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Exercise 10.1： Least Squares 题目描述 代码： import numpy as np m = 20 n = 10 A = np.random.randint(0, 10, (m,n)) B = np.random.randint(0, 10, (m,1)) res = np.linalg.lstsq(A, B, rcond=0) #...

题目总览 1、Plotting a function 代码 import numpy as np import matplotlib.pyplot as plt X = np.arange(0,2,0.01) # 可以生成实数 # ~ X = np.linspace(0,0.001,2) # 生成整数 Y = np.sin(X-2)*np.sin(X-2)*np.exp(-X...

Anscombe’s quartet Anscombe’s quartet comprises of four datasets, and is rather famous. Why? You’ll find out in this exercise. Part 1 Compute the mean and variance of both x and y Compute the corr...

题目清单 生成矩阵 代码 import numpy as np from scipy.linalg import toeplitz A = np.random.normal(loc = 0, scale = 1, size=(200,500)) B = np.random.normal(loc = 0, scale = 0.8, size=(500,500)) row = list(...

题目叙述 代码   按照PPT上的教程进行代码的编写即可，代码如下： from sklearn import metrics from sklearn import datasets from sklearn import cross_validation from sklearn.naive_bayes import GaussianNB from sklearn.svm im...