def scan_points(points):
scans = {}
for pos in points:
X = points[pos]
(eps, min_pts) = get_params(pos)
scan = DBScan(eps, min_pts)
scan.fit(X)
scans[pos] = scan
return scans
plt.figure()
sum_squared_errors = []
for n_clusters in range(2, 10):
kmeans = KMeans(n_clusters=n_clusters, max_iter=100)
kmeans.fit(iris_data, normalize=True)
sse = kmeans.sum_squared_error()
sum_squared_errors.append(sse)
plt.plot(sum_squared_errors)
plt.xlabel('# of clusters')
plt.ylabel('SSE')
plt.show()
# TODO 7: DBSCAN nad Iris podacima, prikazati rezultate na grafiku isto kao kod K-means
dbscan = DBScan(epsilon=0.5, min_points=3)
dbscan.fit(iris_data)
colors = {0: 'red', 1: 'green', 2: 'blue'}
plt.figure()
for idx, cluster in enumerate(dbscan.clusters):
for datum in cluster.data: # iscrtavanje tacaka
plt.scatter(datum[0], datum[1], c=colors[idx])
plt.xlabel('Sepal width')
plt.ylabel('Petal length')
plt.show()
data.append((x1, y1))
data.append((x2, y2))
plt.show()
# TODO 5: K-means nad ovim podacima
kmeans = KMeans(n_clusters=2, max_iter=100)
kmeans.fit(data)
colors = {0: 'red', 1: 'green'}
plt.figure()
for idx, cluster in enumerate(kmeans.clusters):
plt.scatter(cluster.center[0], cluster.center[1], c=colors[idx], marker='x', s=200) # iscrtavanje centara
for datum in cluster.data: # iscrtavanje tacaka
plt.scatter(datum[0], datum[1], c=colors[idx])
plt.show()
# TODO 7: DBSCAN nad ovim podacima
dbscan = DBScan(epsilon=1.2, min_points=3)
dbscan.fit(data)
colors = {0: 'red', 1: 'pink', 2: 'yellow', 3: 'cyan', 4: 'green', 5: 'blue'}
plt.figure()
for idx, cluster in enumerate(dbscan.clusters):
for datum in cluster.data: # iscrtavanje tacaka
plt.scatter(datum[0], datum[1], c=colors[idx%6])
plt.show()
