一、不包含分类型变量 from numpy import genfromtxt import numpy as np from sklearn import datasets,linear_model path=r'D:\daacheng\Python\PythonCode\machineLearning\Delivery.csv' data=genfromtxt(path,delimiter='... Linear Regression 本文代码,见github: 一, 简单线性回归原理 1.线性回归算法的优点...
J_history=np.zeros((num_iters,1))foriterinrange(num_iters):# 对J求导,得到 alpha/m*(WX-Y)*x(i),(3,m)*(m,1)X(m,3)*(3,1)=(m,1)theta=theta-(alpha/m)*(X.T.dot(X.dot(theta)-y))J_history[iter]=computeCost(X,y,theta)returnJ_history,theta iterations=10000#迭代次数 alph...
Multiple_LinearRegression_Test2 1importcsv2importnumpy as np3fromsklearnimportdatasets,linear_model45with open("car_2.1.csv") as f:6car_data = list(csv.reader(f))#转换为list7data_X = [row[:5]forrowincar_data[:-1]]#变量x8data_Y = [row[-1]forrowincar_data[:-1]]#值y9xPred = ...
1 import csv 2 import numpy as np 3 import pandas as pd 4 from sklearn import datasets,linear_model 5 6 with open("car.csv","r") as f: 7 data = list(csv.reader(f)) 8 data_X = [row[:2] for row in data[1:]] 9 data_Y = [row[-1] for row in data[1:]] 10 regres...
import numpy as np import matplotlib.pyplot as plt import pandas as pd Step 2: Importing the dataset In the next step, we shall use pandas to store the data obtained from my github repository and store it as a Pandas DataFrame named as “dataset” using the function “pd.read_csv”. ...
We have our dataset as data.csv file. We will use it to understand the implementation of the multiple linear regression in Python.We need to import libraries before loading the dataset.# import libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd Load the dataset...
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import numpy as np x = np.arange(0, 10, 0.01) y = x # 损失函数 def calCost(theta): h = [i * theta for i in x] cost = 0 for i in range(len(h)): cost += (y[i]-h[i])*(y[i]-h[i]) return cost thetas = np.arange(0, 2, 0.001) ...
吴恩达《Machine Learning》-Linear Regression with Multiple Variables多元线性回归(四),程序员大本营,技术文章内容聚合第一站。
from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train,y_train) # 通过train集找到曲线 y_pred = regressor.predict(X_test) # visualising the Traning set results plt.scatter(X_train, y_train, color = 'red') ...