http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/getting_started/permanent-functions.html
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diff --git a/userguide/getting_started/permanent-functions.html 
b/userguide/getting_started/permanent-functions.html
index 8bb30b7..e344abd 100644
--- a/userguide/getting_started/permanent-functions.html
+++ b/userguide/getting_started/permanent-functions.html
@@ -2215,7 +2215,7 @@
   specific language governing permissions and limitations
   under the License.
 -->
-<p>Hive v0.13 or later supports <a 
href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/DropFunction";
 target="_blank">permanent functions</a> that live across sessions.</p>
+<p>Hive v0.13 or later supports <a 
href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/DropFunction";
 target="_blank">permanent functions</a> that lives across sessions.</p>
 <p>Permanent functions are useful when you are using Hive through Hiveserver 
or to avoid hivemall installation for each session.</p>
 <!-- toc --><div id="toc" class="toc">
 
@@ -2229,15 +2229,15 @@
 <h1 id="put-hivemall-jar-to-hdfs">Put hivemall jar to HDFS</h1>
 <p>First, put hivemall jar to HDFS as follows:</p>
 <pre><code class="lang-sh">hadoop fs -mkdir -p /apps/hivemall
-hadoop fs -put hivemall-with-dependencies.jar /apps/hivemall
+hadoop fs -put hivemall-all-xxx.jar /apps/hivemall
 </code></pre>
 <h1 id="create-permanent-functions">Create permanent functions</h1>
 <p><em>The following is an auxiliary step to define functions for hivemall 
databases, not for the default database.</em></p>
 <pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span 
class="hljs-keyword">DATABASE</span> <span class="hljs-keyword">IF</span> <span 
class="hljs-keyword">NOT</span> <span class="hljs-keyword">EXISTS</span> 
hivemall;
 <span class="hljs-keyword">USE</span> hivemall;
 </code></pre>
-<p>Then, create permanent functions using <a 
href="https://github.com/myui/hivemall/blob/master/resources/ddl/define-all-as-permanent.hive";
 target="_blank">define-all-as-permanent.hive</a>, a DDL script to define 
permanent UDFs.</p>
-<pre><code class="lang-sql"><span class="hljs-keyword">set</span> 
hivevar:hivemall_jar=hdfs:///apps/hivemall/hivemall-<span 
class="hljs-keyword">with</span>-dependencies.jar;
+<p>Then, create permanent functions using <a 
href="https://github.com/apache/incubator-hivemall/blob/master/resources/ddl/define-all-as-permanent.hive";
 target="_blank">define-all-as-permanent.hive</a>, a DDL script to define 
permanent UDFs.</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">set</span> 
hivevar:hivemall_jar=hdfs:///apps/hivemall/hivemall-all-xxx.jar.jar;
 
 source /tmp/define-all-as-permanent.hive;
 </code></pre>
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index 0288ac3..09c3c64 100644
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/misc/approx.html
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diff --git a/userguide/misc/approx.html b/userguide/misc/approx.html
index c5fefd9..04ee48a 100644
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@@ -2227,7 +2227,7 @@
 
 </div><!-- tocstop -->
 <h1 id="approximate-counting-using-hyperloglog">Approximate Counting using 
HyperLogLog</h1>
-<p><code>count(distinct value)</code> can often cause memory exhausted errors 
where input data and the cardinarity of value are large.</p>
+<p><code>count(distinct value)</code> can often cause memory exhausted errors 
where input data and the cardinality of value are large.</p>
 <p><a href="https://en.wikipedia.org/wiki/HyperLogLog"; 
target="_blank">HyperLogLog</a> is an efficient algorithm for approximating the 
number of distinct elements in a <a 
href="https://en.wikipedia.org/wiki/Multiset"; target="_blank">multiset</a>. 
 Hivemall implements <a 
href="https://en.wikipedia.org/wiki/HyperLogLog#HLL.2B.2B"; 
target="_blank">HyperLogLog++</a> in <code>approx_count_distinct</code>.</p>
 <h2 id="usage">Usage</h2>
@@ -2359,7 +2359,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/misc/prediction.html
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/misc/tokenizer.html
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diff --git a/userguide/misc/tokenizer.html b/userguide/misc/tokenizer.html
index 31109df..509a82e 100644
--- a/userguide/misc/tokenizer.html
+++ b/userguide/misc/tokenizer.html
@@ -2266,7 +2266,7 @@
 <blockquote>
 
