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()
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()
