/
app.py
153 lines (118 loc) · 4.58 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import alg_project3
import alg_cluster
import alg_project3_viz as viz
import alg_clusters_matplotlib
import matplotlib.pyplot as plt
import random
import time
def gen_random_clusters(num_clusters):
random_clusters = list()
for cluster in range(num_clusters):
x = random.random() * 2 - 1
y = random.random() * 2 -1
random_clusters.append(alg_cluster.Cluster(set([]), x, y, 0, 0))
return random_clusters
def q1():
num_clusters_list = range(2, 200 + 1)
slow_time_list = list()
fast_time_list = list()
for num_clusters in num_clusters_list:
clusters = gen_random_clusters(num_clusters)
start = time.time()
alg_project3.slow_closest_pair(clusters)
end = time.time()
slow_time_list.append(end - start)
start = time.time()
alg_project3.fast_closest_pair(clusters)
end = time.time()
fast_time_list.append(end - start)
plt.xlabel('Number of initial clusters')
plt.ylabel('Running time in seconds')
line1, = plt.plot(num_clusters_list, slow_time_list,'g')
line2, = plt.plot(num_clusters_list, fast_time_list,'b')
plt.legend((line1, line2), ('slow_time_list', 'fast_time_list'))
plt.show()
def q2():
data_table = viz.load_data_table(viz.DATA_3108_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 15)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def q3():
data_table = viz.load_data_table(viz.DATA_3108_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = alg_project3.kmeans_clustering(singleton_list, 15, 5)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def q5():
data_table = viz.load_data_table(viz.DATA_111_URL)
singleton_list=[]
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def q6():
data_table = viz.load_data_table(viz.DATA_111_URL)
singleton_list=[]
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
alg_clusters_matplotlib.plot_clusters(data_table, cluster_list, True)
def compute_distortion(cluster_list, data_table):
error = 0
for cluster in cluster_list:
error += cluster.cluster_error(data_table)
return error
def q7():
data_table = viz.load_data_table(viz.DATA_111_URL)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
cluster_list = alg_project3.kmeans_clustering(singleton_list, 9, 5)
error2 = compute_distortion(cluster_list, data_table)
cluster_list = alg_project3.hierarchical_clustering(singleton_list, 9)
error1 = compute_distortion(cluster_list, data_table)
print 'hierarchical clustering',error1
print 'kmeans clustering', error2
def q10():
nodes_list = {viz.DATA_111_URL:111, viz.DATA_290_URL:290, viz.DATA_896_URL:896}
url_list = [viz.DATA_111_URL, viz.DATA_290_URL, viz.DATA_896_URL]
kmeans_dict = dict()
hierarchical_dict = dict()
for url in url_list:
data_table = viz.load_data_table(url)
singleton_list = []
for line in data_table:
singleton_list.append(alg_cluster.Cluster(set([line[0]]), line[1], line[2], line[3], line[4]))
kmeans_dict[url] = list()
hierarchical_dict[url] = list()
cluster_range = range(6, 20 + 1)
for cluster_count in cluster_range:
#kmeans
cluster_list = alg_project3.kmeans_clustering(singleton_list, cluster_count, 5)
kmeans_error = compute_distortion(cluster_list, data_table)
kmeans_dict[url].append(kmeans_error)
#hierarchical
count = 20
while count >= 6:
alg_project3.hierarchical_clustering(singleton_list, count)
hierarchical_error = compute_distortion(singleton_list, data_table)
hierarchical_dict[url].insert(0, hierarchical_error)
count -= 1
for url in url_list:
plt.title('Distortion for hierarchical and k-means clustering for '+str(nodes_list[url])+' points')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
line1, = plt.plot(cluster_range, kmeans_dict[url],'g')
line2, = plt.plot(cluster_range, hierarchical_dict[url],'b')
plt.legend((line1, line2), ('kmeans clustering', 'hierarchical clustering'))
plt.show()
#q2()
#q2()
#q3()
#q5()
#q6()
#q7()
#q10()