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import matplotlib.pyplot as plt
import numpy as np
import cascade_creation
import rip_condition
def plot_rip_numberofnodes(max_proba, n_min, n_max, p_init, n_cascades, K_max):
"""
Plots the RIP constant for varying number of nodes (n_max included)
"""
x = np.arange(n_min, n_max+1)
y = []
for n_nodes in x:
print n_nodes
G = cascade_creation.InfluenceGraph(max_proba=.3)
G.erdos_init(n=n_nodes, p=.5) #TODO: handle different inits!
cascades = cascade_creation.generate_cascades(G, p_init=p_init,
n_cascades=n_cascades)
M, __ = cascade_creation.icc_matrixvector_for_node(cascades, None)
M = cascade_creation.normalize_matrix(M)
y.append(rip_condition.find_kth_rip_constants(M, 5))
plt.clf()
plt.plot(x, y)
plt.show()
return x, y
def test():
"""
unit test
"""
plot_rip_numberofnodes(max_proba=.3, n_min=10, n_max=15,
p_init=.3, n_cascades=10, K_max=5)
if __name__=="__main__":
test()
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