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
del nodeList
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}
# 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 = uniform(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))
if __name__ == '__main__':
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from network import SOM, DSOM
from distribution import uniform, normal, ring
size = 8
epochs = 10000
n = 10000
np.random.seed(12345)
samples_1 = uniform(n=n, center=(0.25,0.25), scale=(0.2,0.2))
samples_2 = uniform(n=n, center=(0.25,0.75), scale=(0.2,0.2))
samples_3 = uniform(n=n, center=(0.75,0.25), scale=(0.2,0.2))
samples_4 = uniform(n=n, center=(0.75,0.75), scale=(0.2,0.2))
mag = 1000
s1 = np.array([0,0])
for x in range(0,mag):
v = x/float(mag)
b = np.array([v, np.sin(7*v)/5 + np.random.random_sample()/3+ 0.3])
s1 = np.row_stack((s1,b))
s2 = np.array([0,0])
for x in range(0,mag):
v = x/float(mag)
b = np.array([v, np.sin(7*(v+.1))/5 + np.random.random_sample()/3+ 0.3])
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 = uniform(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))
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
size = 8
epochs = 20000
n = 10000
np.random.seed(12345)
samples_1 = uniform(n=n, center=(0.25,0.25), scale=(0.25,0.25))
samples_2 = uniform(n=n, center=(0.75,0.75), scale=(0.25,0.25))
samples_3 = uniform(n=n, center=(0.25,0.75), scale=(0.25,0.25))
samples_4 = uniform(n=n, center=(0.75,0.25), scale=(0.25,0.25))
print 'Neural gas'
np.random.seed(12345)
ng = NG((size,size,2))
ng.learn([samples_1, samples_2, samples_3, samples_4],
[2*epochs//8, 2*epochs//8, 2*epochs//8, 2*epochs//8])
print 'Self-Organizing Map'
np.random.seed(12345)
som = SOM((size,size,2))
som.learn([samples_1, samples_2, samples_3, samples_4],
