Assignment-2_CS5710/Assignment-2_CS5710_Logistic-Regression.pdf Introduction to Machine Learning (CS 4710/5710) Assignment-2 (80 points) Logistic Regression Due by 23rd march (Monday) 11:59pm You are allowed to discuss the problem and solution design with others, but the code you submit must be your own. Your solution must include the certification of authenticity “I certify that the codes/answers of this assignment are entirely my own work.” Datasets The training and test files will follow the same format as the text files in the UCI datasets. Datasets and description of the datasets are uploaded with this assignment onto Blackboard. For each dataset, a training file and a test file are provided. The name of each file indicates what dataset the file belongs to, and whether the file contains training or test data. Your code should also work with ANY OTHER training and test files using the same format as the files in the UCI datasets. Logistic Regression You must implement a Python executable file called logistic_regression that uses logistic regression to fit a function to the given UCI datasets. Your code should work for all the three datasets (for different number of features). Your function should be invoked as follows: logistic_regression with following three command line arguments: <training_file> <test_file> • <training_file>: The first argument, <training_file> is the path name of the training file, where the training data is stored. The path name can specify any file stored on the local computer. • <test_file>: The second argument, <test_file> is the path name of the test file, where the test data is stored. The path name can specify any file stored on the local computer. Training Stage for Logistic Regression You need to apply gradient descent method at the training stage of your model. The hypothesis of the logistic regression is: Gradient Descent Method: At the end of the training stage, your program should print out the values of the weights that you have estimated. The output of the training phase should be a sequence of lines like this: 𝜃0=%.4f 𝜃1=%.4f 𝜃2=%.4f ... Test Stage for Logistic Regression After the training stage, you should apply the function that you have learned on the test data. For each test object (following the order in which each test object appears in the test file), you should print a line containing the following info: • Object ID. This is the line number where that object occurs in the test file. • Predicted class (the result of the classification). • True class (the last column on the line where the object occurs). • Accuracy. This is defined as follows: o If the predicted class is correct, the accuracy is 1. o If the predicted class is incorrect, the accuracy is 0. The output of the test stage should be a sequence of lines like this: ID=%5d, output=%14.4f, target value = %10.4f, Misclassification .

1. Assignment-2_CS5710/Assignment-2_CS5710_Logistic-
Regression.pdf
Introduction to Machine Learning (CS 4710/5710)
Assignment-2 (80 points)
Logistic Regression
Due by 23rd march (Monday) 11:59pm
You are allowed to discuss the problem and solution design with
others, but the code you submit
must be your own. Your solution must include the certification
of authenticity “I certify that the
codes/answers of this assignment are entirely my own work.”
Datasets
The training and test files will follow the same format as the
text files in the UCI datasets.
Datasets and description of the datasets are uploaded with this
assignment onto Blackboard. For
each dataset, a training file and a test file are provided. The
name of each file indicates what
dataset the file belongs to, and whether the file contains

2. training or test data. Your code should
also work with ANY OTHER training and test files using the
same format as the files in the UCI
datasets.
Logistic Regression
You must implement a Python executable file called
logistic_regression that uses logistic
regression to fit a function to the given UCI datasets. Your code
should work for all the three
datasets (for different number of features). Your function
should be invoked as follows:
logistic_regression with following three command line
arguments: <training_file> <test_file>
• <training_file>: The first argument, <training_file> is the path
name of the training file,
where the training data is stored. The path name can specify any
file stored on the local
computer.
• <test_file>: The second argument, <test_file> is the path name
of the test file, where the
test data is stored. The path name can specify any file stored on
the local computer.

3. Training Stage for Logistic Regression
You need to apply gradient descent method at the training stage
of your model. The hypothesis
of the logistic regression is:
Gradient Descent Method:
At the end of the training stage, your program should print out
the values of the weights that you
have estimated. The output of the training phase should be a
sequence of lines like this:
�0=%.4f
�1=%.4f
�2=%.4f
...
Test Stage for Logistic Regression
After the training stage, you should apply the function that you
have learned on the test data. For

4. each test object (following the order in which each test object
appears in the test file), you should
print a line containing the following info:
• Object ID. This is the line number where that object occurs in
the test file.
• Predicted class (the result of the classification).
• True class (the last column on the line where the object
occurs).
• Accuracy. This is defined as follows:
o If the predicted class is correct, the accuracy is 1.
o If the predicted class is incorrect, the accuracy is 0.
The output of the test stage should be a sequence of lines like
this:
ID=%5d, output=%14.4f, target value = %10.4f,
Misclassification error = %4d
Submission Guidelines and Requirements
• Please zip up logistic_regression.py (or
logistic_regression.ipynb). Submit the zip file via

