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src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
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+ <div id="projectname">
+ <span id="projectnumber">1.14</span>
+ </div>
+ <div id="projectbrief">User Documentation for Apache MADlib</div>
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+
+<div class="header">
+ <div class="summary">
+<a href="#func-members">Functions</a> </div>
+ <div class="headertitle">
+<div class="title">lda.sql_in File Reference</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for Latent Dirichlet Allocation.
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a
name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:aeb7593251a4dedb695494f65dc2d1f80"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda_train</a> (text
data_table, text model_table, text output_data_table, int4 voc_size, int4
topic_num, int4 iter_num, float8 alpha, float8 beta)</td></tr>
+<tr class="memdesc:aeb7593251a4dedb695494f65dc2d1f80"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF provides an entry
for the lda training process. <a
href="#aeb7593251a4dedb695494f65dc2d1f80">More...</a><br /></td></tr>
+<tr class="separator:aeb7593251a4dedb695494f65dc2d1f80"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:af1fde06c39dd12bb9e5544997f815323"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323">lda_predict</a>
(text data_table, text model_table, text output_table)</td></tr>
+<tr class="memdesc:af1fde06c39dd12bb9e5544997f815323"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF provides an entry
for the lda predicton process. <a
href="#af1fde06c39dd12bb9e5544997f815323">More...</a><br /></td></tr>
+<tr class="separator:af1fde06c39dd12bb9e5544997f815323"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aaa89e30c8fd0ba41b6feee01ee195330"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#aaa89e30c8fd0ba41b6feee01ee195330">lda_predict</a>
(text data_table, text model_table, text output_table, int4 iter_num)</td></tr>
+<tr class="memdesc:aaa89e30c8fd0ba41b6feee01ee195330"><td
class="mdescLeft"> </td><td class="mdescRight">A overloaded version which
allows users to specify iter_num. <a
href="#aaa89e30c8fd0ba41b6feee01ee195330">More...</a><br /></td></tr>
+<tr class="separator:aaa89e30c8fd0ba41b6feee01ee195330"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ace20b6314e5b4e98929a98a6f05d925d"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#ace20b6314e5b4e98929a98a6f05d925d">lda_get_topic_word_count</a>
(text model_table, text output_table)</td></tr>
+<tr class="memdesc:ace20b6314e5b4e98929a98a6f05d925d"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF computes the
per-topic word counts. <a
href="#ace20b6314e5b4e98929a98a6f05d925d">More...</a><br /></td></tr>
+<tr class="separator:ace20b6314e5b4e98929a98a6f05d925d"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a455699e72328d6b105c61a3c9e7ae5dc"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a455699e72328d6b105c61a3c9e7ae5dc">lda_get_word_topic_count</a>
(text model_table, text output_table)</td></tr>
+<tr class="memdesc:a455699e72328d6b105c61a3c9e7ae5dc"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF computes the
per-word topic counts. <a
href="#a455699e72328d6b105c61a3c9e7ae5dc">More...</a><br /></td></tr>
+<tr class="separator:a455699e72328d6b105c61a3c9e7ae5dc"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4ac16d87d50821aadcab0d75d65b3f1b"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a4ac16d87d50821aadcab0d75d65b3f1b">lda_get_topic_desc</a>
(text model_table, text vocab_table, text desc_table, int4 top_k)</td></tr>
+<tr class="memdesc:a4ac16d87d50821aadcab0d75d65b3f1b"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF gets the
description for each topic (top-k words) <a
href="#a4ac16d87d50821aadcab0d75d65b3f1b">More...</a><br /></td></tr>
+<tr class="separator:a4ac16d87d50821aadcab0d75d65b3f1b"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afa13f3db33ee5ef4fef17f5daafa866e"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#afa13f3db33ee5ef4fef17f5daafa866e">lda_get_word_topic_mapping</a>
(text lda_output_table, text mapping_table)</td></tr>
+<tr class="memdesc:afa13f3db33ee5ef4fef17f5daafa866e"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF gets the wordid -
topicid mapping from the lda training output table. <a
href="#afa13f3db33ee5ef4fef17f5daafa866e">More...</a><br /></td></tr>
+<tr class="separator:afa13f3db33ee5ef4fef17f5daafa866e"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3cdff7ba417e1a9c1b221f23bc2aac64"><td class="memItemLeft"
align="right" valign="top">int4 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a3cdff7ba417e1a9c1b221f23bc2aac64">__lda_random_assign</a>
