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<section id="balltree">
<h1>BallTree<a class="headerlink" href="#balltree" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.neighbors.</span></span><span class="sig-name descname"><span class="pre">BallTree</span></span><a class="headerlink" href="#sklearn.neighbors.BallTree" title="Link to this definition">#</a></dt>
<dd><p>BallTree for fast generalized N-point problems</p>
<p>Read more in the <a class="reference internal" href="../neighbors.html#unsupervised-neighbors"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>n_samples is the number of points in the data set, and
n_features is the dimension of the parameter space.
Note: if X is a C-contiguous array of doubles then data will
not be copied. Otherwise, an internal copy will be made.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">positive int, default=40</span></dt><dd><p>Number of points at which to switch to brute-force. Changing
leaf_size will not affect the results of a query, but can
significantly impact the speed of a query and the memory required
to store the constructed tree. The amount of memory needed to
store the tree scales as approximately n_samples / leaf_size.
For a specified <code class="docutils literal notranslate"><span class="pre">leaf_size</span></code>, a leaf node is guaranteed to
satisfy <code class="docutils literal notranslate"><span class="pre">leaf_size</span> <span class="pre"><=</span> <span class="pre">n_points</span> <span class="pre"><=</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">leaf_size</span></code>, except in
the case that <code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre"><</span> <span class="pre">leaf_size</span></code>.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">str or DistanceMetric64 object, default=’minkowski’</span></dt><dd><p>Metric to use for distance computation. Default is “minkowski”, which
results in the standard Euclidean distance when p = 2.
A list of valid metrics for BallTree is given by the attribute
<code class="docutils literal notranslate"><span class="pre">valid_metrics</span></code>.
See the documentation of <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/spatial.distance.html">scipy.spatial.distance</a> and
the metrics listed in <a class="reference internal" href="sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics" title="sklearn.metrics.pairwise.distance_metrics"><code class="xref py py-class docutils literal notranslate"><span class="pre">distance_metrics</span></code></a> for
more information on any distance metric.</p>
</dd>
<dt><strong>Additional keywords are passed to the distance metric class.</strong></dt><dd></dd>
<dt><strong>Note: Callable functions in the metric parameter are NOT supported for KDTree</strong></dt><dd></dd>
<dt><strong>and Ball Tree. Function call overhead will result in very poor performance.</strong></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>data</strong><span class="classifier">memory view</span></dt><dd><p>The training data</p>
</dd>
<dt><strong>valid_metrics: list of str</strong></dt><dd><p>List of valid distance metrics.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>Query for k-nearest neighbors</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">BallTree</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c1"># 10 points in 3 dimensions</span>
<span class="gp">>>> </span><span class="n">tree</span> <span class="o">=</span> <span class="n">BallTree</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">leaf_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">dist</span><span class="p">,</span> <span class="n">ind</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span> <span class="c1"># indices of 3 closest neighbors</span>
<span class="go">[0 3 1]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span> <span class="c1"># distances to 3 closest neighbors</span>
<span class="go">[ 0. 0.19662693 0.29473397]</span>
</pre></div>
</div>
<p>Pickle and Unpickle a tree. Note that the state of the tree is saved in the
pickle operation: the tree needs not be rebuilt upon unpickling.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pickle</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c1"># 10 points in 3 dimensions</span>
<span class="gp">>>> </span><span class="n">tree</span> <span class="o">=</span> <span class="n">BallTree</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">leaf_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tree_copy</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">dist</span><span class="p">,</span> <span class="n">ind</span> <span class="o">=</span> <span class="n">tree_copy</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span> <span class="c1"># indices of 3 closest neighbors</span>
<span class="go">[0 3 1]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span> <span class="c1"># distances to 3 closest neighbors</span>
<span class="go">[ 0. 0.19662693 0.29473397]</span>
</pre></div>
</div>
<p>Query for neighbors within a given radius</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c1"># 10 points in 3 dimensions</span>
<span class="gp">>>> </span><span class="n">tree</span> <span class="o">=</span> <span class="n">BallTree</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">leaf_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">tree</span><span class="o">.</span><span class="n">query_radius</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">count_only</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="go">3</span>
<span class="gp">>>> </span><span class="n">ind</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">query_radius</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span> <span class="c1"># indices of neighbors within distance 0.3</span>
<span class="go">[3 0 1]</span>
</pre></div>
</div>
<p>Compute a gaussian kernel density estimate:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">tree</span> <span class="o">=</span> <span class="n">BallTree</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tree</span><span class="o">.</span><span class="n">kernel_density</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">h</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">)</span>
<span class="go">array([ 6.94114649, 7.83281226, 7.2071716 ])</span>
</pre></div>
