import Formatter
import ANN as nn
import numpy as np
train_period = 24 * 7 # 7 days
test_period = 24 # 1 day
bin_count = 8
period_class = Formatter.PeriodSample(60)
target = []
change_data = []
matrix = [
period_class.getChangeVolData(train_period, test_period)
for i in range(900000)
]
for index in range(0, len(matrix), 1):
change_data.append(matrix[index][0][:, 1])
bin_no = np.zeros([bin_count], dtype=float)
bin_no[matrix[index][1]] = 1.0
target.append(bin_no)
change_data = np.array(change_data, dtype=float)
model = nn.ann()
cost = model.train(change_data, target)
# plt.plot(cost)
print(cost)
# plt.show()
# print(cost)
print((model.test(change_data, target)))
import numpy as np
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.layers import Dense, LSTM, Merge
from keras.models import Sequential, model_from_json
from keras.optimizers import RMSprop
import keras
bin_count = 8 # no of bins
BATCH_SIZE = 1000
import Formatter
period_sample = Formatter.PeriodSample(60)
INPUT_SIZE = 24
def createModel(train_period, target):
cost = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
EMA_lstm = Sequential()
EMA_lstm.add(
LSTM(INPUT_SIZE,
input_shape=(INPUT_SIZE, 1),
batch_input_shape=(BATCH_SIZE, INPUT_SIZE, 1),
dropout=0.2,
return_sequences=False))
K_lstm = Sequential()
K_lstm.add(
LSTM(INPUT_SIZE,
input_shape=(INPUT_SIZE, 1),
batch_input_shape=(BATCH_SIZE, INPUT_SIZE, 1),
import Data import Formatter import grouping_changecalc as grp print(Data.getNames()) formatter = Formatter.PeriodSample(10) formatter.getChangeVolData(50, 5) # # # train, test = Data.randomSample(10,2) # print(Data.getNames())