(int4 word_count, int4 topic_num)</td></tr>
+<tr class="memdesc:a3cdff7ba417e1a9c1b221f23bc2aac64"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF assigns topics to
words in a document randomly. <a
href="#a3cdff7ba417e1a9c1b221f23bc2aac64">More...</a><br /></td></tr>
+<tr class="separator:a3cdff7ba417e1a9c1b221f23bc2aac64"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab2640fce837da813a3b6fff38dc713d8"><td class="memItemLeft"
align="right" valign="top">int4 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#ab2640fce837da813a3b6fff38dc713d8">__lda_gibbs_sample</a>
(int4[] words, int4[] counts, int4[] doc_topic, int8[] model, float8 alpha,
float8 beta, int4 voc_size, int4 topic_num, int4 iter_num)</td></tr>
+<tr class="memdesc:ab2640fce837da813a3b6fff38dc713d8"><td
class="mdescLeft"> </td><td class="mdescRight">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. <a href="#ab2640fce837da813a3b6fff38dc713d8">More...</a><br
/></td></tr>
+<tr class="separator:ab2640fce837da813a3b6fff38dc713d8"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad72ac6a8db5e26ef6ca8e7c2563bec94"><td class="memItemLeft"
align="right" valign="top">int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#ad72ac6a8db5e26ef6ca8e7c2563bec94">__lda_count_topic_sfunc</a>
(int8[] state, int4[] words, int4[] counts, int4[] topic_assignment, int4
voc_size, int4 topic_num)</td></tr>
+<tr class="memdesc:ad72ac6a8db5e26ef6ca8e7c2563bec94"><td
class="mdescLeft"> </td><td class="mdescRight">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. <a href="#ad72ac6a8db5e26ef6ca8e7c2563bec94">More...</a><br
/></td></tr>
+<tr class="separator:ad72ac6a8db5e26ef6ca8e7c2563bec94"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae9987628d56799cd5fadccc92dff3110"><td class="memItemLeft"
align="right" valign="top">int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#ae9987628d56799cd5fadccc92dff3110">__lda_count_topic_prefunc</a>
(int8[] state1, int8[] state2)</td></tr>
+<tr class="memdesc:ae9987628d56799cd5fadccc92dff3110"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF is the prefunc for
the aggregator computing the per-word topic counts. <a
href="#ae9987628d56799cd5fadccc92dff3110">More...</a><br /></td></tr>
+<tr class="separator:ae9987628d56799cd5fadccc92dff3110"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:acdae83c5837155ea3663b34db4521e86"><td class="memItemLeft"
align="right" valign="top">aggregate int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#acdae83c5837155ea3663b34db4521e86">__lda_count_topic_agg</a>
(int4[], int4[], int4[], int4, int4)</td></tr>
+<tr class="memdesc:acdae83c5837155ea3663b34db4521e86"><td
class="mdescLeft"> </td><td class="mdescRight">This uda computes the word
topic counts by scanning and summing up topic assignments in each document. <a
href="#acdae83c5837155ea3663b34db4521e86">More...</a><br /></td></tr>
+<tr class="separator:acdae83c5837155ea3663b34db4521e86"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a25c3ef12d9808d8a38c5fd2630f3b5a9"><td class="memItemLeft"
align="right" valign="top">float8 </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda_get_perplexity</a>
(text model_table, text output_data_table)</td></tr>
+<tr class="memdesc:a25c3ef12d9808d8a38c5fd2630f3b5a9"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF computes the
perplexity given the output data table and the model table. <a
href="#a25c3ef12d9808d8a38c5fd2630f3b5a9">More...</a><br /></td></tr>
+<tr class="separator:a25c3ef12d9808d8a38c5fd2630f3b5a9"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a5defc3ef61cabeaf343a2e1d9988f8da"><td class="memItemLeft"
align="right" valign="top">int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a5defc3ef61cabeaf343a2e1d9988f8da">__lda_perplexity_sfunc</a>
(int8[] state, int4[] words, int4[] counts, int4[] doc_topic, int8[] model,
float8 alpha, float8 beta, int4 voc_size, int4 topic_num)</td></tr>
+<tr class="memdesc:a5defc3ef61cabeaf343a2e1d9988f8da"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF is the sfunc for
the aggregator computing the perpleixty. <a
href="#a5defc3ef61cabeaf343a2e1d9988f8da">More...</a><br /></td></tr>
+<tr class="separator:a5defc3ef61cabeaf343a2e1d9988f8da"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a78a90c564e975e9a170b83ddeb6bd11c"><td class="memItemLeft"
align="right" valign="top">int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a78a90c564e975e9a170b83ddeb6bd11c">__lda_perplexity_prefunc</a>
(int8[] state1, int8[] state2)</td></tr>
+<tr class="memdesc:a78a90c564e975e9a170b83ddeb6bd11c"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF is the prefunc for
the aggregator computing the perplexity. <a
