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ex_dict.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Mar 30 18:29:05 2018
@author: J N BALAKUMARAN
"""
from math import sqrt
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
Dataset_for_15mx11={
'Alumni':
{
'Alumni1':4,
'Alumni2':5,
'Alumni3':5,
'Alumni4':4,
'Alumni5':5,
'Alumni6':5
},
"industry":
{
"industry1":4,
"industry2":5,
"industry3":4,
"inudstry4":5,
"industry5":4
},
"student":
{
"student1":5,
"student2":4,
"student3":5,
"student4":4,
"student5":5,
"student6":5
},
"faculty":
{
"faculty1":5,
"faculty2":4,
"faculty3":5,
"faculty4":5,
"faculty5":4,
"faculty6":5
},
"experts":
{
"expert1":5,
"expert2":4,
"expert3":4,
"expert4":5,
"expert5":5
}
}
dataset=Dataset_for_15mx11
Dataset_for_15mx12={
'Alumni':
{
'Alumni1':4,
'Alumni2':5,
'Alumni3':5,
'Alumni4':4,
'Alumni5':5,
'Alumni6':5
},
"industry":
{
"industry1":4,
"industry2":5,
"industry3":4,
"inudstry4":5,
"industry5":4
},
"student":
{
"student1":5,
"student2":4,
"student3":5,
"student4":4,
"student5":5,
"student6":5
},
"faculty":
{
"faculty1":5,
"faculty2":4,
"faculty3":5,
"faculty4":5,
"faculty5":4,
"faculty6":5
},
"experts":
{
"expert1":5,
"expert2":4,
"expert3":4,
"expert4":5,
"expert5":5
}
}
#dataset=Dataset_for_15mx12
print(dataset)
"""def pearson_correlation(person1,person2):
# To get both rated items
both_rated = {}
for item in dataset[person1]:
if item in dataset[person2]:
both_rated[item] = 1
number_of_ratings = len(both_rated)
# Checking for number of ratings in common
if number_of_ratings == 0:
return 0
# Add up all the preferences of each user
person1_preferences_sum = sum([dataset[person1][item] for item in both_rated])
person2_preferences_sum = sum([dataset[person2][item] for item in both_rated])
# Sum up the squares of preferences of each user
person1_square_preferences_sum = sum([pow(dataset[person1][item],2) for item in both_rated])
person2_square_preferences_sum = sum([pow(dataset[person2][item],2) for item in both_rated])
# Sum up the product value of both preferences for each item
product_sum_of_both_users = sum([dataset[person1][item] * dataset[person2][item] for item in both_rated])
# Calculate the pearson score
numerator_value = product_sum_of_both_users - (person1_preferences_sum*person2_preferences_sum/number_of_ratings)
denominator_value = sqrt((person1_square_preferences_sum - pow(person1_preferences_sum,2)/number_of_ratings) * (person2_square_preferences_sum -pow(person2_preferences_sum,2)/number_of_ratings))
if denominator_value == 0:
return 0
else:
r = numerator_value/denominator_value
return r
def most_similar_users(person,number_of_users):
# returns the number_of_users (similar persons) for a given specific person.
scores = [(pearson_correlation(person,other_person),other_person) for other_person in dataset if other_person != person ]
# Sort the similar persons so that highest scores person will appear at the first
scores.sort()
scores.reverse()
return scores[0:number_of_users]
def user_recommendations(person):
# Gets recommendations for a person by using a weighted average of every other user's rankings
totals = {}
simSums = {}
rankings_list =[]
for other in dataset:
if other == person:
continue
sim = pearson_correlation(person,other)
if sim <=0:
continue
for item in dataset[other]:
# only score movies i haven't seen yet
if item not in dataset[person] or dataset[person][item] == 0:
# Similrity * score
totals.setdefault(item,0)
totals[item] += dataset[other][item]* sim
# sum of similarities
simSums.setdefault(item,0)
simSums[item]+= sim
# Create the normalized list
rankings = [(total/simSums[item],item) for item,total in totals.items()]
rankings.sort()
rankings.reverse()
#print(rankings)
# returns the recommended items
recommendataions_list = [(recommend_item,score) for score,recommend_item in rankings]
return recommendataions_list"""
#print(dataset)
alu=ind=exp=fac=stu=0
ac=ic=ec=sc=fc=0
for key in dataset:
for i in dataset[key].values():
if(key=="Alumni"):
alu+=i
ac+=1
if(key=="industry"):
ind+=i
ic+=1
if(key=="experts"):
exp+=i
ec+=1
if(key=="student"):
stu+=i
sc+=1
if(key=="faculty"):
fac+=i
fc+=1
print(alu,ind,exp,stu,fac)
mean=[]
print("length of dataset",len(dataset))
mean.append(round((alu/ac),1))
mean.append(round((ind/ic),1))
mean.append(round((exp/ec),1))
mean.append(round((stu/sc),1))
mean.append(round((fac/fc),1))
print(mean)
mean.sort()
mean.reverse()
print(mean)
objects = ('alumni','experts','students','faculty','industry')
y_pos = np.arange(len(objects))
plt.bar(y_pos, mean, align='center', alpha=0.75)
plt.xticks(y_pos, objects)
plt.ylabel('rating')
plt.xlabel('participants')
plt.title('overall rating for 15mx11')
plt.show()