In order to successfully complete this assignment you need to participate both individually and in groups during class. If you attend class in-person then have one of the instructors check your notebook and sign you out before leaving class. If you are attending asynchronously, turn in your assignment using D2L no later than 11:59pm on the day of class. See links at the end of this document for access to the class timeline for your section.
In order to successfully complete this assignment you need to participate both individually and in groups during class. If you attend class in-person then have one of the instructors check your notebook and sign you out before leaving class. If you are attending asyncronously, turn in your assignment using D2L no later than 11:59pm on the day of class. See links at the end of this document for access to the class timeline for your section.
In this in-class assignment, we are going to avoid some of the more advanced libraries ((i.e. no numpy or scipy or sympy) to try to get a better understanding about what is going on in the math.
The following code implements some common linear algebra functions:
#Standard Python Libraries only
import math
import copy
def dot(u,v):
'''Calculate the dot product between vectors u and v'''
temp = 0;
for i in range(len(u)):
temp += u[i]*v[i]
return temp
def multiply(m1,m2):
'''Calculate the matrix multiplication between m1 and m2 represented as list-of-list.'''
n = len(m1)
d = len(m2)
m = len(m2[0])
if len(m1[0]) != d:
print("ERROR - inner dimentions not equal")
result = [[0 for i in range(n)] for j in range(m)]
for i in range(0,n):
for j in range(0,m):
for k in range(0,d):
result[i][j] = result[i][j] + m1[i][k] * m2[k][j]
return result
def add_vectors(v1,v2):
v3 = []
for i in range(len(v1)):
v3.append(v1[i]+v2[i])
return v3
def sub_vectors(v1,v2):
v3 = []
for i in range(len(v1)):
v3.append(v1[i]-v2[i])
return v3
def norm(u):
'''Calculate the norm of vector u'''
nm = 0
for i in range(len(u)):
nm += u[i]*u[i]
return math.sqrt(nm)
def transpose(A):
'''Calculate the transpose of matrix A represented as list of lists'''
n = len(A)
m = len(A[0])
AT = list()
for j in range(0,m):
temp = list()
for i in range(0,n):
temp.append(A[i][j])
AT.append(temp)
return AT
✅ DO THIS: Write a function that projects vector $v$ onto vector $u$. Do not use the numpy library. Instead use the functions provided above:
$$\mbox{proj}_u v = \frac{v \cdot u}{u \cdot u} u$$Make sure this function will work for any size of $v$ and $u$.
def proj(v,u):
## Put your code here
return pv
Let's test your function. Below are two example vectors. Make sure you get the correct answers. You may want to test this code with more than one set of vectors.
u = [1,2,0,3]
v = [4,0,5,8]
print(proj(u,v))
from answercheck import checkanswer
checkanswer.vector(proj(u,v),'53216508af49c616fa0f4e9676ce3b9d');
✅ DO THIS:See if you can design and implement a small function that takes two vectors ($a$ and $b$) as inputs and generates a figure similar to the one above.
I.e. a black line from the origin to "$b$", a black line from origin to "$a$"; a green line showing the "$a$" component in the "$b$" direction and a red line showing the "$a$" component orthogonal to the green line. Also see section titled "Projection of One Vector onto Another Vector" in Section 4.6 on page 258 of the book.
When complete, show your solution to the instructor.
%matplotlib inline
import matplotlib.pylab as plt
b = [3,2]
a = [2,3]
def show_projection(a,b):
plt.plot([0,a[0]], [0,a[1]], color='black')
plt.annotate('b', b,
xytext=(0.9, 0.7), textcoords='axes fraction',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='right', verticalalignment='top')
plt.annotate('a', a,
xytext=(0.7, 0.95), textcoords='axes fraction',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='right', verticalalignment='top')
plt.plot([0,b[0]], [0,b[1]], color='black')
#Finish your code here
plt.axis('equal')
x = show_projection(a,b) ;
✅ DO THIS: Implement the Gram-Schmidt orthoganalization process from the Hefron textbook (page 282). This function takes a $m \times n$ Matrix $A$ with linearly independent columns as input and return a $m \times n$ Matrix $G$ with orthogonal column vectors. The basic algorithm works as follows:
AT = transpose(A) (this process works with the columns of the matrix so it is easier to work with the transpose. Think about a list of list, it is easy to get a row (a list)). AT and call it GT (G transpose)Loop index i over all of the rows in AT (i.e. columns of A)
GT[i] = AT[i]j from 0 to iGT[i] -= proj(GT[i], GT[j])G = transpose(GT)Use the following function definition as a template:
def GramSchmidt(A):
return G
Here, we are going to test your function using the vectors:
A4 = [[1,4,8],[2,0,1],[0,5,5],[3,8,6]]
print(transpose(A4))
G4 = GramSchmidt(A4)
print(transpose(G4))
from answercheck import checkanswer
checkanswer.matrix(G4,'a472a81eef411c0df03ae9a072dfa040');
A2 = [[-4,-6],[3,5]]
print(transpose(A2))
G2 = GramSchmidt(A2)
print(transpose(G2))
from answercheck import checkanswer
checkanswer.matrix(G2,'23b9860b72dbe5b84d7c598c08af9688');
✅ QUESTION: What is the Big-O complexity of the Gram-Schmidt process?
Put your answer here
If you attend class in-person then have one of the instructors check your notebook and sign you out before leaving class. If you are attending asynchronously, turn in your assignment using D2L.
Written by Dr. Dirk Colbry, Michigan State University
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.