href="#a78a90c564e975e9a170b83ddeb6bd11c">More...</a><br /></td></tr>
+<tr class="separator:a78a90c564e975e9a170b83ddeb6bd11c"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:abf691da12a45408203b82940644dc34e"><td class="memItemLeft"
align="right" valign="top">float8 </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#abf691da12a45408203b82940644dc34e">__lda_perplexity_ffunc</a>
(int8[] state)</td></tr>
+<tr class="memdesc:abf691da12a45408203b82940644dc34e"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF is the finalfunc
for the aggregator computing the perplexity. <a
href="#abf691da12a45408203b82940644dc34e">More...</a><br /></td></tr>
+<tr class="separator:abf691da12a45408203b82940644dc34e"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa9bc2a38ba29636740deb01aad241050"><td class="memItemLeft"
align="right" valign="top">aggregate int8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#aa9bc2a38ba29636740deb01aad241050">__lda_perplexity_agg</a>
(int4[], int4[], int4[], int8[], float8, float8, int4, int4)</td></tr>
+<tr class="separator:aa9bc2a38ba29636740deb01aad241050"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0eefd7b201fda5289554b7d43366865b"><td class="memItemLeft"
align="right" valign="top">int4 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a0eefd7b201fda5289554b7d43366865b">__lda_check_count_ceiling</a>
(int8[] arr, int4 r, int4 c)</td></tr>
+<tr class="separator:a0eefd7b201fda5289554b7d43366865b"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2fcec45a37b459567ad0bbcd42c07142"><td class="memItemLeft"
align="right" valign="top">set< int4[]> </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a2fcec45a37b459567ad0bbcd42c07142">__lda_util_unnest</a>
(int8[] arr, int4 r, int4 c)</td></tr>
+<tr class="memdesc:a2fcec45a37b459567ad0bbcd42c07142"><td
class="mdescLeft"> </td><td class="mdescRight">Unnest a 2-D array into a
set of 1-D arrays. <a href="#a2fcec45a37b459567ad0bbcd42c07142">More...</a><br
/></td></tr>
+<tr class="separator:a2fcec45a37b459567ad0bbcd42c07142"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a219c8432558301878384e5ef8aca1e3d"><td class="memItemLeft"
align="right" valign="top">set< int4[]> </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a219c8432558301878384e5ef8aca1e3d">__lda_util_unnest_transpose</a>
(int8[] arr, int4 r, int4 c)</td></tr>
+<tr class="separator:a219c8432558301878384e5ef8aca1e3d"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a2db0019220f10a1fca78ed77a5c2dcd5"><td class="memItemLeft"
align="right" valign="top">int8 [][] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a2db0019220f10a1fca78ed77a5c2dcd5">__lda_util_transpose</a>
(int8[][] matrix)</td></tr>
+<tr class="memdesc:a2db0019220f10a1fca78ed77a5c2dcd5"><td
class="mdescLeft"> </td><td class="mdescRight">Transpose a 2-D array. <a
href="#a2db0019220f10a1fca78ed77a5c2dcd5">More...</a><br /></td></tr>
+<tr class="separator:a2db0019220f10a1fca78ed77a5c2dcd5"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a9b522ce2d080956d7fb3d5ae79195b8a"><td class="memItemLeft"
align="right" valign="top">float8 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#a9b522ce2d080956d7fb3d5ae79195b8a">__lda_util_norm_with_smoothing</a>
(float8[] arr, float8 smooth)</td></tr>
+<tr class="memdesc:a9b522ce2d080956d7fb3d5ae79195b8a"><td
class="mdescLeft"> </td><td class="mdescRight">L1 normalization with
smoothing. <a href="#a9b522ce2d080956d7fb3d5ae79195b8a">More...</a><br
/></td></tr>
+<tr class="separator:a9b522ce2d080956d7fb3d5ae79195b8a"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa9729b6bcc20d00936b6831382747c2f"><td class="memItemLeft"
align="right" valign="top">int4 [] </td><td class="memItemRight"
valign="bottom"><a class="el"
href="lda_8sql__in.html#aa9729b6bcc20d00936b6831382747c2f">__lda_util_index_sort</a>
(float8[] arr)</td></tr>
+<tr class="memdesc:aa9729b6bcc20d00936b6831382747c2f"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF returns the index
of elements in a sorted order. <a
href="#aa9729b6bcc20d00936b6831382747c2f">More...</a><br /></td></tr>
+<tr class="separator:aa9729b6bcc20d00936b6831382747c2f"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0032ed0be0c41a1b6ed08dca93f4a59f"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a0032ed0be0c41a1b6ed08dca93f4a59f">__lda_util_norm_vocab</a>
(text vocab_table, text output_vocab_table)</td></tr>
+<tr class="memdesc:a0032ed0be0c41a1b6ed08dca93f4a59f"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF checks the
vocabulary and converts non-continous wordids into continuous integers ranging
from 0 to voc_size - 1. <a
href="#a0032ed0be0c41a1b6ed08dca93f4a59f">More...</a><br /></td></tr>
