Deployed b4c56cc with MkDocs version: 1.6.1

Author: tausiaj

datamining/clustering_datamining/index.html

@@ -779,7 +779,7 @@ <h2 id="bluemath_tk.datamining.kma.KMA" class="doc doc-heading">
                   (<code>int</code>)
               –
               <div class="doc-md-description">
-                <p>The random seed to use.</p>
+                <p>The random seed to use as initial datapoint.</p>
               </div>
             </li>
             <li>
@@ -1005,8 +1005,8 @@ <h3 id="bluemath_tk.datamining.kma.KMA.__init__" class="doc doc-heading">
 )
               –
               <div class="doc-md-description">
-                <p>The random seed to use.
-Must be greater or equal to 0.
+                <p>The random seed to use as initial datapoint.
+Must be greater or equal to 0 and less than number of datapoints.
 Default is 0.</p>
               </div>
             </li>
@@ -1612,7 +1612,7 @@ <h2 id="bluemath_tk.datamining.som.SOM" class="doc doc-heading">
 
 
 <h3 id="bluemath_tk.datamining.som.SOM.distance_map" class="doc doc-heading">
-            <code class=" language-python"><span class="n">distance_map</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span></code>
+            <code class=" language-python"><span class="n">distance_map</span></code>
 
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-property"><code>property</code></small>

index.html

@@ -168,5 +168,5 @@ <h2 id="project-status">Project status</h2>
 
 <!--
 MkDocs version : 1.6.1
-Build Date UTC : 2024-12-20 12:01:10.066633+00:00
+Build Date UTC : 2025-01-07 15:38:50.198483+00:00
 -->

interpolation/rbf_interpolation/index.html

@@ -143,6 +143,8 @@ <h2 id="bluemath_tk.interpolation.rbf.RBF" class="doc doc-heading">
 
 
         <p>Radial Basis Function (RBF) interpolation model.</p>
+<p>Here, scipy's RBFInterpolator is used to interpolate the data.
+https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.RBFInterpolator.html</p>
 
