scikit-learn-contrib
diff --git a/‎.buildinfo‎
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diff --git a/‎_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎
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diff --git a/‎_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip‎
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diff --git a/‎_downloads/77b9e52ede6dc6a82f8015e94df3d763/plot_iris_example.zip‎
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diff --git a/‎_downloads/a68903bdfeb7318b3698a11444880c49/plot_iris_example.py‎
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diff --git a/‎_downloads/b4cf190b888ee49e92905caa55256121/plot_iris_example.ipynb‎
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diff --git a/‎_modules/index.html‎
Lines changed: 4 additions & 4 deletions b/‎_modules/index.html‎
Lines changed: 4 additions & 4 deletions
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 085436ad50aa002898f470a7ac7a6d5e
+config: 57862ec0b34b99c6af9e6689c91d1629
 tags: 645f666f9bcd5a90fca523b33c5a78b7
@@ -82,7 +82,7 @@
 # We create an instance of the `RadiusClustering` class and fit it to the Iris dataset.
 import time
 
-rad = RadiusClustering(manner="exact", threshold=1.43)
+rad = RadiusClustering(manner="exact", radius=1.43)
 t0 = time.time()
 rad.fit(X)
 t_rad = time.time() - t0
@@ -242,7 +242,7 @@ def get_order_labels(kmeans, rad, data):
 
 # Compute clustering with MDS
 
-rad = RadiusClustering(manner="exact", threshold=232.09)
+rad = RadiusClustering(manner="exact", radius=232.09)
 t0 = time.time()
 rad.fit(X)
 t_rad = time.time() - t0
 
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\n\nrad = RadiusClustering(manner=\"exact\", threshold=1.43)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0"
+        "import time\n\nrad = RadiusClustering(manner=\"exact\", radius=1.43)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0"
       ]
     },
     {
@@ -141,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "wine = datasets.load_wine()\nX = wine.data\ny = wine.target\npca = PCA(n_components=3)\nwine_reduced = pca.fit_transform(X)\n\n# Compute clustering with MDS\n\nrad = RadiusClustering(manner=\"exact\", threshold=232.09)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0\n\n# Compute KMeans clustering for comparison\n\nk_means = KMeans(n_clusters=3, n_init=10)\nt0 = time.time()\nk_means.fit(X)\nt_kmeans = time.time() - t0"
+        "wine = datasets.load_wine()\nX = wine.data\ny = wine.target\npca = PCA(n_components=3)\nwine_reduced = pca.fit_transform(X)\n\n# Compute clustering with MDS\n\nrad = RadiusClustering(manner=\"exact\", radius=232.09)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0\n\n# Compute KMeans clustering for comparison\n\nk_means = KMeans(n_clusters=3, n_init=10)\nt0 = time.time()\nk_means.fit(X)\nt_kmeans = time.time() - t0"
       ]
     },
     {
 
@@ -7,7 +7,7 @@
   <head>
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Overview: module code &#8212; Radius Clustering 1.0 documentation</title>
+    <title>Overview: module code &#8212; Radius Clustering 1.3.0 documentation</title>
 
 
 
@@ -44,7 +44,7 @@
   <link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
 <link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />
 
-    <script src="../_static/documentation_options.js?v=f2a433a1"></script>
+    <script src="../_static/documentation_options.js?v=1f29e9d3"></script>
     <script src="../_static/doctools.js?v=9bcbadda"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
@@ -121,8 +121,8 @@
 
 
 
-    <img src="../_static/logo-lias.jpg" class="logo__image only-light" alt="Radius Clustering 1.0 documentation - Home"/>
-    <img src="../_static/logo-lias.jpg" class="logo__image only-dark pst-js-only" alt="Radius Clustering 1.0 documentation - Home"/>
+    <img src="../_static/logo-lias.jpg" class="logo__image only-light" alt="Radius Clustering 1.3.0 documentation - Home"/>
+    <img src="../_static/logo-lias.jpg" class="logo__image only-dark pst-js-only" alt="Radius Clustering 1.3.0 documentation - Home"/>
 
 
 </a></div>
Original file line number	Diff line number	Diff line change
`@@ -69,7 +69,7 @@`
`69`	`69`	`},`
`70`	`70`	`"outputs": [],`
`71`	`71`	`"source": [`
`72`		`- "import time\n\nrad = RadiusClustering(manner=\"exact\", threshold=1.43)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0"`
	`72`	`+ "import time\n\nrad = RadiusClustering(manner=\"exact\", radius=1.43)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0"`
`73`	`73`	`]`
`74`	`74`	`},`
`75`	`75`	`{`
`@@ -141,7 +141,7 @@`
`141`	`141`	`},`
`142`	`142`	`"outputs": [],`
`143`	`143`	`"source": [`
`144`		`- "wine = datasets.load_wine()\nX = wine.data\ny = wine.target\npca = PCA(n_components=3)\nwine_reduced = pca.fit_transform(X)\n\n# Compute clustering with MDS\n\nrad = RadiusClustering(manner=\"exact\", threshold=232.09)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0\n\n# Compute KMeans clustering for comparison\n\nk_means = KMeans(n_clusters=3, n_init=10)\nt0 = time.time()\nk_means.fit(X)\nt_kmeans = time.time() - t0"`
	`144`	`+ "wine = datasets.load_wine()\nX = wine.data\ny = wine.target\npca = PCA(n_components=3)\nwine_reduced = pca.fit_transform(X)\n\n# Compute clustering with MDS\n\nrad = RadiusClustering(manner=\"exact\", radius=232.09)\nt0 = time.time()\nrad.fit(X)\nt_rad = time.time() - t0\n\n# Compute KMeans clustering for comparison\n\nk_means = KMeans(n_clusters=3, n_init=10)\nt0 = time.time()\nk_means.fit(X)\nt_kmeans = time.time() - t0"`
`145`	`145`	`]`
`146`	`146`	`},`
`147`	`147`	`{`