def test_simulation_gen(self):
random.seed(123)
time,trap,trajectory = datagen.generate(self.num_points,0.005,self.k,(1.,1.),(1e-5,2e-5),1e-6,8.9e-4,self.phi,self.position,293,1,True)
mean_traj = np.mean(np.fabs(trajectory), axis=0)
self.assertTrue(np.isclose(time[self.num_points - 1], self.last_time, rtol=1e-05, atol=1e-08))
self.assertTrue(np.allclose(mean_traj, self.expected_mean_traj_prob, rtol=1e-05, atol=1e-08))
def test_force_single(self):
random.seed(123)
time, traps, trajectories = datagen.generate(1000, 0.005, self.k,
(2, 1), (1e-6, 1e-6),
0.5e-6, 9.7e-4, self.phi,
(0., 0.), 300, 1, False)
_, means = forcecalc.calculate(time, trajectories[:, 0:2],
traps[:, 0:2], self.k, self.phi)
self.assertTrue(
np.allclose(means,
self.expected_means_calc,
rtol=1e-05,
atol=1e-08))
import os
import torch
import pickle
from MeLU import MeLU
from options import config
from model_training import training
from data_generation import generate
from evidence_candidate import selection
if __name__ == "__main__":
master_path = "/home/sunpeijie/files/task/out/melu/ml"
if not os.path.exists("{}/".format(master_path)):
os.mkdir("{}/".format(master_path))
# preparing dataset. It needs about 22GB of your hard disk space.
generate(master_path)
# training model.
melu = MeLU(config)
model_filename = "{}/models.pkl".format(master_path)
if not os.path.exists(model_filename):
# Load training dataset.
training_set_size = int(
len(os.listdir("{}/warm_state".format(master_path))) / 4)
supp_xs_s = []
supp_ys_s = []
query_xs_s = []
query_ys_s = []
for idx in range(training_set_size):
supp_xs_s.append(
pickle.load(
import data_generation as dg
import draw as d
from algorithm.km_smote import Over_Sample
from algorithm.under_sample import Under_Sample
if __name__ == "__main__":
dg = dg.DataGenerator(1000, 0.9)
data, label = dg.generate()
di = {"n_clusters": 10}
osampler = Over_Sample(data=data, label=label, n=3, categorical_features=[], **di)
syth = osampler.do_synthetic()
print("syth leng", len(syth))
syth_label = [1.0] * len(syth)
dr = d.Drawer(data, label)
dr.plot_scatter()
dr1 = d.Drawer(syth, syth_label)
dr1.plot_scatter()
under_sample = Under_Sample(major=data[0:900].tolist(), major_label=[0.]*900, synthetics=syth, synthetics_label=syth_label, categorical_features=[], rate=0.5, **di)
under = under_sample.do_undersample()
print("under length", len(under))
under_label = [0.0] * len(under)
x = under + syth
y = under_label + syth_label
dr2 = d.Drawer(x, y)
dr2.plot_scatter()