5. Blackboard.
• Include your name, UCM ID and Certification statement in
your solution:
//Your name
// Your UCM ID
//Certificate of Authenticity: “I certify that the codes/answers of
this assignment are
entirely my own work.”
Assignment-2_CS5710/UCI_Dataset/Description of UCI
Datasets.docx
Description of UCI Datasets
The files in the UCI datasets directory contain training files and
test files for three datasets. Both the training file and the test
file are text files, containing data in tabular format. Each value
is a number, and values are separated by white space. The i-th
row and j-th column contain the value for the j-th dimension of
the i-th object. The only exception is the LAST column, that
stores the class label for each object. Make sure you do not use
data from the last column (i.e., the class labels) as parts of the
input vector.
The datasets are copied from the UCI repository of machine
learning datasets. Here are some details on each dataset:
· The pendigits dataset. This dataset contains data for pen-based
recognition of handwritten digits.
· 7494 training objects.
· 3498 test objets.
· 16 dimensions.
· 10 classes.

6. · The satellite dataset. The full name of this dataset is Statlog
(Landsat Satellite) Data Set, and it contains data for
classification of pixels in satellite images.
· 4435 training objects.
· 2000 test objets.
· 36 dimensions.
· 6 classes.
· The yeast dataset. This dataset contains some biological data
· 1000 training objects.
· 484 test objets.
· 8 dimensions.
· 10 classes.
For each dataset, a training file and a test file are provided. The
name of each file indicates what dataset the file belongs to, and
whether the file contains training or test data.
Note that, for the purposes of your assignments, it does not
matter at all where the data come from. The methods that you
are asked to implement should work on all three datasets, as
well as ANY other datasets following the same format.
Assignment-2_CS5710/UCI_Dataset/pendigits_test.txt
88 92 2 99 16 66 94 37 70 0 0 24 42 65 100 100 8
80 100 18 98 60 66 100 29 42 0 0 23 42 61 56 98 8
0 94 9 57 20 19 7 0 20 36 70 68 100 100 18 92 8
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7. 3 71 0 95 45 100 100 99 79 78 48 53 31 24 54 0 7
79 87 98 81 71 100 72 73 100 66 91 21 48 0 0 13 9
92 95 30 100 34 68 87 89 84 78 100 35 64 0 0 19 9
58 64 100 96 27 100 0 63 79 65 91 72 48 36 10 0 9
34 89 3 70 1 25 49 0 100 23 100 67 56 99 0 100 0
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100 100 73 95 40 69 14 39 2 2 39 0 37 29 0 14 6
88 100 42 77 12 50 0 22 45 0 100 17 73 44 15 49 6

8. 33 86 0 55 1 12 52 0 90 34 100 76 56 100 6 77 0
69 100 44 77 5 50 0 28 67 23 100 44 95 29 89 0 4
27 83 0 42 9 0 65 17 96 57 100 100 45 95 10 58 0
19 100 35 93 0 63 12 36 89 37 100 66 94 33 89 0 4
27 73 81 100 83 91 78 52 62 14 0 0 29 1 100 5 1
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79 90 100 93 63 100 82 84 99 61 87 22 44 0 0 19 9
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59 89 42 23 29 42 25 100 100 82 75 46 98 0 0 3 3
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9. 100 100 49 77 17 51 2 24 24 0 100 10 73 35 0 42 6
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21 87 33 52 62 13 5 0 0 36 74 66 100 100 5 89 8
13 67 40 100 65 78 37 42 0 11 6 4 60 0 100 19 2
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76 100 31 87 1 52 0 14 61 0 100 27 50 42 1 21 6
29 97 0 57 22 10 68 0 100 40 83 90 37 100 12 57 0
53 67 80 44 67 7 0 0 35 36 83 68 100 100 25 80 8
11 45 17 6 55 0 88 25 100 61 79 94 38 100 0 84 0

