print Z.shape s = 0 print Z[0, 0] print Z[399, 399] for x in range(400): for y in range(400): s = s + Z[x, y] print s surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap("coolwarm"), linewidth=0, antialiased=True) fig.colorbar(surf, shrink=0.5, aspect=5) # plt.savefig('3dgauss.png') # plt.clf() plt.show() if __name__ == "__main__": headers, attacks = preprocessing.get_header_data() headers.remove("protocol_type") headers.remove("attack") headers.remove("difficulty") df_training_20, df_training_full, gmms_20, gmms_full = preprocessing.get_preprocessed_training_data() df_test_20, df_test_full, gmms_test_20, gmms_test_full = preprocessing.get_preprocessed_test_data() title = "training20_only" logger.debug("#################################################") logger.debug(title) test()
df_abnormal.drop('protocol_type', 1, inplace=True) df_abnormal.reset_index(drop=True) df_abnormal = df_abnormal[0:10] gmm_normals_protcl = gmms[0][protocol_index] gmm_abnormals_protcl = gmms[1][protocol_index] gmms_protcl = [gmm_normals_protcl, gmm_abnormals_protcl] generate_plots(df_abnormal, df_normal, headers, gmms_protcl, attack_type, path=path, protcls_name=protocol_type) if __name__ == '__main__': import time start = time.time() df_training_20, df_training_full, gmms_training_20, gmms_training_full = preprocessing.get_preprocessed_training_data( ) df_test_plus, df_test_21, gmms_test_plus, gmms_test_21 = preprocessing.get_preprocessed_test_data( ) generate_plots_for_df(df_training_20, gmms_training_20, "training20") generate_plots_for_df(df_training_full, gmms_training_full, "trainingfull") generate_plots_for_df(df_test_plus, gmms_test_plus, "testplus") generate_plots_for_df(df_test_21, gmms_test_21, "test21")
Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('coolwarm'), linewidth=0, antialiased=True) fig.colorbar(surf, shrink=0.5, aspect=5) # plt.savefig('3dgauss.png') # plt.clf() plt.show() if __name__ == '__main__': headers, attacks = preprocessing.get_header_data() headers.remove('protocol_type') headers.remove('attack') headers.remove('difficulty') df_training_20, df_training_full, gmms_20, gmms_full = preprocessing.get_preprocessed_training_data( ) df_test_20, df_test_full, gmms_test_20, gmms_test_full = preprocessing.get_preprocessed_test_data( ) title = "training20_only" logger.debug("#################################################") logger.debug(title) test()
df_abnormal = df_abnormal[(df_abnormal["attack"] == i)] # only select for 1 class df_abnormal = df_abnormal[(df_abnormal["protocol_type"] == protocol_index)] if 1 > len(df_abnormal) : continue df_abnormal.drop('attack',1,inplace=True) # remove useless df_abnormal.drop('difficulty',1,inplace=True) # remove useless df_abnormal.drop('protocol_type',1,inplace=True) df_abnormal.reset_index(drop=True) df_abnormal = df_abnormal[0:10] gmm_normals_protcl = gmms[0][protocol_index] gmm_abnormals_protcl = gmms[1][protocol_index] gmms_protcl = [gmm_normals_protcl, gmm_abnormals_protcl] generate_plots(df_abnormal, df_normal, headers, gmms_protcl, attack_type, path=path, protcls_name = protocol_type) if __name__ == '__main__': import time start = time.time() df_training_20, df_training_full, gmms_training_20, gmms_training_full = preprocessing.get_preprocessed_training_data() df_test_plus, df_test_21, gmms_test_plus, gmms_test_21 = preprocessing.get_preprocessed_test_data() generate_plots_for_df(df_training_20, gmms_training_20, "training20") generate_plots_for_df(df_training_full, gmms_training_full, "trainingfull") generate_plots_for_df(df_test_plus, gmms_test_plus, "testplus") generate_plots_for_df(df_test_21, gmms_test_21, "test21")