+<tr class="separator:a0032ed0be0c41a1b6ed08dca93f4a59f"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a85990f0bc07a2ab8834301561662939e"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a85990f0bc07a2ab8834301561662939e">__lda_util_norm_dataset</a>
(text data_table, text norm_vocab_table, text output_data_table)</td></tr>
+<tr class="memdesc:a85990f0bc07a2ab8834301561662939e"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF converts the data
table according to the normalized vocabulary, and all rows with non-positive
count values will be removed. <a
href="#a85990f0bc07a2ab8834301561662939e">More...</a><br /></td></tr>
+<tr class="separator:a85990f0bc07a2ab8834301561662939e"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa4fd0a274f1c400014f2ea9549507436"><td class="memItemLeft"
align="right" valign="top">set< lda_result > </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#aa4fd0a274f1c400014f2ea9549507436">__lda_util_conorm_data</a>
(text data_table, text vocab_table, text output_data_table, text
output_vocab_table)</td></tr>
+<tr class="memdesc:aa4fd0a274f1c400014f2ea9549507436"><td
class="mdescLeft"> </td><td class="mdescRight">This UDF extracts the list
of wordids from the data table and joins it with the vocabulary table to get
the list of common wordids, next it will normalize the vocabulary based on the
common wordids and then normalize the data table based on the normalized
vocabulary. <a href="#aa4fd0a274f1c400014f2ea9549507436">More...</a><br
/></td></tr>
+<tr class="separator:aa4fd0a274f1c400014f2ea9549507436"><td
class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3ea35eb8ae6670a07948a56b85c1710c"><td class="memItemLeft"
align="right" valign="top">_pivotalr_lda_model </td><td
class="memItemRight" valign="bottom"><a class="el"
href="lda_8sql__in.html#a3ea35eb8ae6670a07948a56b85c1710c">lda_parse_model</a>
(bigint[] lda_model, integer voc_size, integer topic_num)</td></tr>
+<tr class="separator:a3ea35eb8ae6670a07948a56b85c1710c"><td
class="memSeparator" colspan="2"> </td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed
Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>Dec
2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For an introduction to Latent
Dirichlet Allocation models, see the module description <a class="el"
href="group__grp__lda.html">Latent Dirichlet Allocation</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a id="a0eefd7b201fda5289554b7d43366865b"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a0eefd7b201fda5289554b7d43366865b">◆ </a></span>__lda_check_count_ceiling()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int4 [] __lda_check_count_ceiling </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>arr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>r</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>c</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="acdae83c5837155ea3663b34db4521e86"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#acdae83c5837155ea3663b34db4521e86">◆ </a></span>__lda_count_topic_agg()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">aggregate int8 [] __lda_count_topic_agg </td>
+ <td>(</td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">words</td><td>The unique words in the document
</td></tr>
+ <tr><td class="paramname">counts</td><td>The counts of each unique words
in the document </td></tr>
+ <tr><td class="paramname">topic_assignment</td><td>The topic assignments
in the document </td></tr>
+ <tr><td class="paramname">voc_size</td><td>The size of vocabulary
</td></tr>
+ <tr><td class="paramname">topic_num</td><td>The number of topics </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The word topic counts (a 1-d
array embeding a 2-d array) </dd></dl>
+
+</div>
+</div>
+<a id="ae9987628d56799cd5fadccc92dff3110"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#ae9987628d56799cd5fadccc92dff3110">◆ </a></span>__lda_count_topic_prefunc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int8 [] __lda_count_topic_prefunc </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state1</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state2</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">state1</td><td>The local word topic counts
</td></tr>
+ <tr><td class="paramname">state2</td><td>The local word topic counts
</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The element-wise sum of two
local states </dd></dl>
+
+</div>
+</div>
+<a id="ad72ac6a8db5e26ef6ca8e7c2563bec94"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#ad72ac6a8db5e26ef6ca8e7c2563bec94">◆ </a></span>__lda_count_topic_sfunc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int8 [] __lda_count_topic_sfunc </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>words</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>counts</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>topic_assignment</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>voc_size</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>topic_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">state</td><td>The topic counts </td></tr>
