from __future__ import print_function
from keras.callbacks import LambdaCallback
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import matplotlib.pyplot as plt  # 追加
import numpy as np
import random
import sys
import io
import neologdn

path = './yobo3_1_6_r.txt'
with io.open(path, encoding='utf-8') as f:
    text1 = f.read().lower()
print('corpus length:', len(text1))

text=neologdn.normalize(text1)
text = text.replace(".", "")
text = text.replace("-", " ")
text = text.replace("‐", " ")
text = text.replace("", " ")
text = text.replace("■", " ")
text = text.replace("●", " ")
text = text.replace("●", " ")

import re
text = re.sub(r'[0-9]+', "0", text)
print(text)

chars = sorted(list(set(text)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

# cut the text in semi-redundant sequences of maxlen characters
maxlen = 8
step = 1
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i: i + maxlen])
    next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))

print('Vectorization...')
x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1


# build the model: a single LSTM
print('Build model...')
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)


def sample(preds, temperature=1.0):
    # helper function to sample an index from a probability array
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1, preds, 1)
    return np.argmax(probas)


def on_epoch_end(epoch, logs):
    # Function invoked at end of each epoch. Prints generated text.
    print()
    print('----- Generating text after Epoch: %d' % epoch)

    start_index = random.randint(0, len(text) - maxlen - 1)
    #start_index = 0  # 毎回、「老人は老いていた」から文章生成
    for diversity in [0.2]:  # diversity = 0.2 のみとする
        print('----- diversity:', diversity)

        generated = ''
        sentence = text[start_index: start_index + maxlen]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)

        for i in range(160):
            x_pred = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_indices[char]] = 1.

            preds = model.predict(x_pred, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char

            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()

print_callback = LambdaCallback(on_epoch_end=on_epoch_end)

history = model.fit(x, y,
                    batch_size=128,
                    epochs=60,
                    callbacks=[print_callback])

# Plot Training loss & Validation Loss
loss = history.history["loss"]
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, "bo", label = "Training loss" )
plt.title("Training loss")
plt.legend()
plt.savefig("loss.png")
plt.close()

model.summary()
model.save_weights('param.hdf5')