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Last active June 15, 2023 04:11
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Tada's usage (see discussion)
""" From: """
from keras.models import Sequential
from keras.layers.core import TimeDistributedDense, Activation, Dropout
from keras.layers.recurrent import GRU
import numpy as np
def _load_data(data, steps = 40):
docX, docY = [], []
for i in range(0, data.shape[0]/steps-1):
alsX = np.array(docX)
alsY = np.array(docY)
return alsX, alsY
def train_test_split(data, test_size=0.15):
# This just splits data to training and testing parts
X,Y = _load_data(data)
ntrn = round(X.shape[0] * (1 - test_size))
perms = np.random.permutation(X.shape[0])
X_train, Y_train = X.take(perms[0:ntrn],axis=0), Y.take(perms[0:ntrn],axis=0)
X_test, Y_test = X.take(perms[ntrn:],axis=0),Y.take(perms[ntrn:],axis=0)
return (X_train, Y_train), (X_test, Y_test)
np.random.seed(0) # For reproducability
data = np.genfromtxt('closingAdjLog.csv', delimiter=',')
(X_train, y_train), (X_test, y_test) = train_test_split(np.flipud(data)) # retrieve data
print "Data loaded."
in_out_neurons = 20
hidden_neurons = 200
model = Sequential()
model.add(GRU(hidden_neurons, input_dim=in_out_neurons, return_sequences=True))
model.compile(loss="mean_squared_error", optimizer="rmsprop")
print "Model compiled."
# and now train the model., y_train, batch_size=30, nb_epoch=200, validation_split=0.1)
predicted = model.predict(X_test)
print np.sqrt(((predicted - y_test) ** 2).mean(axis=0)).mean() # Printing RMSE
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andrewcz commented Nov 7, 2015

me to please.
I was wondering what the file looks like?
Many thanks

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is there any chance to have access to 'closingAdjLog.csv'?

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@hnykda Hi I wonder what are in the closingAdjLog file? Could you share that or just tell what should be the input format? Thank you.

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emigmo commented Apr 6, 2016

can you share what are in the closingAdjLog file? or the format

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