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import tensorflow.keras as tf
import numpy as np
from sklearn.preprocessing import StandardScaler
verbose = 0
def predict_series(values, r1_nodes=5, r2_nodes=0, fc1_nodes=0, steps=20, use_lstm=True, *args, **kwargs):
train = np.array(values)
train_last_value = train[-1]
train = train[1:] - train[:-1]
sc = StandardScaler()
train = sc.fit_transform(train.reshape(-1, 1))
T = 25
X = []
Y = []
for t in range(len(train) - T):
x = train[t:t + T]
X.append(x)
Y.append(train[t + T])
X = np.array(X).reshape(-1, T, 1)
Y = np.array(Y)
nb_stats = 0
"""
X_temp = np.zeros(X.size + nb_stats * len(X)).reshape(-1, T + nb_stats)
step_size = 1 / (len(X) + steps)
def update_stats(row):
new_stat = row[T:]
new_stat[0] += step_size # number of sample
minimum = min(row[:T]) # minimum value, and when it occurred
if minimum < row[T + 1]:
new_stat[1], new_stat[2] = minimum, new_stat[0]
maximum = max(row[:T]) # maximum value, and when it occurred
if maximum > row[T + 3]:
new_stat[3], new_stat[4] = maximum, new_stat[0]
new_stat[5] = (row[T + 5] * row[T] + row[T - 1]) / (new_stat[0]) # rolling average
difference10 = row[T - 1] - row[T - 11] # the biggest difference within 10 items
if difference10 > row[T + 6]:
new_stat[6], new_stat[7] = difference10, new_stat[0]
if difference10 < row[T + 8]:
new_stat[8], new_stat[9] = difference10, new_stat[0]
abs_difference10 = abs(difference10) # the biggest absolute difference within 10 items
if abs_difference10 > row[T + 10]:
new_stat[10], new_stat[11] = abs_difference10, new_stat[0]
if abs_difference10 < row[T + 12]:
new_stat[12], new_stat[13] = abs_difference10, new_stat[0]
return new_stat
X_temp[0] = X[0] #np.append(X[0])#, [0, np.inf, 0, -np.inf, 0]) #, 0, -np.inf, 0, +np.inf, 0, 0, 0, np.inf, 0])
for i in range(1, len(X)):
X_temp[i] = np.append(X[i][:T], X_temp[i - 1][T:])
X_temp[i][T:] = update_stats(X_temp[i])
"""
#X = X_temp[1:].reshape(-1, T + nb_stats, 1)
#Y = Y[1:]
i = tf.layers.Input(shape=(T + nb_stats, 1))
if use_lstm:
rnn_layer = tf.layers.LSTM
else:
rnn_layer = tf.layers.GRU
if r2_nodes:
x = rnn_layer(r1_nodes, return_sequences=True)(i)
x = rnn_layer(r2_nodes)(x)
else:
x = rnn_layer(r1_nodes)(i)
if fc1_nodes:
x = tf.layers.Dense(fc1_nodes, activation='relu')(x)
x = tf.layers.Dense(1)(x)
model = tf.models.Model(i, x)
"""lr_schedule = tf.optimizers.schedules.ExponentialDecay(
initial_learning_rate=0.2,
decay_steps=10,
decay_rate=0.8)
optimizer = tf.optimizers.Ftrl(learning_rate=0.001, learning_rate_power=-0.1)"""
#for i in range(0, 500, 10):
#print('{}: {}'.format(i, lr_schedule(i)))
model.compile(
loss='mse', #tf.losses.LogCosh(),
optimizer=tf.optimizers.Adamax(lr=0.1) #LogCosh()'sgd'
)
callbacks = [tf.callbacks.EarlyStopping(patience=150, monitor='loss', restore_best_weights=True)]
r = model.fit(
X, Y,
epochs=500,
callbacks=callbacks,
verbose=verbose,
validation_split=0.0
)
pred = np.array([])
last_x = X[-1]
for _ in range(steps):
p = model.predict(last_x.reshape(1, -1, 1))[0, 0]
pred = np.append(pred, p)
#last_x[:T] = np.roll(last_x[:T], -1)
#last_x[T - 1] = p
#last_x[T:] = update_stats(last_x)
last_x = np.roll(last_x, -1)
last_x[-1] = p
pred = sc.inverse_transform(pred.reshape(-1, 1))
# pred = np.array(pred).astype('float64')
# pred = list(pred)
# logging.info(pred)
pred.reshape(-1)
pred[0] = train_last_value + pred[0]
for i in range(1, len(pred)):
pred[i] += pred[i-1]
result = {'result': list(pred.reshape(-1)), 'epochs': r.epoch[-1] + 1, 'loss': min(r.history['loss']), 'loss_last': r.history['loss'][-1]}
return result
if __name__ == "__main__":
from time import time
t1 = time()
verbose = 2
data = np.sin(np.arange(0.0, 28.0, 0.35)*2)
result = predict_series(data, steps=66, r1_nodes=14, r2_nodes=14, fc1_nodes=20)
print('exec time: {:8.3f}'.format(time()-t1))
#print(result['result'][:2])
print(print(result['epochs'], result['loss']))
import seaborn as sns
sns.lineplot(x=range(30), y=data[-30:], color='r')
sns.lineplot(x=range(30, 30+len(result['result'])), y=result['result'], color='b')
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