http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/ft_engineering/tfidf.html
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diff --git a/userguide/ft_engineering/tfidf.html 
b/userguide/ft_engineering/tfidf.html
index 13f0464..aacb189 100644
--- a/userguide/ft_engineering/tfidf.html
+++ b/userguide/ft_engineering/tfidf.html
@@ -125,7 +125,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2281,7 +2281,7 @@
   specific language governing permissions and limitations
   under the License.
 -->
-<p>This document explains how to compute <a 
href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf"; target="_blank">TF-IDF</a> 
with Apache Hive/Hivemall.</p>
+<p>This document explains how to compute <a 
href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf"; target="_blank">TF-IDF</a> 
with Apache Hive/Hivemall.</p>
 <p>What you need to compute TF-IDF is a table/view composing (docid, word) 
pair, 2 views, and 1 query.</p>
 <!-- toc --><div id="toc" class="toc">
 
@@ -2477,7 +2477,7 @@ 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/ce452ddb/userguide/ft_engineering/vectorization.html
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diff --git a/userguide/ft_engineering/vectorization.html 
b/userguide/ft_engineering/vectorization.html
index 3baa5b7..58dfe7d 100644
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class="custom-link"><i class="fa fa-home"></i> Home</a>
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class="custom-link"><i class="fa fa-home"></i> Home</a>
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/geospatial/latlon.html
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-<p>Refer <a href="http://wiki.openstreetmap.org/wiki/Slippy_map_tilenames"; 
target="_blank">this page</a> for detail. Zoom level is well described in <a 
href="http://wiki.openstreetmap.org/wiki/Zoom_levels"; target="_blank">this 
page</a>.</p>
+<p>Refer <a href="https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames"; 
target="_blank">this page</a> for detail. Zoom level is well described in <a 
href="https://wiki.openstreetmap.org/wiki/Zoom_levels"; target="_blank">this 
page</a>.</p>
 <h3 id="usage">Usage</h3>
 <pre><code class="lang-sql">WITH data as (
   <span class="hljs-keyword">select</span> <span 
class="hljs-number">51.51202</span> <span class="hljs-keyword">as</span> lat, 
<span class="hljs-number">0.02435</span> <span class="hljs-keyword">as</span> 
lon, <span class="hljs-number">17</span> <span class="hljs-keyword">as</span> 
zoom
@@ -2389,7 +2389,7 @@ y &amp;=
 </tbody>
 </table>
 <h1 id="distance-function">Distance function</h1>
-<p><code>haversine_distance(double lat1, double lon1, double lat2, double 
lon2, [const boolean mile=false])</code> returns <a 
href="http://www.movable-type.co.uk/scripts/latlong.html"; 
target="_blank">Haversine distance</a> between given two Geo locations.</p>
+<p><code>haversine_distance(double lat1, double lon1, double lat2, double 
lon2, [const boolean mile=false])</code> returns <a 
href="https://www.movable-type.co.uk/scripts/latlong.html"; 
target="_blank">Haversine distance</a> between given two Geo locations.</p>
 <pre><code class="lang-sql"><span class="hljs-comment">-- Tokyo (lat: 35.6833, 
lon: 139.7667)</span>
 <span class="hljs-comment">-- Osaka (lat: 34.6603, lon: 135.5232)</span>
 <span class="hljs-keyword">select</span> 
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----------------------------------------------------------------------
diff --git a/userguide/getting_started/index.html 
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index 7345eeb..76cb9aa 100644
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+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/userguide/getting_started/input-format.html
----------------------------------------------------------------------
diff --git a/userguide/getting_started/input-format.html 
b/userguide/getting_started/input-format.html
index 05290b4..93f0d91 100644
--- a/userguide/getting_started/input-format.html
+++ b/userguide/getting_started/input-format.html
@@ -125,7 +125,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2282,7 +2282,7 @@
   under the License.
 -->
 <p>This page explains the input format of training data in Hivemall. 
-Here, we use <a 
href="http://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_Form"; 
target="_blank">EBNF</a>-like notation for describing the format.</p>
+Here, we use <a 
href="https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_Form"; 
target="_blank">EBNF</a>-like notation for describing the format.</p>
 <!-- toc --><div id="toc" class="toc">
 
 <ul>
@@ -2310,12 +2310,12 @@ Here, we use <a 
href="http://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_
 
