def create_series(in_array,
window_size,
period,
minV,
maxV,
layer_nodes=[2, 3],
sigmoid='tanh',
epochs=50000):
global_max = maxV
global_min = minV
X_train = []
y_train = []
for i in range(len(in_array) - window_size):
X = []
for j in range(window_size):
X.append(_scale_to_binary(in_array[i + j], global_min, global_max))
X_train.append(X)
y_train.append(
_scale_to_binary(in_array[i + window_size], global_min,
global_max))
X_train = np.array(X_train)
y_train = np.array(y_train)
layers = []
layers.append(window_size)
for i in range(len(layer_nodes)):
layers.append(layer_nodes[i])
n = NeuralNetwork(layers, sigmoid)
n.fit(X_train, y_train, epochs)
X_test = in_array[-window_size:]
for i in range(len(X_test)):
X_test[i] = _scale_to_binary(X_test[i], global_min, global_max)
preds = []
X_test = deque(X_test)
for i in range(period):
val = n.predict(X_test)
preds.append(rescale_from_binary(val[0], global_min, global_max))
X_test.rotate(-1)
X_test[window_size - 1] = val[0]
return preds
def create_series(in_array,window_size,period, minV, maxV, layer_nodes = [2,3], sigmoid = 'tanh', epochs = 50000):
global_max = maxV
global_min = minV
X_train = []
y_train = []
for i in range(len(in_array)-window_size):
X = []
for j in range(window_size):
X.append(_scale_to_binary(in_array[i+j],global_min,global_max))
X_train.append(X)
y_train.append(_scale_to_binary(in_array[i+window_size],global_min,global_max))
X_train = np.array(X_train)
y_train = np.array(y_train)
layers = []
layers.append(window_size)
for i in range(len(layer_nodes)):
layers.append(layer_nodes[i])
n = NeuralNetwork(layers,sigmoid)
n.fit(X_train,y_train, epochs)
X_test = in_array[-window_size:]
for i in range(len(X_test)):
X_test[i]=_scale_to_binary(X_test[i],global_min,global_max)
preds = []
X_test = deque(X_test)
for i in range(period):
val = n.predict(X_test)
preds.append(rescale_from_binary(val[0], global_min, global_max))
X_test.rotate(-1)
X_test[window_size-1] = val[0]
return preds
