def get_random_example(n, trim_length, trimed=True, standardize=True):
if standardize:
data = standardize_all_data()
else:
data = get_trajectory()
if trimed:
data = trim_data(data, length=trim_length)
examples = random.choices(data, k=n)
return examples
def play_real(length, translation=True):
data = standardize_all_data()
data = trim_data(data, length)
data = istandardize_data(data)
x = random.choice(data)
print(type(x))
print(x.shape)
x = np.array(x)
write_one_file('example', x, format='rov')
cmd = CMD + PAR1 + 'example' + PAR2
if translation:
cmd += TRANSLATION
os.system(cmd)
return x
from data_processor import time_stamp
from data_processor import trim_data
from dynamic_reporter import init_dynamic_report
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
# time.sleep(13500)
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
train_set = trim_data(standardize_all_data())
train_set, WIN = window(train_set, 'hann')
print(WIN.shape)
train_set = fft_data(train_set)
train_set, dim = lpf_dimension_reduction(train_set, frequency=10)
train_set = flatten_complex_data(train_set)
print(dim)
print('Training set is ready!')
class Complex_Fully_Connected_Linear_Discriminator_LPF(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Linear_Discriminator_LPF,
self).__init__()
self.n_in = dimension * 2 * 6 # real part and imaginary part are saperated
from data_processor import iflatten_complex_data
from data_processor import time_stamp
from data_processor import trim_data
from dynamic_reporter import init_dynamic_report
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
origin = trim_data(standardize_all_data())
data = fft_all_data()
train_set = flatten_complex_data(data)
print('Training set is ready!')
class Complex_Fully_Connected_Linear_Discriminator(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Linear_Discriminator, self).__init__()
self.n_in = dimension * (config.TRIM_LENGTH // 2 + 1) * 2 # real part and imaginary part are saperated
# hidden linear layers
self.linear1 = nn.Linear(self.n_in, self.n_in)
self.linear2 = nn.Linear(self.n_in, self.n_in)
self.linear3 = nn.Linear(self.n_in, self.n_in)
self.linear4 = nn.Linear(self.n_in, 1)
from data_processor import time_stamp
from data_processor import trim_data
from dynamic_reporter import init_dynamic_report
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
fft_all_data()
data = trim_data(standardize_all_data())
train_set = flatten_real_data(data)
print('Training set is ready!')
class Complex_Fully_Connected_Discriminator(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Discriminator, self).__init__()
self.n_in = dimension * config.TRIM_LENGTH
# hidden linear layers
self.linear1 = nn.Linear(self.n_in, self.n_in)
self.linear2 = nn.Linear(self.n_in, self.n_in)
self.linear3 = nn.Linear(self.n_in, 1)
self.drop_layer = nn.Dropout(0.87)