 </div><!-- tocstop -->
 <h1 id="input-format-for-classification">Input Format for Classification</h1>
-<p>The classifiers of Hivemall takes 2 (or 3) arguments: <em>features</em>, 
<em>label</em>, and <em>options</em> (a.k.a. <a 
href="http://en.wikipedia.org/wiki/Hyperparameter"; 
target="_blank">hyperparameters</a>). The first two arguments of training 
functions represents training examples. </p>
+<p>The classifiers of Hivemall takes 2 (or 3) arguments: <em>features</em>, 
<em>label</em>, and <em>options</em> (a.k.a. <a 
href="https://en.wikipedia.org/wiki/Hyperparameter"; 
target="_blank">hyperparameters</a>). The first two arguments of training 
functions represents training examples. </p>
 <p>In Statistics, <em>features</em> and <em>label</em> are called <a 
href="http://www.oswego.edu/~srp/stats/variable_types.htm"; 
target="_blank">Explanatory variable and Response Variable</a>, 
respectively.</p>
 <h1 id="features-format-for-classification-and-regression">Features format 
(for classification and regression)</h1>
 <p>The format of <em>features</em> is common between (binary and multi-class) 
classification and regression.
 Hivemall accepts <code>ARRAY&amp;lt;INT|BIGINT|TEXT&gt;</code> for the type of 
<em>features</em> column.</p>
-<p>Hivemall uses a <em>sparse</em> data format (cf. <a 
href="http://netlib.org/linalg/html_templates/node91.html"; 
target="_blank">Compressed Row Storage</a>) which is similar to <a 
href="http://stackoverflow.com/questions/12112558/read-write-data-in-libsvm-format";
 target="_blank">LIBSVM</a> and <a 
href="https://github.com/JohnLangford/vowpal_wabbit/wiki/Input-format"; 
target="_blank">Vowpal Wabbit</a>.</p>
+<p>Hivemall uses a <em>sparse</em> data format (cf. <a 
href="https://netlib.org/linalg/html_templates/node91.html"; 
target="_blank">Compressed Row Storage</a>) which is similar to <a 
href="https://stackoverflow.com/questions/12112558/read-write-data-in-libsvm-format";
 target="_blank">LIBSVM</a> and <a 
href="https://github.com/JohnLangford/vowpal_wabbit/wiki/Input-format"; 
target="_blank">Vowpal Wabbit</a>.</p>
 <p>The format of each feature in an array is as follows:</p>
 <pre><code>feature ::= &lt;index&gt;:&lt;weight&gt; or &lt;index&gt;
 </code></pre><p>Each element of <em>index</em> or <em>weight</em> then accepts 
the following format:</p>
@@ -2342,9 +2342,9 @@ Here is an instance of a features.</p>
 <p>Note that &quot;0&quot; is reserved for a Bias variable (called dummy 
variable in Statistics). </p>
 <p>The <a href="../tips/addbias.html">add_bias</a> function is Hivemall 
appends &quot;0:1.0&quot; as an element of array in <em>features</em>.</p>
 <h2 id="feature-hashing">Feature hashing</h2>
-<p>Hivemall supports <a href="http://en.wikipedia.org/wiki/Feature_hashing"; 
target="_blank">feature hashing/hashing trick</a> through <a 
href="../ft_engineering/hashing.html#mhash-function">mhash function</a>.</p>
+<p>Hivemall supports <a href="https://en.wikipedia.org/wiki/Feature_hashing"; 
target="_blank">feature hashing/hashing trick</a> through <a 
href="../ft_engineering/hashing.html#mhash-function">mhash function</a>.</p>
 <p>The mhash function takes a feature (i.e., <em>index</em>) of TEXT format 
and generates a hash number of a range from 1 to 2^24 (=16777216) by the 
default setting.</p>
-<p>Feature hashing is useful where the dimension of feature vector (i.e., the 
number of elements in <em>features</em>) is so large. Consider applying <a 
href="(../ft_engineering/hashing.html#mhash-function">mhash function</a>) when 
a prediction model does not fit in memory and OutOfMemory exception happens.</p>
+<p>Feature hashing is useful where the dimension of feature vector (i.e., the 
number of elements in <em>features</em>) is so large. Consider applying <a 
href="../ft_engineering/hashing.html#mhash-function">mhash function</a>) when a 
prediction model does not fit in memory and OutOfMemory exception happens.</p>
 <p>In general, you don&apos;t need to use mhash when the dimension of feature 
vector is less than 16777216.
 If feature <em>index</em> is very long TEXT (e.g., 
&quot;xxxxxxx-yyyyyy-weight:55.3&quot;) and uses huge memory spaces, consider 
using mhash as follows:</p>
 <pre><code class="lang-sql"><span class="hljs-comment">-- feature is v0.3.2 or 
before</span>
@@ -2525,7 +2525,7 @@ 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/ce452ddb/userguide/getting_started/installation.html
----------------------------------------------------------------------
diff --git a/userguide/getting_started/installation.html 
b/userguide/getting_started/installation.html
index eae1928..afd1a62 100644
--- a/userguide/getting_started/installation.html
+++ b/userguide/getting_started/installation.html
@@ -125,7 +125,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2366,7 +2366,7 @@ Apache Hivemall is an effort undergoing incubation at The 
Apache Software Founda
     <script>
         var gitbook = gitbook || [];
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/ce452ddb/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 97b61cb..59407f5 100644
--- a/userguide/getting_started/permanent-functions.html
+++ b/userguide/getting_started/permanent-functions.html
@@ -125,7 +125,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2369,7 +2369,7 @@ 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/ce452ddb/userguide/index.html
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diff --git a/userguide/index.html b/userguide/index.html
index 8231dd5..3c53e12 100644
--- a/userguide/index.html
+++ b/userguide/index.html
@@ -123,7 +123,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2288,7 +2288,7 @@ Apache Hivemall is a collection of machine learning 
algorithms and versatile dat
 
