def run_suite():
"""
Testing code for the functions written for Word Wrangler
"""
# create a TestSuite (and an object)
suite = poc_simpletest.TestSuite()
# create a set of 3 clusters
cluster1 = CC.Cluster([1, 1], 0, 0, 100, 0.00001)
cluster2 = CC.Cluster([2, 2, 2], 3, 4, 200, 0.00002)
cluster3 = CC.Cluster([3, 3, 3, 3], 6, 8, 300, 0.00003)
list_of_clusters = [cluster1, cluster2, cluster3]
# testing the slow_closest_pair function with the 3 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (5., 0, 1),
"Test #1: testing slow_closest_pair on 3 clusters")
# testing the fast_closest_pair function with the 3 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (5., 0, 1),
"Test #2: testing fast_closest_pair on 3 clusters")
# add a fourth cluster to the list
cluster4 = CC.Cluster([4, 4, 4, 4, 4], 12, 16, 400, 0.00004)
list_of_clusters.append(cluster4)
# testing the slow_closest_pair function with the 4 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (5., 0, 1),
"Test #3: testing slow_closest_pair on 4 clusters")
# testing the fast_closest_pair function with the 4 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (5., 0, 1),
"Test #4: testing fast_closest_pair on 4 clusters")
# create a set of 4 clusters
cluster1 = CC.Cluster(set([]), 0, 0, 1, 0)
cluster2 = CC.Cluster(set([]), 1, 0, 1, 0)
cluster3 = CC.Cluster(set([]), 2, 0, 1, 0)
cluster4 = CC.Cluster(set([]), 3, 0, 1, 0)
list_of_clusters = [cluster1, cluster2, cluster3, cluster4]
# testing closest_pair_strip on 4 clusters
suite.run_test(student.closest_pair_strip(list_of_clusters, 1.5, 1.0), (1.0, 1, 2),
"Test #5: testing closest_pair_strip on 4 clusters")
# create a set of 4 clusters
cluster1 = CC.Cluster(set([]), 1.0, 0.0, 1, 0)
cluster2 = CC.Cluster(set([]), 4.0, 0.0, 1, 0)
cluster3 = CC.Cluster(set([]), 5.0, 0.0, 1, 0)
cluster4 = CC.Cluster(set([]), 7.0, 0.0, 1, 0)
list_of_clusters = [cluster1, cluster2, cluster3, cluster4]
# testing fast_closest_pair on 4 clusters
suite.run_test(student.fast_closest_pair(list_of_clusters), (1.0, 1, 2),
"Test #6: testing closest_pair_strip on 4 clusters")
# create a set of 4 clusters
cluster1 = CC.Cluster(set([]), -4.0, 0.0, 1, 0)
cluster2 = CC.Cluster(set([]), 0.0, -1.0, 1, 0)
cluster3 = CC.Cluster(set([]), 0.0, 1.0, 1, 0)
cluster4 = CC.Cluster(set([]), 4.0, 0.0, 1, 0)
list_of_clusters = [cluster1, cluster2, cluster3, cluster4]
# testing closest_pair_strip on 4 clusters
suite.run_test(student.closest_pair_strip(list_of_clusters, 0.0, 4.1231059999999999), (2.0, 1, 2),
"Test #7: testing closest_pair_strip on 4 clusters")
# create a set of 4 clusters
cluster1 = CC.Cluster(set([]), -4.0, 0.0, 1, 0)
cluster2 = CC.Cluster(set([]), 0.0, -1.0, 1, 0)
cluster3 = CC.Cluster(set([]), 0.0, 1.0, 1, 0)
cluster4 = CC.Cluster(set([]), 4.0, 0.0, 1, 0)
list_of_clusters = [cluster1, cluster2, cluster3, cluster4]
# testing fast_closest_pair on 4 clusters
suite.run_test(student.fast_closest_pair(list_of_clusters), (2.0, 1, 2),
"Test #8: testing fast_closest_pair on 4 clusters")
# create a sorted list_of_clusters from a small dataset containing 8 clusters
fhandle = open("unifiedCancerData_8.txt")
list_of_clusters = []
for line in fhandle:
tokens = line.split(',')
cluster = CC.Cluster(set([tokens[0]]), float(tokens[1]), float(tokens[2]),
int(tokens[3]), float(tokens[4]))
list_of_clusters.append(cluster)
list_of_clusters.sort(key = lambda cluster: cluster.horiz_center())
print "The following list_of_clusters was loaded:"
for index in range(len(list_of_clusters)):
print index, list_of_clusters[index]
print
# testing the slow_closest_pair function with 8 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (2.4479655653349655, 5, 7),
"Test #9: testing slow_closest_pair on 8 clusters")
# testing the fast_closest_pair function with 8 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (2.4479655653349655, 5, 7),
"Test #10: testing fast_closest_pair on 8 clusters")
# testing the hierarchical_clustering function with 8 clusters
clustering_result = student.hierarchical_clustering(list_of_clusters, 5)
for index in range(len(clustering_result)):
print clustering_result[index]
print
# testing the kmeans_clustering function with 8 clusters
clustering_result = student.kmeans_clustering(list_of_clusters, 5, 3)
for index in range(len(clustering_result)):
print clustering_result[index]
print
# create a sorted list_of_clusters from a small dataset containing 17 clusters
fhandle = open("unifiedCancerData_17.txt")
