diff options
Diffstat (limited to 'src/make_plots.py')
| -rw-r--r-- | src/make_plots.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/src/make_plots.py b/src/make_plots.py index c45479e..8970cdf 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -54,7 +54,7 @@ def plot_watts_strogatz_graph(): plt.clf() fig = plt.figure(1) labels = [50, 100, 500, 1000, 2000, 5000] - x = [np.log(50), np.log(100), np.log(500), + x = [np.log(50), np.log(100), np.log(500), np.log(1000), np.log(2000), np.log(5000)] sparse_recov = [.25, .32, .7, .82, .89, .92] max_likel = [.21, .29, .67, .8, .87, .9] @@ -84,7 +84,7 @@ def plot_barabasi_albert_graph(): plt.clf() fig = plt.figure(1) labels = [50, 100, 500, 1000, 2000, 5000] - x = [np.log(50), np.log(100), np.log(500), + x = [np.log(50), np.log(100), np.log(500), np.log(1000), np.log(2000), np.log(5000)] sparse_recov = [.35, .38, .58, .69, .79, .86] max_likel = [.35, .38, .56, .68, .78, .85] @@ -188,13 +188,13 @@ def plot_ROC_curve(figure_name): plt.ylabel("Precision") plt.grid(color="lightgrey") - ax.plot(recall_lasso_200, precision_lasso_200, 'ko-', + ax.plot(recall_lasso_200, precision_lasso_200, 'ko-', color="lightseagreen", label="Lasso-200 cascades") - ax.plot(recall_sparse_200, precision_sparse_200, 'ko-', + ax.plot(recall_sparse_200, precision_sparse_200, 'ko-', color="k", label="Our Method-200 cascades") - ax.plot(recall_lasso_50, precision_lasso_50, 'ko-', + ax.plot(recall_lasso_50, precision_lasso_50, 'ko-', color="orange", label="Lasso-50 cascades") - ax.plot(recall_sparse_50, precision_sparse_50, 'ko-', + ax.plot(recall_sparse_50, precision_sparse_50, 'ko-', color="cornflowerblue", label="Our Method-50 cascades") plt.legend(loc="upper right", fontsize=14) plt.savefig("../paper/figures/"+"ROC_curve.pdf") @@ -253,9 +253,9 @@ if __name__=="__main__": #algorithms.greedy_prediction) #convex_optimization.type_lasso) if 0: - compute_graph("../datasets/kronecker_graph_256_cross.txt", + compute_graph("../datasets/kronecker_graph_256_cross.txt", n_cascades=50, lbda=0., min_proba=.2, max_proba=.7, passed_function= - convex_optimization.sparse_recovery, + convex_optimization.sparse_recovery, #convex_optimization.type_lasso, - sparse_edges=True)
\ No newline at end of file + sparse_edges=True) |