appveyor.yml

README.rst

@@ -125,30 +125,26 @@ https://genieclust.gagolewski.com/.
 Installation
 ------------
 
-   *This package is in beta-stage (development and testing is currently
-   Linux-only).*
 
 Python Version
 ~~~~~~~~~~~~~~
 
 PyPI
 ^^^^
 
-**TODO** To install via ``pip`` (the current version is a little
-outdated, see `PyPI <https://pypi.org/project/genieclust/>`__):
+To install via ``pip`` (see `PyPI <https://pypi.org/project/genieclust/>`_):
 
 .. code:: bash
 
-   pip3 install genieclust --user # or sudo pip3 install genieclust
+   pip3 install genieclust
 
-**TODO**: Windows builds
-
-**TODO**: OS X builds
 
 The package requires Python 3.6+ together with ``cython`` as well as
 ``numpy``, ``scipy``, ``matplotlib``, and ``sklearn``. Optional
 dependencies: ``mlpack`` and ``nmslib``.
 
+
+
 Development Version
 ^^^^^^^^^^^^^^^^^^^
 
@@ -168,9 +164,15 @@ R Version
 CRAN
 ^^^^
 
-**TODO**: CRAN
+To install the most recent release, call:
+
+.. code:: r
+
+    install.packages("genieclust")
+
+
+See the package entry on `CRAN <https://cran.r-project.org/web/packages/genieclust/>`_.
 
-.. _development-version-1:
 
 Development Version
 ^^^^^^^^^^^^^^^^^^^

appveyor.yml

@@ -50,15 +50,6 @@ environment:
       PYTHON_VERSION: "3.5"
       PYTHON_ARCH: "64"
 
-    - PYTHON: "C:\\Python34"
-      PYTHON_VERSION: "3.4"
-      PYTHON_ARCH: "32"
-      PARALLEL: ""
-
-    - PYTHON: "C:\\Python34-x64"
-      PYTHON_VERSION: "3.4"
-      PYTHON_ARCH: "64"
-      PARALLEL: ""
 
 clone_depth: 5
 

devel/sphinx/index.rst

@@ -66,10 +66,12 @@ R's interface is compatible with ``hclust()``, but there is more.
     # or genie(X, k=2)
 
 
-The `genieclust` package is available for Python (**TODO**: PyPI--link, how to install)
-and R (**TODO**: CRAN--link, how to install). Its source code is distributed
+The `genieclust` package is available for Python (via
+`PyPI <https://pypi.org/project/genieclust/>`_\ )
+and R (on `CRAN <https://cran.r-project.org/web/packages/genieclust/>`_\ ).
+Its source code is distributed
 under the open source GNU AGPL v3 license and can be downloaded from
-`https://github.com/gagolews/genieclust <https://github.com/gagolews/genieclust>`_.
+`GitHub <https://github.com/gagolews/genieclust>`_.
 Note that the core functionality is implemented in the form of a header-only C++
 library, hence it might be relatively easily adapted to new environments.
 

devel/sphinx/rmd/basics.rst

@@ -44,6 +44,7 @@ with the true corresponding partition (by an expert).
 
 .. code:: python
 
+    # see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
     dataset = "jain"
     # Load an example 2D dataset:
     X = np.loadtxt("%s.data.gz" % dataset, ndmin=2)

devel/sphinx/rmd/basics.rstw

@@ -47,6 +47,7 @@ Let's load an example benchmark set, ``jain`` [2]_, which  comes along
 with the true corresponding partition (by an expert).
