Code Issues Pull requests Find the electricity market clearing price and clearing quantity (graphical method) using python. python mcp mcq electricity-market matplotlib-pyplot market-clearing Updated Nov 22, 2021 Python saiftheboss7 / onlinemcqexam Star 10 Code Issues Pull requests Online Exam...
importnumpyasnpimportmatplotlib.pyplotasplt np.random.seed(666) X=np.random.normal(0,1,size=(200,2)) y=np.array(X[:,0]**2+X[:,1]<1.5,dtype='int')for_inrange(20):#添加噪音y[np.random.randint(200)]=1plt.scatter(X[y==0,0],X[y==0,1]) plt.scatter(X[y==1,0],X[y=...
图中右下角为最终答案,这是因为Xb是m(样本数)行n(特征值数)列的,所以Xb*theta是m行1列的,即列向量,python中默认是列向量,所以y也是列向量,那么Xb * theta - y要转置一下才能变成图中第一行的行向量,最后计算结果还要转置一下才能变成列向量。 添加了梯度下降训练的LinearRegression类: importnumpyasnpfrom...
importnumpyasnpimportmatplotlib.pyplotaspltfromsklearnimportdatasets iris = datasets.load_iris() X=iris.data y=iris.target %run F:/python3玩转机器学习/K近邻算法/model_selection.py X_train, X_test, y_train, y_test=train_test_split(X,y,test_ratio=0.2) %run F:/python3玩转机器学习/K近邻...
importnumpyasnpimportmatplotlib.pyplotasplt X=np.empty((100,2))#100行2列X[:,0]=np.random.uniform(0.,100.,size=100)#100个0~100的均匀分布点X[:,1]=0.75*X[:,0]+3.+np.random.normal(0,10.,size=100)#100个均值为0,标准差为10的正态分布点%run f:\python3玩转机器学习\PCA与梯度上升...