def test_default_5d_model(self):
contents = read_file("random_5d.csv", "")
time_series, dt, contents, variable_names = clean_contents(contents)
model = ps.SINDy(feature_names=variable_names)
model.fit(contents, t=dt)
_ = model.coefficients()
actual_score = model.score(contents, t=time_series)
# expected_coefficients should be incomprehensible
expected_score_max = 0.1 # model is expected to fail
# assert (pytest.approx(actual_co, 0.1) == expected_co)
assert actual_score < expected_score_max # model is expected to score poorly (less than 0.1)
def test_default_lorenz_model(self):
contents = read_file("data_Lorenz3d.csv", "")
time_series, dt, contents, variable_names = clean_contents(contents)
model = ps.SINDy(feature_names=variable_names)
model.fit(contents, t=dt)
actual_co = model.coefficients()
actual_score = model.score(contents, t=time_series)
expected_co = [[0.0, -10.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 28.0, -1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, -2.666, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]]
expected_score = 1.0
assert (pytest.approx(actual_co, 0.1) == expected_co)
assert (pytest.approx(actual_score, 0.01) == expected_score)
def test_default_lorenz(self):
ts, dt, cont, var = clean_contents(read_file("data_Lorenz3d.csv", ""))
ts = ts[0:4]
cont = cont[0:4]
expected_ts = [0., 0.002, 0.004, 0.006]
expected_dt = 0.002
expected_cont = [[-8, 8, 27], [-7.683508382, 7.966250523, 26.73151873],
[-7.373984577, 7.92929174, 26.46998112],
[-7.071350036, 7.889537629, 26.21523996]]
expected_vars = ["x", "y", "z"]
assert np.all(ts == expected_ts)
assert np.all(dt == expected_dt)
assert np.all(cont == expected_cont)
assert np.all(var == expected_vars)
def test_random_5d(self):
ts, dt, cont, var = clean_contents(read_file("random_5d.csv", ""))
ts = ts[0:4]
cont = cont[0:4]
expected_ts = [0., 0.001, 0.002, 0.003]
expected_dt = 0.001
expected_cont = [
[3.216510998, 9.914181513, 3.866592635, 0.746334938, 4.606170334],
[0.650685986, 4.486606167, 5.84805047, 1.991102499, 5.211598163],
[1.318906913, 8.869120427, 2.594125945, 9.226401504, 2.357597024],
[8.26282217, 2.762088665, 1.941167642, 5.280758711, 4.70049113]
]
expected_vars = ["a", "b", "c", "d", "e"]
assert np.all(ts == expected_ts)
assert np.all(dt == expected_dt)
assert np.all(cont == expected_cont)
assert np.all(var == expected_vars)
def test_default_lorenz_plot(self):
contents = read_file("data_Lorenz3d.csv", "")
time_series, dt, contents, variable_names = clean_contents(contents)
model = ps.SINDy(feature_names=variable_names)
model.fit(contents, t=dt)
coefs = model.coefficients()
feats = model.get_feature_names(
) # Get the feature names from the obtained model
conds = np.array(
[float(val) for val in contents[0]]
) # Convert the system's initial conditions into a numpy array of float values as this is what is expected by the model.simulate() function
sim_data = model.simulate(
conds, time_series
) # Create the forward simulated data. This uses the original initial conditions evolved with the model output equations to obtain new data
fig, _ = create_plot(time_series, contents, variable_names, coefs,
feats, sim_data)
return fig
def test_clean_contents_control(self):
ts, dt, cont, u, var = clean_contents_control(
read_file("predatorpreydata.csv", ""))
ts = ts[0:4]
cont = cont[0:4]
u = u[0:4]
expected_ts = [0.1, 0.2, 0.3, 0.4]
expected_dt = 0.1
expected_cont = [[1.0, 1.0], [0.93815867, 1.0],
[0.894741036, 0.993815867],
[0.878213584, 0.983355064]]
expected_u = np.array(
['0.2196665', '0.437335995', '0.651031414', '0.858815353'])
expected_vars = ["x", "y", "u"]
assert np.all(ts == expected_ts)
assert np.all(dt == expected_dt)
assert np.all(cont == expected_cont)
assert np.all(u == expected_u)
assert np.all(var == expected_vars)
import numpy as np
from base import read_file, nearest_neighbor_network
if __name__ == '__main__':
training_x, training_y = read_file('./hw4_knn_train.dat')
testing_x, testing_y = read_file('./hw4_knn_test.dat')
print "Q17, ein:", nearest_neighbor_network(training_x, training_y, training_x, training_y,5)
print "Q18, eout:", nearest_neighbor_network(testing_x, testing_y, training_x, training_y, 5)
#!/usr/bin/env python3
import base
import WLSK_Keygen, WLSK_Init, WLSK_Resp, WLSK_S
if __name__ == '__main__':
print('Generating keys')
WLSK_Keygen.init()(base.get_hostname())
print(' enc_key = {}\n mac_key = {}\b table = {}'.format(
base.read_file('wlsk_enc_key'), base.read_file('wlsk_mac_key'),
base.read_file('keytbl')))
print('\x1b[31m[A1]\x1b[0m')
(a3, idA) = WLSK_Init.init()(base.get_hostname())
print(' idA = {}'.format(idA))
print('\x1b[34m[B2]\x1b[0m')
(b4, n) = WLSK_Resp.init()(idA)
print(' n = {}'.format(n))
print('\x1b[31m[A3]\x1b[0m')
(_, iv1, e, m) = a3(n)
print(' e = {}\n m = {}'.format(e, m))
print('\x1b[34m[B4]\x1b[0m')
(b6, idA_, idB_, iv2, e_, m_) = b4(iv1, e, m)
print(' idA\' = {}\n idB\' = {}\n e\' = {}\n m\' = {}'.format(
idA_, idB_, e_, m_))
print('\x1b[33m[S5]\x1b[0m')
(_, iv3, e__, m__) = WLSK_S.init()(idA_, idB_, iv2, e_, m_)
print(' e\'\' = {}\n m\'\' = {}'.format(e__, m__))
#!/usr/bin/env python3
import base, WLSK_Keygen
from common import *
if __name__ == '__main__':
my_hostname = base.get_hostname()
print('Generating keys for {}...'.format(my_hostname))
WLSK_Keygen.init()(my_hostname)
print('Done')
debug('enc_key = {}\nmac_key = {}\ntable = {}'.format(base.read_file('wlsk_enc_key'),
base.read_file('wlsk_mac_key'), base.read_file('keytbl')))
import ONS_Keygen, ONS_AGenKey, ONS_BGenKey
import ONS_S, ONS_A, ONS_B
import time
def delay(): time.sleep(0.7)
if __name__ == '__main__':
print('Generating \x1b[33mserver\x1b[0m key')
(_, pkS) = ONS_Keygen.init()()
print('Generating \x1b[31mA\x1b[0m key')
(_, pkA) = ONS_AGenKey.init()()
print('Generating \x1b[34mB\x1b[0m key')
(_, pkB) = ONS_BGenKey.init()()
b = base.read_file('idB')
delay()
print('\x1b[31mA:\x1b[0m Message 1')
(oa3, hA, hB) = ONS_A.init()(b)
print(' hA: {}\n hB: {}'.format(hA, hB))
delay()
print('\x1b[33mS:\x1b[0m Message 2')
(_, rk, h2, s) = ONS_S.init()(hA, hB)
delay()
print('\x1b[31mA:\x1b[0m Message 3')
(oa5, c) = oa3(rk, h2, s)
delay()
