ソフトウェアエンジニアの技術ブログ：Software engineer tech blog

MEAN, MEDIAN, MODE

house prices
190k 170k 165k 180k 165k
Mean = 1/N ΣXi = 175

Median
picks the one in the middle

Mode
most frequently used number

3, 9, 3, 8, 2, 9, 1, 9, 2, 4
mean = 5
median = 3, 4
mode = 9

def sim_distance(prefs, person1, preson2)
	shared_items_a = shared_items_a(prefs, person1, person2)
	return 0 if shared_items_a.size == 0
	sum_of_squares = shared_items_a.inject(0){|result, item|
		result + (prefs[person1][item]-prefs[person2][item])**2
	}
	return 1/(1+sum_of_squares)
end

def shared_items_a(prefs, person1, person2)
	prefs[person1].keys & prefs[person2].keys
end

ピアソン相関係数

def sim_pearson(prefs, person1, person2)
	shared_items_a = shared_items_a(prefs, person1, person2)

	n = shared_items_a.size
	return 0 if n == 0

	sum1 = shared_items_a.inject(0) {|result,si|
		result + prefs[person1][si]
	}
	sum2 = shared_items_a.inject(0) {|result,si|
		result + prefs[person2][si]
	}
	sum1_sq = shared_items_a.inject(0) {|result,si|
		result + prefs[person1][si]**2
	}
	sum2_sq = shared_items_a.inject(0) {|result,si|
		result + prefs[person2][si]**2
	}
	sum_products = shared_items_a.inject(0) {|result,si|
		result + prefs[person1][si]*prefs[person2][si]
	}

	num = sum_products - (sum1*sum2/n)
	den = Math.sqrt((sum1_sq - sum1**2/n)*(sum2_sq - sum2**2/n))
	return 0 if den == 0
	return num/den
end

類似度

def top_matches(prefs, person, n=5, similarity=:sim_pearson)
	scores = Array.new
	prefs.each do |key,value|
		if key != person
			scores << [__send__(similarity, prefs, person, key),key]
		end
	end
	scores.sort.reverse[0,n]
end

p top_matches(critics_ja, 'xxx')

def get_recommendations(prefs, person, similarity=:sim_pearson)
	totals_h = Hash.new(0)
	sim_sums_h = Hash.new(0)

	prefs.each do |other,val|
		next if other == person
		sim = __send__(similarity,prefs,person,other)
		next if sim <= 0
		prefs[other].each do |item, val|
			if !prefs[person].keys.include?(item)||pref[person][item]==0
				totals_h[item] += prefs[other][item]*sim
				sim_sums_h[item] += sim
			end
		end
	end

	rankings = Array.new
	totals_h.each do |item,total|
		rankings << [total/sim_sums_h[item], item]
	end
	rankings.sort.reverse
end

p get_recommendations(critics_ja, 'xxx')

def transform_prefs(prefs)
	result = Hash.new
	prefs.each do |person, score_h|
		score_h.each do |item, score|
			result[item] ||= Hash.new
			result[item][person] = score
		end
	end
	result
end

menu = transform_prefs(ciritics_ja)
p top_matches(menu, 'xxx')

for goods in goods.get_all():
	Recomender.register(goods.id, tag=goods.tag)

for user in user.get_all():
	Recomender.like(user.id, user.history.goods_ids)

Recomender.update_all()

Recomender.update_all(proc=4)

Recomender.update_all(proc=4, scope=[1, 4])
Recomender.update_all(proc=4, scope=[2, 4])
Recomender.update_all(proc=4, scope=[3, 4])
Recomender.update_all(proc=4, scope=[4, 4])

new_goods_id = 2100
tag = "book"
Recomender.register(new_goods_id, tag=tag)

goods_id = 102
print Recomender.get(good_id, count=5)

Recomender.update(goods_id)

Recomender.update_all()

user_id = "xxxx"
goods_ids = [102, 102, 103, 104]
Recomender.like(user_id, goods_ids)

new_tag = "computer"
Recomender.change_tag(goods_id, new_tag)

Recomender.remove(goods_id)

Recomender.remove_user(user_id)

# -*- coding: utf-8 -*-
__future__ import absolute_import, unicode_literals

# 商品ID:10の購入者
from collections import defaultdict

ITEM_10_BUY_USERS = ['A', 'C', 'E', 'G']

INDEX_BASE = 'INDEX_BUY_HISTORY_USER_{}'
INDEX = {
	'INDEX_BUY_HISTORY_USER_A':[10,20,50,60,90],
	'INDEX_BUY_HISTORY_USER_B':[20,20,50,60,90],
	'INDEX_BUY_HISTORY_USER_A':[10,30,50,60,90],
	'INDEX_BUY_HISTORY_USER_A':[30,40,50,60],
	'INDEX_BUY_HISTORY_USER_A':[10],
	'INDEX_BUY_HISTORY_USER_A':[70,80,90],
	'INDEX_BUY_HISTORY_USER_A':[10,70,90],
}

result = defaultdict(int)
for user_id in ITEM_10_BUY_USERS:
	buy_history = INDEX.get(INDEX_BASE.format(user_id))
		for item_id in buy_history:
			result[item_id] += 1

	l = []
	for key in result:
		l.append((key, result[key]))

	l.sort(key=lambda x: x[1], reverse=True)
	print l

Deep insight
correlation, causation

Sick
In hospital 40, died 4 10%
home 8000, died 20 0.25%

Chances of dying in hospital are 40 times larger than at home

hospital died
sick 36 4 11.1%
health 4 0 0%

At home
sick 40 20 50%
healthy 7960 20 0.251%

P(exactly one head)
–
P(first flip is only head)
= 4

def test(coins, flips):
	f=FlipPredictor(coins)
	quesses=[]
	for flip in flips:
		f.update(flip)
		quesses.append(f.Pheads())
	return guesses

print test([0.5,0.4,0.3],'HHTH')

from __future__ import division
class FlipPredictor(object):
	def __init__(self,coins):
		self.coins=coins
		n=len(coins)
		self.probs=[1/n]*n
	def Pheads(self):

	def update(self,result):

p(x)=0

in continuous distribution
every outcome has probability 0

outcome:x
P(0

P(c)= p0 = 0.1, p(¬c)=0.9
p(pos|c)= p1 = 0.9, p(pos|¬c)=0.1
p(neg|¬c)= p2 = 0.8, p(neg|c)= 0.2

p(p)= 0.09 + 0.18 = 0.27

def f(p0, p1, p2):
	return p0*p1 + (1-p0)*(1-p2)
print f(0.1, 0.9, 0.8)

program bayes rule

def f(p0, p1, p2):
	return p0*p1 / (p0 * p1 + (1-p0)*(1-p2))
print f(0.1, 0.9, 0.8)

def f(p0,p1,p2):
	return p0 * (1-p1)/(p0 * (1-p1)+(1-p0)*p2)
print f(0.1, 0.9, 0.8)