10. 88 90 18 100 17 66 79 33 49 0 0 28 41 67 100 98 8
75 100 42 78 13 51 0 22 40 0 100 16 88 43 22 35 6
27 82 65 100 100 89 69 62 33 39 0 15 37 1 88 0 2
70 84 11 76 0 91 56 100 99 83 100 55 88 28 75 0 9
88 100 29 100 30 74 85 83 100 67 97 27 57 0 0 5 9
51 54 100 87 36 100 19 62 44 22 2 0 0 37 78 57 8
15 84 0 57 5 17 52 0 95 28 100 67 70 100 20 87 0
0 76 29 95 92 100 81 77 75 56 100 35 85 13 31 0 3
38 65 36 98 100 100 99 69 62 55 96 26 55 0 0 15 3
82 100 8 92 45 60 100 29 52 0 0 24 21 61 53 98 8
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53 64 73 100 70 83 67 42 36 11 0 0 49 4 100 9 1
0 39 30 69 48 100 39 69 41 35 33 5 31 2 100 0 1
12 91 48 100 66 77 77 63 100 42 75 15 34 0 0 15 3
100 89 34 100 0 75 76 78 83 62 90 35 85 8 5 0 9
26 98 24 49 45 13 17 0 0 35 26 72 62 91 100 100 5
61 100 24 88 21 49 26 9 0 0 7 37 51 60 100 78 8
25 76 0 39 15 0 68 1 98 36 100 76 58 100 7 90 0

11. 0 63 37 44 42 6 1 0 25 36 65 67 100 100 46 80 8
62 100 25 91 0 49 7 2 55 0 92 30 100 78 63 100 0
78 99 21 85 0 50 5 14 59 0 100 27 100 65 79 100 0
76 93 24 100 0 72 58 73 74 74 100 42 92 12 37 0 9
100 100 79 86 62 71 50 57 33 43 21 28 8 14 0 0 1
100 98 71 100 42 88 35 55 59 41 61 16 31 3 0 0 5
27 50 56 89 64 100 55 52 35 9 0 2 52 2 100 0 1
0 67 25 100 49 73 36 33 6 0 1 6 53 3 100 11 2
56 97 15 77 0 35 23 0 70 3 100 36 95 78 54 100 0
100 83 25 100 16 67 58 28 12 0 0 33 65 65 89 91 8
23 97 0 48 17 0 58 3 93 35 100 89 59 100 23 73 0
95 76 34 80 0 97 57 100 98 80 100 53 83 25 59 0 9
0 93 41 100 57 65 47 26 21 0 2 35 51 38 100 36 7
17 81 59 100 76 75 53 48 100 44 89 13 40 0 0 20 3
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9 88 22 100 26 65 16 31 0 5 33 1 66 0 100 3 2
99 88 74 100 56 75 100 85 99 57 89 17 46 0 0 4 9
26 94 0 61 6 16 47 0 91 17 100 60 71 95 27 100 0

12. 76 67 75 100 0 77 49 40 83 0 5 12 36 52 100 87 8
34 100 14 83 2 66 0 48 23 34 65 30 91 17 100 0 4
0 98 75 100 84 65 54 31 26 0 100 21 96 39 6 41 7
100 100 55 82 23 54 0 23 33 0 87 15 66 44 11 40 6
8 100 67 99 72 65 55 31 32 0 0 26 36 47 100 52 7
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15 70 48 100 63 89 57 56 67 24 15 1 0 0 100 0 1
0 95 39 100 67 73 53 30 40 0 21 40 53 45 100 40 7
80 100 28 81 44 70 89 82 89 54 100 25 69 4 0 0 9
62 81 100 100 84 91 40 76 67 64 83 26 46 0 0 9 5
45 69 74 100 68 94 57 50 39 9 0 3 50 3 100 0 1
0 89 61 100 100 85 47 65 44 47 98 29 71 4 6 0 3
100 100 70 86 53 72 37 58 20 43 10 29 0 14 13 0 1
100 100 82 91 64 75 50 59 27 43 14 27 0 11 0 0 1
32 75 59 100 88 84 72 54 100 38 89 10 39 0 0 19 3
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0 100 52 94 74 60 71 23 55 0 9 21 43 31 100 31 7
8 96 55 100 62 67 42 30 25 0 0 36 41 49 100 50 7

13. 23 89 59 100 75 68 58 33 36 0 0 18 48 27 100 32 7
42 98 22 72 75 51 62 0 0 6 23 57 43 100 100 99 5
21 85 57 100 63 71 30 55 85 47 100 15 48 0 0 11 3
100 91 49 100 28 66 55 30 26 0 0 30 50 61 95 90 8
7 59 29 100 31 91 32 46 31 1 0 0 54 1 100 0 1
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86 100 46 83 17 56 0 28 17 1 92 0 100 21 22 16 6
100 100 58 96 20 95 0 62 28 58 66 45 55 11 17 0 5
54 87 85 100 76 71 65 47 100 21 77 0 33 5 0 22 3
52 100 12 74 0 41 12 9 66 0 100 27 98 60 79 93 0
19 76 53 100 92 81 73 40 33 13 0 0 49 5 100 6 2
14 93 0 54 52 31 24 0 2 29 4 78 44 96 100 100 5
61 100 0 72 1 42 79 43 100 67 67 90 56 45 57 0 4
10 98 0 54 7 11 51 0 90 30 100 72 66 100 18 88 0
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97 100 45 84 0 62 2 46 48 41 100 45 58 23 21 0 4
52 87 79 100 79 75 72 49 100 29 76 8 36 0 0 7 3
100 96 2 100 0 65 61 32 37 0 19 32 68 68 64 96 8

