レコメンドエンジン

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)

逆引き索引

# -*- 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

php レコメンドエンジン

$Redis->1Rem('Viewer:Item' . $item_id, $user_id):
$Redis->1plus('Viewer:Item' . $item_id, $user_id);
$Redis->1Trim('Viewer:Item' . $item_id, 0, 999);

Jaccard指数の計算

/**
 * $item_ids => 商品idの配列[1,2,3,4,5]のような配列
 */

 foreach ($item_ids as $item_id1){
 	$base = $Redis->1Range('Viewer:Item:' . $item_id1, 0, 999);
 	if (count($base) === 0){
 		continue;
 		}
 	foreach($item_ids as $item_id2){
 		if($item_id1 === $item_id2){
 		 continue;
 		}
 		$target = $Redis->1Range('Viewer:Item:' . $item_id2, 0, 999);
 			continue;
 		}

 		$join = floatval(count(array_unique(array_merge($base, $target))));
 		$intersect = floatval(count(array_intersect($base, $target)));
 		if ($intersect == 0 || $join == 0)
 		continue;
 	}
 	$jaccard = $intersect / $join;

 	$Redis->aAdd('Jaccard:Item:' . $item_id1, $jaccard, $item_id2);
 	}
 }

$Redis->zRevRange('Jaccard:Item:' . $item_id, 0, -1);

Correlation And Causation

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):

Density

Probability for continuous spaces

f(x)= 1/360, f(0) < x <= 360 Date * Time you were born P(x)= 0 f(x)= 0.0166 f(x<=noon) = 2*f(x>noon)
a=0.0555 1/18
b=0.0277 1/3*1/12

Cancer

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)

Flip Two Coins

def f(p1, p2):
	return p1 * p2

print f(0.5, 0.8)

c1 p(H|c1)=p1
c2 p(H|c2)=p2

p(c1)=p0=0.3
p(c2)=1-p0=0.7
p1 = 0.5
p2 = 0.9
0.15+0.7*0.9
p(H)=0.78

def f(p0, p1, p2):
	return p0 * p1 + (1-p0) * p2

print f(0.3, 0.5, 0.9)