+anyelementarray_max
(anyarray x)
+This function finds the
maximum value in the array. NULLs are ignored. Return type is the same as the
input type. More...
+
+float8 []array_max_index
(anyarray x)
+This function finds the
maximum value and corresponding index in the array. NULLs are ignored. Return
type is the same as the input type. More...
+
+anyelementarray_min
(anyarray x)
+This function finds the
minimum value in the array. NULLs are ignored. Return type is the same as the
input type. More...
+
+float8 []array_min_index
(anyarray x)
+This function finds the
minimum value and corresponding index in the array. NULLs are ignored. Return
type is the same as the input type. More...
+
+anyelementarray_sum
(anyarray x)
+This function finds the sum
of the values in the array. NULLs are ignored. Return type is the same as the
input type. More...
+
+float8array_sum_big
(anyarray x)
+This function finds the sum
of the values in the array. NULLs are ignored. Return type is always FLOAT8
regardless of input. This function is meant to replace array_sum() in the cases when sum may overflow the
element type. More...
+
+anyelementarray_abs_sum
(anyarray x)
+This function finds the sum
of abs of the values in the array. NULLs are ignored. Return type is the same
as the input type. More...
+
[07/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__svec.html
--
diff --git a/docs/v1.15.1/group__grp__svec.html
b/docs/v1.15.1/group__grp__svec.html
new file mode 100644
index 000..cffbba4
--- /dev/null
+++ b/docs/v1.15.1/group__grp__svec.html
@@ -0,0 +1,455 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Sparse Vectors
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__svec.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Sparse VectorsData Types and Transformations Arrays and
Matrices
+
+
+Contents
+
+Using Sparse Vectors
+
+Document Vectorization into Sparse Vectors
+
+Examples
+
+Related Topics
+
+This module implements a sparse vector data type, named "svec", which
provides compressed storage of vectors that have many duplicate elements.
+Arrays of floating point numbers for various calculations sometimes have
long runs of zeros (or some other default value). This is common in
applications like scientific computing, retail optimization, and text
processing. Each floating point number takes 8 bytes of storage in memory
and/or disk, so saving those zeros is often worthwhile. There are also many
computations that can benefit from skipping over the zeros.
+Consider, for example, the following array of doubles stored as a
Postgres/Greenplum "float8[]" data type:
+
+'{0, 33,...40,000 zeros..., 12, 22 }'::float8[]
+This array would occupy slightly more than 320KB of memory or disk,
most of it zeros. Even if we were to exploit the null bitmap and store the
zeros as nulls, we would still end up with a 5KB null bitmap, which is still
not nearly as memory efficient as we'd like. Also, as we perform various
operations on the array, we do work on 40,000 fields that turn out to be
unimportant.
+To solve the problems associated with the processing of vectors discussed
above, the svec type employs a simple Run Length Encoding (RLE) scheme to
represent sparse vectors as pairs of count-value arrays. For example, the array
above would be represented as
+
+'{1,1,4,1,1}:{0,33,0,12,22}'::madlib.svec
+which says there is 1 occurrence of 0, followed by 1 occurrence of
33, followed by 40,000 occurrences of 0, etc. This uses just 5 integers and 5
floating point numbers to store the array. Further, it is easy to implement
vector operations that can take advantage of the RLE representation to make
computations faster. The SVEC module provides a library of such functions.
+The current version only supports sparse vectors of float8 values. Future
versions will support other base types.
+Using
Sparse Vectors
+An SVEC can be constructed directly with a constant expression, as follows:
+SELECT '{n1,n2,...,nk}:{v1,v2,...vk}'::madlib.svec;
+ where n1,n2,...,nk specifies the counts for the values
v1,v2,...,vk.
+A float array can be cast to an SVEC:
+SELECT ('{v1,v2,...vk}'::float[])::madlib.svec;
+An SVEC can be created with an aggregation:
+SELECT madlib.svec_agg(v1) FROM generate_series(1,k);
+An SVEC can be created using the
madlib.svec_cast_positions_float8arr() function by supplying an
array of positions and an array of values at those positions:
+SELECT madlib.svec_cast_positions_float8arr(
+array[n1,n2,...nk],-- positions of
[05/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__validation.html
--
diff --git a/docs/v1.15.1/group__grp__validation.html
b/docs/v1.15.1/group__grp__validation.html
new file mode 100644
index 000..6f44ea9
--- /dev/null
+++ b/docs/v1.15.1/group__grp__validation.html
@@ -0,0 +1,273 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Cross Validation
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__validation.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Cross ValidationModel Selection
+
+
+Contents
+
+Cross-Validation Function
+
+Examples
+
+Notes
+
+Technical Background
+
+Related Topics
+
+Estimates the fit of a predictive model given a data set and
specifications for the training, prediction, and error estimation functions.
+Cross validation, sometimes called rotation estimation, is a technique for
assessing how the results of a statistical analysis will generalize to an
independent data set. It is mainly used in settings where the goal is
prediction, and you want to estimate how accurately a predictive model will
perform in practice.
+The cross-validation function provided by this module is very flexible and
can work with algorithms you want to cross validate, including algorithms you
write yourself. Among the inputs to the cross-validation function are
specifications of the modelling, prediction, and error metric functions. These
three-part specifications include the name of the function, an array of
arguments to pass to the function, and an array of the data types of the
arguments. This makes it possible to use functions from other MADlib modules or
user-defined functions that you supply.
+
+The modelling (training) function takes in a given data set with
independent and dependent variables and produces a model, which is stored in an
output table.
+The prediction function takes in the model generated by the modelling
function and a different data set with independent variables, and produces a
prediction of the dependent variables based on the model, which is stored in an
output table. The prediction function should take a unique ID column name in
the data table as one of the inputs, so that the prediction result can be
compared with the validation values. Note: Prediction function in some MADlib
modules do not save results into an output table. These prediction functions
are not suitable for cross-validation.
