docs(README): remove API details

winkjs · Oct 10, 2018 · c5e3eae · c5e3eae
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diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@ Configurable [Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
 
 Classify text, analyse sentiments, recognize user intents for chatbot using **`wink-naive-bayes-text-classifier`**. It is a part of [wink](http://winkjs.org/) — a growing family of high quality packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS.
 
-It's [API](#api) offers a rich set of features:
+It's [API](http://winkjs.org/wink-naive-bayes-text-classifier/NaiveBayesTextClassifier.html) offers a rich set of features:
 
 1. Configure text preparation task such as **amplify negation**, **tokenize**, **stem**, **remove stop words**, and **propagate negation** using [wink-nlp-utils](https://www.npmjs.com/package/wink-nlp-utils) or any other package of your choice.
 2. Configure **Lidstone** or **Lapalce** additive smoothing.
@@ -18,14 +18,14 @@ It's [API](#api) offers a rich set of features:
 5. Evaluate learning to perform n-fold cross validation.
 6. Obtain comprehensive metrics including **confusion matrix**, **precision**, and **recall**.
 
-## Installation
+### Installation
 Use [npm](https://www.npmjs.com/package/wink-naive-bayes-text-classifier) to install:
 ```
 npm install wink-naive-bayes-text-classifier --save
 ```
 
 
-## Example
+### Example
 ```javascript
 
 // Load Naive Bayes Text Classifier
@@ -66,67 +66,15 @@ console.log( nbc.predict( 'I want to pay my car loan early' ) );
 
 ```
 
-## API
+### Documentation
+Check out the [Naive Bayes Text Classifier](http://winkjs.org/wink-naive-bayes-text-classifier/) API documentation to learn more.
 
-
-#### definePrepTasks( tasks )
-
-Defines the text preparation `tasks` to transform raw incoming text into an array of tokens required during `learn()`, `evaluate()` and `predict()` operations. The `tasks` should be an array of functions. The first function in this array must accept a string as input; and the last function must return an array of tokens as JavaScript Strings. Each function must accept one input argument and return a single value. `definePrepTasks` returns the count of `tasks`.
-
-As illustrated in the usage, [wink-nlp-utils](https://www.npmjs.com/package/wink-nlp-utils) offers a rich set of such functions.
-
-#### defineConfig( config )
-Defines the configuration from the `config` object. This object must define 2 properties viz. (a) `considerOnlyPresence` and `smoothingFactor`. The `considerOnlyPresence` must be a boolean — true indicates a binarized model; default value is false. The `smoothingFactor` defines the value for additive smoothing; its default value is **1**. The `defineConfig()` must be called before attempting to learn.
-
-#### learn( input, label )
-Simply learns that the `input` belongs to the `label`. If the input is a JavaScript String, then `definePrepTasks()` must be called before learning.
-
-
-#### consolidate()
-Consolidates the learning. It is a prerequisite for `evaluate()` and/or `predict()`.
-
-#### evaluate( input, label )
-
-It is used to evaluate the learning against a test data set. The `input` is used to predict the label, which is compared with the `label` to populate a confusion matrix.
-
-#### metrics()
-
-It computes a detailed metrics consisting of macro-averaged *precision*, *recall* and *f-measure* along with their label-wise values and the *confusion matrix*.
-
-#### predict( input )
-Predicts the label for the `input`. If it is unable to predict then it returns a value **`'unknown'`**.
-
-#### computeOdds( input )
-Computes the log base-2 of odds of every label for the `input`; and returns the array of `[ label, odds ]` in descending
-order of `odds`. Here is an example of the returned array:
-
-```javascript
-[
-  [ 'prepay', 6.169686751688911 ],
-  [ 'autoloan', -6.169686751688911 ]
-]
-```
-
-If it is unable to make prediction then it returns a value **`[ [ 'unknown', 0 ] ]`**.
-
-#### exportJSON()
-The learning can be exported as JSON text that may be saved in a file.
-
-#### importJSON( json )
-An existing JSON learning can be imported for prediction. It is essential to `definePrepTasks()` and `consolidate()` before attempting to predict.
-
-#### stats()
-Returns basic stats of learning in terms of count of samples under each label, total words, and the size of vocabulary.
-
-#### reset()
-It completely resets the classifier by re-initializing all the learning related variables, except the preparatory tasks. It is useful during cross fold-validation.
-
-## Need Help?
+### Need Help?
 If you spot a bug and the same has not yet been reported, raise a new [issue](https://github.com/winkjs/wink-naive-bayes-text-classifier/issues) or consider fixing it and sending a pull request.
 
 
-## Copyright & License
-**wink-naive-bayes-text-classifier** is copyright 2017 [GRAYPE Systems Private Limited](http://graype.in/).
+### Copyright & License
+**wink-naive-bayes-text-classifier** is copyright 2017-18 [GRAYPE Systems Private Limited](http://graype.in/).
 