</div>
<p>Compute a two-point auto-correlation function</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">30</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tree</span> <span class="o">=</span> <span class="n">BallTree</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tree</span><span class="o">.</span><span class="n">two_point_correlation</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
<span class="go">array([ 30, 62, 278, 580, 820])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.get_arrays">
<span class="sig-name descname"><span class="pre">get_arrays</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.get_arrays" title="Link to this definition">#</a></dt>
<dd><p>Get data and node arrays.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>arrays: tuple of array</dt><dd><p>Arrays for storing tree data, index, node data and node bounds.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.get_n_calls">
<span class="sig-name descname"><span class="pre">get_n_calls</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.get_n_calls" title="Link to this definition">#</a></dt>
<dd><p>Get number of calls.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>n_calls: int</dt><dd><p>number of distance computation calls</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.get_tree_stats">
<span class="sig-name descname"><span class="pre">get_tree_stats</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.get_tree_stats" title="Link to this definition">#</a></dt>
<dd><p>Get tree status.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>tree_stats: tuple of int</dt><dd><p>(number of trims, number of leaves, number of splits)</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.kernel_density">
<span class="sig-name descname"><span class="pre">kernel_density</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1E-8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">breadth_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.kernel_density" title="Link to this definition">#</a></dt>
<dd><p>Compute the kernel density estimate at points X with the given kernel,
using the distance metric specified at tree creation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>An array of points to query. Last dimension should match dimension
of training data.</p>
</dd>
<dt><strong>h</strong><span class="classifier">float</span></dt><dd><p>the bandwidth of the kernel</p>
</dd>
<dt><strong>kernel</strong><span class="classifier">str, default=”gaussian”</span></dt><dd><p>specify the kernel to use. Options are
- ‘gaussian’
- ‘tophat’
- ‘epanechnikov’
- ‘exponential’
- ‘linear’
- ‘cosine’
Default is kernel = ‘gaussian’</p>
</dd>
<dt><strong>atol</strong><span class="classifier">float, default=0</span></dt><dd><p>Specify the desired absolute tolerance of the result.
If the true result is <code class="docutils literal notranslate"><span class="pre">K_true</span></code>, then the returned result <code class="docutils literal notranslate"><span class="pre">K_ret</span></code>
satisfies <code class="docutils literal notranslate"><span class="pre">abs(K_true</span> <span class="pre">-</span> <span class="pre">K_ret)</span> <span class="pre"><</span> <span class="pre">atol</span> <span class="pre">+</span> <span class="pre">rtol</span> <span class="pre">*</span> <span class="pre">K_ret</span></code>
The default is zero (i.e. machine precision).</p>
</dd>
<dt><strong>rtol</strong><span class="classifier">float, default=1e-8</span></dt><dd><p>Specify the desired relative tolerance of the result.
If the true result is <code class="docutils literal notranslate"><span class="pre">K_true</span></code>, then the returned result <code class="docutils literal notranslate"><span class="pre">K_ret</span></code>
satisfies <code class="docutils literal notranslate"><span class="pre">abs(K_true</span> <span class="pre">-</span> <span class="pre">K_ret)</span> <span class="pre"><</span> <span class="pre">atol</span> <span class="pre">+</span> <span class="pre">rtol</span> <span class="pre">*</span> <span class="pre">K_ret</span></code>
The default is <code class="docutils literal notranslate"><span class="pre">1e-8</span></code> (i.e. machine precision).</p>
</dd>
<dt><strong>breadth_first</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, use a breadth-first search. If False (default) use a
depth-first search. Breadth-first is generally faster for
compact kernels and/or high tolerances.</p>
</dd>
<dt><strong>return_log</strong><span class="classifier">bool, default=False</span></dt><dd><p>Return the logarithm of the result. This can be more accurate
than returning the result itself for narrow kernels.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>density</strong><span class="classifier">ndarray of shape X.shape[:-1]</span></dt><dd><p>The array of (log)-density evaluations</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.query">
<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dualtree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">breadth_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.query" title="Link to this definition">#</a></dt>
<dd><p>query the tree for the k nearest neighbors</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>An array of points to query</p>
</dd>
<dt><strong>k</strong><span class="classifier">int, default=1</span></dt><dd><p>The number of nearest neighbors to return</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">bool, default=True</span></dt><dd><p>if True, return a tuple (d, i) of distances and indices
if False, return array i</p>
</dd>
<dt><strong>dualtree</strong><span class="classifier">bool, default=False</span></dt><dd><p>if True, use the dual tree formalism for the query: a tree is
built for the query points, and the pair of trees is used to
efficiently search this space. This can lead to better
performance as the number of points grows large.</p>
</dd>
<dt><strong>breadth_first</strong><span class="classifier">bool, default=False</span></dt><dd><p>if True, then query the nodes in a breadth-first manner.
Otherwise, query the nodes in a depth-first manner.</p>
</dd>
<dt><strong>sort_results</strong><span class="classifier">bool, default=True</span></dt><dd><p>if True, then distances and indices of each point are sorted
on return, so that the first column contains the closest points.