 <p>Apache Hivemall offers a variety of functionalities: <strong>regression, 
classification, recommendation, anomaly detection, k-nearest neighbor, and 
feature engineering</strong>. It also supports state-of-the-art machine 
learning algorithms such as Soft Confidence Weighted, Adaptive Regularization 
of Weight Vectors, Factorization Machines, and AdaDelta. </p>
 <h2 id="architecture">Architecture</h2>
-<p>Apache Hivemall is mainly designed to run on <a 
href="https://hive.apache.org/"; target="_blank">Apache Hive</a> but it also 
supports <a href="https://pig.apache.org/"; target="_blank">Apache Pig</a> and 
<a href="http://spark.apache.org/"; target="_blank">Apache Spark</a> for the 
runtime.
+<p>Apache Hivemall is mainly designed to run on <a 
href="https://hive.apache.org/"; target="_blank">Apache Hive</a> but it also 
supports <a href="https://pig.apache.org/"; target="_blank">Apache Pig</a> and 
<a href="https://spark.apache.org/"; target="_blank">Apache Spark</a> for the 
runtime.
 Thus, it can be considered as a cross platform library for machine learning; 
prediction models built by a batch query of Apache Hive can be used on Apache 
Spark/Pig, and conversely, prediction models build by Apache Spark can be used 
from Apache Hive/Pig.</p>
 <div style="text-align:center"><img src="resources/images/techstack.png" 
width="80%" height="80%"></div>
 
@@ -2347,7 +2347,7 @@ 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/ce452ddb/userguide/misc/approx.html
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diff --git a/userguide/misc/approx.html b/userguide/misc/approx.html
index 90235dd..7e75eee 100644
--- a/userguide/misc/approx.html
+++ b/userguide/misc/approx.html
@@ -125,7 +125,7 @@
     
         
         <li>
-            <a href="http://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
+            <a href="https://hivemall.incubator.apache.org/"; target="_blank" 
class="custom-link"><i class="fa fa-home"></i> Home</a>
         </li>
     
     
@@ -2356,7 +2356,7 @@ Hivemall implements <a 
href="https://en.wikipedia.org/wiki/HyperLogLog#HLL.2B.2B
 </tr>
 </tbody>
 </table>
-<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>p</code> controls expected precision and memory 
consumption tradeoff and <code>default p=15</code> generally works well. Find 
More information on <a href="https://research.google.com/pubs/pub40671.html"; 
target="_blank">this paper</a>.</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><code>p</code> controls expected precision and memory 
consumption tradeoff and <code>default p=15</code> generally works well. Find 
More information on <a href="https://ai.google/research/pubs/pub40671"; 
target="_blank">this paper</a>.</p></div></div>
 <h2 id="function-signature">Function Signature</h2>
 <p>You can find the function signature and options of 
<code>approx_count_distinct</code> is as follows:</p>
 <pre><code class="lang-sql"><span class="hljs-keyword">select</span> 
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Apache Software Founda
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