+The error metric function compares the prediction results with the known
values of the dependent variables in the data set that was fed into the
prediction function. It computes the error metric using the specified error
metric function, storing the results in a table.
+
+Other inputs include the output table name, k value for the k-fold cross
validation, and how many folds to try. For example, you can choose to run a
simple validation instead of a full cross validation.
+Cross-Validation Function
+
+cross_validation_general( modelling_func,
+ modelling_params,
+ modelling_params_type,
+ param_explored,
+ explore_values,
+ predict_func,
+ predict_params,
+
[03/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/jquery.js
--
diff --git a/docs/v1.15.1/jquery.js b/docs/v1.15.1/jquery.js
new file mode 100644
index 000..2771c74
--- /dev/null
+++ b/docs/v1.15.1/jquery.js
@@ -0,0 +1,115 @@
+/*
+ @licstart The following is the entire license notice for the
+ JavaScript code in this file.
+
+ Copyright (C) 1997-2017 by Dimitri van Heesch
+
+ Permission is hereby granted, free of charge, to any person obtaining
+ a copy of this software and associated documentation files (the
+ "Software"), to deal in the Software without restriction, including
+ without limitation the rights to use, copy, modify, merge, publish,
+ distribute, sublicense, and/or sell copies of the Software, and to
+ permit persons to whom the Software is furnished to do so, subject to
+ the following conditions:
+
+ The above copyright notice and this permission notice shall be included
+ in all copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+ EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+ @licend The above is the entire license notice
+ for the JavaScript code in this file
+ */
+/*!
+ * jQuery JavaScript Library v1.7.1
+ * http://jquery.com/
+ *
+ * Copyright 2011, John Resig
+ * Dual licensed under the MIT or GPL Version 2 licenses.
+ * http://jquery.org/license
+ *
+ * Includes Sizzle.js
+ * http://sizzlejs.com/
+ * Copyright 2011, The Dojo Foundation
+ * Released under the MIT, BSD, and GPL Licenses.
+ *
+ * Date: Mon Nov 21 21:11:03 2011 -0500
+ */
+(function(bb,L){var av=bb.document,bu=bb.navigator,bl=bb.location;var
b=(function(){var bF=function(b0,b1){return new
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\/]([\w.]+)/,bR=/(opera)(?:.*version)?[ \/]([\w.]+)/,bQ=/(msie)
([\w.]+)/,bS=/(mozilla)(?:.*?
rv:([\w.]+))?/,bB=/-([a-z]|[0-9])/ig,bZ=/^-ms-/,bT=function(b0,b1){return(b1+"").toUpperCase()},bX=bu.userAgent,bV,bC,e,bL=Object.prototype.toString,bG=Object.prototype.hasOwnProperty,bz=Array.prototype.push,bK=Array.prototype.slice,bO=String.prototype.trim,bv=Array.prototype.indexOf,bx={};bF.fn=bF.prototype={constructor:bF,init:function(b0,b4,b3){var
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[40/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/dense__linear__systems_8sql__in.html
--
diff --git a/docs/v1.15.1/dense__linear__systems_8sql__in.html
b/docs/v1.15.1/dense__linear__systems_8sql__in.html
new file mode 100644
index 000..74ac9ee
--- /dev/null
+++ b/docs/v1.15.1/dense__linear__systems_8sql__in.html
@@ -0,0 +1,647 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: dense_linear_systems.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('dense__linear__systems_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+dense_linear_systems.sql_in File Reference
+
+
+
+SQL functions for linear systems.
+More...
+
+
+Functions
+bytea8dense_residual_norm_transition
(bytea8 state, float8[] a, float8 b, float8[] x)
+
+bytea8dense_residual_norm_merge_states
(bytea8 state1, bytea8 state2)
+
+residual_norm_resultdense_residual_norm_final
(bytea8 state)
+
+aggregate residual_norm_resultdense_residual_norm
(float8[] left_hand_side, float8 right_hand_side, float8[] solution)
+Compute the residual after
solving the dense linear systems. More...
+
+float8 []dense_direct_linear_system_transition
(float8[] state, integer row_id, float8[] a, float8 b, integer num_rows,
integer algorithm)
+
+float8 []dense_direct_linear_system_merge_states
(float8[] state1, float8[] state2)
+
+dense_linear_solver_resultdense_direct_linear_system_final
(float8[] state)
+
+aggregate dense_linear_solver_resultdense_direct_linear_system
(integer row_id, float8[] left_hand_side, float8 right_hand_side, integer
numEquations, integer algorithm)
+Solve a system of linear
equations using the direct method. More...
+
+varcharlinear_solver_dense
(varchar input_string)
+Help function, to print out
the supported families. More...
+
+varcharlinear_solver_dense
()
+
+voidlinear_solver_dense
(varchar source_table, varchar out_table, varchar row_id, varchar
left_hand_side, varchar right_hand_side, varchar grouping_cols, varchar
optimizer, varchar optimizer_options)
+A wrapper function for the
various marginal linear_systemsion analyzes. More...
+
+voidlinear_solver_dense
(varchar source_table, varchar out_table, varchar row_id, varchar
left_hand_side, varchar right_hand_side)
+Marginal effects with
default variables. More...
+
+
+Detailed
Description
+DateJuly
2013
+See alsoComputes the solution of a
consistent linear system, for more details see the module description at Dense Linear
Systems
+Function Documentation
+
+dense_direct_linear_system()
+
+
+
+
+
+ aggregate dense_linear_solver_result
dense_direct_linear_system
+ (
+ integer
+ row_id,
+
+
+
+
+ float8 []
+ left_hand_side,
+
+
+
+
+ float8
+ right_hand_side,
+
+
+
+
+ integer
+ numEquations,
+
+
+
+
+ integer
+ algorithm
+
+
+
+ )
+
+
+
+
+Parameters
+
+row_idColumn containing the row_id
+left_hand_sideColumn containing the
left hand side of the
[46/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/clustered__variance_8sql__in.html
--
diff --git a/docs/v1.15.1/clustered__variance_8sql__in.html
b/docs/v1.15.1/clustered__variance_8sql__in.html
new file mode 100644
index 000..17c20cd
--- /dev/null
+++ b/docs/v1.15.1/clustered__variance_8sql__in.html
@@ -0,0 +1,1954 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: clustered_variance.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('clustered__variance_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+clustered_variance.sql_in File Reference
+
+
+
+
+Functions
+voidclustered_variance_linregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col)
+Compute the clustered
errors. More...
