application_vnd.jupyter.stderr_output_1.png
import matplotlib.pyplot as plt

import numpy as np

import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
plt.figure(figsize=((len(clustering_names) * 2 + 3) * 2, 9.5 * 2))
plt.subplots_adjust(left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01)