 
 <<basics-load>>=
+# see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
 dataset = "jain"
 # Load an example 2D dataset:
 X = np.loadtxt("%s.data.gz" % dataset, ndmin=2)

devel/sphinx/rmd/string.rst

@@ -23,6 +23,7 @@ We'll use one of the benchmark datasets mentioned in [1]_ as an example:
 
 .. code:: python
 
+    # see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
     strings = np.loadtxt("actg1.data.gz", dtype=np.str).tolist()
     strings[:5] # preview
 

devel/sphinx/rmd/string.rstw

@@ -28,6 +28,7 @@ np.set_printoptions(precision=3, threshold=50, edgeitems=50)
 We'll use one of the benchmark datasets mentioned in [1]_ as an example:
 
 <<string-example-strings>>=
+# see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
 strings = np.loadtxt("actg1.data.gz", dtype=np.str).tolist()
 strings[:5] # preview
 @

docs/_sources/index.rst.txt

@@ -66,10 +66,12 @@ R's interface is compatible with ``hclust()``, but there is more.
     # or genie(X, k=2)
 
 
-The `genieclust` package is available for Python (**TODO**: PyPI--link, how to install)
-and R (**TODO**: CRAN--link, how to install). Its source code is distributed
+The `genieclust` package is available for Python (via
+`PyPI <https://pypi.org/project/genieclust/>`_\ )
+and R (on `CRAN <https://cran.r-project.org/web/packages/genieclust/>`_\ ).
+Its source code is distributed
 under the open source GNU AGPL v3 license and can be downloaded from
-`https://github.com/gagolews/genieclust <https://github.com/gagolews/genieclust>`_.
+`GitHub <https://github.com/gagolews/genieclust>`_.
 Note that the core functionality is implemented in the form of a header-only C++
 library, hence it might be relatively easily adapted to new environments.
 

docs/_sources/rmd/basics.rst.txt

@@ -44,6 +44,7 @@ with the true corresponding partition (by an expert).
 
 .. code:: python
 
+    # see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
     dataset = "jain"
     # Load an example 2D dataset:
     X = np.loadtxt("%s.data.gz" % dataset, ndmin=2)

docs/_sources/rmd/string.rst.txt

@@ -23,6 +23,7 @@ We'll use one of the benchmark datasets mentioned in [1]_ as an example:
 
 .. code:: python
 
+    # see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd
     strings = np.loadtxt("actg1.data.gz", dtype=np.str).tolist()
     strings[:5] # preview
 

docs/index.html

@@ -221,10 +221,12 @@ <h1><cite>genieclust</cite>: Fast and Robust Hierarchical Clustering with Noise
 <span class="c1"># or genie(X, k=2)</span>
 </pre></div>
 </div>
-<p>The <cite>genieclust</cite> package is available for Python (<strong>TODO</strong>: PyPI–link, how to install)
-and R (<strong>TODO</strong>: CRAN–link, how to install). Its source code is distributed
+<p>The <cite>genieclust</cite> package is available for Python (via
+<a class="reference external" href="https://pypi.org/project/genieclust/">PyPI</a>)
+and R (on <a class="reference external" href="https://cran.r-project.org/web/packages/genieclust/">CRAN</a>).
+Its source code is distributed
 under the open source GNU AGPL v3 license and can be downloaded from
-<a class="reference external" href="https://github.com/gagolews/genieclust">https://github.com/gagolews/genieclust</a>.
+<a class="reference external" href="https://github.com/gagolews/genieclust">GitHub</a>.