+
+voidclustered_variance_linregr
(text source_table, text out_table, text depvar, text indvar, text
clustervar)
+
+textclustered_variance_linregr
()
+
+textclustered_variance_linregr
(text msg)
+
+bytea8__clustered_err_lin_transition
(bytea8 state, float8 y, float8[] x, float8[] coef)
+
+bytea8__clustered_err_lin_merge
(bytea8 state1, bytea8 state2)
+
+__clustered_agg_result__clustered_err_lin_final
(bytea8 state)
+
+aggregate __clustered_agg_result__clustered_err_lin_step
(float8, float8[], float8[])
+
+__clustered_lin_result__clustered_lin_compute_stats
(float8[] coef, float8[] meatvec, float8[] breadvec, integer mcluster, integer
numrows)
+
+float8 []__array_add
(float8[] x, float8[] y)
+
+aggregate float8 []__array_sum
(float8[])
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col, integer max_iter, text optimizer, float8 tolerance, boolean
verbose_mode)
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text
clustervar)
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col)
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col, integer max_iter)
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col, integer max_iter, text optimizer)
+
+voidclustered_variance_logregr
(text source_table, text out_table, text depvar, text indvar, text clustervar,
text grouping_col, integer max_iter, text optimizer, float8 tolerance)
+
+textclustered_variance_logregr
()
+
+textclustered_variance_logregr
(text msg)
+
+bytea8__clustered_err_log_transition
(bytea8 state, boolean y, float8[] x, float8[] coef)
+
+bytea8__clustered_err_log_merge
(bytea8 state1, bytea8 state2)
+
+__clustered_agg_result__clustered_err_log_final
(bytea8 state)
+
+aggregate __clustered_agg_result__clustered_err_log_step
(boolean, float8[], float8[])
+
+__clustered_log_result__clustered_log_compute_stats
(float8[] coef, float8[] meatvec, float8[] breadvec, integer mcluster, integer
numrows)
+
+voidclustered_variance_mlogregr
(text source_table, text out_table, text dependent_varname, text
independent_varname, text cluster_varname, integer ref_category, text
[08/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__strs.html
--
diff --git a/docs/v1.15.1/group__grp__strs.html
b/docs/v1.15.1/group__grp__strs.html
new file mode 100644
index 000..74f8305
--- /dev/null
+++ b/docs/v1.15.1/group__grp__strs.html
@@ -0,0 +1,269 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Stratified Sampling
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__strs.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Stratified SamplingSampling
+
+
+Contents
+
+Stratified Sampling
+
+Examples
+
+Stratified sampling is a method for independently sampling
subpopulations (strata). It is commonly used to reduce sampling error by
ensuring that subgroups are adequately represented in the sample.
+Stratified
Sampling
+
+stratified_sample( source_table,
+output_table,
+proportion,
+grouping_cols,
+target_cols,
+with_replacement
+ )
+Arguments
+source_table
+TEXT. Name of the table containing the input data.
+
+
+output_table
+TEXT. Name of output table that contains the sampled
data. The output table contains all columns present in the source table unless
otherwise specified in the 'target_cols' parameter below.
+
+
+proportion
+FLOAT8 in the range (0,1). Each stratum is sampled
independently.
+
+
+grouping_cols (optional)
+TEXT, default: NULL. A single column or a list of
comma-separated columns that defines the strata. When this parameter is NULL,
no grouping is used so the sampling is non-stratified, that is, the whole table
is treated as a single group.
+
+
+target_cols (optional)
+TEXT, default NULL. A comma-separated list of columns
to appear in the 'output_table'. If NULL or '*', all columns from the
'source_table' will appear in the 'output_table'.
+NoteDo not include 'grouping_cols' in the parameter
'target_cols', because they are always included in the 'output_table'.
+
+with_replacement (optional)
+BOOLEAN, default FALSE. Determines whether to sample with replacement or
without replacement (default). With replacement means that it is possible that
the same row may appear in the sample set more than once. Without replacement
means a given row can be selected only once.
+
+Examples
+Please note that due to the random nature of sampling, your results may
look different from those below.
+
+Create an input table:
+DROP TABLE IF EXISTS test;
+CREATE TABLE test(
+id1 INTEGER,
+id2 INTEGER,
+gr1 INTEGER,
+gr2 INTEGER
+);
+INSERT INTO test VALUES
+(1,0,1,1),
+(2,0,1,1),
+(3,0,1,1),
+(4,0,1,1),
+(5,0,1,1),
+(6,0,1,1),
+(7,0,1,1),
+(8,0,1,1),
+(9,0,1,1),
+(9,0,1,1),
+(9,0,1,1),
+(9,0,1,1),
+(0,1,1,2),
+(0,2,1,2),
+(0,3,1,2),
+(0,4,1,2),
+(0,5,1,2),
+(0,6,1,2),
+(10,10,2,2),
+(20,20,2,2),
+(30,30,2,2),
+(40,40,2,2),
+(50,50,2,2),
+(60,60,2,2),
+(70,70,2,2);
+
+Sample without replacement:
+DROP TABLE IF EXISTS out;
+SELECT madlib.stratified_sample(
+'test',-- Source table
+'out', -- Output table
+0.5, -- Sample proportion
+'gr1,gr2', -- Strata
[01/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
Repository: madlib-site
Updated Branches:
refs/heads/asf-site 6d7f908b5 -> af0e5f141
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/lda_8sql__in.html
--
diff --git a/docs/v1.15.1/lda_8sql__in.html b/docs/v1.15.1/lda_8sql__in.html
new file mode 100644
index 000..feb68a3
--- /dev/null
+++ b/docs/v1.15.1/lda_8sql__in.html
@@ -0,0 +1,1422 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: lda.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('lda_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+lda.sql_in File Reference
+
+
+
+SQL functions for Latent Dirichlet Allocation.
