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import numpy as np | |
import pandas as pd | |
from numpy import concatenate | |
from matplotlib import pyplot | |
from keras.models import Sequential | |
from keras.callbacks import Callback | |
from keras.layers import LSTM, Dense, Activation | |
import matplotlib.pyplot as plt | |
%matplotlib inline | |
N_DATA_ORIG = 3000 # length of data to generate | |
N_FEATURES = 5 # number of syntethic features to create | |
N_ROLLING = 1000 # length of window over which to smooth the random data to make it look realistic | |
N_TIMESTEPS = 5 # number of timesteps you want to both train on and predict out to | |
N_DATA = N_DATA_ORIG - N_ROLLING # length of final data after smoothing | |
N_TRAIN_ITERATIONS = 5 # number of times to iterate training of the model | |
N_EPOCHS = 5 # within each train call how many epochs to run for | |
BATCH_SIZE = 100 # batch size to train on | |
N_LAYERS = 3 # number of layers for the LSTM | |
N_LSTM_UNITS = 2 # number of hidden unit in each LSTM layer | |
BREAK_LEN = 1000 # length of the break in the data we will create | |
random_break_point = np.random.choice(N_DATA) # pick a random point in the data to swap in the broken data into |
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