 Note that the core functionality is implemented in the form of a header-only C++
 library, hence it might be relatively easily adapted to new environments.</p>
 <div class="toctree-wrapper compound">

docs/r.html

@@ -346,8 +346,8 @@ <h3>References<a class="headerlink" href="#references" title="Permalink to this
 </div>
 <div class="section" id="examples">
 <h3>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h3>
-<div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">&lt;-</span> <span class="n">iris[[5]]</span>
-<span class="n">y_pred</span> <span class="o">&lt;-</span> <span class="nf">kmeans</span><span class="p">(</span><span class="nf">as.matrix</span><span class="p">(</span><span class="n">iris[1</span><span class="o">:</span><span class="m">4</span><span class="n">]</span><span class="p">),</span> <span class="m">3</span><span class="p">)</span><span class="o">$</span><span class="n">cluster</span>
+<div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="n">y_true</span> <span class="o">&lt;-</span> <span class="n">iris</span><span class="p">[[</span><span class="m">5</span><span class="p">]]</span>
+<span class="n">y_pred</span> <span class="o">&lt;-</span> <span class="nf">kmeans</span><span class="p">(</span><span class="nf">as.matrix</span><span class="p">(</span><span class="n">iris</span><span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">]),</span> <span class="m">3</span><span class="p">)</span><span class="o">$</span><span class="n">cluster</span>
 <span class="nf">adjusted_rand_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 <span class="nf">rand_score</span><span class="p">(</span><span class="nf">table</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span> <span class="c1"># the same</span>
 <span class="nf">adjusted_fm_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
@@ -639,12 +639,12 @@ <h3>Seealso<a class="headerlink" href="#seealso" title="Permalink to this headli
 <span id="id13"></span><h3>Examples<a class="headerlink" href="#examples-1" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="nf">library</span><span class="p">(</span><span class="s">&quot;datasets&quot;</span><span class="p">)</span>
 <span class="nf">data</span><span class="p">(</span><span class="s">&quot;iris&quot;</span><span class="p">)</span>
-<span class="n">X</span> <span class="o">&lt;-</span> <span class="n">iris[1</span><span class="o">:</span><span class="m">4</span><span class="n">]</span>
+<span class="n">X</span> <span class="o">&lt;-</span> <span class="n">iris</span><span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">]</span>
 <span class="n">h</span> <span class="o">&lt;-</span> <span class="nf">gclust</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="n">y_pred</span> <span class="o">&lt;-</span> <span class="nf">cutree</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="m">3</span><span class="p">)</span>
-<span class="n">y_test</span> <span class="o">&lt;-</span> <span class="n">iris[</span><span class="p">,</span><span class="m">5</span><span class="n">]</span>
-<span class="nf">plot</span><span class="p">(</span><span class="n">iris[</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span> <span class="n">iris[</span><span class="p">,</span><span class="m">3</span><span class="n">]</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="n">y_pred</span><span class="p">,</span>
-<span class="n">pch</span><span class="o">=</span><span class="nf">as.integer</span><span class="p">(</span><span class="n">iris[</span><span class="p">,</span><span class="m">5</span><span class="n">]</span><span class="p">),</span> <span class="n">asp</span><span class="o">=</span><span class="m">1</span><span class="p">,</span> <span class="n">las</span><span class="o">=</span><span class="m">1</span><span class="p">)</span>
+<span class="n">y_test</span> <span class="o">&lt;-</span> <span class="n">iris</span><span class="p">[,</span><span class="m">5</span><span class="p">]</span>
+<span class="nf">plot</span><span class="p">(</span><span class="n">iris</span><span class="p">[,</span><span class="m">2</span><span class="p">],</span> <span class="n">iris</span><span class="p">[,</span><span class="m">3</span><span class="p">],</span> <span class="n">col</span><span class="o">=</span><span class="n">y_pred</span><span class="p">,</span>
+<span class="n">pch</span><span class="o">=</span><span class="nf">as.integer</span><span class="p">(</span><span class="n">iris</span><span class="p">[,</span><span class="m">5</span><span class="p">]),</span> <span class="n">asp</span><span class="o">=</span><span class="m">1</span><span class="p">,</span> <span class="n">las</span><span class="o">=</span><span class="m">1</span><span class="p">)</span>
 <span class="nf">adjusted_rand_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 <span class="nf">pair_sets_index</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
 