+More...
+
+
+Functions
+set lda_result lda_train (text
data_table, text model_table, text output_data_table, int4 voc_size, int4
topic_num, int4 iter_num, float8 alpha, float8 beta)
+This UDF provides an entry
for the lda training process. More...
+
+set lda_result lda_predict
(text data_table, text model_table, text output_table)
+This UDF provides an entry
for the lda predicton process. More...
+
+set lda_result lda_predict
(text data_table, text model_table, text output_table, int4 iter_num)
+A overloaded version which
allows users to specify iter_num. More...
+
+set lda_result lda_get_topic_word_count
(text model_table, text output_table)
+This UDF computes the
per-topic word counts. More...
+
+set lda_result lda_get_word_topic_count
(text model_table, text output_table)
+This UDF computes the
per-word topic counts. More...
+
+set lda_result lda_get_topic_desc
(text model_table, text vocab_table, text desc_table, int4 top_k)
+This UDF gets the
description for each topic (top-k words) More...
+
+set lda_result lda_get_word_topic_mapping
(text lda_output_table, text mapping_table)
+This UDF gets the wordid -
topicid mapping from the lda training output table. More...
+
+int4 []__lda_random_assign
(int4 word_count, int4 topic_num)
+This UDF assigns topics to
words in a document randomly. More...
+
+int4 []__lda_gibbs_sample
(int4[] words, int4[] counts, int4[] doc_topic, int8[] model, float8 alpha,
float8 beta, int4 voc_size, int4 topic_num, int4 iter_num)
+This UDF learns the topics
of words in a document and is the main step of a Gibbs sampling iteration. The
model parameter (including the per-word topic counts and corpus-level topic
counts) is passed to this function in the first call and then transfered to the
rest calls through fcinfo-flinfo-fn_extra to allow the immediate
update. More...
+
+int8 []__lda_count_topic_sfunc
(int8[] state, int4[] words, int4[] counts, int4[] topic_assignment, int4
voc_size, int4 topic_num)
+This UDF is the sfunc for
the aggregator computing the topic counts for each word and the topic count in
the whole corpus. It scans the topic assignments in a document and updates the
topic counts. More...
+
+int8 []__lda_count_topic_prefunc
(int8[] state1, int8[] state2)
+This UDF is the prefunc for
the aggregator computing the per-word topic counts. More...
+
+aggregate int8 []__lda_count_topic_agg
(int4[], int4[], int4[], int4, int4)
+This uda computes the word
topic counts by scanning and summing up topic assignments in each document. More...
+
+float8lda_get_perplexity
(text
[28/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__bfs.html
--
diff --git a/docs/v1.15.1/group__grp__bfs.html
b/docs/v1.15.1/group__grp__bfs.html
new file mode 100644
index 000..0bf55a8
--- /dev/null
+++ b/docs/v1.15.1/group__grp__bfs.html
@@ -0,0 +1,421 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Breadth-First Search
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__bfs.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Breadth-First SearchGraph
+
+
+Contents
+
+Breadth-First Search
+
+Notes
+
+Examples
+
+Literature
+
+Given a graph and a source vertex, the breadth-first search (BFS)
algorithm finds all nodes reachable from the source vertex by searching /
traversing the graph in a breadth-first manner.
+BFS
+graph_bfs( vertex_table,
+ vertex_id,
+ edge_table,
+ edge_args,
+ source_vertex,
+ out_table,
+ max_distance,
+ directed,
+ grouping_cols
+ )
+
+Arguments
+vertex_table
+TEXT. Name of the table containing the vertex data for
the graph. Must contain the column specified in the 'vertex_id' parameter
below.
+
+
+vertex_id
+TEXT, default = 'id'. Name of the column in
'vertex_table' containing vertex ids. The vertex ids are of type INTEGER with
no duplicates. They do not need to be contiguous.
+
+
+edge_table
+TEXT. Name of the table containing the edge data. The
edge table must contain columns for source vertex and destination vertex.
Column naming convention is described below in the 'edge_args' parameter. In
addition to vertex columns, if grouping is used then the columns specified in
the 'grouping_cols' parameter must be present.
+
+
+edge_args
+TEXT. A comma-delimited string containing multiple
named arguments of the form "name=value". The following parameters are
supported for this string argument:
+src (INTEGER): Name of the column containing the source vertex ids in the
edge table. Default column name is 'src'. (This is not to be confused with the
'source_vertex' argument passed to the BFS function.)
+dest (INTEGER): Name of the column containing the destination vertex ids
in the edge table. Default column name is 'dest'.
+
+
+
+source_vertex
+INTEGER. The source vertex id for the algorithm to
start. This vertex id must exist in the 'vertex_id' column of
'vertex_table'.
+
+
+out_table
+TEXT. Name of the table to store the result of BFS. It
contains a row for every vertex that is reachable from the source_vertex. In
the presence of grouping columns, only those edges are used for which there are
no NULL values in any grouping column. The output table will have the following
columns (in addition to the grouping columns):
+vertex_id : The id for any node reachable from source_vertex in addition
to the source_vertex. Will use the input parameter 'vertex_id' for column
naming.
+dist : The distance in number of edges (or hops) from the source_vertex to
where this vertex is located.
+parent : The parent of this vertex in BFS traversal of the graph from
source_vertex. Will use 'parent' for column naming. For the case where
vertex_id = source_vertex, the value for parent is NULL.
