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tommyod · Apr 1, 2018 · d7330ea · d7330ea
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diff --git a/docs/source/_static/img/create_images.ipynb b/docs/source/_static/img/create_images.ipynb
diff --git a/docs/source/_static/img/logo.png b/docs/source/_static/img/logo.png
diff --git a/docs/source/_static/img/minimal_working_example.pdf b/docs/source/_static/img/minimal_working_example.pdf
diff --git a/docs/source/_static/img/minimal_working_example.png b/docs/source/_static/img/minimal_working_example.png
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -91,6 +91,7 @@
 # documentation.
 #
 html_theme_options = {
+    # 'logo': 'logo.png',
     # 'logo': 'logo.png',
     'github_user': 'tommyod',
     'github_repo': 'KDEpy',
@@ -104,7 +105,8 @@
     'font_size': '15px',
     'head_font_family': '-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,\
         "Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji"\
-        ,"Segoe UI Symbol"'
+        ,"Segoe UI Symbol"',
+    'page_width': '940px'
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -6,60 +6,59 @@
 KDEpy
 =====
 
-Example
--------
+KDEpy implements univariate kernel density estimators.
 
-The package works roughly like this.
-Since Pythagoras, we know that :math:`a^2 + b^2 = c^2`.
+Highlights
+----------
 
-.. code-block:: python
-   :linenos:
+KDEpy is a Python library for kernel density estimation.
 
-   from KDEpy import KDE
-   kde = KDE()
+Currently, the following features are implemented:
 
+* **Many kernels**: There are 10 kernel functions implemented.
+* **Weighted KDE**: The user may weight the data.
+* **Weighted KDE**: The user may weight the data.
 
-.. code-block:: ipython
 
-   >>> print('test')
 
+Minimal working example
+-----------------------
 
-.. code-block:: python
-   :linenos:
+Here's a minimal working example::
 
-   from KDEpy import KDE
-   kde = KDE()
+    >>> from KDEpy import KDE
+    >>> import numpy as npmake 
+    >>> from scipy.stats import norm
+    >>> data = norm(loc=0, scale=1).rvs(100)
+    >>> x = np.linspace(-3, 3)
+    >>> y = KDE(kernel='gaussian', bw=0.5).fit(data).evaluate(x)
 
+.. image:: _static/img/minimal_working_example.png
+   :width: 400 px
+   :target: #
 
+It's really that simple.
 
-Here's some code::
-
-   >>> print('test')
-   test
-   >>> 2 + 2
-   4
-
-
-Doctest example:
-
-.. doctest::
-
-   >>> 2 + 2
-   4
 
+Table of contents
+-----------------
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
 
    intro_kde
    notebook.ipynb
 
 
+Contribute
+----------
+
+You are very welcome to contribute.
+To do so, please go to GitHub.
 
 
 Indices and tables
-==================
+------------------
 
 * :ref:`genindex`
 * :ref:`modindex`

diff --git a/docs/source/intro_kde.rst b/docs/source/intro_kde.rst
@@ -10,14 +10,7 @@ Testing
 
 :class:`KDE` will take in its ``fit`` method arrays X, y
 and will store the coefficients :math:`w` of the linear model in its
-``coef_`` member::
-
-    >>> from sklearn import linear_model
-    >>> reg = linear_model.LinearRegression()
-    >>> reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
-    LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
-    >>> reg.coef_
-    array([ 0.5,  0.5])
+``coef_`` member.
 
 Histograms
 ----------