Fix all references to templates.prediction.io

RELEASE.md

@@ -99,9 +99,9 @@ March 4th, 2015
 
 Release Notes
 
-- [E-Commerce Recommendation Template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-ecommercerecommendation) which includes 1) out-of-stock items support 2) new user recommendation 3) unseen items only
-- [Complementary Purchase Template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-complementarypurchase) for shopping cart recommendation
-- [Lead Scoring Template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-leadscoring) predicts the probability of an user will convert in the current session
+- [E-Commerce Recommendation Template](http:https://predictionio.incubator.apache.org/gallery/template-gallery#recommender-systems) which includes 1) out-of-stock items support 2) new user recommendation 3) unseen items only
+- [Complementary Purchase Template](http:https://predictionio.incubator.apache.org/gallery/template-gallery#unsupervised-learning) for shopping cart recommendation
+- [Lead Scoring Template](http:https://predictionio.incubator.apache.org/gallery/template-gallery#classification) predicts the probability of an user will convert in the current session
 - `pio-start-all`, `pio-stop-all` commands to start and stop all PredictionIO related services
 
 ###v0.8.6

docs/manual/source/algorithm/index.html.md

@@ -6,6 +6,6 @@ An engine can virtually call any algorithm in the Algorithm class. Apache
 PredictionIO (incubating) currently offers native support to [Spark
 MLlib](http:https://spark.apache.org/docs/latest/mllib-guide.html) machine learning
 library. It is being used by some of the engine templates in the [template
-gallery](http:https://templates.prediction.io/).
+gallery](/gallery/template-gallery).
 
 More library support will be added soon.

docs/manual/source/community/projects.html.md

@@ -18,7 +18,7 @@ Community-powered SDKs are [separately listed](/sdk/).
 
 URL: https://github.com/richdynamix/personalised-products
 
-Personalised Products is a Magento 2 module that will serve realtime predicted suggestions for product upsells on the product page and complimentary suggestions for cross sells on the basket page. All powered by PredictionIO using the [Similar Product](https://templates.prediction.io/PredictionIO/template-scala-parallel-similarproduct "Similar Product") engine and the [Complementary Purchase](https://templates.prediction.io/PredictionIO/template-scala-parallel-complementarypurchase "Complementary Purchase") engine.
+Personalised Products is a Magento 2 module that will serve realtime predicted suggestions for product upsells on the product page and complimentary suggestions for cross sells on the basket page. All powered by PredictionIO using the [Similar Product](/gallery/template-gallery/#recommender-systems "Similar Product") engine and the [Complementary Purchase](/gallery/template-gallery/#unsupervised-learning "Complementary Purchase") engine.
 
 - Core Author: Steven Richardson
 

docs/manual/source/demo/textclassification.html.md.erb

@@ -8,7 +8,7 @@ title: Text Classification Engine Tutorial
 
 In the real world, there are many applications that collect text as data. For example, spam detectors take email and header content to automatically determine what is or is not spam; applications can gague the general sentiment in a geographical area by analyzing Twitter data; and news articles can be automatically categorized based solely on the text content.There are a wide array of machine learning models you can use to create, or train, a predictive model to assign an incoming article, or query, to an existing category. Before you can use these techniques you must first transform the text data (in this case the set of news articles) into numeric vectors, or feature vectors, that can be used to train your model.
 
-The purpose of this tutorial is to illustrate how you can go about doing this using PredictionIO's platform. The advantages of using this platform include: a dynamic engine that responds to queries in real-time; [separation of concerns](http:https://en.wikipedia.org/wiki/Separation_of_concerns), which offers code re-use and maintainability, and distributed computing capabilities for scalability and efficiency. Moreover, it is easy to incorporate non-trivial data modeling tasks into the DASE architecture allowing Data Scientists to focus on tasks related to modeling. This tutorial will exemplify some of these ideas by guiding you through PredictionIO's [text classification template(http:https://templates.prediction.io/PredictionIO/template-scala-parallel-textclassification/).
+The purpose of this tutorial is to illustrate how you can go about doing this using PredictionIO's platform. The advantages of using this platform include: a dynamic engine that responds to queries in real-time; [separation of concerns](http:https://en.wikipedia.org/wiki/Separation_of_concerns), which offers code re-use and maintainability, and distributed computing capabilities for scalability and efficiency. Moreover, it is easy to incorporate non-trivial data modeling tasks into the DASE architecture allowing Data Scientists to focus on tasks related to modeling. This tutorial will exemplify some of these ideas by guiding you through PredictionIO's [text classification template](/gallery/template-gallery/#natural-language-processing).
 
