Added some docs and refactored some

docs/source/index.rst

@@ -6,8 +6,12 @@
 Welcome to MultiviewPlatform's documentation!
 =============================================
 
+This package is used as an easy-to-use platform to estimate different mono- and multi-view classifiers' performance on a multiview dataset.
+
+The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).
+
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 3
    :caption: Contents:
 
    api

docs/source/monomulti/monoview_classifier.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monoview classifier framework"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File addition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* In the `MonoviewClassifiers` package, you need to add a python module called after your monoview classifier (let's call it MOC for **MO**noview **C**lassifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `MOC.py` file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this file, you need to add several functions forthe platform to be able to use your classifier, they are alllisted below :  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `canProbas`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function is just used to knowif the classifier can predict a probability for each label instead of just predicting the a label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def canProbas():\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `fit`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function returns a fitted sklearn classifier object"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2.0
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file

docs/source/monomultidoc.rst

@@ -6,6 +6,7 @@ Mono and mutliview classification
    :caption: Contents:
 
    monomulti/metrics
+   monomulti/monoview_classifier
+   monomulti/multiview_classifier
    monomulti/exec_classif
    monomulti/result_analysis
-   monomulti/multiview_classifier
\ No newline at end of file