 It is licensed under the under the terms of the GNU Affero General Public License as published by the Free
 Software Foundation, version 3 of the License.
diff --git a/docs/NaiveBayesTextClassifier.html b/docs/NaiveBayesTextClassifier.html
@@ -2078,7 +2078,7 @@ <h5>Returns:</h5>
 <br class="clear">
 
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-    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:16:37 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
+    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:30:26 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
 </footer>
 
 <script>prettyPrint();</script>

diff --git a/docs/global.html b/docs/global.html
@@ -251,7 +251,7 @@ <h5>Returns:</h5>
 <br class="clear">
 
 <footer>
-    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:16:37 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
+    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:30:26 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
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diff --git a/docs/index.html b/docs/index.html
@@ -53,7 +53,7 @@ <h2><a href="index.html">Home</a></h2><h2><a href="http://winkjs.org" target="_b
         <article><h1>wink-naive-bayes-text-classifier</h1><p>Configurable <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier">Naive Bayes</a> Classifier for text with cross-validation support</p>
 <h3><a href="https://travis-ci.org/winkjs/wink-naive-bayes-text-classifier"><img src="https://api.travis-ci.org/winkjs/wink-naive-bayes-text-classifier.svg?branch=master" alt="Build Status"></a> <a href="https://coveralls.io/github/winkjs/wink-naive-bayes-text-classifier?branch=master"><img src="https://coveralls.io/repos/github/winkjs/wink-naive-bayes-text-classifier/badge.svg?branch=master" alt="Coverage Status"></a> <a href="http://inch-ci.org/github/winkjs/wink-naive-bayes-text-classifier"><img src="http://inch-ci.org/github/winkjs/wink-naive-bayes-text-classifier.svg?branch=master" alt="Inline docs"></a> <a href="https://david-dm.org/winkjs/wink-naive-bayes-text-classifier"><img src="https://david-dm.org/winkjs/wink-naive-bayes-text-classifier/status.svg" alt="dependencies Status"></a> <a href="https://david-dm.org/winkjs/wink-naive-bayes-text-classifier?type=dev"><img src="https://david-dm.org/winkjs/wink-naive-bayes-text-classifier/dev-status.svg" alt="devDependencies Status"></a> <a href="https://gitter.im/winkjs/Lobby"><img src="https://img.shields.io/gitter/room/nwjs/nw.js.svg" alt="Gitter"></a></h3><p><img align="right" src="https://decisively.github.io/wink-logos/logo-title.png" width="100px" ></p>
 <p>Classify text, analyse sentiments, recognize user intents for chatbot using <strong><code>wink-naive-bayes-text-classifier</code></strong>. It is a part of <a href="http://winkjs.org/">wink</a> — a growing family of high quality packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS.</p>
-<p>It's <a href="#api">API</a> offers a rich set of features:</p>
+<p>It's <a href="http://winkjs.org/wink-naive-bayes-text-classifier/NaiveBayesTextClassifier.html">API</a> offers a rich set of features:</p>
 <ol>
 <li>Configure text preparation task such as <strong>amplify negation</strong>, <strong>tokenize</strong>, <strong>stem</strong>, <strong>remove stop words</strong>, and <strong>propagate negation</strong> using <a href="https://www.npmjs.com/package/wink-nlp-utils">wink-nlp-utils</a> or any other package of your choice.</li>
 <li>Configure <strong>Lidstone</strong> or <strong>Lapalce</strong> additive smoothing.</li>
@@ -62,8 +62,8 @@ <h3><a href="https://travis-ci.org/winkjs/wink-naive-bayes-text-classifier"><img
 <li>Evaluate learning to perform n-fold cross validation.</li>
 <li>Obtain comprehensive metrics including <strong>confusion matrix</strong>, <strong>precision</strong>, and <strong>recall</strong>.</li>
 </ol>
-<h2>Installation</h2><p>Use <a href="https://www.npmjs.com/package/wink-naive-bayes-text-classifier">npm</a> to install:</p>
-<pre class="prettyprint source"><code>npm install wink-naive-bayes-text-classifier --save</code></pre><h2>Example</h2><pre class="prettyprint source lang-javascript"><code>
+<h3>Installation</h3><p>Use <a href="https://www.npmjs.com/package/wink-naive-bayes-text-classifier">npm</a> to install:</p>
+<pre class="prettyprint source"><code>npm install wink-naive-bayes-text-classifier --save</code></pre><h3>Example</h3><pre class="prettyprint source lang-javascript"><code>
 // Load Naive Bayes Text Classifier
 var Classifier = require( 'wink-naive-bayes-text-classifier' );
 // Instantiate
@@ -99,26 +99,9 @@ <h2>Installation</h2><p>Use <a href="https://www.npmjs.com/package/wink-naive-ba
 // -> autoloan
 console.log( nbc.predict( 'I want to pay my car loan early' ) );
 // -> prepay
-</code></pre><h2>API</h2><h4>definePrepTasks( tasks )</h4><p>Defines the text preparation <code>tasks</code> to transform raw incoming text into an array of tokens required during <code>learn()</code>, <code>evaluate()</code> and <code>predict()</code> operations. The <code>tasks</code> should be an array of functions. The first function in this array must accept a string as input; and the last function must return an array of tokens as JavaScript Strings. Each function must accept one input argument and return a single value. <code>definePrepTasks</code> returns the count of <code>tasks</code>.</p>
