| 1 |
from scipy.cluster.hierarchy import linkage, dendrogram import matplotlib.pyplot as plt mergings = linkage(samples, method='complete') dendrogram(mergings, labels=varieties, leaf_rotation=90, leaf_font_size=6, ) plt.show() |
| 2 |
from sklearn.preprocessing import normalize normalized_movements = normalize(movements) mergings = linkage(normalized_movements, method='complete') dendrogram(mergings, labels=companies, leaf_rotation=90, leaf_font_size=6) plt.show() |
| 3 |
import matplotlib.pyplot as plt from scipy.cluster.hierarchy import linkage, dendrogram mergings = linkage(samples, method='single') dendrogram(mergings, labels=country_names, leaf_rotation=90, leaf_font_size=6) plt.show() |
| 4 |
import pandas as pd from scipy.cluster.hierarchy import fcluster labels = fcluster(mergings, 6, criterion='distance') df = pd.DataFrame({'labels': labels, 'varieties': varieties}) ct = pd.crosstab(df['labels'], df['varieties']) print(ct) |
| 5 |
from sklearn.manifold import TSNE model = TSNE(learning_rate=300) tsne_features = model.fit_transform(samples) xs = tsne_features[:,0] ys = tsne_features[:,1] plt.scatter(xs, ys, c=variety_numbers) plt.show() |
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