+
+A summary table named out_table_summary
[18/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
diff --git a/docs/v1.15.1/group__grp__linreg.html
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@@ -0,0 +1,474 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Linear Regression
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
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+
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+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
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+
+
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+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__linreg.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Linear RegressionSupervised Learning Regression Models
+
+
+Contents
+
+Training Function
+
+Prediction Function
+
+Examples
+
+Technical Background
+
+Literature
+
+Related Topics
+
+Linear regression models a linear relationship of a scalar dependent
variable \( y \) to one or more explanatory independent variables \( x \) and
builds a model of coefficients.
+Training
Function
+The linear regression training function has the following syntax.
+linregr_train( source_table,
+ out_table,
+ dependent_varname,
+ independent_varname,
+ grouping_cols,
+ heteroskedasticity_option
+ )
+Arguments
+source_table
+TEXT. Name of the table containing the training
data.
+
+
+out_table
+TEXT. Name of the generated table containing the output
model.
+The output table contains the following columns:
+
+... Any grouping columns provided during training.
Present only if the grouping option is used.
+
+coef FLOAT8[]. Vector of the coefficients of the regression.
+
+r2 FLOAT8. R-squared coefficient of determination of the model.
+
+std_err FLOAT8[]. Vector of the standard error of the
coefficients.
+
+t_stats FLOAT8[]. Vector of the t-statistics of the coefficients.
+
+p_values FLOAT8[]. Vector of the p-values of the coefficients.
+
+condition_no FLOAT8 array. The condition number of the \(X^{*}X\)
matrix. A high condition number is usually an indication that there may be some
numeric instability in the result yielding a less reliable model. A high
condition number often results when there is a significant amount of
colinearity in the underlying design matrix, in which case other regression
techniques, such as elastic net regression, may be more appropriate.
+
+bp_stats FLOAT8. The Breush-Pagan statistic of heteroskedacity.
Present only if the heteroskedacity argument was set to True when the model was
trained.
+
+bp_p_value FLOAT8. The Breush-Pagan calculated p-value. Present
only if the heteroskedacity parameter was set to True when the model was
trained.
+
+num_rows_processed INTEGER. The number of rows that are actually
used in each group.
+
+num_missing_rows_skipped INTEGER. The number of rows that have
NULL values in the dependent and independent variables, and were skipped in the
computation for each group.
+
+variance_covariance FLOAT[]. Variance/covariance matrix.
+
+A summary table named out_table_summary is created
together with the output table. It has the following columns:
+
+method 'linregr' for linear regression.
+
+source_table The data source table name
+
+out_table The output table name
+
+dependent_varname The dependent variable
+
+independent_varname The independent variables
[23/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
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--- /dev/null
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@@ -0,0 +1,700 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Encoding Categorical Variables
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
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+
+
+/* @license
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+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
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+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
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+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
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+$(document).ready(function(){initNavTree('group__grp__encode__categorical.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Encoding Categorical VariablesData Types and
Transformations
+
+
+Contents
+
+Coding Systems for Categorical Variables
+
+Examples
+
+Literature
+
+Coding Systems for Categorical VariablesCategorical
variables [1] require special attention in regression analysis because, unlike
dichotomous or continuous variables, they cannot be entered into the regression
equation just as they are. For example, if you have a variable called race that
is coded with 1=Hispanic, 2=Asian, 3=Black, 4=White, then entering race in your
regression will look at the linear effect of the race variable, which is
probably not what you intended. Instead, categorical variables like this need
to be coded into a series of indicator variables which can then be entered into
the regression model. There are a variety of coding systems that can be used
for coding categorical variables, including one-hot, dummy, effects,
orthogonal, and Helmert.
+We currently support one-hot and dummy coding techniques.
+Dummy coding is used when a researcher wants to compare other groups of the
predictor variable with one specific group of the predictor variable. Often,
the specific group to compare with is called the reference group.
+One-hot encoding is similar to dummy coding except it builds indicator
(0/1) columns (cast as numeric) for each value of each category. Only one of
these columns could take on the value 1 for each row (data point). There is no
reference category for this function.
+
+encode_categorical_variables (
+source_table,
+output_table,
+categorical_cols,
+categorical_cols_to_exclude,-- Optional
+row_id, -- Optional
+top,-- Optional
+value_to_drop, -- Optional
+encode_null,-- Optional
+output_type,-- Optional
+output_dictionary, -- Optional
+distributed_by -- Optional
+)
+ Arguments
+source_table
+VARCHAR. Name of the table containing the source
categorical data to encode.
+
+
+output_table
+VARCHAR. Name of the result table.
+NoteIf there are index columns in the
'source_table' specified by the parameter 'row_id' (see below), then the output
table will contain only the index columns 'row_id' and the encoded columns. If
the parameter 'row_id' is not specified, then all columns from the
'source_table', with the exception of the original columns that have been
encoded, will be included in the 'output_table'.
+
+categorical_cols
+VARCHAR. Comma-separated string of column names of
categorical variables to encode.
[09/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
diff --git a/docs/v1.15.1/group__grp__sparse__linear__solver.html
b/docs/v1.15.1/group__grp__sparse__linear__solver.html
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--- /dev/null
+++ b/docs/v1.15.1/group__grp__sparse__linear__solver.html
@@ -0,0 +1,361 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Sparse Linear Systems
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
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GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
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+
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__sparse__linear__solver.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Sparse Linear SystemsUtilities Linear Solvers
+
+
+Contents
+
+Solution Function
+
+Optimizer Parameters
+
+Output Tables
+
+Examples
+
+Related Topics
+
+The sparse linear systems module implements solution methods for
systems of consistent linear equations. Systems of linear equations take the
form:
+\[ Ax = b \]
+
+where \(x \in \mathbb{R}^{n}\), \(A \in \mathbb{R}^{m \times n} \) and \(b
\in \mathbb{R}^{m}\). This module accepts sparse matrix input formats for \(A\)
and \(b\). We assume that there are no rows of \(A\) where all elements are
zero.
+NoteAlgorithms with fail if there is an
row of the input matrix containing all zeros.
