self-learning

with processor flags output

https://github.com/yrp604/rappel

msf provided repl

msf-nasm_shell

neo4j desktop provide free multi graph databases support, enable you to create and use multiple graph datases at once

to query undirected relationships:

match () – (p) return p

create fulltext index

create fulltext index lucene for (n:Person) on each [n.title, n.description]

call db.index.fulltext.queryNodes(“titlesAndDescriptions”, “Full Metal Jacket”) yield node, score return node, score

calculate customer rating cosine similarity for recommendation:

https://neo4j.com/graphgists/northwind-recommendation-engine

match (c1:customer)-[r1:rated]-(:product)-[r2:rated]-(c2:customer)

with sum(r1.score*r2.score) as dot_product,

sqrt(reduce(x=0, a in r1.score | x+a^2)) as r1_length,

sqrt(reduce(y=0, b in r2.score | y+b^2)) as r2_length

merge (c1)-[s:similarity}]-(c2)

set s += {score:dot_product/(r1_length*r2_length)

use collect to turn maps into lists:

match (p) return collect(p.names)

exist subquery:

match (n:Person) where exists {

match (n) –(t:Tech)

where size((t)-[:likes]-(:Person)) >2

}

return n.name

list comprehension:

return [x in range(0,10) where x%3 = 0| x/2] as list

return [x in range(0,10) where not x in range(4,10) |x ] as list

to use conditional matches or regular expressions:

match () – (p) where p.name in [“helen”] or p.name =~ “.chinese.“ return p

create index on properties:

create index for (n:Category) on (n.categoryName)

asterisks:

load csv:

load csv with headers from “http://localhost/person.csv“ as line

call {with line

merge (n:person {id: toInteger(line.id)})

set n.name = line.name

} in transactions of 2 rows

count nodes:

match (n) return count(n)

match relationship patterns:

match (n) -[:friend|hater*3]->(p) return p limit 20

node can have multiple labels, while relationship can only have one type, both specified after the colon.

simple case expression:

match(n)

return

case n.eyes

when “blue” then 1

when “brown” then 2

else 3

end as result

generic case expression

match (n)

return

case

when n.eyes = “blue” then 1

when n.age > 40 then 2

else 3 // if without else then return null

end as result

inequality symbol: <>

mutating updating node properties:

match(n)

set n+={name:”helen”} // if using = the properties will be totally replaced instead of update.

return n.name

merge can only ensure the existance of one node or pattern at a time, no comma

plus can concatenate strings

tenporal dataformat can be used as numbers to compute.

Map operators

. for static value access by key, [] for dynamic value access by key

List operators

• for concatenation, IN to check existence of an element in a list, [] for accessing element(s) dynamically

recommendation steps:

first find targets by meta relatonships

next sort recommendation by frequency, ratings or occurance

third filter items by topics or properties