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dbscan_lab_helper.py
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dbscan_lab_helper.py
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import matplotlib.pyplot as plt
import numpy as np
from itertools import cycle, islice
from sklearn import cluster
figsize = (10,10)
point_size=150
point_border=0.8
def plot_dataset(dataset, xlim=(-15, 15), ylim=(-15, 15)):
plt.figure(figsize=figsize)
plt.scatter(dataset[:,0], dataset[:,1], s=point_size, color="#00B3E9", edgecolor='black', lw=point_border)
plt.xlim(xlim)
plt.ylim(ylim)
plt.show()
def plot_clustered_dataset(dataset, y_pred, xlim=(-15, 15), ylim=(-15, 15), neighborhood=False, epsilon=0.5):
fig, ax = plt.subplots(figsize=figsize)
colors = np.array(list(islice(cycle(['#df8efd', '#78c465', '#ff8e34',
'#f65e97', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']),
int(max(y_pred) + 1))))
colors = np.append(colors, '#BECBD6')
if neighborhood:
for point in dataset:
circle1 = plt.Circle(point, epsilon, color='#666666', fill=False, zorder=0, alpha=0.3)
ax.add_artist(circle1)
ax.scatter(dataset[:, 0], dataset[:, 1], s=point_size, color=colors[y_pred], zorder=10, edgecolor='black', lw=point_border)
plt.xlim(xlim)
plt.ylim(ylim)
plt.show()
def plot_dbscan_grid(dataset, eps_values, min_samples_values):
fig = plt.figure(figsize=(16, 20))
plt.subplots_adjust(left=.02, right=.98, bottom=0.001, top=.96, wspace=.05,
hspace=0.25)
plot_num = 1
for i, min_samples in enumerate(min_samples_values):
for j, eps in enumerate(eps_values):
ax = fig.add_subplot( len(min_samples_values) , len(eps_values), plot_num)
dbscan = cluster.DBSCAN(eps=eps, min_samples=min_samples)
y_pred_2 = dbscan.fit_predict(dataset)
colors = np.array(list(islice(cycle(['#df8efd', '#78c465', '#ff8e34',
'#f65e97', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']),
int(max(y_pred_2) + 1))))
colors = np.append(colors, '#BECBD6')
for point in dataset:
circle1 = plt.Circle(point, eps, color='#666666', fill=False, zorder=0, alpha=0.3)
ax.add_artist(circle1)
ax.text(0, -0.03, 'Epsilon: {} \nMin_samples: {}'.format(eps, min_samples), transform=ax.transAxes, fontsize=16, va='top')
ax.scatter(dataset[:, 0], dataset[:, 1], s=50, color=colors[y_pred_2], zorder=10, edgecolor='black', lw=0.5)
plt.xticks(())
plt.yticks(())
plt.xlim(-14, 5)
plt.ylim(-12, 7)
plot_num = plot_num + 1
plt.show()