{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"![](\"data/cover.jpg\")
\n",
"*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
"The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
"and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
"\n",
"*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
"If you find this content useful, please consider supporting the work by\n",
"[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"< [Classifying handwritten digits using k-means](08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb) | [Contents](../README.md) | [9. Using Deep Learning to Classify Handwritten Digits](09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb) >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing Agglomerative Hierarchical Clustering\n",
"\n",
"Although OpenCV does not provide an implementation of agglomerative hierarchical\n",
"clustering, it is a popular algorithm that should, by all means, belong to our machine\n",
"learning repertoire.\n",
"\n",
"We start out by generating 10 random data points, just like in the previous figure:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.datasets import make_blobs\n",
"X, y = make_blobs(n_samples=10, random_state=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the familiar statistical modeling API, we import the `AgglomerativeClustering`\n",
"algorithm and specify the desired number of clusters:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn import cluster\n",
"agg = cluster.AgglomerativeClustering(n_clusters=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fitting the model to the data works, as usual, via the `fit_predict` method:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"labels = agg.fit_predict(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can generate a scatter plot where every data point is colored according to the predicted\n",
"label:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2xowCTDawyFUAOAgj6uvjlGuvjZ9feWXOx0+59trd/3pxD7Ld3dHT2dnf4x2QYUceGfN/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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, s=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it! This marks the end of another wonderful adventure."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"< [Classifying handwritten digits using k-means](08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb) | [Contents](../README.md) | [9. Using Deep Learning to Classify Handwritten Digits](09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb) >"
]
}
],
"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",
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