import time

x = line.split(',')

date = x[0] # 日期

wday = time.strptime(date,'%Y-%m-%d').tm_wday # 0-6, 0-Monday

if x[9]=='None':

zhang_die = 0.0

percent = 0.0

else:

zhang_die = float(x[8])

#percent = float(x[9])/100.0 # 涨跌幅

percent = float(x[9]) # 涨跌幅

#return [float(wday)/10.0, percent] # 都用浮点数表示

return [

float(wday), # 星期几

percent, # 涨跌幅

float(x[3]), # 收盘价

float(x[4]), # 最高价

float(x[5]), # 最低价

float(x[6]), # 开盘价

zhang_die, # 涨跌额

float(x[10]), # 成交量／换手率

import json

f = open(file_name, 'r')

lines = f.readlines()

f.close()

all_data = []

for index, i in enumerate(lines):

if index<INPUT_LENGTH+1: # 从第8行开始， 0行是标题

continue

# 如果指定了日期，只返回指定日期相关的数据

if specific_date and specific_date not in lines[index]:

#print(lines[index], specific_date)

continue

question = []

for j in xrange(index-INPUT_LENGTH, index):

question.append(gen_data(lines[j]))

_answer = gen_data(lines[index])

# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

# -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

answer = int(_answer[1])+10+cmp(_answer[1],0)

answer = 0 if answer<0 else answer

answer = 20 if answer>20 else answer

#if _answer[1]<=-5.0:

# answer = 0

#elif _answer[1]<0.0 and _answer[1]>-5.0:

# answer = 1

#elif _answer[1]==0.0:

# answer = 2

#elif _answer[1]>0.0 and _answer[1]<5.0:

# answer = 3

#else:

# answer = 4

all_data.append([question, answer])

questions = []

expected = []

seen = set()

print('Generating data...')

train_data = load_data(DATA_FILE, specific_date)

train_data = train_data[:data_size] # for test

for x in train_data:

#print(x)

X = np.zeros((MAXLEN_q, MAXW_q))

for i, c in enumerate(x[0]):

X[i] = c

questions.append(X)

Y = np.zeros((MAXLEN_a, MAXW_a))

Y[0, x[1]] = 1

expected.append(Y)

print('Total addition questions:', len(questions))

#print(questions)

#print(expected)

print('Vectorization...')

X = np.zeros((len(questions), MAXLEN_q, MAXW_q))

y = np.zeros((len(questions), MAXLEN_a, MAXW_a))

for i, sentence in enumerate(questions):

X[i] = sentence

for i, sentence in enumerate(expected):

y[i] = sentence

X, y = prepare_data(TRAINING_SIZE)

print('Split data ... ')

# Shuffle (X, y)

#indices = np.arange(len(y))

#np.random.shuffle(indices)

#X = X[indices]

#y = y[indices]

# Explicitly set apart 10% for validation data that we never train over

split_at = len(X) - len(X) / 10

(X_train, X_val) = (slice_X(X, 0, split_at), slice_X(X, split_at))

(y_train, y_val) = (y[:split_at], y[split_at:])

print(X_train.shape)

print(y_train.shape)

#print(X_train)

#print(y_train)

#return X_train, y_train

if model is None:

print('Build model...')

model = Sequential()

model.add(RNN(HIDDEN_SIZE, input_shape=(MAXLEN_q, MAXW_q)))

model.add(RepeatVector(MAXLEN_a))

for _ in range(LAYERS):

model.add(RNN(HIDDEN_SIZE, return_sequences=True))

model.add(TimeDistributed(Dense(MAXW_a)))

model.add(Activation('softmax'))

model.compile(

loss='categorical_crossentropy',

optimizer='adam',

metrics=['accuracy']

)

else:

print('Use existed model...')

# Train the model each generation and show predictions against the validation dataset

for iteration in range(1, 50):

print()

print('-' * 50)

print('Iteration', iteration)

model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=10, #validation_split=0.1,

validation_data=(X_val, y_val)

)

###

# Select samples from the validation set at random so we can visualize errors

for i in range(5):

ind = np.random.randint(0, len(X_val))

rowX, rowy = X_val[np.array([ind])], y_val[np.array([ind])]

preds = model.predict_classes(rowX, verbose=0)

#print(rowX)

#print(rowy)

#print(preds)

q = rowX[0]

correct = rowy[0]

guess = preds[0]

#print('Q', q)

print('T', correct)

#print('G', preds)

print(colors.ok + '☑' + colors.close if correct[0][guess[0]] > 0 else colors.fail + '☒' + colors.close, guess)

print('---')

print('Save model ... ')

X, y = prepare_data(TRAINING_SIZE)

# Explicitly set apart 10% for validation data that we never train over

split_at = len(X) - len(X) / 10

(X_train, X_val) = (slice_X(X, 0, split_at), slice_X(X, split_at))

(y_train, y_val) = (y[:split_at], y[split_at:])

X = X_val

y = y_val

#X = X_train

#y = y_train

print('Guessing ... ')

b = b2 = 0

for i in range(len(X)):

rowX, rowy = np.array([X[i]]), np.array([y[i]])

preds = model.predict_classes(rowX, verbose=0)

q = rowX[0]

correct = rowy[0]

guess = preds[0]

print('Q', q)

print('T', correct)

for xx in range(MAXW_a):

if correct[0][xx]>0:

g2=xx

break

print(colors.ok + '☑' + colors.close if correct[0][guess[0]] > 0 else colors.fail + '☒' + colors.close, guess, g2)

b += 1 if correct[0][guess[0]] > 0 else 0

b2 += 1 if (guess[0]<10 and g2<10) or (guess[0]>10 and g2>10) or (guess[0]==10 and g2==10) else 0

print('---')

print('t=', len(X))

print('b=', b)

print('b/t= %.4f'%(b*1.0/len(X)))

print('b2=', b2)

X, y = prepare_data(TRAINING_SIZE, date)

print('Guessing ... ')

b = 0

for i in range(len(X)):

rowX, rowy = np.array([X[i]]), np.array([y[i]])

preds = model.predict_classes(rowX, verbose=0)

q = rowX[0]

correct = rowy[0]

guess = preds[0]

print('Q', q)

print('T', correct)

for xx in range(MAXW_a):

if correct[0][xx]>0:

g2=xx

break

print(colors.ok + '☑' + colors.close if correct[0][guess[0]] > 0 else colors.fail + '☒' + colors.close, guess, g2)

#b += 1 if correct[0][guess[0]] > 0 else 0

print('---')

print('t=', len(X))

#print('b=', b)