 
 

docs/manual/source/index.html.md.erb

@@ -10,7 +10,7 @@ built on top of state-of-the-art open source stack for developers and data
 scientists create predictive engines for any machine learning task. It lets you:
 
 * quickly build and deploy an engine as a web service on production with
- [customizable templates](http:https://templates.prediction.io);
+ [customizable templates](/gallery/template-gallery);
 * respond to dynamic queries in **real-time** once deployed as a web service;
 * evaluate and tune multiple engine variants systematically;
 * unify data from multiple platforms in batch or in real-time for comprehensive

docs/manual/source/machinelearning/dimensionalityreduction.html.md

@@ -2,7 +2,7 @@
 title: Dimensionality Reduction With PredictionIO
 ---
 
-The purpose of this guide is to teach developers how to incorporate "dimensionality reduction" into a PredictionIO engine [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA) on the [MNIST digit recognition dataset](https://www.kaggle.com/c/digit-recognizer). To do this, you will be modifying the PredictionIO [classification engine template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-classification). This guide will demonstrate how to import the specific data set in batch, and also how to change the engine components in order to incorporate the new sample data and implement PCA.
+The purpose of this guide is to teach developers how to incorporate "dimensionality reduction" into a PredictionIO engine [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA) on the [MNIST digit recognition dataset](https://www.kaggle.com/c/digit-recognizer). To do this, you will be modifying the PredictionIO [classification engine template](/gallery/template-gallery/#classification). This guide will demonstrate how to import the specific data set in batch, and also how to change the engine components in order to incorporate the new sample data and implement PCA.
 
 In machine learning, specifically in [supervised learning](http:https://en.wikipedia.org/wiki/Supervised_learning), the general problem at hand is to predict a numeric outcome \\(y\\) from a numeric vector \\(\bf{x}\\). The different components of \\(\bf{x}\\) are called **features**, and usually represent observed values such as a hospital patient's age, weight, height, sex, etc. There are subtle issues that begin to arise as the number of features contained in each feature vector increases. We briefly list some of the issues that arise as the number of features grows in size:
 
@@ -27,7 +27,7 @@ This guide will also help to solidify the concept of taking an engine template a
 
 As a guiding example, a base data set, the [MNIST digit recognition dataset](https://www.kaggle.com/c/digit-recognizer/data), is used. This is a perfect data set for dimensionality reduction, for, in this data set, the features that will be used for learning are pixel entries in a \\(28 \times 28\\) pixel image. There is really no direct interpretation of any one feature, so that you do not lose anything in applying a transformation that will treat the features as [linear combinations](https://en.wikipedia.org/wiki/Linear_combination) of some set "convenient" vectors. 
 
-Now, we first pull the [classification engine template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-classification) via the following bash line
+Now, we first pull the [classification engine template](/gallery/template-gallery/#classification) via the following bash line
 
 ```
 pio template get PredictionIO/template-scala-parallel-classification <Your new engine directory>
@@ -357,7 +357,7 @@ The default algorithm used in the classification template is Naive Bayes. Now, t
 
 The implementation details are not discussed in this guide, as the point of this guide is to show how to incorporate **dimensionality reduction** techniques by incorporating PCA. The latter paragraph is mentioned in order to emphasize the fact that applying the PCA transformation (or possibly other dimensionality reduction techniques) will largely remove the interpretability of features, so that model assumptions relying on such interpretations may no longer be satisfied. This is just something to keep in mind.
 
-The following code is taken from the [text classification engine template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-textclassification) and adapted to match the project definitions. Copy and paste into the new scala script, `LRAlgorithm.scala`: 
+The following code is taken from the [text classification engine template](/gallery/template-gallery/#classification) and adapted to match the project definitions. Copy and paste into the new scala script, `LRAlgorithm.scala`: 
 
 ```scala
 package FeatureReduction

docs/manual/source/partials/_header.html.slim

@@ -9,6 +9,6 @@ header
  = image_tag 'logos/logo.png', alt: 'PredictionIO', id: 'logo'
  #menu-wrapper
  #pill-wrapper
- a.pill.left> href="//templates.prediction.io/" TEMPLATES
+ a.pill.left> href="/gallery/template-gallery" TEMPLATES
  a.pill.right href="//github.com/apache/incubator-predictionio/" OPEN SOURCE
  = image_tag 'icons/search-glass.png', class: 'mobile-search-bar-toggler hidden-md hidden-lg'

docs/manual/source/partials/nav/_header.html.slim

@@ -1,6 +1,6 @@
 nav#nav-header
  ul
  li = link_to 'Docs', '/'
- li = link_to 'Engine Templates', 'http:https://templates.prediction.io/'
+ li = link_to 'Engine Templates', '/gallery/template-gallery'
  li = link_to 'Community', '/community/'
  li = link_to 'Blog', 'http:https://blog.prediction.io/'

docs/manual/source/resources/intellij.html.md.erb

@@ -187,7 +187,7 @@ response is created or run the query with no breakpoints.
 
