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Gaussian Mixture Models Implementation
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| #training k-means model | |
| from sklearn.cluster import KMeans | |
| kmeans = KMeans(n_clusters=4) | |
| kmeans.fit(data) | |
| #predictions from kmeans | |
| pred = kmeans.predict(data) | |
| frame = pd.DataFrame(data) | |
| frame['cluster'] = pred | |
| frame.columns = ['Weight', 'Height', 'cluster'] | |
| #plotting results | |
| color=['blue','green','cyan', 'black'] | |
| for k in range(0,4): | |
| data = frame[frame["cluster"]==k] | |
| plt.scatter(data["Weight"],data["Height"],c=color[k]) | |
| plt.show() |
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| import pandas as pd | |
| data = pd.read_csv('Clustering_gmm.csv') | |
| plt.figure(figsize=(7,7)) | |
| plt.scatter(data["Weight"],data["Height"]) | |
| plt.xlabel('Weight') | |
| plt.ylabel('Height') | |
| plt.title('Data Distribution') | |
| plt.show() |
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| import pandas as pd | |
| data = pd.read_csv('Clustering_gmm.csv') | |
| # training gaussian mixture model | |
| from sklearn.mixture import GaussianMixture | |
| gmm = GaussianMixture(n_components=4) | |
| gmm.fit(data) | |
| #predictions from gmm | |
| labels = gmm.predict(data) | |
| frame = pd.DataFrame(data) | |
| frame['cluster'] = labels | |
| frame.columns = ['Weight', 'Height', 'cluster'] | |
| color=['blue','green','cyan', 'black'] | |
| for k in range(0,4): | |
| data = frame[frame["cluster"]==k] | |
| plt.scatter(data["Weight"],data["Height"],c=color[k]) | |
| plt.show() |
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