def lnfaSub(mu, sigma, m, ssize, nodes):
cn = [2,2,2]; ft=[]; fit =[];
g = [[1,2],[0,2],[0,1]];
ft.append(r.lognormvariate(mu,sigma)); Fit = ft[0]; fit.append(Fit)
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[1]; fit.append(Fit)
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[2]; fit.append(Fit)
for j in range(3,ssize):
g.append([]); cn.append(m);
for i in range(m):
prob=r.uniform(0,Fit); n = binsearch(prob, fit, j);
cn[n] += 1; g[n].append(j); g[j].append(n);
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[j]; fit.append(Fit)
for j in range(ssize, nodes):
print j;
nn1=r.sample(xrange(0,j),ssize);
fft=[]; ffit = []; FFit = 0;
for i in range(0, ssize):
fft.append(ft[nn1[i]]); FFit += fft[i]; ffit.append(FFit);
g.append([]); cn.append(m);
for i in range(m):
prob=r.uniform(0, FFit); n = binsearch(prob, ffit, ssize);
cn[nn1[n]] += 1; g[nn1[n]].append(j); g[j].append(nn1[n]);
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[j]; fit.append(Fit)
return g, cn, ft
def lnfa(mu, sigma, m, perc, nodes):
cn = [1, 1]
ft = []
fit = []
g = [[1], [0]]
ft.append(r.lognormvariate(mu, sigma))
Fit = ft[0]
fit.append(Fit)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[1]
fit.append(Fit)
for j in range(2, 1000):
g.append([])
cn.append(m)
for i in range(m):
prob = r.uniform(0, Fit)
n = binsearch(prob, fit, j)
cn[n] += 1
g[n].append(j)
g[j].append(n)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[j]
fit.append(Fit)
for j in range(1000, nodes):
j
#ssize = int(perc*j);
ssize = 1000
nn1 = r.sample(xrange(0, j), ssize)
fft = []
ffit = []
FFit = 0
for i in range(0, ssize):
fft.append(ft[nn1[i]])
FFit += fft[i]
ffit.append(FFit)
g.append([])
cn.append(m)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[j]
fit.append(Fit)
for i in range(m):
prob = r.uniform(0, FFit)
n = binsearch(prob, ffit, ssize)
cn[nn1[n]] += 1
g[nn1[n]].append(j)
g[j].append(nn1[n])
return g, cn, ft
def lognormdd(mu, sigma, m, nodes):
cn = [2, 2, 2]
ft = []
fit = []
g = [[1, 2], [0, 2], [0, 1]]
ft.append(r.lognormvariate(mu, sigma))
Fit = ft[0]
fit.append(Fit)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[1]
fit.append(Fit)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[2]
fit.append(Fit)
for j in range(3, nodes):
g.append([])
cn.append(m)
for i in range(m):
prob = r.uniform(0, Fit)
n = binsearch(prob, fit, j)
cn[n] += 1
g[n].append(j)
g[j].append(n)
ft.append(r.lognormvariate(mu, sigma))
Fit += ft[j]
fit.append(Fit)
return g, cn, ft
def expo_search(arr, size, target):
if (size == 0):
print("Element not found")
return -1
bound = 1
while (bound < size and arr[bound] < target):
bound *= 2
#print("Searching for range in: ",bound)
return bin_s.binsearch(arr, bound // 2, min(bound, size - 1), target)
def EqualFalse(self):
for _list, key, index in (([0, 1, 2, 3, 4,
5], 3, 3), ([0, 1, 2, 3, 4, 5], 0, 0),
([0, 1, 2, 3, 4,
5], 4, 4), ([0, 1, 2, 3, 4, 5], 5, 5),
([0, 1, 2, 3, 4,
5], 1, 1), ([0, 1, 2, 3, 4, 5], 2, 2),
([0, 1, 2, 3, 4,
6], 6, 5), ([0, 1, 4, 7, 9, 10], 4, 2),
([0, 2, 53, 364, 1235, 23523], 2,
1), ([0, 2, 53, 364, 1235,
23523], 23523, 5)):
result = bs.binsearch(_list, key)
self.assertEqual(index, result)
def inRange(self):
for _list, key, index in (([0, 1, 2, 3, 4,