+The algorithms implemented in this module can handle large sparse square
linear systems. Currently, the algorithms implemented in this module solve the
linear system using direct or iterative methods.
+Sparse Linear Systems Solution Function
+
+linear_solver_sparse( tbl_source_lhs,
+ tbl_source_rhs,
+ tbl_result,
+ lhs_row_id,
+ lhs_col_id,
+ lhs_value,
+ rhs_row_id,
+ rhs_value,
+ grouping_cols := NULL,
+ optimizer := 'direct',
+ optimizer_params :=
+ 'algorithm = llt'
+)
+ Arguments
+tbl_source_lhs
+The name of the table containing the left hand side
matrix. For the LHS matrix, the input data is expected to be of the following
form:
+{TABLE|VIEW} sourceName (
+...
+row_id FLOAT8,
+col_id FLOAT8,
+value FLOAT8,
+...
+) Each row represents a single equation. The rhs columns
refer to the right hand side of the equations and the lhs columns
refer to the multipliers on the variables on the left hand side of the same
equations.
+
+
+tbl_source_rhs
+TEXT. The name of the table containing the right hand
side vector. For the RHS matrix, the input data is expected to be of the
following form: {TABLE|VIEW}
emsourceName/em (
+...
+emrow_id/em FLOAT8,
+emvalue/em FLOAT8
+...
+) Each row represents a single equation. The rhs columns
refer to the right hand side of the equations while the lhs columns
refers to the multipliers on the variables on the left hand side of the same
equations.
+
+
+tbl_result
+TEXT. The name of the table where the output is saved.
Output is stored in the tabled named by the tbl_result argument. The
table contains the following columns. The output contains the following
columns:
+
+solution FLOAT8[]. The solution is an array with the variables in
the same order as that
[06/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
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--
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b/docs/v1.15.1/group__grp__text__utilities.html
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@@ -0,0 +1,365 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Term Frequency
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
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+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__text__utilities.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Term FrequencyUtilities
+
+
+Contents
+
+Function Syntax
+
+Examples
+
+Related Topics
+
+Term frequency computes the number of times that a word or term
occurs in a document. Term frequency is often used as part of a larger text
processing pipeline, which may include operations such as stemming, stop word
removal and topic modelling.
+Function Syntax
+
+term_frequency(input_table,
+ doc_id_col,
+ word_col,
+ output_table,
+ compute_vocab)
+Arguments:
+input_table
+TEXT. The name of the table containing the documents,
with one document per row. Each row is in the form doc_id, word_vector
where doc_id is an id unique to each document, and
word_vector is a text array containing the words in the document.
The word_vector should contain multiple entries of a word if the
document contains multiple occurrence of that word.
+
+
+doc_id_col
+TEXT. The name of the column containing the document
id.
+
+
+word_col
+TEXT. The name of the column containing the vector of
words/terms in the document. This column should be of type that can be cast to
TEXT[].
+
+
+output_table
+TEXT. The name of the table to store the term frequency
output. The output table contains the following columns:
+doc_id_col: This the document id column (name will be same as
the one provided as input).
+word: Word/term present in a document. Depending on the value
of compute_vocab below, this is either the original word as it
appears in word_col, or an id representing the word. Note that
word id's start from 0 not 1.
+count: The number of times this word is found in the
document.
+
+
+
+compute_vocab
+BOOLEAN. (Optional, Default=FALSE) Flag to indicate if a vocabulary table
is to be created. If TRUE, an additional output table is created containing the
vocabulary of all words, with an id assigned to each word in alphabetical
order. The table is called output_table_vocabulary (i.e., suffix added
to the output_table name) and contains the following columns:
+wordid: An id for each word in alphabetical order.
+word: The word/term corresponding to the id.
+
+
+
+Examples
+
+First we create a document table with one document per row:
+DROP TABLE IF EXISTS documents;
+CREATE TABLE documents(docid INT4, contents TEXT);
+INSERT INTO documents VALUES
+(0, 'I like to eat broccoli and bananas. I ate a banana and spinach smoothie
for breakfast.'),
+(1, 'Chinchillas and kittens are cute.'),
+(2, 'My sister adopted two kittens yesterday.'),
+(3, 'Look at this cute hamster munching on a piece of broccoli.');
+ You can apply stemming, stop word removal and tokenization at this
point in order to prepare the documents for text
[02/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/kmeans_8sql__in.html
--
diff --git a/docs/v1.15.1/kmeans_8sql__in.html
b/docs/v1.15.1/kmeans_8sql__in.html
new file mode 100644
index 000..30675eb
--- /dev/null
+++ b/docs/v1.15.1/kmeans_8sql__in.html
@@ -0,0 +1,300 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: kmeans.sql_in File Reference
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('kmeans_8sql__in.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Functions
+
+kmeans.sql_in File Reference
+
+
+
+Set of functions for k-means clustering.
+More...
+
+
+Functions
+voidinternal_execute_using_kmeans_args
(varchar sql, float8[][], regproc, integer, float8, varchar)
+
+integerinternal_compute_kmeans
(varchar rel_args, varchar rel_state, varchar rel_source, varchar expr_point,
varchar agg_centroid)
+
+void__kmeans_validate_src
(varchar rel_source)
+
+boolean__kmeans_validate_expr
(varchar rel_source, varchar expr_point)
+
+
+Detailed
Description
+See alsoFor a
brief introduction to k-means clustering, see the module description k-Means Clustering.