<p>[&quot;&#x65E5;&#x672C;&quot;,&quot;&#x7D4C;&#x6E08;&quot;,&quot;&#x65B0;&#x805E;&quot;,&quot;&#x95A2;&#x897F;&quot;,&quot;&#x56FD;&#x969B;&quot;,&quot;&#x7A7A;&#x6E2F;&quot;]</p>
 </blockquote>
-<p>Note that you can pass <code>null</code> to each of the third and fourth 
argument to explicitly use Kuromoji&apos;s default stop words and stop tags. 
</p>
+<p>Note that you can pass <code>null</code> to each of the third and fourth 
argument to explicitly use Kuromoji&apos;s <a 
href="https://github.com/apache/lucene-solr/blob/master/lucene/analysis/kuromoji/src/resources/org/apache/lucene/analysis/ja/stopwords.txt";
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 target="_blank">stop tags</a>.</p>
 <p>If you have a large custom dictionary as an external file, 
<code>userDict</code> can also be <code>const string userDictURL</code> which 
indicates URL of the external file on somewhere like Amazon S3:</p>
 <pre><code class="lang-sql"><span class="hljs-keyword">select</span> 
tokenize_ja(<span 
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class="hljs-literal">null</span>, <span class="hljs-literal">null</span>,
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@@ -2372,7 +2372,7 @@ Apache Hivemall is an effort undergoing incubation at The 
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diff --git a/userguide/misc/topk.html b/userguide/misc/topk.html
index 6db27fa..1726e9e 100644
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diff --git a/userguide/multiclass/iris.html b/userguide/multiclass/iris.html
index 49b110e..3032a6d 100644
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diff --git a/userguide/multiclass/iris_dataset.html 
b/userguide/multiclass/iris_dataset.html
index bf4ad42..411f677 100644
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@@ -2416,7 +2416,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
         
     
         
-        <script 
src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js";></script>
+        <script 
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/9d62b788/userguide/multiclass/iris_randomforest.html
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diff --git a/userguide/multiclass/iris_randomforest.html 
b/userguide/multiclass/iris_randomforest.html
index 0745306..151a218 100644
--- a/userguide/multiclass/iris_randomforest.html
+++ b/userguide/multiclass/iris_randomforest.html
@@ -2299,15 +2299,17 @@ $ sed &apos;/^$/d&apos; iris.data | hadoop fs -put - 
/dataset/iris/raw/iris.data
   <span class="hljs-keyword">STORED</span> <span 
class="hljs-keyword">AS</span> SEQUENCEFILE 
 <span class="hljs-keyword">AS</span>
 <span class="hljs-keyword">select</span> 
-  train_randomforest_classifier(features, label) 
-  <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 and before</span>
+  train_randomforest_classifier(features, label)
+  <span class="hljs-comment">-- v0.5.0 and later</span>
+  <span class="hljs-comment">-- train_randomforest_classifier(features, label) 
as (model_id, model_weight, model, var_importance, oob_errors, oob_tests)</span>
+  <span class="hljs-comment">-- v0.4.1-alpha.2 and before</span>
   <span class="hljs-comment">-- train_randomforest_classifier(features, label) 
as (pred_model, var_importance, oob_errors, oob_tests)</span>
-  <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+  <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span>
   <span class="hljs-comment">-- train_randomforest_classifier(features, label) 
as (model_id, model_type, pred_model, var_importance, oob_errors, 
oob_tests)</span>
 <span class="hljs-keyword">from</span>
   training;
 </code></pre>
-<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p>The default <code>TEXTFILE</code> 
should not be used for model table when using Javascript output through 
<code>-output javascript</code> option.</p></div></div>
+<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p>Note that model storage format is 
different between versions as seen the above.</p></div></div>
 <pre><code class="lang-sql">hive&gt; desc extended model;
 </code></pre>
 <table>
@@ -2384,7 +2386,7 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
                                      features [default:
                                      ceil(sqrt(x[0].length))].
                                      int(num_variables * x[0].length) is
-                                     considered if num_variable is (0,1
+                                     considered if num_variable is (0,1]
 </code></pre>
 <div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p><code>-num_trees</code> controls 
the number of trees for each task, not the total number of 
trees.</p></div></div>
 <h3 id="parallelize-training">Parallelize Training</h3>
@@ -2416,7 +2418,7 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
 