@@ -891,7 +891,7 @@ <h2>mst<a class="headerlink" href="#mst" title="Permalink to this headline">¶</
 <span id="id29"></span><h3>Examples<a class="headerlink" href="#examples-3" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="nf">library</span><span class="p">(</span><span class="s">&quot;datasets&quot;</span><span class="p">)</span>
 <span class="nf">data</span><span class="p">(</span><span class="s">&quot;iris&quot;</span><span class="p">)</span>
-<span class="n">X</span> <span class="o">&lt;-</span> <span class="n">iris[1</span><span class="o">:</span><span class="m">4</span><span class="n">]</span>
+<span class="n">X</span> <span class="o">&lt;-</span> <span class="n">iris</span><span class="p">[</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">]</span>
 <span class="n">tree</span> <span class="o">&lt;-</span> <span class="nf">mst</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>

docs/rmd/basics.html

@@ -197,17 +197,18 @@ <h1>Basics<a class="headerlink" href="#basics" title="Permalink to this headline
 and <a class="reference internal" href="timings.html"><span class="doc">speed</span></a>.</p>
 <p>Here are a few examples of basic interactions with the Python version
 of the <cite>genieclust</cite> package.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">genieclust</span>
 </pre></div>
 </div>
 <div class="section" id="breaking-the-ice">
 <h2>Breaking the Ice<a class="headerlink" href="#breaking-the-ice" title="Permalink to this headline">¶</a></h2>
 <p>Let’s load an example benchmark set, <code class="docutils literal notranslate"><span class="pre">jain</span></code> <a class="footnote-reference brackets" href="#id7" id="id2">2</a>, which  comes along
 with the true corresponding partition (by an expert).</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="s2">&quot;jain&quot;</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># see https://github.com/gagolews/genieclust/tree/master/devel/sphinx/rmd</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="s2">&quot;jain&quot;</span>
 <span class="c1"># Load an example 2D dataset:</span>
 <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">.data.gz&quot;</span> <span class="o">%</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 
@@ -237,7 +238,7 @@ <h2>Breaking the Ice<a class="headerlink" href="#breaking-the-ice" title="Permal
 In particular, an object of class <cite>Genie</cite> is equipped with the
 <cite>fit</cite> and <cite>fit_predict</cite> methods.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">g</span> <span class="o">=</span> <span class="n">genieclust</span><span class="o">.</span><span class="n">Genie</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Genie</span><span class="p">()</span>
@@ -406,11 +407,11 @@ <h2>A Comparison with HDBSCAN*<a class="headerlink" href="#a-comparison-with-hdb
 <h2>Dendrograms<a class="headerlink" href="#dendrograms" title="Permalink to this headline">¶</a></h2>
 <p>Plotting of dendrograms is possible with <cite>scipy.cluster.hierarchy</cite>:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.cluster.hierarchy</span>
-<span class="n">g</span> <span class="o">=</span> <span class="n">genieclust</span><span class="o">.</span><span class="n">Genie</span><span class="p">(</span><span class="n">compute_full_tree</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">g</span> <span class="o">=</span> <span class="n">genieclust</span><span class="o">.</span><span class="n">Genie</span><span class="p">(</span><span class="n">compute_full_tree</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">g</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="n">linkage_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="n">g</span><span class="o">.</span><span class="n">children_</span><span class="p">,</span> <span class="n">g</span><span class="o">.</span><span class="n">distances_</span><span class="p">,</span> <span class="n">g</span><span class="o">.</span><span class="n">counts_</span><span class="p">])</span>
 <span class="n">scipy</span><span class="o">.</span><span class="n">cluster</span><span class="o">.</span><span class="n">hierarchy</span><span class="o">.</span><span class="n">dendrogram</span><span class="p">(</span><span class="n">linkage_matrix</span><span class="p">,</span>
-    <span class="n">show_leaf_counts</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">no_labels</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+    <span class="n">show_leaf_counts</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">no_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>

docs/rmd/noise.html

@@ -183,9 +183,9 @@
 
   <div class="section" id="clustering-with-noise-points-detection">
 <h1>Clustering with Noise Points Detection<a class="headerlink" href="#clustering-with-noise-points-detection" title="Permalink to this headline">¶</a></h1>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">genieclust</span>
 </pre></div>
 </div>