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)