def main():
i_gen = InputGenerator(0, T, NUM_TIME_STEPS)
data = i_gen.generate_sin(amplitude=AMPLITUDE)
num_train = int(len(data) * RATIO_TRAIN)
train_data = data[:num_train]
model = ReservoirNetWork(inputs=train_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.train() # 訓練
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(data[num_train:]))
predict_result = model.predict(num_predict)
t = np.linspace(0, T, NUM_TIME_STEPS)
## plot
plt.plot(t, data, label="inputs")
plt.plot(t[:num_train], train_result, label="trained")
plt.plot(t[num_train:], predict_result, label="predicted")
plt.axvline(x=int(T * RATIO_TRAIN), label="end of train",
color="green") # border of train and prediction
plt.legend()
plt.title("Echo State Network Sin Prediction")
plt.xlabel("time[ms]")
plt.ylabel("y(t)")
plt.show()
print(model.test())
def main():
print(NUM_TIME_STEPS)
ing = InputGenerator(0,T,NUM_TIME_STEPS)
train_data = ing.generate_sin()
num_train = int(len(train_data) * RATIO_TRAIN)
print(train_data)
target_data = np.sign(train_data)
num_target = int(len(target_data) * RATIO_TRAIN)
print(target_data)
model = ReservoirNetWork(inputs=train_data, #ReservoirNetwork.pyを参照
outputs_target = target_data,
num_input_nodes=NUM_INPUT_NODES,
num_reservoir_nodes=NUM_RESERVOIR_NODES,
num_output_nodes=NUM_OUTPUT_NODES,
leak_rate=LEAK_RATE)
model.train() # 訓練
train_result = model.get_train_result() # 訓練の結果を取得
num_predict = int(len(train_data[num_target:]))
predict_result = model.predict(num_predict)
t = np.linspace(0,T,NUM_TIME_STEPS)
## plot
plt.plot(t, train_data, label="inputs")
plt.plot(t[:num_train], train_result[:num_train], label="trained")
plt.plot(t[num_target:], predict_result, label="predicted")
plt.axvline(x=int(T * RATIO_TRAIN), label="end of train", color="green") # border of train and prediction
plt.legend()
plt.title("Echo State Network TEST Prediction")
plt.xlabel("time[ms]")
plt.ylabel("y(t)")
plt.show()