<p>[6.837674865013268,4.1317115752776665,24.331571871930226,25.677497925673062] 
   0.056666666666666664</p>
 </blockquote>
 <h1 id="prediction">Prediction</h1>
-<pre><code class="lang-sql"><span class="hljs-keyword">set</span> 
hivevar:classification=<span class="hljs-literal">true</span>;
+<pre><code class="lang-sql"><span class="hljs-comment">-- set 
hivevar:classification=true;</span>
 <span class="hljs-keyword">set</span> hive.<span 
class="hljs-keyword">auto</span>.<span 
class="hljs-keyword">convert</span>.<span 
class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>;
 <span class="hljs-keyword">set</span> hive.mapjoin.optimized.hashtable=<span 
class="hljs-literal">false</span>;
 
@@ -2425,18 +2427,19 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
 <span class="hljs-keyword">SELECT</span>
   <span class="hljs-keyword">rowid</span>,
   <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span>
-  <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+  <span class="hljs-comment">-- v0.5.0 or later</span>
   rf_ensemble(predicted.<span class="hljs-keyword">value</span>, 
predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> 
predicted
   <span class="hljs-comment">-- rf_ensemble(predicted.value, 
predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., 
model_weight)</span>
 <span class="hljs-keyword">FROM</span> (
   <span class="hljs-keyword">SELECT</span>
     <span class="hljs-keyword">rowid</span>, 
-    <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+    <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span>
     <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted</span>
-    <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+    <span class="hljs-comment">-- v0.5.0 or later</span>
     p.model_weight,
-    tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, ${classification}) <span class="hljs-keyword">as</span> predicted
-    <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5-rc.1 or later</span>
+    tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, <span class="hljs-string">&quot;-classification&quot;</span>) <span 
class="hljs-keyword">as</span> predicted
+    <span class="hljs-comment">-- tree_predict(p.model_id, p.model, 
t.features, ${classification}) as predicted</span>
+    <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted -- to use the old 
model in v0.5.0 or later</span>
   <span class="hljs-keyword">FROM</span>
     <span class="hljs-keyword">model</span> p
     <span class="hljs-keyword">LEFT</span> <span 
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> <span 
class="hljs-comment">-- CROSS JOIN</span>
@@ -2446,7 +2449,7 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
   <span class="hljs-keyword">rowid</span>
 ;
 </code></pre>
-<div class="panel panel-warning"><div class="panel-heading"><h3 
class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> 
Caution</h3></div><div class="panel-body"><p><code>tree_predict_v1</code> is 
for the backward compatibility for using prediction models built before 
<code>v0.5-rc.1</code> on <code>v0.5-rc.1</code> or later.</p></div></div>
+<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p>Left outer join without a join condition (i.e., 
<code>model p LEFT OUTER JOIN training t</code>) is a trick to fix the left 
table for cross join.</p><h4 
id="caution">Caution</h4><p><code>tree_predict_v1</code> is for the backward 
compatibility for using prediction models built before <code>v0.5</code> on 
<code>v0.5</code> or later.</p></div></div>
 <h3 id="parallelize-prediction">Parallelize Prediction</h3>
 <p>The following query runs predictions in N-parallel. It would reduce elapsed 
time for prediction almost by N.</p>
 <pre><code class="lang-sql"><span class="hljs-keyword">SET</span> 
hivevar:classification=<span class="hljs-literal">true</span>;
@@ -2454,28 +2457,30 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
 <span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span 
class="hljs-literal">false</span>;
 <span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span 
class="hljs-number">8</span>;
 