5], 3, 3), ([0, 1, 2, 3, 4, 5], 0, 0),
([0, 1, 2, 3, 4,
5], 4, 4), ([0, 1, 2, 3, 4, 5], 5, 5),
([0, 1, 2, 3, 4,
5], 1, 1), ([0, 1, 2, 3, 4, 5], 2, 2),
([0, 1, 2, 3, 4,
6], 6, 5), ([0, 1, 4, 7, 9, 10], 4, 2),
([0, 2, 53, 364, 1235, 23523], 2,
1), ([0, 2, 53, 364, 1235,
23523], 23523, 5)):
result = bs.binsearch(_list, key)
self.assertIn(result, _list)
def NoneFalse(self):
for _list, key, index in (([0, 1, 2, 3, 4,
5], 3, 3), ([0, 1, 2, 3, 4, 5], 0, 0),
([0, 1, 2, 3, 4,
5], 4, 4), ([0, 1, 2, 3, 4, 5], 5, 5),
([0, 1, 2, 3, 4,
5], 1, 1), ([0, 1, 2, 3, 4, 5], 2, 2),
([0, 1, 2, 3, 4,
6], 6, 5), ([0, 1, 4, 7, 9, 10], 11, None),
([0, 2, 53, 364, 1235, 23523], 2,
1), ([0, 2, 53, 364, 1235,
23523], 23523, 5)):
result = bs.binsearch(_list, key)
if key not in _list:
self.assertEqual(key, result)
def lognormdd(mu, sigma, m, alpha, nodes):
cn = [1,1]; ft=[]; fit =[];
g = [[1],[0]];
ft.append((r.lognormvariate(mu,sigma))**alpha); Fit = ft[0]; fit.append(Fit)
ft.append((r.lognormvariate(mu,sigma))**alpha); Fit += ft[1]; fit.append(Fit)
for j in range(2,nodes):
g.append([]); cn.append(m);
for i in range(m):
prob=r.uniform(0,Fit); n = binsearch(prob, fit, j);
cn[n] += 1;
g[n].append(j);
g[j].append(n);
ft.append((r.lognormvariate(mu,sigma))**alpha); Fit += ft[j]; fit.append(Fit)
return g, cn, ft
graph.add_vertices(2)
graph.add_edges([(0, 1)])
ft.append((r.lognormvariate(mu, sigma))**alpha)
Fit = ft[0]
fit.append(Fit)
ft.append((r.lognormvariate(mu, sigma))**alpha)
Fit += ft[1]
fit.append(Fit)
for j in range(2, nodes):
g.append([])
cn.append(m)
graph.add_vertices(1)
for i in range(m):
prob = r.uniform(0, Fit)
n = binsearch(prob, fit, j)
cn[n] += 1
g[n].append(j)
g[j].append(n)
graph.add_edges([(n, j)])
ft.append((r.lognormvariate(mu, sigma))**alpha)
Fit += ft[j]
fit.append(Fit)
igraph.plot(graph,
'a10.pdf',
layout="fr",
vertex_label=None,
vertex_color="blue",
vertex_size=7,
edge_width=.1,
m=2; mu=0; sigma=10.; nodes=1000;
cn = [1,1]; ft=[]; fit =[];
g = [[1],[0]];
graph = igraph.Graph()
graph.add_vertices(2)
graph.add_edges([(0,1)])
ft.append(r.lognormvariate(mu,sigma)); Fit = ft[0]; fit.append(Fit)
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[1]; fit.append(Fit)
for j in range(2,nodes):
g.append([]); cn.append(m);
graph.add_vertices(1)
for i in range(m):
prob=r.uniform(0,Fit); n = binsearch(prob, fit, j);
cn[n] += 1;
g[n].append(j);
g[j].append(n);
graph.add_edges([(n,j)])
ft.append(r.lognormvariate(mu,sigma)); Fit += ft[j]; fit.append(Fit)
igraph.plot(graph, layout="fr", vertex_label=None, vertex_size=4, edge_width=.2)
igraph.plot(graph, layout="kk", vertex_label=None, vertex_size=5, edge_width=.3)
igraph.plot(graph, layout="fr", vertex_label=None, vertex_size=5, edge_width=.3)
def testBinsearchKnownValues(self):
for _list, key, index in self.knowValues:
result = bs.binsearch(_list, key)
self.assertEqual(index, result)
from binsearch import binsearch bs = binsearch() array = [1, 3, 5, 17, 25, 31, 33, 45, 55, 56, 57, 87, 90, 99] q = 1 print bs.search(array, q) == 0 q = 5 print bs.search(array, q) == 2 q = 87 print bs.search(array, q) == 11 q = 99 print bs.search(array, q) == 13 q = 6 print bs.search(array, q) == -1 q = 60 print bs.search(array, q) == -1
