DDA3020讲解 、辅导 Python语言编程
DDA3020 Homework 1
Due date: Oct 14, 2024
Instructions
• The deadline is 23:59, Oct 14, 2024.
• The weight of this assignment in the ffnal grade is 20%.
• Electronic submission: Turn in solutions electronically via Blackboard. Be sure to submit
your homework as one pdf ffle plus two python scripts. Please name your solution ffles as
”DDA3020HW1 studentID name.pdf”, ”HW1 yourID Q1.ipynb” and ”HW1 yourID Q2.ipynb”.
(.py ffles also acceptable)
• Note that late submissions will result in discounted scores: 0-24 hours → 80%, 24-120 hours
→ 50%, 120 or more hours → 0%.
• Answer the questions in English. Otherwise, you’ll lose half of the points.
• Collaboration policy: You need to solve all questions independently and collaboration between
students is NOT allowed.
1 Written Problems (50 points)
1.1. (Learning of Linear Regression, 25 points) Suppose we have training data:
{(x1, y1),(x2, y2), . . . ,(xN , yN )},
where xi ∈ R
d and yi ∈ R
k
, i = 1, 2, . . . , N.
i) (9 pts) Find the closed-form solution of the following problem.
min
W,b
X
N
i=1
∥yi − Wxi − b∥
2
2
,
ii) (8 pts) Show how to use gradient descent to solve the problem. (Please state at least one
possible Stopping Criterion)
1DDA3020 Machine Learning Autumn 2024, CUHKSZ
iii) (8 pts) We further suppose that x1, x2, . . . , xN are drawn from N (µ, σ
2
). Show that the
maximum likelihood estimation (MLE) of σ
2
is σˆ
2
MLE =
1
N
PN
n=1
(xn − µMLE)
2
.
1.2. (Support Vector Machine, 25 points) Given two positive samples x1 = (3, 3)
T
, x2 =
(4, 3)
T
, and one negative sample x3 = (1, 1)
T
, ffnd the maximum-margin separating hyperplane and
support vectors.
Solution steps:
i) Formulating the Optimization Problem (5 pts)
ii) Constructing the Lagrangian (5 pts)
iii) Using KKT Conditions (5 pts)
iv) Solving the Equations (5 pts)
v) Determining the Hyperplane Equation and Support Vectors (5 pts)
2 Programming (50 points)
2.1. (Linear regression, 25 points) We have a labeled dataset D = {(x1, y1),(x2, y2),
· · · ,(xn, yn)}, with xi ∈ R
d being the d-dimensional feature vector of the i-th sample, and yi ∈ R
being real valued target (label).
A linear regression model is give by
fw0,...,wd
(x) = w0 + w1x1 + w2x2 + · · · + wdxd, (1)
where w0 is often called bias and w1, w2, . . . , wd are often called coefffcients.
Now, we want to utilize the dataset D to build a linear model based on linear regression.
We provide a training set Dtrain that includes 2024 labeled samples with 11 features (See linear
regression train.txt) to fft model, and a test set Dtest that includes 10 unlabeled samples with
11 features (see linear regression test.txt) to estimate model.
1. Using the LinearRegression class from Sklearn package to get the bias w0 and the coefffcients
w1, w2, . . . , w11, then computing the yˆ = f(x) of test set Dtest by the model trained well. (Put
the estimation of w0, w1, . . . , w11 and these yˆ in your answers.)
2. Implementing the linear regression by yourself to obtain the bias w0 and the coefffcients
w1, w2, . . . , w11, then computing the yˆ = f(x) of test set Dtest. (Put the estimation of
w0, w1, . . . , w11 and these yˆ in your answers. It is allowed to compute the inverse of a matrix
using the existing python package.)
2DDA3020 Machine Learning Autumn 2024, CUHKSZ
(Hint: Note that for linear regression train.txt, there are 2024 rows with 12 columns where the
ffrst 11 columns are features x and the last column is target y and linear regression test.txt
only contains 10 rows with 11 columns (features). Both of two tasks require the submission of
code and results. Put all the code in a “HW1 yourID Q1.ipynb” Jupyter notebook. ffle.(”.py”
ffle is also acceptable))
2.2. (SVM, 25 points)
Task Description You are asked to write a program that constructs support vector machine
models with different kernel functions and slack variables.
Datasets You are provided with the iris dataset. The data set contains 3 classes of 50 instances
each, where each class refers to a type of iris plant. There are four features: 1. sepal length in cm;
2. sepal width in cm; 3. petal length in cm; 4. petal width in cm. You need to use these features
to classify each iris plant as one of the three possible types.