+<span class="hljs-keyword">drop</span> <span class="hljs-keyword">table</span> 
predicted;
 <span class="hljs-keyword">create</span> <span 
class="hljs-keyword">table</span> predicted
 <span class="hljs-keyword">as</span>
 <span class="hljs-keyword">SELECT</span>
   <span class="hljs-keyword">rowid</span>,
   <span class="hljs-comment">-- rf_ensemble(predicted) as predicted</span>
-  <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+  <span class="hljs-comment">-- v0.5.0 or later</span>
   rf_ensemble(predicted.<span class="hljs-keyword">value</span>, 
predicted.posteriori, model_weight) <span class="hljs-keyword">as</span> 
predicted
   <span class="hljs-comment">-- rf_ensemble(predicted.value, 
predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., 
model_weight)</span>
 <span class="hljs-keyword">FROM</span> (
   <span class="hljs-keyword">SELECT</span>
     t.<span class="hljs-keyword">rowid</span>, 
-    <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+    <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span>
     <span class="hljs-comment">-- tree_predict(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted</span>
-    <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+    <span class="hljs-comment">-- v0.5.0 or later</span>
     p.model_weight,
-    tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, ${classification}) <span class="hljs-keyword">as</span> predicted
-    <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted as predicted -- to 
use the old model in v0.5-rc.1 or later</span>
+    tree_predict(p.model_id, p.<span class="hljs-keyword">model</span>, 
t.features, <span class="hljs-string">&quot;-classification&quot;</span>) <span 
class="hljs-keyword">as</span> predicted
+    <span class="hljs-comment">-- tree_predict(p.model_id, p.model, 
t.features, ${classification}) as predicted</span>
+    <span class="hljs-comment">-- tree_predict_v1(p.model_id, p.model_type, 
p.pred_model, t.features, ${classification}) as predicted as predicted -- to 
use the old model in v0.5.0 or later</span>
   <span class="hljs-keyword">FROM</span> (
     <span class="hljs-keyword">SELECT</span> 
-      <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+      <span class="hljs-comment">-- from v0.4.1 to v0.4.2-rc4</span>
       <span class="hljs-comment">-- model_id, model_type, pred_model</span>
-      <span class="hljs-comment">-- hivemall v0.5-rc.1 or later</span>
+      <span class="hljs-comment">-- v0.5.0 or later</span>
       model_id, model_weight, <span class="hljs-keyword">model</span>
     <span class="hljs-keyword">FROM</span> <span 
class="hljs-keyword">model</span>
     <span class="hljs-keyword">DISTRIBUTE</span> <span 
class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span 
class="hljs-number">1</span>)
@@ -2494,13 +2499,13 @@ usage: 
train_randomforest_classifier(array&lt;double|string&gt; features, int
 <pre><code class="lang-sql"><span class="hljs-keyword">set</span> 
hivevar:total_cnt=<span class="hljs-number">150</span>;
 
 WITH t1 as (
-<span class="hljs-keyword">SELECT</span>
-  t.<span class="hljs-keyword">rowid</span>,
-  t.label <span class="hljs-keyword">as</span> actual,
-  p.predicted.label <span class="hljs-keyword">as</span> predicted
-<span class="hljs-keyword">FROM</span>
-  predicted p
-  <span class="hljs-keyword">LEFT</span> <span 
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> 
training t <span class="hljs-keyword">ON</span> (t.<span 
class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>)
+  <span class="hljs-keyword">SELECT</span>
+    t.<span class="hljs-keyword">rowid</span>,
+    t.label <span class="hljs-keyword">as</span> actual,
+    p.predicted.label <span class="hljs-keyword">as</span> predicted
+  <span class="hljs-keyword">FROM</span>
+    predicted p
+    <span class="hljs-keyword">LEFT</span> <span 
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> 
training t <span class="hljs-keyword">ON</span> (t.<span 
class="hljs-keyword">rowid</span> = p.<span class="hljs-keyword">rowid</span>)
 )
 <span class="hljs-keyword">SELECT</span>
   <span class="hljs-keyword">count</span>(<span class="hljs-number">1</span>) 
/ ${total_cnt}
@@ -2514,7 +2519,7 @@ WITH t1 as (
 <p>0.98</p>
 </blockquote>
 <h1 id="graphvis-export">Graphvis export</h1>
-<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p><code>tree_export</code> feature is supported from 
Hivemall v0.5-rc.1 or later.
+<div class="panel panel-primary"><div class="panel-heading"><h3 
class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div 
class="panel-body"><p><code>tree_export</code> feature is supported from 
Hivemall v0.5.0 or later.
 Better to limit tree depth on training by <code>-depth</code> option to plot a 
Decision Tree.</p></div></div>
 <p>Hivemall provide <code>tree_export</code> to export a decision tree into <a 
href="http://www.graphviz.org/"; target="_blank">Graphviz</a> or human-readable 
Javascript format. You can find the usage by issuing the following query:</p>
 <pre><code>&gt; select tree_export(&quot;&quot;,&quot;-help&quot;);
@@ -2626,7 +2631,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
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@@ -2656,7 +2661,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
         
     
         
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src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/3.1.1/anchor.min.js";></script>
+        <script 
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