 ## Loading a Template Into Intellij IDEA
 
-To customize an existing [template](http:https://templates.prediction.io) using Intellij IDEA, first pull it from the template gallery:
+To customize an existing [template](/gallery/template-gallery) using Intellij IDEA, first pull it from the template gallery:
 
 ```bash
 $ pio template get <Template Source> <New Engine Directory>

docs/manual/source/start/download.html.md

@@ -2,16 +2,16 @@
 title: Downloading an Engine Template
 ---
 
-The first step to create a new engine is to browse [PredictionIO template gallery](http:https://templates.prediction.io/) where you could find Engine Templates for all kinds of machine learning tasks. Choose an engine template that matches your use case the best. You can further customize the engine later if you like.
+The first step to create a new engine is to browse [PredictionIO template gallery](/gallery/template-gallery) where you could find Engine Templates for all kinds of machine learning tasks. Choose an engine template that matches your use case the best. You can further customize the engine later if you like.
 
 To download a template, run:
 
 ```
 $ pio template get <template-repo-path> <your-new-engine-directory>
 ```
 
-You will find the `<template-repo-path>` of the chosen the template in the [PredictionIO template gallery](http:https://templates.prediction.io/).
+You will find the `<template-repo-path>` of the chosen the template in the [PredictionIO template gallery](/gallery/template-gallery).
 
 NOTE: `pio` is a command available in the `bin/` of the installed PredictionIO directory. You may add the installed Prediction's bin/ directory path to you environment PATH.
 
-Please browse the [PredictionIO template gallery](http:https://templates.prediction.io/) to choose an engine template.
+Please browse the [PredictionIO template gallery](/gallery/template-gallery) to choose an engine template.

docs/manual/source/start/index.html.md

@@ -40,7 +40,7 @@ Engine is responsible for making prediction.
 It contains one or more machine learning algorithms. An engine reads training data and build predictive model(s).
 It is then deployed as a web service. A deployed engine responds to prediction queries from your application through REST API in real-time.
 
-PredictionIO's [template gallery](http:https://templates.prediction.io/) offers Engine Templates for all kinds of machine learning tasks.
+PredictionIO's [template gallery](/gallery/template-gallery) offers Engine Templates for all kinds of machine learning tasks.
 You can easily create one or more engines from these templates .
 
 The components of a template, namely **Data Source**, **Data Preparator**, **Algorithm(s)**, and **Serving**, are all [customizable](/start/customize/) for your specific needs.

docs/manual/source/templates/recommendation/blacklist-items.html.md

@@ -4,9 +4,9 @@ title: Filter Recommended Items by Blacklist in Query (Recommendation)
 
 Let's say you want to supply a backList for each query to exclude some items from recommendation (For example, in the browsing session, the user just added some items to shopping cart, or you have a list of items you want to filter out, you may want to supply blackList in Query). This how-to will demonstrate how you can do it.
 
-Note that you may also use [E-Commerce Recommendation Template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-ecommercerecommendation) which supports this feature by default.
+Note that you may also use [E-Commerce Recommendation Template](/gallery/template-gallery#recommender-systems) which supports this feature by default.
 
-If you are looking for filtering out items based on the specific user-to-item events logged by EventServer (eg. filter all items which the user has "buy" events on), you can use the [E-Commerce Recommendation Template](http:https://templates.prediction.io/PredictionIO/template-scala-parallel-ecommercerecommendation). Please refer to the algorithm parameters "unseenOnly" and "seenEvents" of the E-Commerce Recommenation Template.
+If you are looking for filtering out items based on the specific user-to-item events logged by EventServer (eg. filter all items which the user has "buy" events on), you can use the [E-Commerce Recommendation Template](/gallery/template-gallery#recommender-systems). Please refer to the algorithm parameters "unseenOnly" and "seenEvents" of the E-Commerce Recommenation Template.
 
 ## Add Query Parameter