What you should do You should use the SVM function from python sklearn package, which
provides various forms of SVM functions. For multiclass SVM you should use the one vs rest
strategy. You are recommended to use sklearn.svm.svc() function. You can use numpy for vector
manipulation. For technical report, you should report the results required as mentioned below (e.g.
training error, testing error, and so on).
1. (2 points) Split training set and test set. Split the data into a training set and a test set.
The training set should contain 70% of the samples, while the test set should include 30%.
The number of samples from each category in both the training and test sets should reffect
this 70-30 split; for each category, the ffrst 70% of the samples will form the training set, and
the remaining 30% will form the test set. Ensure that the split maintains the original order
of the data. You should report instance ids in the split training set and test set. The output
format is as follows:
Q2.2.1 Split training set and test set:
Training set: xx
Test set: xx
You should ffll up xx in the template. You should write ids for each set in the same line with
comma separated, e.g. Training set:[1, 4, 19].
2. (10 points) Calculation using Standard SVM Model (Linear Kernel). Employ the
standard SVM model with a linear kernel. Train your SVM on the split training dataset and
3DDA3020 Machine Learning Autumn 2024, CUHKSZ
validate it on the testing dataset. Calculate the classiffcation error for both the training and
testing datasets, output the weight vector w, the bias b, and the indices of support vectors
(start with 0). Note that the scikit-learn package does not offer a function with hard margin,
so we will simulate this using C = 1e5. You should ffrst print out the total training error
and testing error, where the error is
wrong prediction
number of data
. Then, print out the results for each class
separately (note that you should calculate errors for each class separately in this part). You
should also mention in your report which classes are linear separable with SVM without slack.
The output format is as follows:
Q2.2.2 Calculation using Standard SVM Model:
total training error: xx, total testing error: xx,
class setosa:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
class versicolor:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
class virginica:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
Linear separable classes: xx
If we view the one vs all strategy as combining the multiple different SVM, each one being
a separating hyperplane for one class and the rest of the points, then the w, b and support
vector indices for that class is the corresponding parameters for the SVM separating this class
and the rest of the points. If a variable is of vector form, say a =
1
2
3
, then you should write
each entry in the same line with comma separated e.g. [1,2,3].
3. (6 points) Calculation using SVM with Slack Variables (Linear Kernel). For each
C = 0.25 × t, where t = 1, 2, . . . , 4, train your SVM on the training dataset, and subsequently
validate it on the testing dataset. Calculate the classiffcation error for both the training and
testing datasets, the weight vector w, the bias b, and the indices of support vectors, and the
slack variable ζ of support vectors (you may compute it as max(0, 1 − y · f(X)). The output
format is as follows:
Q2.2.3 Calculation using SVM with Slack Variables (C = 0.25 × t, where t = 1, . . . , 4):
4DDA3020 Machine Learning Autumn 2024, CUHKSZ
-------------------------------------------
C=0.25,
total training error: xx, total testing error: xx,
class setosa:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
slack variable: xx,
class versicolor:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
slack variable: xx,
class virginica:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
slack variable: xx,
-------------------------------------------
C=0.5,
<... results for (C=0.5) ...>
-------------------------------------------
C=0.75,
<... results for (C=0.75) ...>
-------------------------------------------
C=1,
<... results for (C=1) ...>
4. (7 points) Calculation using SVM with Kernel Functions. Conduct experiments with
different kernel functions for SVM without slack variable. Calculate the classiffcation error
for both the training and testing datasets, and the indices of support vectors for each kernel
type:
(a) 2nd-order Polynomial Kernel
(b) 3nd-order Polynomial Kernel
(c) Radial Basis Function Kernel with σ = 1
(d) Sigmoidal Kernel with σ = 1
The output format is as follows:
5DDA3020 Machine Learning Autumn 2024, CUHKSZ
Q2.2.4 Calculation using SVM with Kernel Functions:
-------------------------------------------
(a) 2nd-order Polynomial Kernel,
total training error: xx, total testing error: xx,
class setosa:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
class versicolor:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
class virginica:
training error: xx, testing error: xx,
w: xx, b: xx,
support vector indices: xx,
-------------------------------------------
(b) 3nd-order Polynomial Kernel,
<... results for (b) ...>
-------------------------------------------
(c) Radial Basis Function Kernel with σ = 1,
<... results for (c) ...>
-------------------------------------------
(d) Sigmoidal Kernel with σ = 1,
<... results for (d) ...>
Submission Submit your executable code in a “HW1 yourID Q2.ipynb” Jupyter notebook(”.py”
file is also acceptable). Indicate the corresponding question number in the comment for each cell,
and ensure that your code can logically produce the required results for each question in the required
format. Please note that you need to write clear comments and use appropriate function/variable
names. Excessively unreadable code may result in point deductions.
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