+ <tr><td class="paramname">words</td><td>The unique words in the document
</td></tr>
+ <tr><td class="paramname">counts</td><td>The counts of each unique words
in the document (sum(counts) = word_count) </td></tr>
+ <tr><td class="paramname">topic_assignment</td><td>The topic assignments
in the document </td></tr>
+ <tr><td class="paramname">voc_size</td><td>The size of vocabulary
</td></tr>
+ <tr><td class="paramname">topic_num</td><td>The number of topics </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The updated state </dd></dl>
+
+</div>
+</div>
+<a id="ab2640fce837da813a3b6fff38dc713d8"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#ab2640fce837da813a3b6fff38dc713d8">◆ </a></span>__lda_gibbs_sample()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int4 [] __lda_gibbs_sample </td>
+ <td>(</td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>words</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>counts</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>doc_topic</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>model</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>alpha</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>beta</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>voc_size</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>topic_num</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>iter_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">words</td><td>The set of unique words in the
document </td></tr>
+ <tr><td class="paramname">counts</td><td>The counts of each unique words
in the document (sum(counts) = word_count) </td></tr>
+ <tr><td class="paramname">doc_topic</td><td>The current per-doc topic
counts and topic assignments </td></tr>
+ <tr><td class="paramname">model</td><td>The current model (including the
per-word topic counts and the corpus-level topic counts) </td></tr>
+ <tr><td class="paramname">alpha</td><td>The Dirichlet parameter for
per-document topic multinomial </td></tr>
+ <tr><td class="paramname">beta</td><td>The Dirichlet parameter for
per-topic word multinomial </td></tr>
+ <tr><td class="paramname">voc_size</td><td>The size of vocabulary
</td></tr>
+ <tr><td class="paramname">topic_num</td><td>The number of topics </td></tr>
+ <tr><td class="paramname">iter_num</td><td>The number of iterations
</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The learned topic counts and
topic assignments </dd></dl>
+
+</div>
+</div>
+<a id="aa9bc2a38ba29636740deb01aad241050"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#aa9bc2a38ba29636740deb01aad241050">◆ </a></span>__lda_perplexity_agg()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">aggregate int8 [] __lda_perplexity_agg </td>
+ <td>(</td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int8 </td>
+ <td class="paramname">[], </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname">, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="abf691da12a45408203b82940644dc34e"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#abf691da12a45408203b82940644dc34e">◆ </a></span>__lda_perplexity_ffunc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 __lda_perplexity_ffunc </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">state</td><td>The merged state </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The perpleixty </dd></dl>
+
+</div>
+</div>
+<a id="a78a90c564e975e9a170b83ddeb6bd11c"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a78a90c564e975e9a170b83ddeb6bd11c">◆ </a></span>__lda_perplexity_prefunc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int8 [] __lda_perplexity_prefunc </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state1</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state2</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">state1</td><td>The local state </td></tr>
+ <tr><td class="paramname">state2</td><td>The local state </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The merged state </dd></dl>
+
+</div>
+</div>
+<a id="a5defc3ef61cabeaf343a2e1d9988f8da"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a5defc3ef61cabeaf343a2e1d9988f8da">◆ </a></span>__lda_perplexity_sfunc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int8 [] __lda_perplexity_sfunc </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>state</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>words</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>counts</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 [] </td>