 
 <table class="field-list">
@@ -178,31 +180,72 @@ <h2 id="bluemath_tk.interpolation.rbf.RBF" class="doc doc-heading">
                   (<code>float</code>)
               –
               <div class="doc-md-description">
-                <p>The minimum difference between the optimal sigma and the minimum and maximum sigma values.</p>
+                <p>The minimum difference between the optimal sigma and the minimum and maximum
+sigma values.</p>
               </div>
             </li>
             <li>
               <b><code>kernel</code></b>
                   (<code>str</code>)
               –
               <div class="doc-md-description">
-                <p>The kernel to use for the RBF model.</p>
+                <p>Type of RBF. This should be one of</p>
+<div class="codehilite"><pre><span></span><code><span class="k">-</span> &#39;linear&#39;               : ``-r``
+<span class="k">-</span> &#39;thin_plate_spline&#39;    : ``r**2 * log(r)``
+<span class="k">-</span> &#39;cubic&#39;                : ``r**3``
+<span class="k">-</span> &#39;quintic&#39;              : ``-r**5``
+<span class="k">-</span> &#39;multiquadric&#39;         : ``-sqrt(1 + r**2)``
+<span class="k">-</span> &#39;inverse_multiquadric&#39; : ``1/sqrt(1 + r**2)``
+<span class="k">-</span> &#39;inverse_quadratic&#39;    : ``1/(1 + r**2)``
+<span class="k">-</span> &#39;gaussian&#39;             : ``exp(-r**2)``
+</code></pre></div>
               </div>
             </li>
             <li>
-              <b><code>kernel_func</code></b>
-                  (<code>function</code>)
+              <b><code>smoothing</code></b>
+                  (<code>float or (npoints, ) array_like</code>)
               –
               <div class="doc-md-description">
-                <p>The kernel function to use for the RBF model.</p>
+                <p>Smoothing parameter. The interpolant perfectly fits the data when this
+is set to 0. For large values, the interpolant approaches a least
+squares fit of a polynomial with the specified degree.</p>
               </div>
             </li>
             <li>
-              <b><code>smooth</code></b>
-                  (<code>float</code>)
+              <b><code>degree</code></b>
+                  (<code>int</code>)
+              –
+              <div class="doc-md-description">
+                <p>Degree of the added polynomial. For some RBFs the interpolant may not
+be well-posed if the polynomial degree is too small. Those RBFs and
+their corresponding minimum degrees are</p>
+<div class="codehilite"><pre><span></span><code><span class="k">-</span> &#39;multiquadric&#39;      : 0
+<span class="k">-</span> &#39;linear&#39;            : 0
+<span class="k">-</span> &#39;thin_plate_spline&#39; : 1
+<span class="k">-</span> &#39;cubic&#39;             : 1
+<span class="k">-</span> &#39;quintic&#39;           : 2
+</code></pre></div>
+
+<p>The default value is the minimum degree for <code>kernel</code> or 0 if there is
+no minimum degree. Set this to -1 for no added polynomial.</p>
+              </div>
+            </li>
+            <li>
+              <b><code>neighbors</code></b>
+                  (<code>int</code>)
+              –
+              <div class="doc-md-description">
+                <p>If specified, the value of the interpolant at each evaluation point
+will be computed using only this many nearest data points. All the data
+points are used by default.</p>
+              </div>
+            </li>
+            <li>
+              <b><code>rbfs</code></b>
+                  (<code>dict</code>)
               –
               <div class="doc-md-description">
-                <p>The smoothness parameter.</p>
+                <p>Dict with RBFInterpolator instances.</p>
               </div>
             </li>
             <li>
@@ -334,52 +377,23 @@ <h2 id="bluemath_tk.interpolation.rbf.RBF" class="doc doc-heading">
       </thead>
       <tbody>
             <tr class="doc-section-item">
-              <td><code><a class="autorefs autorefs-internal" title="bluemath_tk.interpolation.rbf.RBF.fit" href="#bluemath_tk.interpolation.rbf.RBF.fit">fit</a></code></td>
-              <td>
-                <div class="doc-md-description">
-                  <p>subset_data: pd.DataFrame,
-target_data: pd.DataFrame,
-subset_directional_variables: List[str] = [],
-target_directional_variables: List[str] = [],
-subset_custom_scale_factor: dict = {},
-normalize_target_data: bool = True,
-target_custom_scale_factor: dict = {},</p>
-                </div>
-              </td>
-            </tr>
-            <tr class="doc-section-item">
-              <td><code><span title="bluemath_tk.interpolation.rbf.RBF.) -> None">) -&gt; None</span></code></td>
+              <td><code><span title="bluemath_tk.interpolation.rbf.RBF.fit : Fits the model to the data.">fit : Fits the model to the data.</span></code></td>
               <td>
                 <div class="doc-md-description">
 
                 </div>
               </td>
             </tr>
             <tr class="doc-section-item">
-              <td><code><a class="autorefs autorefs-internal" title="bluemath_tk.interpolation.rbf.RBF.predict" href="#bluemath_tk.interpolation.rbf.RBF.predict">predict</a></code></td>
+              <td><code><span title="bluemath_tk.interpolation.rbf.RBF.predict : Predicts the data for the provided dataset.">predict : Predicts the data for the provided dataset.</span></code></td>
               <td>
                 <div class="doc-md-description">
 
