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")