-<p>As illustrated in the usage, <a href="https://www.npmjs.com/package/wink-nlp-utils">wink-nlp-utils</a> offers a rich set of such functions.</p>
-<h4>defineConfig( config )</h4><p>Defines the configuration from the <code>config</code> object. This object must define 2 properties viz. (a) <code>considerOnlyPresence</code> and <code>smoothingFactor</code>. The <code>considerOnlyPresence</code> must be a boolean — true indicates a binarized model; default value is false. The <code>smoothingFactor</code> defines the value for additive smoothing; its default value is <strong>1</strong>. The <code>defineConfig()</code> must be called before attempting to learn.</p>
-<h4>learn( input, label )</h4><p>Simply learns that the <code>input</code> belongs to the <code>label</code>. If the input is a JavaScript String, then <code>definePrepTasks()</code> must be called before learning.</p>
-<h4>consolidate()</h4><p>Consolidates the learning. It is a prerequisite for <code>evaluate()</code> and/or <code>predict()</code>.</p>
-<h4>evaluate( input, label )</h4><p>It is used to evaluate the learning against a test data set. The <code>input</code> is used to predict the label, which is compared with the <code>label</code> to populate a confusion matrix.</p>
-<h4>metrics()</h4><p>It computes a detailed metrics consisting of macro-averaged <em>precision</em>, <em>recall</em> and <em>f-measure</em> along with their label-wise values and the <em>confusion matrix</em>.</p>
-<h4>predict( input )</h4><p>Predicts the label for the <code>input</code>. If it is unable to predict then it returns a value <strong><code>'unknown'</code></strong>.</p>
-<h4>computeOdds( input )</h4><p>Computes the log base-2 of odds of every label for the <code>input</code>; and returns the array of <code>[ label, odds ]</code> in descending
-order of <code>odds</code>. Here is an example of the returned array:</p>
-<pre class="prettyprint source lang-javascript"><code>[
-  [ 'prepay', 6.169686751688911 ],
-  [ 'autoloan', -6.169686751688911 ]
-]</code></pre><p>If it is unable to make prediction then it returns a value <strong><code>[ [ 'unknown', 0 ] ]</code></strong>.</p>
-<h4>exportJSON()</h4><p>The learning can be exported as JSON text that may be saved in a file.</p>
-<h4>importJSON( json )</h4><p>An existing JSON learning can be imported for prediction. It is essential to <code>definePrepTasks()</code> and <code>consolidate()</code> before attempting to predict.</p>
-<h4>stats()</h4><p>Returns basic stats of learning in terms of count of samples under each label, total words, and the size of vocabulary.</p>
-<h4>reset()</h4><p>It completely resets the classifier by re-initializing all the learning related variables, except the preparatory tasks. It is useful during cross fold-validation.</p>
-<h2>Need Help?</h2><p>If you spot a bug and the same has not yet been reported, raise a new <a href="https://github.com/winkjs/wink-naive-bayes-text-classifier/issues">issue</a> or consider fixing it and sending a pull request.</p>
-<h2>Copyright &amp; License</h2><p><strong>wink-naive-bayes-text-classifier</strong> is copyright 2017 <a href="http://graype.in/">GRAYPE Systems Private Limited</a>.</p>
+</code></pre><h3>Documentation</h3><p>Check out the <a href="http://winkjs.org/wink-naive-bayes-text-classifier/">Naive Bayes Text Classifier</a> API documentation to learn more.</p>
+<h3>Need Help?</h3><p>If you spot a bug and the same has not yet been reported, raise a new <a href="https://github.com/winkjs/wink-naive-bayes-text-classifier/issues">issue</a> or consider fixing it and sending a pull request.</p>
+<h3>Copyright &amp; License</h3><p><strong>wink-naive-bayes-text-classifier</strong> is copyright 2017-18 <a href="http://graype.in/">GRAYPE Systems Private Limited</a>.</p>
 <p>It is licensed under the under the terms of the GNU Affero General Public License as published by the Free
 Software Foundation, version 3 of the License.</p></article>
     </section>
@@ -135,7 +118,7 @@ <h2>Copyright &amp; License</h2><p><strong>wink-naive-bayes-text-classifier</str
 <br class="clear">
 
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+    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:30:26 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
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diff --git a/docs/wink-naive-bayes-text-classifier.js.html b/docs/wink-naive-bayes-text-classifier.js.html
@@ -870,7 +870,7 @@ <h1 class="page-title">wink-naive-bayes-text-classifier.js</h1>
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+    Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Wed Oct 10 2018 17:30:26 GMT+0530 (IST) using the <a href="https://github.com/clenemt/docdash">docdash</a> theme.
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