在这个作业中，我们将用梯度下降方法预测PM2.5的值
hw1要求： 1、要求python3.5+ 2、只能用（1）numpy（2）scipy（3）pandas 3、请用梯度下降手写线性回归 4、最好的公共简单基线 5、对于想加载模型而并不想运行整个训练过程的人：hw_best要求： 1、要求python3.5+ 2、任何库都可以用 3、在kaggle上获得你选择的更高的分 读取数据： import numpy as np import scipy import pandas as pd lr = 0.001 # lr = 0.001 def get_train(): # 获得PM2.5的训练数据 with open("train.csv") as f: data = f.readlines() data = [i.strip().split(',') for i in data] train_data = [];train_label = []; for i in data[10::18]: for j in range(len(i[3:]) - 10): train_data.append(i[3:][j : j + 9]) train_label.append(i[3:][j + 10]) return np.array(train_data,dtype = np.float64),np.array(train_label,dtype = np.float64) def get_test(): # 获得PM2.5的测试数据 with open("test(1).csv") as f: data = f.readlines() data = [i.strip().split(',') for i in data] test_data = [];test_label = []; for i in data[9::18]: test_data.append(i[2:]) with open("answer.csv") as f: data = f.readlines() data = [i.strip().split(',') for i in data] for i in data[1:]: test_label.append(i[1]) return np.array(test_data,dtype = np.float64),np.array(test_label,dtype = np.float64) import numpy as np import math import pandas as pd from sklearn.preprocessing import StandardScaler # 损失函数 def lossfunc(x_train,y_train,theta): x_train = np.array(x_train,float) y_train = np.array(y_train, float) return np.sum((y_train - x_train.dot(theta)) ** 2) / len(y_train) # '损失函数求导 def dlossfunc(x_train,y_train,theta): x_train = np.array(x_train,float) y_train = np.array(y_train, float) return x_train.T.dot(x_train.dot(theta) - y_train) * 2. / len(y_train) # 归一化 def standardfunc(data): standard = StandardScaler() standard.fit(data) data =standard.transform(data) return data 待补充：