Otherwise, neighbors are returned in an arbitrary order.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>i</strong><span class="classifier">if return_distance == False</span></dt><dd></dd>
<dt><strong>(d,i)</strong><span class="classifier">if return_distance == True</span></dt><dd></dd>
<dt><strong>d</strong><span class="classifier">ndarray of shape X.shape[:-1] + (k,), dtype=double</span></dt><dd><p>Each entry gives the list of distances to the neighbors of the
corresponding point.</p>
</dd>
<dt><strong>i</strong><span class="classifier">ndarray of shape X.shape[:-1] + (k,), dtype=int</span></dt><dd><p>Each entry gives the list of indices of neighbors of the
corresponding point.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.query_radius">
<span class="sig-name descname"><span class="pre">query_radius</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.query_radius" title="Link to this definition">#</a></dt>
<dd><p>query the tree for neighbors within a radius r</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>An array of points to query</p>
</dd>
<dt><strong>r</strong><span class="classifier">distance within which neighbors are returned</span></dt><dd><p>r can be a single value, or an array of values of shape
x.shape[:-1] if different radii are desired for each point.</p>
</dd>
<dt><strong>return_distance</strong><span class="classifier">bool, default=False</span></dt><dd><p>if True, return distances to neighbors of each point
if False, return only neighbors
Note that unlike the query() method, setting return_distance=True
here adds to the computation time. Not all distances need to be
calculated explicitly for return_distance=False. Results are
not sorted by default: see <code class="docutils literal notranslate"><span class="pre">sort_results</span></code> keyword.</p>
</dd>
<dt><strong>count_only</strong><span class="classifier">bool, default=False</span></dt><dd><p>if True, return only the count of points within distance r
if False, return the indices of all points within distance r
If return_distance==True, setting count_only=True will
result in an error.</p>
</dd>
<dt><strong>sort_results</strong><span class="classifier">bool, default=False</span></dt><dd><p>if True, the distances and indices will be sorted before being
returned. If False, the results will not be sorted. If
return_distance == False, setting sort_results = True will
result in an error.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>count</strong><span class="classifier">if count_only == True</span></dt><dd></dd>
<dt><strong>ind</strong><span class="classifier">if count_only == False and return_distance == False</span></dt><dd></dd>
<dt><strong>(ind, dist)</strong><span class="classifier">if count_only == False and return_distance == True</span></dt><dd></dd>
<dt><strong>count</strong><span class="classifier">ndarray of shape X.shape[:-1], dtype=int</span></dt><dd><p>Each entry gives the number of neighbors within a distance r of the
corresponding point.</p>
</dd>
<dt><strong>ind</strong><span class="classifier">ndarray of shape X.shape[:-1], dtype=object</span></dt><dd><p>Each element is a numpy integer array listing the indices of
neighbors of the corresponding point. Note that unlike
the results of a k-neighbors query, the returned neighbors
are not sorted by distance by default.</p>
</dd>
<dt><strong>dist</strong><span class="classifier">ndarray of shape X.shape[:-1], dtype=object</span></dt><dd><p>Each element is a numpy double array listing the distances
corresponding to indices in i.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.reset_n_calls">
<span class="sig-name descname"><span class="pre">reset_n_calls</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.reset_n_calls" title="Link to this definition">#</a></dt>
<dd><p>Reset number of calls to 0.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.neighbors.BallTree.two_point_correlation">
<span class="sig-name descname"><span class="pre">two_point_correlation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dualtree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.neighbors.BallTree.two_point_correlation" title="Link to this definition">#</a></dt>
<dd><p>Compute the two-point correlation function</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>An array of points to query. Last dimension should match dimension
of training data.</p>
</dd>
<dt><strong>r</strong><span class="classifier">array-like</span></dt><dd><p>A one-dimensional array of distances</p>
</dd>
<dt><strong>dualtree</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, use a dualtree algorithm. Otherwise, use a single-tree
algorithm. Dual tree algorithms can have better scaling for
large N.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>counts</strong><span class="classifier">ndarray</span></dt><dd><p>counts[i] contains the number of pairs of points with distance
less than or equal to r[i]</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree"><code class="docutils literal notranslate"><span class="pre">BallTree</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.get_arrays"><code class="docutils literal notranslate"><span class="pre">get_arrays</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.get_n_calls"><code class="docutils literal notranslate"><span class="pre">get_n_calls</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.get_tree_stats"><code class="docutils literal notranslate"><span class="pre">get_tree_stats</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.kernel_density"><code class="docutils literal notranslate"><span class="pre">kernel_density</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.query"><code class="docutils literal notranslate"><span class="pre">query</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.query_radius"><code class="docutils literal notranslate"><span class="pre">query_radius</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.reset_n_calls"><code class="docutils literal notranslate"><span class="pre">reset_n_calls</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.neighbors.BallTree.two_point_correlation"><code class="docutils literal notranslate"><span class="pre">two_point_correlation</span></code></a></li>
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