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Update to Rubix ML 0.3.0
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LICENSE.md LICENSE

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README.md

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Now, we can plot the values using our favorite plotting software. As you can see, the value of the cost function decreases at each epoch until it stops when K Means has met its stopping criteria.
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![Inertia Loss](https://raw.githubusercontent.com/RubixML/Colors/master/docs/images/training-loss.svg?sanitize=true)
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![Inertia Loss](https://raw.githubusercontent.com/RubixML/Colors/master/docs/images/training-loss.png)
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### Making Predictions
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To make the predictions, pass the testing set to the `predict()` method on the estimator instance.
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Congratulations on completing the tutorial on K Means and synthetic data generation. Try generating some more data in other shapes using the [Circle](https://docs.rubixml.com/en/latest/datasets/generators/circle.html) or [Half Moon](https://docs.rubixml.com/en/latest/datasets/generators/half-moon.html) generator. Is K Means able to detect clusters of different shapes and sizes?
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## License
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The code is licensed [MIT](LICENSE.md) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
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The code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

composer.json

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"type": "project",
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"description": "Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.",
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"homepage": "https://github.com/RubixML/Colors",
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"license": "Apache-2.0",
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"license": "MIT",
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"readme": "README.md",
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"keywords": [
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"clustering", "clusterer", "color clustering", "contingency table", "cross validation",
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"authors": [
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{
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"name": "Andrew DalPino",
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"email": "[email protected]",
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"homepage": "https://andrewdalpino.com",
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"homepage": "https://github.com/andrewdalpino",
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"role": "Lead Engineer"
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}
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],
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"require": {
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"php": ">=7.2",
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"rubix/ml": "^0.1.0"
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"rubix/ml": "^0.3.0"
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},
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"scripts": {
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"train": "@php train.php"

docs/images/training-loss.png

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