                 </div>
               </td>
             </tr>
             <tr class="doc-section-item">
-              <td><code><a class="autorefs autorefs-internal" title="bluemath_tk.interpolation.rbf.RBF.fit_predict" href="#bluemath_tk.interpolation.rbf.RBF.fit_predict">fit_predict</a></code></td>
-              <td>
-                <div class="doc-md-description">
-                  <p>subset_data: pd.DataFrame,
-target_data: pd.DataFrame,
-dataset: pd.DataFrame,
-subset_directional_variables: List[str] = [],
-target_directional_variables: List[str] = [],
-subset_custom_scale_factor: dict = {},
-normalize_target_data: bool = True,
-target_custom_scale_factor: dict = {},</p>
-                </div>
-              </td>
-            </tr>
-            <tr class="doc-section-item">
-              <td><code><span title="bluemath_tk.interpolation.rbf.RBF.) -> pd.DataFrame">) -&gt; pd.DataFrame</span></code></td>
+              <td><code><span title="bluemath_tk.interpolation.rbf.RBF.fit_predict : Fits the model to the subset and predicts the interpolated dataset.">fit_predict : Fits the model to the subset and predicts the interpolated dataset.</span></code></td>
               <td>
                 <div class="doc-md-description">
 
@@ -390,9 +404,29 @@ <h2 id="bluemath_tk.interpolation.rbf.RBF" class="doc doc-heading">
     </table>
 
 
+<details class="see-also" open>
+  <summary>See Also</summary>
+  <p>OtherInterpolationMethod</p>
+</details>
+
+<details class="references" open>
+  <summary>References</summary>
+  <p>.. [1] Fasshauer, G., 2007. Meshfree Approximation Methods with Matlab.
+    World Scientific Publishing Co.</p>
+<p>.. [2] http://amadeus.math.iit.edu/~fass/603_ch3.pdf</p>
+<p>.. [3] Wahba, G., 1990. Spline Models for Observational Data. SIAM.</p>
+<p>.. [4] http://pages.stat.wisc.edu/~wahba/stat860public/lect/lect8/lect8.pdf</p>
+</details>
+
+<p><span class="doc-section-title">Examples:</span></p>
+    <div class="codehilite"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</code></pre></div>
+
+
 <details class="note" open>
   <summary>Notes</summary>
-  <p>TODO: For the moment, this class only supports optimization for one parameter kernels.
+  <p>.. versionadded:: 1.0.3
+TODO: For the moment, this class only supports optimization for one parameter kernels.
       For this reason, we only have sigma as the parameter to optimize.
       This sigma refers to the sigma parameter in the Gaussian kernel (but is used for all kernels).</p>
 </details>
@@ -418,7 +452,7 @@ <h2 id="bluemath_tk.interpolation.rbf.RBF" class="doc doc-heading">
 
 
 <h3 id="bluemath_tk.interpolation.rbf.RBF.__init__" class="doc doc-heading">
-            <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">sigma_min</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">sigma_max</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">sigma_diff</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">smooth</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">)</span></code>
+            <code class=" language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">sigma_min</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">sigma_max</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">sigma_diff</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">neighbors</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
 
