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- # Imports
- import numpy as np
- from sklearn.datasets import make_blobs
- import matplotlib.pyplot as plt
- plt.xkcd();
- import seaborn as sns
- sns.set_style('white')
- # Set transparency off when exporting / Only if you want to save the figure
- from matplotlib import patheffects, rcParams
- rcParams['path.effects'] = [patheffects.withStroke(linewidth=0)]
- # Set font
- font = {'family' : 'Caveat',
- 'size' : 14}
- plt.rc('font', **font)
- # Make sure its the same dataset
- np.random.seed(42*42)
- # Generate data from scikit learn make_blobs
- # Here it would also work to create a two-dimensional dataset,
- # for my purpose a slice through six-dimensional blobs gave better results
- X, y = make_blobs(n_samples=200, centers=2, n_features=6, cluster_std=3.5)
- X = X.astype(np.float32)
- # Plot data with matplotlib
- plt.plot(X[y>0,0], X[y>0,1], 'C0o')
- plt.plot(X[y<1,0]*-1, X[y<1,1], 'C1o')
- plt.legend(['Swedish', 'Norwegian'], loc='lower right', title='Nationality')
- plt.xlabel('Alcohol expenses', fontsize=18)
- plt.ylabel('Brunost consumption', fontsize=18)
- plt.gca().set_xticks([-9.,-4.,1.,6.,11.,16.])
- plt.gca().set_xticklabels([0,500,1000,1500,2000,2500])
- plt.gca().set_yticklabels([0,0,1,2,3,4,5,6,7])
- plt.show()
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