コード例 #1
0
ファイル: zadacha.py プロジェクト: golood/magistr
 def model(self, param):
     """Метод для моделирования исходных данных"""
     if param[2] == "N":
         return distr.normal(param[0], param[1], param[3])
     elif param[2] == "LN":
         return distr.logNormal(param[0], param[1], param[3])
     elif param[2] == "R":
         return distr.uniform(param[0], param[1], param[3])
     elif param[2] == "G":
         return distr.gamma(param[0], param[1], param[3])
     elif param[2] == "BS":
         return distr.birnbaumSaunders(param[0], param[1], param[3])
     elif param[2] == "P":
         return distr.pareto(param[0], param[1], param[3])
     else:
         return 0
コード例 #2
0
ファイル: main.py プロジェクト: kevinrdahl/cascade
centralities = 	{
				'Degree' : centrality.degree(network),
				'Betweenness' : centrality.betweenness(network),
				'Closeness' : centrality.closeness(network)
				}
centralities['Hybrid'] = centrality.hybrid( [centralities[name] for name in centralities] )

results = 0

print ''
for trial in range(numTrials):
	thresholds = []
	if distro == 'UNIFORM':
		thresholds = distribution.uniform(network)
	elif distro == 'NORMAL':
		thresholds = distribution.normal(network)
	elif distro == 'LONGTAIL':
		thresholds = distribution.longtail(network, 20)
	else:
		print '\nDistribution should be UNIFORM, NORMAL, or LONGTAIL'
		quit()
		
	for i in range(len(thresholds)):
		#print 'thresholds[' + str(i) + '] = ' + str(thresholds[i])
		network[i]['threshold'] = thresholds[i]
	
	if results == 0:
		results = {budget:{method:[] for method in centralities} for budget in budgets}
	
	for i in range(len(budgets)):
		budget = budgets[i]
コード例 #3
0
 def reward(self, employer):
     #かなり小さい値になると思うので、標準偏差は小さくする必要がある
     rw = distribution.normal((employer.remained_work() + 1) / (employer.assigned_work() + employer.processed_work() + 1), 0, 0.01)
     return rw
コード例 #4
0
ファイル: figure-normal.py プロジェクト: rougier/dynamic-som
#           FRANCE

if __name__ == '__main__':
    import numpy as np
    import matplotlib
    #matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG,SOM,DSOM
    from distribution import uniform, normal, ring

    n = 8
    epochs = 20000
    N = 10000

    np.random.seed(123)
    samples = normal(n=N) 

    print 'Neural Gas'
    np.random.seed(123)
    ng = NG((n,n,2))
    ng.learn(samples,epochs)
    print 'Self-Organizing Map'
    np.random.seed(123)
    som = SOM((n,n,2))
    som.learn(samples,epochs)
    print 'Dynamic Self-Organizing Map'
    np.random.seed(123)
    dsom = DSOM((n,n,2), elasticity=1.75)
    dsom.learn(samples,epochs)

    fig = plt.figure(figsize=(21,8))
コード例 #5
0
#           FRANCE

if __name__ == '__main__':
    import numpy as np
    import matplotlib
    #matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG, SOM, DSOM
    from distribution import uniform, normal, ring

    n = 8
    epochs = 20000
    N = 10000

    np.random.seed(123)
    samples = normal(n=N)

    print 'Neural Gas'
    np.random.seed(123)
    ng = NG((n, n, 2))
    ng.learn(samples, epochs)
    print 'Self-Organizing Map'
    np.random.seed(123)
    som = SOM((n, n, 2))
    som.learn(samples, epochs)
    print 'Dynamic Self-Organizing Map'
    np.random.seed(123)
    dsom = DSOM((n, n, 2), elasticity=1.75)
    dsom.learn(samples, epochs)

    fig = plt.figure(figsize=(21, 8))