吴恩达机器学习课程有点老了，当时吴教授选的octave/matlab来编程，我也尝试了安装octave，不仅编程语法不熟悉，就是submission也没搞明白，幸运的是github上的Gerges大神，搞了个python版交作业神器，直接在jupyter notebook上运行代码就能提交作业。
传送门：
GitHub - dibgerge/ml-coursera-python-assignments: Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions、- GitHub - dibgerge/ml-coursera-python-assignments: Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.https://github.com/dibgerge/ml-coursera-python-assignments以week2为例：
week2 的练习分成两个部分，一个是算分的required exercises，一个是不算分的附加题
Gerges 大神已经给大家整理好了，我们只需要在your code 部分写上代码，再运行grader.grade()后填上email，以及token就行了。比如 warm up exercise， 这个地方我们需要把A=[] 改为 A = np.eye(5)
然后再运行（不需要自己写，已经放在jupter notebook上了）
运行完这段代码，会让我们填couresa绑定的邮箱地址以及token
token 在我们的couresa assignment里面，点generate new token 就能自动生成，但是每次token只有30分钟有效。（可以提前写好solution，每道题都做好了，最后再来run 每个部分的grader.grade() ）
运行完之后直接就能看到分数：
每交一道，就会显示当前累计得分， 写这个教程时我已经交完作业了，所以看到的分数是100。
二、week 2 编程作业——线性回归 python 代码
先来导包吧
# used for manipulating directory pathsimport os# Scientific and vector computation for pythonimport numpy as np# Plotting libraryfrom matplotlib import pyplotfrom mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces# library written for this exercise providing additional functions for assignment submission, and othersimport utils # define the submission/grader object for this exercisegrader = utils.Grader()# tells matplotlib to embed plots within the notebook%matplotlib inline
1.第一题 warm up exercise
要求：生成一个对角矩阵。
方案：numpy 里的eye()函数就能实现这个功能
def warmUpExercise(): """ Example function in Python which computes the identity matrix. Returns ------- A : array_like The 5x5 identity matrix. Instructions ------------ Return the 5x5 identity matrix. """ # ======== YOUR CODE HERE ====== A = np.eye(5) # modify this line # ============================== return A
2.第二题Computing the cost
要求：补全Computing the cost函数
# 导入数据data = np.loadtxt(os.path.join('Data', 'ex1data1.txt'), delimiter=',')X, y = data[:, 0], data[:, 1] #数据切片m = y.size # 训练集的个数#数据可视化def plotData(x, y): """ Plots the data points x and y into a new figure、Plots the data points and gives the figure axes labels of population and profit. Parameters ---------- x : array_like Data point values for x-axis. y : array_like Data point values for y-axis、Note x and y should have the same size. Instructions ------------ Plot the training data into a figure using the "figure" and "plot" functions、Set the axes labels using the "xlabel" and "ylabel" functions. Assume the population and revenue data have been passed in as the x and y arguments of this function、 Hint ---- You can use the 'ro' option with plot to have the markers appear as red circles、Furthermore, you can make the markers larger by using plot(..., 'ro', ms=10), where `ms` refers to marker size、You can also set the marker edge color using the `mec` property. """ fig = pyplot.figure() # open a new figure # ====================== YOUR CODE HERE ======================= pyplot.plot(x, y, 'ro', ms=10, mec='k') pyplot.ylabel('Profit in $10,000') pyplot.xlabel('Population of City in 10,000s') # =============================================================plotData(X, y)#生成 computecost函数#给x添加值为1的列（吴教授在课程里有讲，这里相当于添加偏置，y=w0*1+w1*x ）X = np.stack([np.ones(m), X], axis=1)def computeCost(X, y, theta): """ Compute cost for linear regression、Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y. Parameters ---------- X : array_like The input dataset of shape (m x n+1), where m is the number of examples, and n is the number of features、We assume a vector of one's already appended to the features so we have n+1 columns. y : array_like The values of the function at each data point、This is a vector of shape (m, ). theta : array_like The parameters for the regression function、This is a vector of shape (n+1, ). Returns ------- J : float The value of the regression cost function. Instructions ------------ Compute the cost of a particular choice of theta、 You should set J to the cost. """ # initialize some useful values m = y.size # number of training examples # You need to return the following variables correctly J = 0 # ====================== YOUR CODE HERE ===================== h = np.dot(X, theta) J = (1/(2 * m)) * np.sum(np.square(np.dot(X, theta) - y)) # =========================================================== return J
3.第三题gradientdescent
def gradientDescent(X, y, theta, alpha, num_iters): """ Performs gradient descent to learn `theta`、Updates theta by taking `num_iters` gradient steps with learning rate `alpha`. Parameters ---------- X : array_like The input dataset of shape (m x n+1). y : arra_like Value at given features、A vector of shape (m, ). theta : array_like Initial values for the linear regression parameters、 A vector of shape (n+1, ). alpha : float The learning rate. num_iters : int The number of iterations for gradient descent、 Returns ------- theta : array_like The learned linear regression parameters、A vector of shape (n+1, ). J_history : list A python list for the values of the cost function after each iteration. Instructions ------------ Peform a single gradient step on the parameter vector theta. While debugging, it can be useful to print out the values of the cost function (computeCost) and gradient here. """ # Initialize some useful values m = y.shape[0] # number of training examples # make a copy of theta, to avoid changing the original array, since numpy arrays # are passed by reference to functions theta = theta.copy() J_history = [] # Use a python list to save cost in every iteration for i in range(num_iters): # ==================== YOUR CODE HERE ================================= theta = theta - (alpha / m) * (np.dot(X, theta) - y).dot(X) # ===================================================================== # save the cost J in every iteration J_history.append(computeCost(X, y, theta)) return theta, J_history# 初始化thetatheta = np.zeros(2)# 初始化迭代次数和学习率iterations = 1500alpha = 0.01theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))print('Expected theta values (approximately): [-3.6303, 1.1664]')# 可视化plotData(X[:, 1], y)pyplot.plot(X[:, 1], np.dot(X, theta), '-')pyplot.legend(['Training data', 'Linear regression']);