From e30c6845c4efc223ef83c2bb1b51098cc7c62805 Mon Sep 17 00:00:00 2001 From: bloodstalker Date: Sat, 17 Nov 2018 18:05:05 +0330 Subject: update --- lstm.py | 34 ++++++++++++++++++++++++++++++---- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/lstm.py b/lstm.py index cb98055..935a8c2 100755 --- a/lstm.py +++ b/lstm.py @@ -20,10 +20,11 @@ from keras.layers import Activation, Dense from keras.layers import LSTM from keras.layers import Dropout from keras.models import load_model +from keras import models +from keras import layers window_len = 10 -#split_date = "2018-03-01" -split_date = "2017.01.01" +split_date = "2018-03-01" def SigHandler_SIGINT(signum, frame): print() @@ -58,7 +59,7 @@ def getData_CMC(crypto, crypto_short): return model_data def getData_Stock(name, period): - info = pd.read_csv("./data/"+name+"/"+period+".csv", encoding="utf-8") + info = pd.read_csv("./data/"+name+"/"+period+".csv", encoding="utf-16") return info def get_sets(crypto, model_data): @@ -98,7 +99,32 @@ def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, return model def stock(): - data = getData_Stock("irxo", "Daily") + split_date = "2017.01.01" + model_data = getData_Stock("irxo", "Daily") + model_data = model_data.sort_values(by='Date') + + training_set, test_set = model_data[model_data['Date']=split_date] + training_set = training_set.drop('Date', 1) + test_set = test_set.drop('Date', 1) + + training_inputs = training_set + training_outputs = training_set.drop(['Open', 'High', 'Low', 'NTx', 'Volume'], axis=1) + test_inputs = test_set + test_outputs = test_set.drop(["Open", "High", "Low", "NTx", "Volume"], axis=1) + + print(training_set.head) + print(test_set.head) + print(training_inputs.shape) + print(test_inputs.shape) + print(training_outputs.shape) + print(test_outputs.shape) + + model = models.Sequential() + model.add(layers.Dense(64, activation="relu", input_shape=(training_inputs.shape[1],))) + model.add(layers.Dense(64, activation="relu")) + model.add(layers.Dense(1)) + model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"]) + history = model.fit(training_inputs, training_outputs, validation_data=(test_inputs, test_outputs), epochs=10, batch_size=1, verbose=2) def lstm_type_1(crypto, crypto_short): model_data = getData_CMC(crypto, crypto_short) -- cgit v1.2.3