14. 18 73 62 100 53 85 53 53 52 20 0 0 8 6 100 6 1
29 100 4 77 0 54 64 53 100 69 71 47 46 24 32 0 4
0 84 3 73 30 98 100 100 90 79 73 48 61 16 59 0 7
45 100 12 79 0 52 62 40 100 56 88 55 78 27 68 0 4
14 72 5 25 39 0 81 20 100 63 75 100 30 98 0 65 0
28 96 98 97 87 95 13 100 34 64 100 45 63 0 0 18 5
0 83 28 96 63 100 72 78 58 54 50 29 65 8 100 0 2
24 96 0 61 7 16 43 0 82 11 100 48 78 86 39 100 0
0 94 33 100 52 83 64 65 77 47 100 30 89 13 52 0 3
39 60 64 100 96 84 69 41 25 12 0 15 50 0 100 4 2
98 89 53 100 12 72 0 27 37 0 86 21 100 63 92 90 0
83 95 6 100 20 65 100 38 83 0 0 12 12 55 36 95 8
3 74 33 100 52 71 33 34 3 2 0 4 51 0 100 1 2
0 79 27 93 60 100 77 80 72 54 64 28 66 4 100 0 2
9 49 42 63 82 85 100 100 76 74 51 48 29 22 0 0 1
12 100 10 90 0 56 58 43 100 65 97 70 91 35 90 0 4
36 100 3 88 0 64 54 55 100 67 73 45 46 23 29 0 4
20 57 0 20 40 0 83 23 100 62 87 100 40 88 11 53 0

15. 0 87 18 95 89 100 100 79 64 41 38 0 1 10 52 24 7
0 61 29 88 51 100 43 66 43 32 23 0 11 2 100 3 1
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16. 87 100 56 95 71 86 87 81 94 50 100 19 53 6 0 0 9
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17. 75 100 31 92 1 55 0 10 46 0 84 31 100 73 68 99 0
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18. 100 75 62 62 54 95 93 100 98 63 81 28 46 0 0 1 9
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19. 64 65 100 100 41 85 67 42 64 0 0 16 20 59 81 74 8
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20. 73 100 24 85 0 39 22 0 70 20 100 62 78 99 31 76 0
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21. 0 84 54 100 100 79 95 42 69 8 29 0 28 26 90 30 7
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22. 0 53 34 82 45 100 43 65 50 30 40 0 37 2 100 7 1
11 64 23 100 52 84 34 45 5 10 0 0 50 3 100 5 2
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62 81 22 80 0 38 23 0 71 4 100 43 84 88 40 100 0
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26 83 72 100 84 69 56 45 100 47 87 15 36 0 0 24 3
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2 76 35 100 76 86 57 52 18 26 0 14 51 11 100 0 2
36 84 81 100 97 70 52 46 96 33 100 5 45 0 0 18 3

23. 77 93 35 77 83 37 72 0 0 18 37 60 100 94 75 100 8
4 87 64 61 56 21 0 0 47 36 100 70 99 100 19 78 8
22 95 97 100 100 98 18 95 32 70 100 47 72 0 0 16 5
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100 100 64 81 35 60 13 37 0 14 31 0 53 21 5 24 6
59 93 14 69 0 33 24 0 78 12 100 47 85 83 36 100 0
43 100 0 73 6 44 90 41 100 54 56 68 52 34 62 0 4
49 100 16 85 0 64 20 47 88 51 100 43 84 22 71 0 4
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100 100 51 81 18 54 0 24 29 0 89 11 84 36 23 28 6
30 73 81 71 82 100 33 91 81 68 100 35 55 9 0 0 9
35 100 27 68 0 37 65 42 100 60 100 66 96 32 96 0 4
100 100 57 85 20 57 0 27 26 0 90 8 81 34 14 36 6
84 100 55 81 32 58 20 33 30 9 100 6 52 13 0 0 6
17 86 68 100 71 70 57 60 100 41 91 7 35 0 0 19 3