+Function Documentation
+
+__kmeans_validate_expr()
+
+
+
+
+
+ boolean __kmeans_validate_expr
+ (
+ varchar
+ rel_source,
+
+
+
+
+ varchar
+ expr_point
+
+
+
+ )
+
+
+
+
+
+
+
+
+__kmeans_validate_src()
+
+
+
+
+
+ void __kmeans_validate_src
+ (
+ varchar
+ rel_source)
+
+
+
+
+
+
+
+
+internal_compute_kmeans()
+
+
+
+
+
+ integer internal_compute_kmeans
+ (
+ varchar
+ rel_args,
+
+
+
+
+ varchar
+ rel_state,
+
+
+
+
+ varchar
+ rel_source,
+
+
+
+
+ varchar
+ expr_point,
+
+
+
+
+ varchar
+ agg_centroid
+
+
+
+ )
+
+
+
+
+
+
+
+
+internal_execute_using_kmeans_args()
+
+
+
+
+
+ void internal_execute_using_kmeans_args
+ (
+ varchar
+ sql,
+
+
+
+
+ float8
+ [][],
+
+
+
+
+ regproc
+ ,
+
+
+
+
+ integer
+ ,
+
+
+
+
+ float8
+ ,
+
+
+
+
+ varchar
+
+
+
+
+ )
+
+
+
+
+
+
+
+
+
+
+
+
+srcportspostgresmoduleskmeanskmeans.sql_in
+Generated on Mon Oct 15 2018 11:24:30 for MADlib by
+http://www.doxygen.org/index.html;>
+ 1.8.14
+
+
+
+
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/knn_8sql__in.html
--
diff --git a/docs/v1.15.1/knn_8sql__in.html
[10/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__robust.html
--
diff --git a/docs/v1.15.1/group__grp__robust.html
b/docs/v1.15.1/group__grp__robust.html
new file mode 100644
index 000..b727f7d
--- /dev/null
+++ b/docs/v1.15.1/group__grp__robust.html
@@ -0,0 +1,447 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Robust Variance
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__robust.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Robust VarianceSupervised Learning Regression Models
+
+
+Contents
+
+Robust Linear Regression Training Function
+
+Robust Logistic Regression Training Function
+
+Robust Multinomial Logistic Regression Training
Function
+
+Robust Variance Function For Cox Proportional
Hazards
+
+Examples
+
+Technical Background
+
+Literature
+
+Related Topics
+
+The functions in this module calculate robust variance (Huber-White
estimates) for linear regression, logistic regression, multinomial logistic
regression, and Cox proportional hazards. They are useful in calculating
variances in a dataset with potentially noisy outliers. The Huber-White
implemented here is identical to the "HC0" sandwich operator in the R module
"sandwich".
+The interfaces for robust linear, logistic, and multinomial logistic
regression are similar. Each regression type has its own training function. The
regression results are saved in an output table with small differences,
depending on the regression type.
+WarningPlease note that the interface
for Cox proportional hazards, unlike the interface of other regression methods,
accepts an output model table produced by coxph_train()
function.
+Robust Linear Regression Training Function
+The robust_variance_linregr()
function has the following syntax:
+robust_variance_linregr( source_table,
+ out_table,
+ dependent_varname,
+ independent_varname,
+ grouping_cols
+ )
+
+source_table
+VARCHAR. The name of the table containing the training data.
+out_table
+VARCHAR. Name of the generated table containing the
output model. The output table contains the following columns.
+
+coef DOUBLE PRECISION[]. Vector of the coefficients of the
regression.
+
+std_err DOUBLE PRECISION[]. Vector of the standard error of the
coefficients.
+
+t_stats DOUBLE PRECISION[]. Vector of the t-stats of the
coefficients.
+
+p_values DOUBLE PRECISION[]. Vector of the p-values of the
coefficients.
+
+A summary table named out_table_summary is also
created, which is the same as the summary table created by linregr_train
function. Please refer to the documentation for linear regression for details.
+
+dependent_varname
+VARCHAR. The name of the column containing the dependent variable.
+independent_varname
+VARCHAR. Expression list to evaluate for the independent variables. An
intercept variable is not assumed. It is common to provide an explicit
intercept term by including a single constant 1 term in the independent
variable list.
+grouping_cols (optional)
+VARCHAR, default: NULL. An expression list used to group the input dataset
into discrete groups, running one regression
[19/51] [partial] madlib-site git commit: Doc: Add v1.15.1 documentation
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/af0e5f14/docs/v1.15.1/group__grp__lda.html
--
diff --git a/docs/v1.15.1/group__grp__lda.html
b/docs/v1.15.1/group__grp__lda.html
new file mode 100644
index 000..a15fd55
--- /dev/null
+++ b/docs/v1.15.1/group__grp__lda.html
@@ -0,0 +1,758 @@
+
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd;>
+http://www.w3.org/1999/xhtml;>
+
+
+
+
+
+MADlib: Latent Dirichlet Allocation
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(initResizable);
+/* @license-end */
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+ $(document).ready(function() { init_search(); });
+/* @license-end */
+
+
+ MathJax.Hub.Config({
+extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+jax: ["input/TeX","output/HTML-CSS"],
+});
+https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/MathJax.js";>
+
+
+
+
+
+
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
+
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+ ga('create', 'UA-45382226-1', 'madlib.apache.org');
+ ga('send', 'pageview');
+
+
+
+
+
+
+
+
+ http://madlib.apache.org;>
+
+
+ 1.15.1
+
+ User Documentation for Apache MADlib
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+/* @license
magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&dn=gpl-2.0.txt
GPL-v2 */
+$(document).ready(function(){initNavTree('group__grp__lda.html','');});
+/* @license-end */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Latent Dirichlet AllocationUnsupervised Learning
Topic
Modelling
+
+
+Contents
+
+Background
+
+Training Function
+
+Prediction Function
+
+Perplexity
+
+Helper Functions
+
+Examples
+
+Literature
+
+Related Topics
+
+
+
+Latent Dirichlet Allocation (LDA) is a generative probabilistic model
for natural texts. It is used in problems such as automated topic discovery,
collaborative filtering, and document classification.
+In addition to an implementation of LDA, this MADlib module also provides a
number of additional helper functions to interpret results of the LDA
output.
+NoteTopic modeling is often used as part
of a larger text processing pipeline, which may include operations such as term
frequency, stemming and stop word removal. You can use the function Term Frequency to generate the
required vocabulary format from raw documents for the LDA training function.
