import matplotlib.pyplot as plt
import numpy as np
import cascade_creation
import convex_optimization
import algorithms
import rip_condition


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=100)

    #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.type_lasso,
            floor_cstt=.1, lbda=10)
    algorithms.correctness_measure(G, G_hat, print_values=True)


def test():
    """
    unit test
    """
    G = cascade_creation.InfluenceGraph(max_proba=.8)
    G.erdos_init(n=20, p=.2)
    print(G.mat)
    A = cascade_creation.generate_cascades(G, p_init=.1, n_cascades=1000)
    G_hat = algorithms.recovery_l1obj_l2constraint(G, A,
                passed_function=convex_optimization.sparse_recovery,
                floor_cstt=.1, lbda=20)
    algorithms.correctness_measure(G, G_hat, print_values=True)

if __name__=="__main__":
    test()