+ <td class="paramname"><em>doc_topic</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>model</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>alpha</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>beta</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>voc_size</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>topic_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">state</td><td>The cached model plus perplexity
</td></tr>
+ <tr><td class="paramname">words</td><td>The unique words in the document
</td></tr>
+ <tr><td class="paramname">counts</td><td>The counts of each unique words
in the document </td></tr>
+ <tr><td class="paramname">doc_topic</td><td>The topic counts in the
document </td></tr>
+ <tr><td class="paramname">model</td><td>The learned model </td></tr>
+ <tr><td class="paramname">alpha</td><td>The Dirichlet parameter for
per-document topic multinomial </td></tr>
+ <tr><td class="paramname">beta</td><td>The Dirichlet parameter for
per-topic word multinomial </td></tr>
+ <tr><td class="paramname">voc_size</td><td>The size of vocabulary
</td></tr>
+ <tr><td class="paramname">topic_num</td><td>The number of topics </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The updated state </dd></dl>
+
+</div>
+</div>
+<a id="a3cdff7ba417e1a9c1b221f23bc2aac64"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a3cdff7ba417e1a9c1b221f23bc2aac64">◆ </a></span>__lda_random_assign()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int4 [] __lda_random_assign </td>
+ <td>(</td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>word_count</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>topic_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">word_count</td><td>The number of words in the
document </td></tr>
+ <tr><td class="paramname">topic_num</td><td>The number of topics
(specified by the user) </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The topic counts and topic
assignments </dd></dl>
+
+</div>
+</div>
+<a id="aa4fd0a274f1c400014f2ea9549507436"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#aa4fd0a274f1c400014f2ea9549507436">◆ </a></span>__lda_util_conorm_data()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> __lda_util_conorm_data
</td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>vocab_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_vocab_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">data_table</td><td>The data table to be
normalized </td></tr>
+ <tr><td class="paramname">vocab_table</td><td>The vocabulary table to be
normalized </td></tr>
+ <tr><td class="paramname">output_data_table</td><td>The normalized data
table </td></tr>
+ <tr><td class="paramname">output_vocab_table</td><td>The normalized
vocabulary table </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="aa9729b6bcc20d00936b6831382747c2f"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#aa9729b6bcc20d00936b6831382747c2f">◆ </a></span>__lda_util_index_sort()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int4 [] __lda_util_index_sort </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>arr</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">arr</td><td>The array to be sorted </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The index of elements </dd></dl>
+
+</div>
+</div>
+<a id="a85990f0bc07a2ab8834301561662939e"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a85990f0bc07a2ab8834301561662939e">◆ </a></span>__lda_util_norm_dataset()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> __lda_util_norm_dataset
</td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>norm_vocab_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_data_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">data_table</td><td>The data table to be
normalized </td></tr>
+ <tr><td class="paramname">vocab_table</td><td>The normalized vocabulary
table </td></tr>
+ <tr><td class="paramname">output_data_table</td><td>The normalized data
table </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a0032ed0be0c41a1b6ed08dca93f4a59f"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a0032ed0be0c41a1b6ed08dca93f4a59f">◆ </a></span>__lda_util_norm_vocab()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> __lda_util_norm_vocab </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>vocab_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_vocab_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">vocab_table</td><td>The vocabulary table in the
form of <wordid::int4, word::text> </td></tr>
+ <tr><td class="paramname">output_vocab_table</td><td>The regularized
