<jupyter_start><jupyter_text><jupyter_code>import numpy as np
from numpy.linalg import eig<jupyter_output><empty_output><jupyter_text>Problem 1<jupyter_code>studC = 19

studA = (studC - 6)
studB = (studC * 3)

print("Student A has ", studA)
print("Student B has ", studB)
print("Student C has ", studC)<jupyter_output>Student A has  13
Student B has  57
Student C has  19<jupyter_text>Problem 2<jupyter_code>A = np.array([[3,-1,1], [9,-3,3], [-12,4,-4]])
print("Matrix A", A)
inv_A = np.linalg.pinv(A)
print("Inv A", inv_A)
B = np.array([[5],[10],[-20]])
print("Matrix A", A)
X = np.dot(inv_A, B)
print("Dot", X)
C = np.dot(A, X)
print("Checking", C)<jupyter_output>Matrix A [[  3  -1   1]
 [  9  -3   3]
 [-12   4  -4]]
Inv A [[ 0.01048951  0.03146853 -0.04195804]
 [-0.0034965  -0.01048951  0.01398601]
 [ 0.0034965   0.01048951 -0.01398601]]
Matrix A [[  3  -1   1]
 [  9  -3   3]
 [-12   4  -4]]
Dot [[ 1.20629371]
 [-0.4020979 ]
 [ 0.4020979 ]]
Checking [[  4.42307692]
 [ 13.26923077]
 [-17.69230769]]<jupyter_text>Problem 3<jupyter_code>matrix3 =np.array ([[8,5,-6],[-12,-9,-12],[-3,-3,5]]) #This line determines the array that will be solved
print(matrix3)

value,vector = np.linalg.eig(matrix3)   #This line contains the code for finding the eigenvalue and eigevector
print ("The eigenvalue/s is/are:",value)
print ("The eigenvectors are:",vector)<jupyter_output>[[  8   5  -6]
 [-12  -9 -12]
 [ -3  -3   5]]
The eigenvalue/s is/are: [-8.11684397  3.          9.11684397]
The eigenvectors are: [[ 2.35632155e-01 -7.07106781e-01 -8.61671223e-01]
 [-9.57707415e-01  7.07106781e-01  2.99299241e-01]
 [-1.65148399e-01 -1.19155502e-16  4.09808086e-01]]
{'fraction_non_alphanumeric': 0.18478260869565216, 'fraction_numerical': 0.1956521739130435, 'mean_word_length': 3.6807909604519775, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '595394', 'license': 'Apache-2.0', 'n_tokens_mistral': 915, 'n_tokens_neox': 750, 'n_words': 167, 'path': 'Final_Exam.ipynb', 'repo_name': 'YushirohMurasakih/Linear-Algebra-58020', 'size': '1656'}