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Ex2 lr&regulariz
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‎README.md

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# Python-AndrewNgML
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Python implementation of Andrew Ng's ML course projects
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- Ex1 (Linear Regression) = Complete
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- Ex2 = Incomplete
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- Ex2 (Logistic Regression & Regulation) = Complete
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- Ex3 = Incomplete
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- Ex4 = Incomplete
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- Ex5 = Incomplete

‎__pycache__/ex2helper.cpython-36.pyc

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‎ex1data1.txt renamed to ‎data/ex1data1.txt

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‎ex1data2.txt renamed to ‎data/ex1data2.txt

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‎data/ex2data1.txt

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‎data/ex2data2.txt

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‎ex1.py

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## ======================= Part 2: Plotting =======================
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print('Plotting Data ...')
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data = np.genfromtxt('ex1data1.txt', delimiter=',')
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data = np.genfromtxt('./data/ex1data1.txt', delimiter=',')
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x=np.array(data[:,0])
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x=np.expand_dims(x,axis=0)

‎ex1_multi.py

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print('Loading data...')
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data = np.genfromtxt('ex1data2.txt', delimiter=',')
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data = np.genfromtxt('./data/ex1data2.txt', delimiter=',')
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x = np.array(data[:,:2])
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y = np.array(data[:,2])
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m = y.shape[0]
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#
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## Load Data
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data = np.genfromtxt('ex1data2.txt', delimiter=',')
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data = np.genfromtxt('./data/ex1data2.txt', delimiter=',')
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x = np.array(data[:,:2])
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y = np.array(data[:,2])
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m = y.shape[0]

‎ex2.py

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## Machine Learning Online Class - Exercise 2: Logistic Regression
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#
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# Instructions
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# ------------
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#
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# This file contains code that helps you get started on the logistic
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# regression exercise. You will need to complete the following functions
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# in this exericse:
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#
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# sigmoid - complete
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# costFunction - complete
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# predict - complete
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# costFunctionReg - complete
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#
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# For this exercise, you will not need to change any code in this file,
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# or any other files other than those mentioned above.
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#
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## Initialization
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## Load Data
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# The first two columns contains the exam scores and the third column
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# contains the label.
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import numpy as np
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import ex2helper as helper
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import matplotlib.pyplot as plt
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data = np.genfromtxt('./data/ex2data1.txt', delimiter=',')
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y = np.array(data[:,2])
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x = np.array(data[:,0:2])
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## ==================== Part 1: Plotting ====================
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# We start the exercise by first plotting the data to understand the
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# the problem we are working with.
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[m,n] = x.shape
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r = x
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x = np.ones((m, n+1))
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x[:,1:] = r
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print('\nPlotting data with \'o\' indicating (y = 1) examples and \'x\' indicating (y = 0) examples.')
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helper.plotData(x,y,'Exam Score 1', 'Exam Score 2')
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input('\nPart 1 completed. Program paused. Press enter to continue: ')
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## ============ Part 2: Compute Cost and Gradient ============
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# In this part of the exercise, you will implement the cost and gradient
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# for logistic regression. You neeed to complete the code in
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# costFunction.m
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#
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# Setup the data matrix appropriately, and add ones for the intercept term
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theta = np.zeros(n+1)
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cost = helper.costFunction(theta,x,y)
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grad = helper.gradient(theta, x, y)
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print('Cost at initial theta (zeros): {0:.3f}'.format(cost))
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print('Expected cost (approx): 0.693')
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print('Gradient at initial theta (zeros): ')
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print(grad)
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print('Expected gradients (approx):\n -0.1000\n -12.0092\n -11.2628')
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# Compute and display cost and gradient with non-zero theta
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test_theta = np.array([-24, 0.2, 0.2])
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cost = helper.costFunction(test_theta, x, y)
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grad = helper.gradient(test_theta, x, y)
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print('Cost at initial theta (zeros): {0:.3f}'.format(cost))
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print('Expected cost (approx): 0.218')
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print('Gradient at initial theta (zeros): ')
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print(grad)
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print('Expected gradients (approx):\n 0.043\n 2.566\n 2.647')
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input('\nPart 2 completed. Program paused. Press enter to continue: ')
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## ============= Part 3: Optimizing using fminunc =============
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# In this exercise, you will use a built-in function (fminunc) to find the
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# optimal parameters theta.
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# Set options for fminunc
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results = helper.optimize(theta,x,y)
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theta = results.x
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cost = results.fun
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# Print theta to screen
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print('Cost at theta found by scipy.optimize.minimize with TNC: {0:.3f}'.format(cost))
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print('Expected cost (approx): 0.203')
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print('theta:')
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print(theta)
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print('Expected theta (approx):')
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print('[ -25.161 0.206 0.201]')
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helper.plotDecisionBoundary(theta,x,y,'Exam Score 1', 'Exam Score 2')
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input('\nPart 3 completed. Program paused. Press enter to continue: ')
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## ============== Part 4: Predict and Accuracies ==============
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# After learning the parameters, you'll like to use it to predict the outcomes
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# on unseen data. In this part, you will use the logistic regression model
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# to predict the probability that a student with score 45 on exam 1 and
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# score 85 on exam 2 will be admitted.
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#
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# Furthermore, you will compute the training and test set accuracies of
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# our model.
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#
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# Your task is to complete the code in predict.m
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# Predict probability for a student with score 45 on exam 1
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# and score 85 on exam 2
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prob = helper.sigmoid(np.matmul(np.array([1, 45, 85]), theta))
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print('For a student with scores 45 and 85, we predict an admission probability of ', prob)
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print('Expected value: 0.775 +/- 0.002');
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# Compute accuracy on our training set
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p = helper.predict(theta, x)
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predictions = np.zeros(p.shape)
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predictions[np.where(p==y)] = 1
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print('Train Accuracy: ', np.mean(predictions) * 100)
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print('Expected accuracy (approx): 89.0\n')

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