vocabulary table </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a9b522ce2d080956d7fb3d5ae79195b8a"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a9b522ce2d080956d7fb3d5ae79195b8a">◆ </a></span>__lda_util_norm_with_smoothing()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 [] __lda_util_norm_with_smoothing </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>arr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>smooth</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">arr</td><td>The array to be normalized </td></tr>
+ <tr><td class="paramname">smooth</td><td>The smoothing parameter </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The normalized vector </dd></dl>
+
+</div>
+</div>
+<a id="a2db0019220f10a1fca78ed77a5c2dcd5"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a2db0019220f10a1fca78ed77a5c2dcd5">◆ </a></span>__lda_util_transpose()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">int8 [][] __lda_util_transpose </td>
+ <td>(</td>
+ <td class="paramtype">int8 </td>
+ <td class="paramname"><em>matrix</em>[][]</td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">matrix</td><td>The input 2-D array </td></tr>
+ <tr><td class="paramname">The</td><td>transposed array </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a2fcec45a37b459567ad0bbcd42c07142"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a2fcec45a37b459567ad0bbcd42c07142">◆ </a></span>__lda_util_unnest()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<int4[]> __lda_util_unnest </td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>arr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>r</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>c</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">arr</td><td>The 2-D array to be unnested
</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The unnested 1-D arrays
</dd></dl>
+
+</div>
+</div>
+<a id="a219c8432558301878384e5ef8aca1e3d"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a219c8432558301878384e5ef8aca1e3d">◆ </a></span>__lda_util_unnest_transpose()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<int4[]> __lda_util_unnest_transpose
</td>
+ <td>(</td>
+ <td class="paramtype">int8 [] </td>
+ <td class="paramname"><em>arr</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>r</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>c</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a25c3ef12d9808d8a38c5fd2630f3b5a9"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a25c3ef12d9808d8a38c5fd2630f3b5a9">◆ </a></span>lda_get_perplexity()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 lda_get_perplexity </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_data_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">model_table</td><td>The model table generated by
lda_train </td></tr>
+ <tr><td class="paramname">output_data_table</td><td>The output data table
generated by lda_predict </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The perplexity </dd></dl>
+
+</div>
+</div>
+<a id="a4ac16d87d50821aadcab0d75d65b3f1b"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a4ac16d87d50821aadcab0d75d65b3f1b">◆ </a></span>lda_get_topic_desc()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_get_topic_desc </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>vocab_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>desc_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>top_k</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">model_table</td><td>The model table generated by
the training process </td></tr>
+ <tr><td class="paramname">vocab_table</td><td>The vocabulary table
(<wordid, word>) </td></tr>
+ <tr><td class="paramname">top_k</td><td>The number of top words for each
topic description </td></tr>
+ <tr><td class="paramname">desc_table</td><td>The output table for storing
the per-topic description </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="ace20b6314e5b4e98929a98a6f05d925d"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#ace20b6314e5b4e98929a98a6f05d925d">◆ </a></span>lda_get_topic_word_count()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_get_topic_word_count
</td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">model_table</td><td>The model table generated by
the training process </td></tr>
+ <tr><td class="paramname">output_table</td><td>The output table storing
the per-topic word counts </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a455699e72328d6b105c61a3c9e7ae5dc"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a455699e72328d6b105c61a3c9e7ae5dc">◆ </a></span>lda_get_word_topic_count()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_get_word_topic_count
</td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">model_table</td><td>The model table generated by