list_of_clusters = []
for line in fhandle:
tokens = line.split(',')
cluster = CC.Cluster(set([tokens[0]]), float(tokens[1]), float(tokens[2]),
int(tokens[3]), float(tokens[4]))
list_of_clusters.append(cluster)
list_of_clusters.sort(key = lambda cluster: cluster.horiz_center())
# testing the slow_closest_pair function with 17 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (1.9439662413427632, 9, 10),
"Test #11: testing slow_closest_pair on 17 clusters")
# testing the fast_closest_pair function with 17 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (1.9439662413427632, 9, 10),
"Test #12: testing fast_closest_pair on 17 clusters")
# create a sorted list_of_clusters from a small dataset containing 24 clusters
fhandle = open("unifiedCancerData_24.txt")
list_of_clusters = []
for line in fhandle:
tokens = line.split(',')
cluster = CC.Cluster(set([tokens[0]]), float(tokens[1]), float(tokens[2]),
int(tokens[3]), float(tokens[4]))
list_of_clusters.append(cluster)
list_of_clusters.sort(key = lambda cluster: cluster.horiz_center())
print "The following list_of_clusters was loaded:"
for index in range(len(list_of_clusters)):
print index, list_of_clusters[index]
print
# testing the kmeans_clustering function with 24 clusters
clustering_result = student.kmeans_clustering(list_of_clusters, 10, 1)
print "This output was created by kmeans_slustering:"
for index in range(len(clustering_result)):
print index, clustering_result[index]
print
# create a sorted list_of_clusters from a small dataset containing 39 clusters
fhandle = open("unifiedCancerData_39.txt")
list_of_clusters = []
for line in fhandle:
tokens = line.split(',')
cluster = CC.Cluster(set([tokens[0]]), float(tokens[1]), float(tokens[2]),
int(tokens[3]), float(tokens[4]))
list_of_clusters.append(cluster)
list_of_clusters.sort(key = lambda cluster: cluster.horiz_center())
# testing the slow_closest_pair function with 39 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (1.6612217536988727, 22, 24),
"Test #13: testing slow_closest_pair on 39 clusters")
# testing the fast_closest_pair function with 39 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (1.6612217536988727, 22, 24),
"Test #14: testing fast_closest_pair on 39 clusters")
# create a sorted list_of_clusters from a small dataset containing 111 clusters
fhandle = open("unifiedCancerData_111.csv")
list_of_clusters = []
for line in fhandle:
tokens = line.split(',')
cluster = CC.Cluster(set([tokens[0]]), float(tokens[1]), float(tokens[2]),
int(tokens[3]), float(tokens[4]))
list_of_clusters.append(cluster)
list_of_clusters.sort(key = lambda cluster: cluster.horiz_center())
print "The following list_of_clusters was loaded:"
for index in range(len(list_of_clusters)):
print index, list_of_clusters[index]
print
# testing the slow_closest_pair function with 111 cluster list
suite.run_test(student.slow_closest_pair(list_of_clusters), (1.266216002018164, 79, 81),
"Test #15: testing slow_closest_pair on 111 clusters")
# testing the fast_closest_pair function with 111 cluster list
suite.run_test(student.fast_closest_pair(list_of_clusters), (1.266216002018164, 79, 81),
"Test #16: testing fast_closest_pair on 111 clusters")
# testing the hierarchical_clustering function with 111 clusters
clustering_result = student.hierarchical_clustering(list_of_clusters, 5)
for index in range(len(clustering_result)):
print clustering_result[index]
print
# report number of tests and failures
print
suite.report_results()
def test02(self):
(distance,idx1,idx2) = closest_pair_strip([Cluster(set([]), 0.32, 0.16, 1, 0), Cluster(set([]), 0.39, 0.4, 1, 0), Cluster(set([]), 0.54, 0.8, 1, 0), Cluster(set([]), 0.61, 0.8, 1, 0), Cluster(set([]), 0.76, 0.94, 1, 0)], 0.46500000000000002, 0.070000000000000007)
self.assertAlmostEqual(distance, float('inf'))
self.assertEqual(idx1, -1)
self.assertEqual(idx2, -1)
def test_cps():
cluster_list = [alg_cluster.Cluster(set([]),1.0,1.0,1,0),alg_cluster.Cluster(set([]),1.0,5.0,1,0),alg_cluster.Cluster(set([]),1.0,4.0,1,0),alg_cluster.Cluster(set([]),1.0,7.0,1,0)]
horiz_center = 1.0
half_width = 3.0
print alg_project3_solution.closest_pair_strip(cluster_list,horiz_center,half_width)
def test01(self):
(distance,idx1,idx2) = closest_pair_strip([Cluster(set([]), 0, 0, 1, 0), Cluster(set([]), 1, 0, 1, 0), Cluster(set([]), 2, 0, 1, 0), Cluster(set([]), 3, 0, 1, 0)], 1.5, 1.0)
self.assertAlmostEqual(distance, 1.0)
self.assertEqual(idx1, 1)
self.assertEqual(idx2, 2)