@@ -465,8 +499,8 @@ <h3 id="bluemath_tk.interpolation.rbf.RBF.__init__" class="doc doc-heading">
 )
               –
               <div class="doc-md-description">
-                <p>The minimum difference between the optimal sigma and the minimum and maximum sigma values.
-Default is 0.0001.</p>
+                <p>The minimum difference between the optimal sigma and the minimum and maximum
+sigma values. Default is 0.0001.</p>
               </div>
             </li>
             <li>
@@ -476,50 +510,38 @@ <h3 id="bluemath_tk.interpolation.rbf.RBF.__init__" class="doc doc-heading">
 )
               –
               <div class="doc-md-description">
-                <p>The kernel to use for the RBF model. Default is "gaussian".
-The available kernels are:
-- "gaussian": Gaussian kernel.
-- "multiquadratic": Multiquadratic kernel.
-- "inverse": Inverse kernel.
-- "cubic": Cubic kernel.
-- "thin_plate": Thin plate kernel.</p>
+                <p>Type of RBF. Default is 'gaussian'.</p>
               </div>
             </li>
             <li>
-              <b><code>smooth</code></b>
+              <b><code>smoothing</code></b>
                   (<code>float</code>, default:
-                      <code>1e-05</code>
+                      <code>0.0</code>
 )
               –
               <div class="doc-md-description">
-                <p>The smoothness parameter. Default is 1e-5.</p>
+                <p>Smoothing parameter. Default is 0.0.</p>
               </div>
             </li>
-        </ul>
-      </td>
-    </tr>
-  </tbody>
-</table>
-
-<table class="field-list">
-  <colgroup>
-    <col class="field-name" />
-    <col class="field-body" />
-  </colgroup>
-  <tbody valign="top">
-    <tr class="field">
-      <th class="field-name">Raises:</th>
-      <td class="field-body">
-        <ul class="first simple">
             <li>
-                  <code>ValueError</code>
+              <b><code>degree</code></b>
+                  (<code>int</code>, default:
+                      <code>None</code>
+)
+              –
+              <div class="doc-md-description">
+                <p>Degree of the added polynomial. Default is None.</p>
+              </div>
+            </li>
+            <li>
+              <b><code>neighbors</code></b>
+                  (<code>int</code>, default:
+                      <code>None</code>
+)
               –
               <div class="doc-md-description">
-                <p>If the sigma_min is not a positive float.
-If the sigma_max is not a positive float greater than sigma_min.
-If the sigma_diff is not a positive float.
-If the kernel is not a string and one of the available kernels.
-If the smooth is not a positive float.</p>
+                <p>If specified, the value of the interpolant at each evaluation point will be
+computed using only this many nearest data points. Default is None.</p>
               </div>
             </li>
         </ul>
@@ -952,87 +974,6 @@ <h2 id="bluemath_tk.interpolation.rbf.RBFError" class="doc doc-heading">
 </div>
 
 
-<div class="doc doc-object doc-function">
-
-
-<h2 id="bluemath_tk.interpolation.rbf.gaussian_kernel" class="doc doc-heading">
-            <code class=" language-python"><span class="n">gaussian_kernel</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">const</span><span class="p">)</span></code>
-
-</h2>
-
-
-    <div class="doc doc-contents ">
-
-        <p>This function calculates the Gaussian kernel for the given distance and constant.</p>
-
-
-<table class="field-list">
-  <colgroup>
-    <col class="field-name" />
-    <col class="field-body" />
-  </colgroup>
-  <tbody valign="top">
-    <tr class="field">
-      <th class="field-name">Parameters:</th>
-      <td class="field-body">
-        <ul class="first simple">
-            <li>
-              <b><code>r</code></b>
-                  (<code>float</code>)
-              –
-              <div class="doc-md-description">
-                <p>The distance.</p>
-              </div>
-            </li>
-            <li>
-              <b><code>const</code></b>
-                  (<code>float</code>)
-              –
-              <div class="doc-md-description">
-                <p>The constant (default name is usually sigma for gaussian kernel).</p>
-              </div>
-            </li>
-        </ul>
-      </td>
-    </tr>
-  </tbody>
-</table>
-
-<table class="field-list">
-  <colgroup>
-    <col class="field-name" />
-    <col class="field-body" />
-  </colgroup>
-  <tbody valign="top">
-    <tr class="field">
-      <th class="field-name">Returns:</th>
-      <td class="field-body">
-        <ul class="first simple">
-            <li>
-                  <code>float</code>
-              –
-              <div class="doc-md-description">
-                <p>The Gaussian kernel value.</p>
-              </div>
-            </li>
-        </ul>
-      </td>
-    </tr>
-  </tbody>
-</table>
-
-<details class="note" open>
-  <summary>Notes</summary>
-  <ul>
-<li>The Gaussian kernel is defined as:
-  K(r) = exp(r^2 / 2 * const^2) (https://en.wikipedia.org/wiki/Gaussian_function)</li>
-<li>Here, we are assuming the mean is 0.</li>
-</ul>
-</details>
-    </div>
-
-</div>
-
 
 
   </div>