the training process </td></tr>
+ <tr><td class="paramname">dist_table</td><td>The output table storing the
per-word topic counts </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="afa13f3db33ee5ef4fef17f5daafa866e"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#afa13f3db33ee5ef4fef17f5daafa866e">◆ </a></span>lda_get_word_topic_mapping()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_get_word_topic_mapping
</td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>lda_output_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>mapping_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">lda_output_table</td><td>The output table from
lda traning or predicting </td></tr>
+ <tr><td class="paramname">mapping_table</td><td>The result table that
saves the mapping info </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+<a id="a3ea35eb8ae6670a07948a56b85c1710c"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#a3ea35eb8ae6670a07948a56b85c1710c">◆ </a></span>lda_parse_model()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">_pivotalr_lda_model lda_parse_model </td>
+ <td>(</td>
+ <td class="paramtype">bigint [] </td>
+ <td class="paramname"><em>lda_model</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>voc_size</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>topic_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="af1fde06c39dd12bb9e5544997f815323"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#af1fde06c39dd12bb9e5544997f815323">◆ </a></span>lda_predict()
<span class="overload">[1/2]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_predict </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_table</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">data_table</td><td>Table storing the testing
dataset, each row is in the form of <docid, wordid, count> where docid,
wordid, and count are all non-negative integers. </td></tr>
+ <tr><td class="paramname">model_table</td><td>Table storing the learned
models </td></tr>
+ <tr><td class="paramname">output_table</td><td>Table storing per-document
topic counts and topic assignments </td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd>default iter_num = 20 </dd></dl>
+
+</div>
+</div>
+<a id="aaa89e30c8fd0ba41b6feee01ee195330"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#aaa89e30c8fd0ba41b6feee01ee195330">◆ </a></span>lda_predict()
<span class="overload">[2/2]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_predict </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>iter_num</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="aeb7593251a4dedb695494f65dc2d1f80"></a>
+<h2 class="memtitle"><span class="permalink"><a
href="#aeb7593251a4dedb695494f65dc2d1f80">◆ </a></span>lda_train()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<lda_result> lda_train </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output_data_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>voc_size</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>topic_num</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">int4 </td>
+ <td class="paramname"><em>iter_num</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>alpha</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>beta</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+ <table class="params">
+ <tr><td class="paramname">data_table</td><td>Table storing the training
dataset, each row is in the form of <docid, wordid, count> where docid,
wordid, and count are all non-negative integers. </td></tr>
+ <tr><td class="paramname">model_table</td><td>Table storing the learned
models (voc_size, topic_num, alpha, beta, per-word topic counts, and
corpus-level topic counts) </td></tr>
+ <tr><td class="paramname">output_data_table</td><td>Table storing the
output data table in the form of <docid, wordcount, words, counts,
topic_count, topic_assignment> </td></tr>
+ <tr><td class="paramname">voc_size</td><td>Size of the vocabulary (Note
that the wordid should be continous integers starting from 0 to voc_size -<ol
type="1">
+<li>A data validation rountine will be called to validate the dataset.) </li>
+</ol>
+</td></tr>
+ <tr><td class="paramname">topic_num</td><td>Number of topics (e.g. 100)
</td></tr>
+ <tr><td class="paramname">iter_num</td><td>Number of iterations (e.g. 60)
</td></tr>
+ <tr><td class="paramname">alpha</td><td>Dirichlet parameter for the
per-doc topic multinomial (e.g. 50/topic_num) </td></tr>
+ <tr><td class="paramname">beta</td><td>Dirichlet parameter for the
per-topic word multinomial (e.g. 0.01) </td></tr>
+ </table>
+ </dd>
+</dl>
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
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