import keras
from keras import Sequential
from keras.layers import Dense, Dropout
from keras import backend
import numpy as np
from matplotlib import pyplot as plt
from keras.utils import to_categorical
np.seterr(divide='ignore', invalid='ignore')
gas = np.loadtxt('all_data.txt', delimiter=' ', dtype='float32')
# gas = all_data[np.where(all_data[:, 0] == 4)]
# np.savetxt('gas.txt', gas, fmt='%.5f')，目的是找出4号气体的浓度和所有数据
np.random.seed(133)
np.random.shuffle(gas)
gas_z_score = gas[:, 2:]
# z-score方法
gas_mean = np.mean(gas_z_score, axis=0)
gas_std = np.std(gas_z_score, axis=0)
gas_new = (gas_z_score - gas_mean) / gas_std
# from sklearn import preprocessing
# min_max_scaler = preprocessing.MinMaxScaler()
# gas_new = min_max_scaler.fit_transform(gas_z_score)
gas_train_attr = gas_new[:int(gas_new.shape[0] * 0.7)]
gas_train_label = gas[:int(gas.shape[0] * 0.7), 0]
gas_train_label = to_categorical(gas_train_label)
gas_train_label = gas_train_label[:,1:]
gas_test_attr = gas_new[int(gas_new.shape[0] * 0.7):]
gas_test_label = gas[int(gas.shape[0] * 0.7):, 0]
gas_test_label = to_categorical(gas_test_label)
gas_test_label = gas_test_label[:,1:]
model = Sequential()
model.add(Dense(100, activation='relu'))
model.add(Dense(100, activation='softmax'))
# model.add(Dropout(0.5))
# model.add(Dense(128, activation='relu'))
# model.add(Dropout(0.5))
# model.add(Dense(128, activation='relu'))
model.add(Dense(6))
sgd = keras.optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='mse', metrics=['accuracy'])
his = model.fit(gas_train_attr, gas_train_label, validation_data=(gas_test_attr[:int(gas_test_attr.shape[0] * 0.5)], gas_test_label[:int(gas_test_label.shape[0] * 0.5)]), epochs=100)
# plt.plot(hist.history['acc'])
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.title("")
plt.ylabel("Classification Accuracy")
plt.xlabel("epoch")
plt.legend(["Training set","Validation set"],loc="lower right")
plt.show()
gas_val_predict = model.predict(gas_test_attr[:int(gas_test_attr.shape[0] * 0.5)])
gas_test_predict = model.predict(gas_test_attr[int(gas_test_attr.shape[0] * 0.5):])
gas_train_predict = model.predict(gas_train_attr)
gas_all_predict = model.predict(gas[:, 2:])
a=range(len(his.history['acc'])) #np.arange()返回的是一个一个array,而range返回的是一个list
np.savetxt('min_max_sgd.txt', np.hstack((a, his.history['acc'])))
# for i in range(len(his.history['acc'])):
# a.append(his.history['acc'][i])
# model.evaluate(gas_test_attr, gas_test_label)
np.savetxt('gas_train_result.txt', np.hstack((gas_train_label.reshape(-1, 1), gas_train_predict.reshape(-1, 1))),
fmt='%.5f')
np.savetxt('gas_val_result.txt', np.hstack(
(gas_test_label[:int(gas_test_attr.shape[0] * 0.5)].reshape(-1, 1), gas_val_predict.reshape(-1, 1))),
fmt='%.5f')
np.savetxt('gas_test_result.txt', np.hstack(
(gas_test_label[int(gas_test_attr.shape[0] * 0.5):].reshape(-1, 1), gas_test_predict.reshape(-1, 1))),
fmt='%.5f')
