import pandas as pd

 import numpy as np

 # 数据预处理

 def dataProcess(df):

     x_list, y_list = [], []

     # df替换指定元素，将空数据填充为0

     df = df.replace(['NR'], [0.0])

     # astype() 转换array中元素数据类型

     array = np.array(df).astype(float)

     # 将数据集拆分为多个数据帧

     for i in range(0, 4320, 18):

         for j in range(24-9):

             mat = array[i:i+18, j:j+9]

             label = array[i+9, j+9] # 第10行是PM2.5

             x_list.append(mat)

             y_list.append(label)

     x = np.array(x_list)

     y = np.array(y_list)

     '''

     # 将每行数据都scale到0到1的范围内，有利于梯度下降，但经尝试发现效果并不好

     for i in range(18):

         if(np.max(x[:, i, :]) != 0):

             x[: , i, :] /= np.max(x[:, i, :])

     '''

     return x, y, array

 # 更新参数，训练模型

 def train(x_train, y_train, epoch):

     bias = 0 # 偏置值初始化

     weights = np.ones(9) # 权重初始化

     learning_rate = 1 # 初始学习率

     reg_rate = 0.001 # 正则项系数

     bg2_sum = 0 # 用于存放偏置值的梯度平方和

     wg2_sum = np.zeros(9) # 用于存放权重的梯度平方和

     for i in range(epoch):

         b_g = 0

         w_g = np.zeros(9)

         # 在所有数据上计算Loss_label的梯度

         for j in range(3200):

             b_g += (y_train[j] - weights.dot(x_train[j, 9, :]) - bias) * (-1)

             for k in range(9):

                 w_g[k] += (y_train[j] - weights.dot(x_train[j, 9, :]) - bias) * (-x_train[j, 9, k])

         # 求平均

         b_g /= 3200

         w_g /= 3200

         #  加上Loss_regularization在w上的梯度

         for m in range(9):

             w_g[m] += reg_rate * weights[m]

         # adagrad

         bg2_sum += b_g**2

         wg2_sum += w_g**2

         # 更新权重和偏置

         bias -= learning_rate/bg2_sum**0.5 * b_g

         weights -= learning_rate/wg2_sum**0.5 * w_g

         # 每训练200轮，输出一次在训练集上的损失

         if i%200 == 0:

             loss = 0

             for j in range(3200):

                 loss += (y_train[j] - weights.dot(x_train[j, 9, :]) - bias)**2

             print('after {} epochs, the loss on train data is:'.format(i), loss/3200)

     return weights, bias

 # 验证模型效果

 def validate(x_val, y_val, weights, bias):

     loss = 0

     for i in range(400):

         loss += (y_val[i] - weights.dot(x_val[i, 9, :]) - bias)**2

     return loss / 400

 def main():

     # 从csv中读取有用的信息

     # 由于大家获取数据集的渠道不同，所以数据集的编码格式可能不同

     # 若读取失败，可在参数栏中加入encoding = 'gb18030'

     df = pd.read_csv('train.csv', usecols=range(3,27))

     x, y, _ = dataProcess(df)

     #划分训练集与验证集

     x_train, y_train = x[0:3200], y[0:3200]

     x_val, y_val = x[3200:3600], y[3200:3600]

     epoch = 2000 # 训练轮数

     # 开始训练

     w, b = train(x_train, y_train, epoch)

     # 在验证集上看效果

     loss = validate(x_val, y_val, w, b)

     print('The loss on val data is:', loss)

 if __name__ == '__main__':

     main()