Pushing the docs to dev/ for branch: master, commit 01ba635cfcd523d01604ae9eb37a13844df6d92e

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plot Hierarchical Clustering Dendrogram\n\nThis example plots the corresponding dendrogram of a hierarchical clustering\nusing AgglomerativeClustering and the dendrogram method available in scipy.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram\nfrom sklearn.datasets import load_iris\nfrom sklearn.cluster import AgglomerativeClustering\n\n\ndef plot_dendrogram(model, **kwargs):\n    # Create linkage matrix and then plot the dendrogram\n\n    # create the counts of samples under each node\n    counts = np.zeros(model.children_.shape[0])\n    n_samples = len(model.labels_)\n    for i, merge in enumerate(model.children_):\n        current_count = 0\n        for child_idx in merge:\n            if child_idx < n_samples:\n                current_count += 1  # leaf node\n            else:\n                current_count += counts[child_idx - n_samples]\n        counts[i] = current_count\n\n    linkage_matrix = np.column_stack([model.children_, model.distances_,\n                                      counts]).astype(float)\n\n    # Plot the corresponding dendrogram\n    dendrogram(linkage_matrix, **kwargs)\n\n\niris = load_iris()\nX = iris.data\n\n# setting distance_threshold=0 ensures we compute the full tree.\nmodel = AgglomerativeClustering(distance_threshold=0, n_clusters=None)\n\nmodel = model.fit(X)\nplt.title('Hierarchical Clustering Dendrogram')\n# plot the top three levels of the dendrogram\nplot_dendrogram(model, truncate_mode='level', p=3)\nplt.xlabel(\"Number of points in node (or index of point if no parenthesis).\")\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/3a6db977f81e62cd589724738e029ea8/plot_agglomerative_dendrogram.ipynb

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+# Authors: Mathew Kallada, Andreas Mueller
+# License: BSD 3 clause
+"""
+=========================================
+Plot Hierarchical Clustering Dendrogram
+=========================================
+This example plots the corresponding dendrogram of a hierarchical clustering
+using AgglomerativeClustering and the dendrogram method available in scipy.
+"""
+
+import numpy as np
+
+from matplotlib import pyplot as plt
+from scipy.cluster.hierarchy import dendrogram
+from sklearn.datasets import load_iris
+from sklearn.cluster import AgglomerativeClustering
+
+
+def plot_dendrogram(model, **kwargs):
+    # Create linkage matrix and then plot the dendrogram
+
+    # create the counts of samples under each node
+    counts = np.zeros(model.children_.shape[0])
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1  # leaf node
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    linkage_matrix = np.column_stack([model.children_, model.distances_,
+                                      counts]).astype(float)
+
+    # Plot the corresponding dendrogram
+    dendrogram(linkage_matrix, **kwargs)
+
+
+iris = load_iris()
+X = iris.data
+
+# setting distance_threshold=0 ensures we compute the full tree.
+model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
+
+model = model.fit(X)
+plt.title('Hierarchical Clustering Dendrogram')
+# plot the top three levels of the dendrogram
+plot_dendrogram(model, truncate_mode='level', p=3)
+plt.xlabel("Number of points in node (or index of point if no parenthesis).")
+plt.show()