graph_lasso_K_estim_tau_pen_main.py

from time import time

from sklearn.mixture import GMM

from graph_lasso_K_estim_tau_pen import GraphLassoMix
from tools.gm_tools import gaussian_mixture_sample, gm_params_generator, best_cont_matrix


def main(d,k,N):

    weights, centers, cov = gm_params_generator(d,k)

    X, Y = gaussian_mixture_sample(weights, centers, cov, N)
    lasso = GraphLassoMix(n_components=k, n_iter=5)
    t1_lasso = time()
    lasso.fit(X)
    t2_lasso=time()
    y_lasso = lasso.clusters_assigned

    gmm = GMM(n_components=k)
    t1_em= time()
    gmm.fit(X)
    y_em = gmm.predict(X)
    t2_em = time()

    print "===Glasso result==="
    algo_score(Y, y_lasso, t2_lasso-t1_lasso)

    print "\n===EM result==="
    algo_score(Y, y_em, t2_em-t1_em)

def algo_score(Y, y_estim, t):
    mat, permut, diag_sum = best_cont_matrix(Y, y_estim)
    print "best cont Matrix: "
    print mat
    print "Best Permutation: "
    print permut
    print "Diagonal Sum:"
    print diag_sum
    print "Correctly assigned ratio: "
    print 1.0*diag_sum/len(Y)
    print "Elapsed time:"
    print t, 's'

if __name__ == '__main__':
    dim_range = [2]
    N_range = [200]
    k_range = [2]
    for n in N_range:
        for k in k_range:
            for d in dim_range:
                print "*******Computing for d =",d," k =",k," and ",n," points********"
                main(d, k, n)
                #brute code to avoid errors
                #err = None
                #while err == None:
                #    try:
                #     #   main(d, k, n)
                #        err = 0
                #    except Exception as inst:
                #        template = "An exception of type {0} occured. Arguments:\n{1!r}"
                #        print template.format(type(inst).__name__, inst.args)
                #        pass