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# Homework-0

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Homework-0

1 Homework 0
You will not have to turn this homework in. The goal of this homework is just to practice the skills learned
in Lecture 1. These skills will be essential for the rest of this course.
Before starting to work on this homework, make a new folder named hw0-submission in your private
repo and move Homework-0.ipynb into that folder.
Let’s make sure that you placed everything in the correct folder
In [ ]: import os
if os.path.basename(os.path.realpath(’.’)) != “hw0-submission”:
print(“The name of the current directory should be ’hw0-submission’”)
else:
print(“You did everything correctly. Congrats!”)
Now, assuming that everything is in their correct place, you should git add all the new directories/files
and make your initial git commit.
1.1 This is a Markdown cell
Insert and edit your own markdown cell below. Write your favorite quote.
In [ ]: # This is a code cell. Try executing it. Do not clear your output.
print(“hi”)
Write a function that takes as argument a string and returns its 3-letter suffix. If the string is shorter
than 3 letters, return the whole string.
In [ ]: def suffix_3(string):
“””
Returns the 3-letter suffix.
Parameters:
string: str
Returns:
suffix: str
If ‘string‘ is more than 3-letters long, ‘suffix‘ is its 3-letter suffix. Otherwise, ‘suffix‘ is the same as ‘string‘.
“””
In [ ]: print(suffix_3(“Hi!”))
print(suffix_3(“Superb”))
print(suffix_3(“Yo”))
1
When you finish with the above function, don’t forget to add and commit to your local git repository.
Let’s print the versions of some packages that we will be using in the future. If any of these packages are
not present, conda install them. Do not clear your output.
In [ ]: import numpy
print(’numpy:’, numpy.__version__)
import scipy
print(’scipy:’, scipy.__version__)
import matplotlib
print(’matplotlib:’, matplotlib.__version__)
import sklearn
print(’scikit-learn:’, sklearn.__version__)
import pandas
print(’pandas:’, pandas.__version__)
Write a function that takes as argument a 2D list representing a matrix M and returns a boolean
indicating whether M is an orthogonal matrix.
Keep in mind the possibility of roundoff error in floating point computations.
To test if two matrices are equal, you may find functions in the numpy.testing package to be useful.
In [ ]: import numpy
def is_matrix_orthogonal(M):
“””
Returns True if M is an orthogonal matrix.
Parameters:
M: 2D list
Returns:
is_orthogonal: boolean
“””
In [ ]: print(is_matrix_orthogonal([[1, 0], [0, -1]]))
print(is_matrix_orthogonal([[-1.2396, -4.3801, -1.7737], [-1.3121, -2.9193, -4.5496], [-2.1143, 0.0363, 0.2022]]))
print(is_matrix_orthogonal([[-0.8185, 0.5740, -0.0249], [-0.2510, -0.3183, 0.9142], [-0.5168, -0.7544, -0.4046]]))
When you finish with the above function, don’t forget to add and commit to your local git repository.
1.2 Submitting
Now, you can push your changes to Github.
2

Homework-0
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