示例#1
0
def get_optimal_n_clusters(dataset):
	algorothms = ['Bigclam']
	datasets = [dataset]
	table = {}
	variants = []
	if dataset == 'amazon.txt':
		optimal_n_clusters = 0 #?????
	elif dataset == 'cliques.txt':
		optimal_n_clusters = 2
	elif dataset == 'cycles.txt':
		optimal_n_clusters = 4
	elif dataset == 'facebook.txt':
		variants = []
	elif dataset == 'football.txt':
		optimal_n_clusters = 12
	elif dataset == 'karate.txt':
		optimal_n_clusters = 2
	elif dataset == 'nested.txt':
		optimal_n_clusters = 6
	elif dataset == 'polbooks.txt':
		optimal_n_clusters = 3
	elif dataset == 'protein_new.txt':
		optimal_n_clusters = 13
	elif dataset == 'scientists_new.txt':
		variants = [250, 275, 300, 325, 350]
	elif dataset == 'stars.txt':
		optimal_n_clusters = 5

	for i in variants:
		result = make_experiment(algorothms, datasets, n_clusters=i)
		print result
		table[i, 'My modularity'] = result['Bigclam', dataset, 'My modularity']
		table[i, 'Time'] = result['Bigclam', dataset, 'Time']
	write_choice(table, variants, dataset, 'n_clusters')
示例#2
0
def get_optimal_n_steps(dataset):
	algorothms = ['Walktrap']
	datasets = [dataset]
	table = {}
	variants = [45, 55]
	
	for i in variants:
		result = make_experiment(algorothms, datasets, n_steps=i)
		for measure in all_measures:
			table[i, measure] = result['Walktrap', dataset, measure]
	write_choice(table, variants, dataset, 'n_steps')
示例#3
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def get_optimal_n_clique_size(dataset):
	algorothms = ['CFinder']
	datasets = [dataset]
	table = {}
	variants = [3]
	
	for i in variants:
		result = make_experiment(algorothms, datasets, clique_size=i)
		for measure in all_measures:
			if ('CFinder', dataset, measure) in result.keys():
			  table[i, measure] = result['CFinder', dataset, measure]
	write_choice(table, variants, dataset, 'clique_size')
示例#4
0
def get_optimal_thresholds(dataset):
	algorothms = ['SCAN']
	datasets = [dataset]
	table = {}
	#variants = [[1, 0.3], [1, 0.4], [1, 0.5], [1, 0.6], [1, 0.7], [1, 0.8], [2, 0.3], [2, 0.4], [2, 0.5], [2, 0.6], [2, 0.7], [2, 0.8], 
	# [3, 0.3], [3, 0.4], [3, 0.5], [3, 0.6], [3, 0.7], [3, 0.8]]
	#str_variants = ['1, 0.3', '1, 0.4','1, 0.5', '1, 0.6', '1, 0.7', '1, 0.8', '2, 0.3', '2, 0.4','2, 0.5', '2, 0.6', '2, 0.7', '2, 0.8',
  	#						'3, 0.3', '3, 0.4','3, 0.5', '3, 0.6', '3, 0.7', '3, 0.8']

	variants = [[2, 0.5]]
	str_variants = ['2, 0.5']
	for i in xrange(len(variants)):
		result = make_experiment(algorothms, datasets, neighbours_threshold=variants[i][0], similarity_threshold=variants[i][1])
		for measure in all_measures:
			table[str_variants[i], measure] = result['SCAN', dataset, measure]

	write_choice(table, str_variants, dataset, 'thresholds')