See the examples later on this page for more details.
+Background
+The LDA model posits that each document is associated with a mixture of
various topics (e.g., a document is related to Topic 1 with probability 0.7,
and Topic 2 with probability 0.3), and that each word in the document is
attributable to one of the document's topics. There is a (symmetric) Dirichlet
prior with parameter \( \alpha \) on each document's topic mixture. In
addition, there is another (symmetric) Dirichlet prior with parameter \( \beta
\) on the distribution of words for each topic.
+The following generative process then defines a distribution over a corpus
of documents:
+
+Sample for each topic \( i \), a per-topic word distribution \( \phi_i \)
from the Dirichlet( \(\beta\)) prior.
+For each document:
+Sample a document length N from a suitable distribution, say, Poisson.
+Sample a topic mixture \( \theta \) for the document from the Dirichlet(
\(\alpha\)) distribution.
+For each of the N words:
+Sample a topic \( z_n \) from the multinomial topic distribution \( \theta
\).
+Sample a word \( w_n \) from the multinomial word distribution \(
\phi_{z_n} \) associated with topic \( z_n \).
+
+
+
+
+
+In practice, only the words in each document are observable. The topic
mixture of each document and the topic for each word in each document are
latent unobservable variables that need to be inferred from the observables,
and this is referred to as the inference problem for LDA. Exact inference is
intractable, but several approximate inference algorithms for LDA have been
developed. The simple and effective Gibbs sampling algorithm described in
Griffiths and Steyvers [2] appears to be the current algorithm of choice.
+This implementation provides a parallel and scalable in-database solution
for LDA based on Gibbs sampling. It takes advantage of the
madlib-site git commit: update website for 1.15.1 release
Repository: madlib-site
Updated Branches:
refs/heads/asf-site 127c0b7e7 -> 6d7f908b5
update website for 1.15.1 release
Project: http://git-wip-us.apache.org/repos/asf/madlib-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/madlib-site/commit/6d7f908b
Tree: http://git-wip-us.apache.org/repos/asf/madlib-site/tree/6d7f908b
Diff: http://git-wip-us.apache.org/repos/asf/madlib-site/diff/6d7f908b
Branch: refs/heads/asf-site
Commit: 6d7f908b550848b438cf94b3176ce963814bf367
Parents: 127c0b7
Author: Frank McQuillan
Authored: Mon Oct 15 10:50:17 2018 -0700
Committer: Frank McQuillan
Committed: Mon Oct 15 10:50:17 2018 -0700
--
documentation.html | 1 +
download.html | 37 -
index.html | 16
3 files changed, 45 insertions(+), 9 deletions(-)
--
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/6d7f908b/documentation.html
--
diff --git a/documentation.html b/documentation.html
index 0d01094..8727541 100644
--- a/documentation.html
+++ b/documentation.html
@@ -55,6 +55,7 @@ jQuery(document).ready(function() {
The primary documentation reference material providing
detailed information on the functions and algorithms within MADlib as well as
background theory and references into the literature.
Older Documentation
+MADlib v1.15
MADlib v1.14
MADlib v1.13
MADlib v1.12
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/6d7f908b/download.html
--
diff --git a/download.html b/download.html
index ce790ad..1781c9d 100644
--- a/download.html
+++ b/download.html
@@ -58,7 +58,7 @@
Current Release
- v1.15
+ v1.15.1
Source Code and Convenience
Binaries
MADlib source code
and convenience binaries are available from the Apache distribution site.
@@ -66,13 +66,15 @@
Latest
stable release:
- http://apache.org/dyn/closer.cgi?filename=madlib/1.15/apache-madlib-1.15-src.tar.gz=download;>Source
code tar.gz (https://www.apache.org/dist/madlib/1.15/apache-madlib-1.15-src.tar.gz.asc;>pgp,
https://www.apache.org/dist/madlib/1.15/apache-madlib-1.15-src.tar.gz.sha512;>sha512)
+ https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-src.tar.gz;>Source
code tar.gz (https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-src.tar.gz.asc;>pgp,
https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-src.tar.gz.sha512;>sha512)
+
+ https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux-GPDB43.rpm;>Linux
(https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux-GPDB43.rpm.asc;>pgp,
https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux-GPDB43.rpm.sha512;>sha512)
â CentOS / Red Hat 5 and higher (64 bit). GPDB 4.3.x.
+
+ https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.rpm;>Linux
(https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.rpm.asc;>pgp,
https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.rpm.sha512;>sha512)
â CentOS / Red Hat 6 and higher (64 bit). GPDB 5.x, PostgreSQL 9.6 and
10.x.
- http://apache.org/dyn/closer.cgi?filename=madlib/1.15/apache-madlib-1.15-bin-Linux-GPDB43.rpm=download;>Linux
(https://www.apache.org/dist/madlib/1.15/apache-madlib-1.15-bin-Linux-GPDB43.rpm.asc;>pgp,
https://www.apache.org/dist/madlib/1.15/apache-madlib-1.15-bin-Linux-GPDB43.rpm.sha512;>sha512)
â CentOS / Red Hat 5 and higher (64 bit). GPDB 4.3.x.
+ https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.deb;>Linux
(https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.deb.asc;>pgp,
https://www.apache.org/dist/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.deb.sha512;>sha512)
â Ubuntu 16.04. GPDB 5.x, PostgreSQL 9.6 and 10.x.
- http://apache.org/dyn/closer.cgi?filename=madlib/1.15/apache-madlib-1.15-bin-Linux.rpm=download;>Linux
(https://www.apache.org/dist/madlib/1.15/apache-madlib-1.15-bin-Linux.rpm.asc;>pgp,
svn commit: r30064 - /release/madlib/KEYS
Author: okislal Date: Mon Oct 15 12:07:03 2018 New Revision: 30064 Log: Update keys Added: release/madlib/KEYS - copied unchanged from r30063, dev/madlib/KEYS
