Download JAR files tagged by item with all dependencies

This plugin provides support for textlint. <h3>What&rsquo;s the textlint?</h3> <p>See <a href="https://github.com/textlint/textlint">https://github.com/textlint/textlint</a></p> <h3>Usage</h3> <h4>Install textlint and rules</h4> <p>Of course, it assumes that nodejs and npm are installed.</p> <p>e.g.</p> <pre><code>$ mkdir txtlint $ cd txtlint $ npm init $ npm install textlint --save-dev $ npm install textlint-rule-max-ten textlint-rule-spellcheck-tech-word textlint-rule-no-mix-dearu-desumasu --save-dev </code></pre> <h4>Create .textlintrc</h4> <pre><code>$ touch .textlintrc </code></pre> <pre><code class="json">{ &quot;rules&quot;: { &quot;max-ten&quot;: { &quot;max&quot;: 3 }, &quot;spellcheck-tech-word&quot;: true, &quot;no-mix-dearu-desumasu&quot;: true } } </code></pre> <p>You can also set parameters to Options (see below).</p> <h4>Set textlint and .textlintrc paths</h4> <p>Set paths to the Options (see below).</p> <p>e.g.</p> <ul> <li>textlint Path: /path/to/txtlint/node_modules/.bin/textlint (textlint.cmd in Windows)</li> <li>.textlintrc Path: /path/to/textlint/.textlintrc</li> </ul> <h4>Open Action Items window</h4> <ul> <li>Click Window > Action Items.</li> <li>Click &ldquo;Show action items for currently edited file only&rdquo; icon.</li> <li>Open your markdown or text file.</li> </ul> <h3>Options</h3> <p>Tools > Options > Editor > textlint</p> <ul> <li>textlint Path: Absolute path to textlint</li> <li>.textlintrc Path: Absolute path to .textlintrc</li> <li>Options : You can set options for the textlint command</li> <li>Enable in HTML files: To use the html plugin, you can check this</li> <li>Refresh on Save: To scan the document on save, you can check this (Checked by default)</li> <li>Show Annotations: To show annotations in the glyph gutter, you can check this (Checked by default)</li> </ul> <h3>Actions</h3> <h4>Fix</h4> <p>You have to save your file before you run this action.<br/> If there is a fixable rule&rsquo;s error, you can fix it. Right-click an item > Click <code>Fix</code>.<br/> To refresh items, your document is saved once.</p> <h4>Fix All</h4> <p>You have to save your file before you run this action.<br/> If there are fixable rule&rsquo;s errors, you can fix them. Right-click an item > Click <code>Fix All</code>.<br/> This action runs <code>textlint --fix</code> command.</p> <h4>Refresh</h4> <p>You can refresh results forcibly by the following action: Right-click your editor > Click "textlint Refresh".</p> <p>You can also set the shortcut key(Tools > Options > Keymap). </p> <h3>NOTE</h3> <ul> <li>The plugin scans only current file.</li> <li>The plugin does not refresh results automatically. Please save your file or run the refresh action.</li> <li>Use <code>UTF-8</code> as file encoding and <code>LF</code> as line endings.</li> <li>This plugin may not work properly in Windows. (Please try to check above.)</li> <li>If you cannot get expected results, just try to run the <code>textlint</code> commands once in your CLI.</li> </ul>

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.