推荐算法-距离算法

#
#  dis.py
#

from math import *

teams = [
"Blues Traveler",
"Broken Bells",
"Deadmau5",
"Norah Jones",
"Phoenix",
"Slightly Stoopid",
"The Strokes",
"Vampire Weekend"
]


users = {
"Angelica": {
"Blues Traveler": 3.5,
"Broken Bells": 2,
"Norah Jones": 4.5,
"Phoenix": 5,
"Slightly Stoopid": 1.5,
"The Strokes": 2.5,
"Vampire Weekend": 2
},
"Bill": {
"Blues Traveler": 2,
"Broken Bells": 3.5,
"Deadmau5": 4,
"Phoenix": 2,
"Slightly Stoopid": 3.5,
"Vampire Weekend": 3
},
"Chan": {
"Blues Traveler": 5,
"Broken Bells": 1,
"Deadmau5": 1,
"Norah Jones": 3,
"Phoenix": 5,
"Slightly Stoopid": 1
},
"Dan": {
"Blues Traveler": 3,
"Broken Bells": 4,
"Deadmau5": 4.5,
"Phoenix": 3,
"Slightly Stoopid": 4.5,
"The Strokes": 4,
"Vampire Weekend": 2
},
"Hailey": {
"Broken Bells": 4,
"Deadmau5": 1,
"Norah Jones": 4,
"The Strokes": 4,
"Vampire Weekend": 1
},
"Jordyn": {
"Broken Bells": 4.5,
"Deadmau5": 4,
"Norah Jones": 5,
"Phoenix": 5,
"Slightly Stoopid": 4.5,
"The Strokes": 4,
"Vampire Weekend": 4
},
"Sam": {
"Blues Traveler": 5,
"Broken Bells": 2,
"Norah Jones": 3,
"Phoenix": 5,
"Slightly Stoopid": 4,
"The Strokes": 5
},
"Veronica": {
"Blues Traveler": 3,
"Norah Jones": 5,
"Phoenix": 4,
"Slightly Stoopid": 2.5,
"The Strokes": 3
}
}

def manhattan(rating1, rating2):
"""Computes the Manhattan distance. Both rating1 and rating2 are dictionaries
of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += abs(rating1[key] - rating2[key])
commonRatings = True
if commonRatings:
return distance
else:
return -1 #Indicates no ratings in common


def euclidean(rating1, rating2):
"""Computes the euclidean distance. Both rating1 and rating2 are dictionaries
of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += pow(rating1[key] - rating2[key],2)
commonRatings = True
if commonRatings:
return sqrt(distance)
else:
return -1 #Indicates no ratings in common


def minkowski(rating1, rating2, r):
"""Computes the minkowski distance. Both rating1 and rating2 are dictionaries
of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += pow(abs(rating1[key] - rating2[key]),r)
commonRatings = True
if commonRatings:
return pow(distance, 1.0/r)
else:
return -1 #Indicates no ratings in common


def cosineSimilarity (rating1, rating2):
"""Computes the Cosine Similarity distance. Both rating1 and rating2 are dictionaries
of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
sum_xy = 0
sum_sqr_x = 0
sum_sqr_y = 0
for key in teams:
if key in rating1 and key in rating2:
sum_xy += rating1[key]* rating2[key]
sum_sqr_x += pow(rating1[key], 2)
sum_sqr_y += pow(rating2[key], 2)
elif key not in rating1 and key in rating2:
sum_xy += 0
sum_sqr_x += 0
sum_sqr_y += pow(rating2[key], 2)
elif key in rating1 and key not in rating2:
sum_xy += 0
sum_sqr_x += pow(rating1[key], 2)
sum_sqr_y += 0
else:
sum_xy += 0
sum_sqr_x += 0
sum_sqr_y += 0

if sum_sqr_x ==0 or sum_sqr_y==0:
return -1 #Indicates no ratings in common
else:
return sum_xy / (sqrt(sum_sqr_x) * sqrt(sum_sqr_y))


def pearson(rating1, rating2):
"""Computes the pearson distance. Both rating1 and rating2 are dictionaries
of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
sum_xy = 0
sum_x = 0
sum_y = 0
sum_x2 = 0
sum_y2 = 0
n = 0
for key in rating1:
if key in rating2:
n += 1
x = rating1[key]
y = rating2[key]
sum_xy += x * y
sum_x += x
sum_y += y
sum_x2 += pow(x, 2)
sum_y2 += pow(y, 2)
# now compute denominator
denominator = sqrt(sum_x2 - pow(sum_x, 2) / n) * sqrt(sum_y2 - pow(sum_y, 2) / n)
if denominator == 0:
return 0
else:
return (sum_xy - (sum_x * sum_y) / n) / denominator

Original: https://www.cnblogs.com/liuning8023/p/5417052.html
Author: 船长CAP
Title: 推荐算法-距离算法

原创文章受到原创版权保护。转载请注明出处:https://www.johngo689.com/6484/

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