import numpy as np
import pymysql, random, json
def connect_db(database):try:conn = pymysql.connect(host='localhost', port=3306, user='root', password='zandaoguang', db=database, charset='utf8')return connexcept:print ('Exception: MySQL Connection')return None

from __future__ import print_function
import numpy as np
#np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.optimizers import SGD, Adam, RMSprop
import gzip
import sys
from six.moves import cPickle
from fetcher import *
batch_size = 256
nb_classes = 2
nb_epoch = 100
champion_dict = fetch_champion_dict("champion136.json")
champion_num = len(champion_dict)
X_train = X_train.astype('int8')
X_test = X_test.astype('int8')
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Dense(1500, input_dim = champion_num, init='uniform'))
model.add(Activation('sigmoid'))
model.add(Dense(2))
model.add(Activation('softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])
history = model.fit(X_train, Y_train,batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])

from sklearn.ensemble import RandomForestClassifier
from sklearn.externals import joblib
from fetcher import *
champion_dict = fetch_champion_dict("champion136.json")
champion_num = len(champion_dict)
X_train, y_train, X_test, y_test = fetch_one_side_riot('12', 'MATCHED_GAME', 'KING_PORO', 'KINGPORO', ('1492660800000', '1493740800000'), champion_dict)
clf = RandomForestClassifier(n_estimators=500, n_jobs=2)
clf.fit(X_train, y_train)
train_score = clf.score(X_train, y_train)
print ("Train Score = "+str(train_score))
test_score = clf.score(X_test, y_test)
print ("Test Score = "+str(test_score))

from sklearn.externals import joblib
from sklearn import svm
from fetcher import *
champion_dict = fetch_champion_dict("champion136.json")
champion_num = len(champion_dict)
X_train, y_train, X_test, y_test = fetch_one_side_riot('12', 'MATCHED_GAME', 'ARAM_UNRANKED_5x5', 'ARAM', ('1492660800000', '1493740800000'), champion_dict)
clf = svm.SVC()
clf.fit(X_train, y_train)
train_score = clf.score(X_train, y_train)
print ("Train Score = "+str(train_score))
test_score = clf.score(X_test, y_test)
print ("Test Score = "+str(test_score))