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import sklearn.cluster as cluster |
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import pandas as pd |
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from matplotlib import pyplot as plt |
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def find_kmeans(mat, k, first, second): |
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km = cluster.KMeans(n_clusters=k) |
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km.fit(mat) |
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# Plot sse against k |
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plt.figure(figsize=(6, 6)) |
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plt.xlabel('Metric: ' + first) |
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plt.ylabel('Metric: ' + second) |
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plt.scatter(mat[:, 0], mat[:, 1], c=km.labels_, cmap='rainbow') |
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plt.show() |
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# Read csv in |
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df = pd.read_csv('../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv') |
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# Specify what things you want |
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df = df[["normFatigue", "normSleepQuality"]] |
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# values, num clusters, axis labelsg |
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find_kmeans(df.values, 2, "normFatigue", "normSleepQuality") |