def problem_3():
(A, B) = create_matrix_A_B()
# B is a song x user matrix, transpose of B is a user x song matrix. Their multiplication gives a song x song matrix with similarity values between songs
song_similarity_mat = B * B.transpose()
# Extracting all similarity values
song_sim_values = [
val for (row_lbl, col_lbl), val in song_similarity_mat.f.items()
if row_lbl != col_lbl
]
# Extracting maximum similarity value
max_song_sim = max(song_sim_values)
song_sim_list = []
for row_label in song_similarity_mat.D[0]:
for col_label in song_similarity_mat.D[1]:
if row_label != col_label and song_similarity_mat.f.get(
(row_label, col_label)) == max_song_sim:
if (row_label, col_label) not in song_sim_list and (
col_label, row_label) not in song_sim_list:
song_sim_list.append((row_label, col_label))
print('Songs most similar to each other : ', song_sim_list)
def problem_1():
(A, B) = create_matrix_A_B()
# A is a user x song matrix, B is song x user matrix. A*B creates a user x user matrix which contains similarity values between every pair of users
user_similarity_mat = A * B
# Extracing all similarity values excluding diagonal elements
user_similarity_values = [
val for (row_label, col_label), val in user_similarity_mat.f.items()
if row_label != col_label
]
# Extracing maximum similarity value
user_max_sim = max(user_similarity_values)
# Extracing minimum similarity value
user_min_sim = min(user_similarity_values)
max_sim_list = []
max_sim_count = 0
for row_label in user_similarity_mat.D[0]:
for col_label in user_similarity_mat.D[1]:
if row_label != col_label and user_similarity_mat.f.get(
(row_label, col_label)) == user_max_sim:
if (row_label, col_label) not in max_sim_list and (
col_label, row_label) not in max_sim_list:
max_sim_list.append((row_label, col_label))
max_sim_count = max_sim_count + 1
print("List of users with most similarity : ")
print(max_sim_list)
min_sim_list = []
min_sim_count = 0
for row_label in user_similarity_mat.D[0]:
for col_label in user_similarity_mat.D[1]:
if row_label != col_label and user_similarity_mat.f.get(
(row_label, col_label)) == user_min_sim:
if (row_label, col_label) not in min_sim_list and (
col_label, row_label) not in min_sim_list:
min_sim_list.append((row_label, col_label))
min_sim_count = min_sim_count + 1
print("List of users with least similarity : ")
print(min_sim_list)
def problem_4():
(A, B) = create_matrix_A_B()
# F is a user x city matrix
F = create_matrix_F()
# Transpose of F is a city x user matrix and A is a user x song matrix. Their multiplication gives the city listening profile matrix, which is a city x song matrix
city_listening_mat = F.transpose() * A
max_row_sum = 0
max_city = ""
for row_label in city_listening_mat.D[0]:
row_sum = 0
for col_label in city_listening_mat.D[1]:
row_sum = row_sum + city_listening_mat.f.get(
(row_label, col_label))
if row_sum > max_row_sum:
max_row_sum = row_sum
max_city = row_label
print("City with most recurrent songs played : ", max_city)
# Multiplication of city listening profile matrix with its transpose will give city similarity matrix
city_similarity_mat = city_listening_mat * city_listening_mat.transpose()
# Extracting all similarity values
city_sim_values = [
val for (row_lbl, col_lbl), val in city_similarity_mat.f.items()
if row_lbl != col_lbl
]
# Extracting the max similarity values
max_city_sim = max(city_sim_values)
city_sim_list = []
for row_label in city_similarity_mat.D[0]:
for col_label in city_similarity_mat.D[1]:
if row_label != col_label and city_similarity_mat.f.get(
(row_label, col_label)) == max_city_sim:
if (row_label, col_label) not in city_sim_list and (
col_label, row_label) not in city_sim_list:
city_sim_list.append((row_label, col_label))
print('Cities most similar to each other are : ', city_sim_list)
def problem_2():
(A, B) = create_matrix_A_B()
# A is a user x song matrix, B is song x user matrix. A*B creates a user x user matrix which contains similarity values between every pair of users
user_similarity_mat = A * B
user_id = 'T86YHdD4C9JSc274b1IlMkLuNdz4BQRB50fWWE7hx9g='
# Extracting all values similarity values corresponding to user 'T86YHdD4C9JSc274b1IlMkLuNdz4BQRB50fWWE7hx9g='
user_similarity_values = [
val for (row_label, col_label), val in user_similarity_mat.f.items()
if ((row_label != col_label) and row_label == user_id)
]
# Extracting max similarity value
user_max_sim = max(user_similarity_values)
max_sim_user = []
for col_label in similarity_mat.D[1]:
if user_id != col_label and user_similarity_mat.f.get(
(user_id, col_label)) == user_max_sim:
max_sim_list.append((row_label, col_label))
print("Users most similar to each other : ", max_sim_list)
def problem_5():
(A, B) = create_matrix_A_B()
G = create_matrix_G()
gender_song_sim_mat = G.transpose() * A
for row_label in gender_song_sim_mat.D[0]:
row_values = []
for col_label in gender_song_sim_mat.D[1]:
row_values.append(gender_song_sim_mat.f.get(
(row_label, col_label)))
max_row_value = max(row_values)
gender = row_label + 's'
gender_song = ''
for col_label in gender_song_sim_mat.D[1]:
if gender_song_sim_mat.f.get(
(row_label, col_label)) == max_row_value:
gender_song = col_label
break
print('Song : ', gender_song, ' is played most by ', gender)
def problem_6():
(A, B) = create_matrix_A_B()
w = create_vec_w()
b = create_vec_b()
print('Fraction Wrong : ', frac_wrong(A, b, w))
from vec import Vec from mat import Mat from matutil import mat2rowdict from matutil import rowdict2mat import math from problems import problem_1 from problems import problem_2 from problems import problem_3 from problems import problem_4 from problems import problem_5 from problems import problem_6 from problems import gradient_descent from problems import frac_wrong from problems import loss from problems import find_grad from problems import gradient_descent_step from read_input import create_matrix_A_B from read_input import create_matrix_F from read_input import create_matrix_G from read_input import create_vec_b from read_input import create_vec_w import matplotlib.pyplot as plt (A, B) = create_matrix_A_B()