96 lines
4.1 KiB
Markdown
96 lines
4.1 KiB
Markdown
Install required dependencies for matplotlib GUI frontend and all pip other packages for this project
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```bash
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sudo apt install python3-tk
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python3.9 -m pip install -r requirements.txt
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```
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CLI to run K-Means clustering algorithm on a set of data.
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Data can be provided or randomly generated for testing.
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```bash
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python3.9 k-means.py -h
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usage: k-means.py [-h] [--data [X,Y ...]] [--seeds [X,Y ...]] [--silent] [--verbose] [--random] [--radius [RADIUS]]
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[--lock-radius] [--file [FILE_PATH]]
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[CLUSTER_COUNT] [CENTROID_SHIFT] [LOOP_COUNT]
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K-means clustering program for clustering data read from a file, terminal, or randomly generated
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positional arguments:
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CLUSTER_COUNT Total number of desired clusters
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(default: '2')
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CENTROID_SHIFT Centroid shift threshold. If cluster centroids move less-than this value, clustering is finished
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(default: '1.0')
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LOOP_COUNT Maximum count of loops to perform clustering
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(default: '3')
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optional arguments:
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-h, --help show this help message and exit
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--data [X,Y ...], -d [X,Y ...]
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A list of data points separated by spaces as: x,y x,y x,y ...
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(default: '[(1.0, 2.0), (2.0, 3.0), (2.0, 2.0), (5.0, 6.0), (6.0, 7.0), (6.0, 8.0), (7.0, 11.0), (1.0, 1.0)]')
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--seeds [X,Y ...], --seed [X,Y ...], -s [X,Y ...]
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A list of seed points separated by spaces as: x,y x,y x,y ...
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Number of seeds provided must match CLUSTER_COUNT, or else CLUSTER_COUNT will be overriden.
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--silent When this flag is set, scatter plot visualizations will not be shown
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(default: 'False')
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--verbose, -v When this flag is set, cluster members will be shown in output
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(default: 'False')
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--random, -r When this flag is set, data will be randomly generated
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(default: 'False')
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--radius [RADIUS] Initial radius to use for clusters
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(default: 'None')
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--lock-radius, -l When this flag is set, centroid radius will not be recalculated
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(default: 'False')
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--file [FILE_PATH], -f [FILE_PATH]
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Optionally provide file for data to be read from. Each point must be on it's own line with format x,y
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```
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Running k-means clustering program
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```bash
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python3.9 k-means.py --file ./input.txt --silent
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Finding K-means clusters for given data [(1.0, 2.0), (2.0, 3.0), (2.0, 2.0), (5.0, 6.0), (6.0, 7.0), (6.0, 8.0), (7.0, 11.0), (1.0, 1.0), (5.0, 5.0), (10.0, 10.0), (15.0, 15.0), (25.0, 25.0), (20.0, 20.0), (21.0, 21.0), (22.0, 22.0)]
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Using 2 clusters, 1.0 max centroid shift, and 3 iterations
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Clustering iteration 0
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Updating cluster membership using cluster seeds, radius:
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((5.0000, 5.0000), 10.6066)
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((20.0000, 20.0000), 10.6066)
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Outliers present: set()
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Updated clusters ([(5.0, 5.0), (20.0, 20.0)]) with new centroids [(4.5, 5.5), (20.6, 20.6)]
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New centroids [(4.5, 5.5), (20.6, 20.6)] shifted [0.7071, 0.8485] respectively
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Showing final cluster result...
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Initial cluster at (5.0000, 5.0000) moved to (4.5000, 5.5000)
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Total shift: 0.7071
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Final radius: 11.0365
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Initial radius: 10.6066
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Initial cluster at (20.0000, 20.0000) moved to (20.6000, 20.6000)
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Total shift: 0.8485
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Final radius: 11.0365
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Initial radius: 10.6066
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Stopping...
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Cluster centroids have not shifted at least 1.0, clusters are stable
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```
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Running k-means clustering program on some random example data shows the following visual output
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```bash
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python3.9 k-means.py --random
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# Output removed for GUI example
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```
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