Merge branch '0.3dev'

doc/clustering.rst

@@ -72,7 +72,7 @@ color for each digit. This will help frame what follows.
     standard_embedding = umap.UMAP(random_state=42).fit_transform(mnist.data)
     plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=mnist.target, s=0.1, cmap='Spectral');
 
-.. image:: clustering_files/clustering_6_1.png
+.. image:: images/clustering_6_1.png
 
 
 Traditional clustering
@@ -97,7 +97,7 @@ out UMAP embedded data by cluster membership.
     plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=kmeans_labels, s=0.1, cmap='Spectral');
 
 
-.. image:: clustering_files/clustering_10_1.png
+.. image:: images/clustering_10_1.png
 
 
 This is not really the result we were looking for (though it does expose
@@ -168,7 +168,7 @@ color the remaining points according to the cluster membership.
 
 
 
-.. image:: clustering_files/clustering_16_1.png
+.. image:: images/clustering_16_1.png
 
 
 This looks somewhat underwhelming. It meets HDBSCAN's approach of "not
@@ -294,7 +294,7 @@ visualization attuned parameters:
                 c=mnist.target, s=0.1, cmap='Spectral');
 
 
-.. image:: clustering_files/clustering_27_1.png
+.. image:: images/clustering_27_1.png
 
 
 As you can see we still have the general global structure, but we are
@@ -330,7 +330,7 @@ And now we can visualize the results, just as before.
                 cmap='Spectral');
 
 
-.. image:: clustering_files/clustering_31_1.png
+.. image:: images/clustering_31_1.png
 
 
 We can see that we have done a much better job of finding clusters

doc/index.rst

@@ -26,6 +26,27 @@ The details for the underlying mathematics can be found in
 McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection
 for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018
 
+You can find the software `on github <https://github.com/lmcinnes/>`_.
+
+**Installation**
+
+Conda install, via the excellent work of the conda-forge team:
+
+.. code:: bash
+
+    conda install -c conda-forge umap-learn
+
+The conda-forge packages are available for linux, OS X, and Windows 64 bit.
+
+PyPI install, presuming you have numba and sklearn and all its requirements
+(numpy and scipy) installed:
+
+.. code:: bash
+
+    pip install umap-learn
+
+
+
 .. toctree::
    :maxdepth: 2
    :caption: User Guide / Tutorial: