Created
November 15, 2019 22:19
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| # pip install sklearn numpy matplotlib | |
| from sklearn.cluster import KMeans | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # make a fake set of data | |
| samples = np.array([ | |
| [1, 2], | |
| [11, 12], | |
| [1, 2], | |
| [12, 10], | |
| [4, 1], | |
| [15, 12], | |
| [10, 9], | |
| [4, 2], | |
| [2, 1], | |
| [5, 1], | |
| [14, 13], | |
| ]) | |
| # plot the data to clearly illustrate the two groups | |
| plt.scatter(samples[:, 0], samples[:, 1]) | |
| plt.show() | |
| # create a KMeans model that will look for two groups | |
| model = KMeans(2) | |
| # fit the model to the data | |
| model.fit(samples) | |
| # ask the model which points correspond to which group | |
| labels = model.predict(samples) | |
| print('samples:', samples) | |
| print('labels:', labels) | |
| # Given a new point, give it the correct group label | |
| new_datapoints = [[2,5]] | |
| label = model.predict(new_datapoints) | |
| print(f"The point {new_datapoints[0]}") | |
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