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import matplotlib.pyplot as plt
import numpy as np
import cascade_creation
import convex_optimization
import algorithms
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=.1) # 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, 4)) #
print(y)
plt.clf()
plt.plot(x, y)
#plt.show()
return x, y
def compare_greedy_and_lagrange_cs284r():
"""
Compares the performance of the greedy algorithm on the
lagrangian sparse recovery objective on the Facebook dataset
for the CS284r project
"""
G = cascade_creation.InfluenceGraph(max_proba = .8)
G.import_from_file("../datasets/subset_facebook_SNAPnormalize.txt")
A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=2000)
#Greedy
G_hat = algorithms.greedy_prediction(G, A)
algorithms.correctness_measure(G, G_hat, print_values=True)
#Lagrange Objective
G_hat = algorithms.recovery_l1obj_l2constraint(G, A,
passed_function=convex_optimization.sparse_recovery,
floor_cstt=.1, lbda=10)
algorithms.correctness_measure(G, G_hat, print_values=True)
def test():
"""
unit test
"""
if 0:
plot_rip_numberofnodes(max_proba=.3, n_min=30, n_max=30,
p_init=.01, n_cascades=100, K_max=4)
if 1:
compare_greedy_and_lagrange_cs284r()
if __name__=="__main__":
test()
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