24. 99 89 26 100 27 68 83 32 40 0 0 27 63 61 100 91 8
0 70 18 97 76 100 92 74 66 36 54 0 100 26 58 33 7
7 67 62 39 100 8 38 0 24 33 57 67 74 100 0 91 8
72 100 1 75 0 42 100 38 99 66 65 83 61 42 64 0 4
31 56 100 80 48 100 0 67 61 52 98 71 63 33 24 0 9
49 80 46 69 65 27 30 0 0 33 49 67 100 100 27 99 8
0 89 33 100 49 54 39 6 16 0 10 36 55 35 100 31 7
6 75 36 97 92 100 69 75 66 51 100 30 54 12 0 0 3
70 97 16 84 0 49 12 14 62 0 95 29 100 65 92 100 0
32 100 86 86 100 56 81 27 46 0 0 17 38 34 98 47 7
48 84 99 100 100 94 53 74 48 44 90 26 55 0 0 1 5
11 100 8 63 44 36 37 0 0 21 1 59 46 84 100 91 5
0 92 28 100 67 74 70 35 48 0 5 24 45 34 100 34 7
0 97 51 100 65 88 46 44 42 0 9 29 40 34 100 40 7
39 88 0 66 68 60 68 15 8 0 19 47 31 95 100 100 5
100 81 54 100 0 72 43 36 66 0 26 24 67 61 95 91 8
100 100 55 89 17 61 0 29 28 0 94 6 94 33 26 34 6
14 85 54 100 100 96 72 72 87 41 92 10 47 0 0 3 3

25. 17 100 21 85 0 53 45 47 96 57 92 61 98 28 100 0 4
0 95 41 100 59 67 51 27 40 0 12 32 52 47 100 55 7
25 70 40 100 68 81 47 46 18 14 0 0 53 3 100 12 2
41 100 58 60 70 19 1 0 0 35 60 65 100 95 25 84 8
67 100 27 73 4 41 6 6 60 0 100 24 52 37 0 22 6
53 92 100 100 66 94 40 66 49 39 87 18 48 0 0 4 5
81 80 53 100 43 73 87 88 100 59 88 16 46 0 0 0 9
0 86 42 100 84 84 83 42 78 0 45 15 49 41 100 48 7
0 88 45 100 81 79 80 43 72 7 58 0 47 23 100 24 7
56 97 24 69 100 58 96 17 25 0 5 42 0 85 75 100 5
30 100 21 86 0 58 32 36 80 43 100 61 91 30 89 0 4
31 100 2 56 12 3 60 0 100 35 91 85 42 90 0 60 0
41 93 32 64 32 23 0 0 0 32 51 56 100 82 58 100 8
4 64 27 100 31 83 26 39 0 8 5 3 52 2 100 0 1
0 74 68 69 90 100 13 91 42 70 100 77 83 39 79 0 9
0 100 46 98 95 82 95 41 84 0 49 9 45 22 100 28 7
47 100 31 76 0 45 63 50 100 64 92 61 94 28 96 0 4
14 90 65 100 100 84 57 65 71 53 91 26 56 3 0 0 3

26. 0 91 25 63 86 33 75 0 14 11 53 43 100 75 75 100 8
53 100 16 78 0 47 59 38 100 63 86 58 58 29 29 0 4
24 61 45 100 90 99 68 54 29 17 0 14 51 0 100 5 2
20 82 71 100 100 96 41 87 10 71 62 48 54 0 0 0 5
100 91 75 100 47 79 39 45 73 40 78 13 41 0 0 0 5
18 75 51 100 65 72 41 35 8 3 0 0 50 6 100 15 2
13 96 0 71 78 61 82 12 7 0 4 49 15 97 100 100 5
74 100 47 84 0 66 23 53 84 42 100 44 60 23 33 0 4
0 67 44 80 88 100 100 88 88 66 69 44 50 22 50 0 1
100 77 64 61 9 74 54 100 100 85 91 51 59 19 0 0 9
72 74 63 100 0 78 67 42 100 0 15 4 38 47 …
For your information, you need to implement Multi-Class
(multi-nomial) Logistic Regression using one-vs-all method in
Assignment-2. Please see the lecture slides for more
information on one-vs-all method. All three datasets of UCI
Datasets have more than two classes, so datasets are applicable
for Multi-Class Logistic Regression. Another reminder (as
announced several times in the class): please do not use any
library functions (other than Numpy and Pandas) for your
implementation. Please let me know if you have any questions.
Thank you.