CanoeFZH

/* ---- MIT LICENSE ----
Copyright (c) 2011 John Gietzen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

CanoeFZH

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

CanoeFZH

from pyspark.sql.types import StringType
from pyspark.sql.functions import udf

maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

df = sqlContext.createDataFrame([{'name': 'Alice', 'age': 1}])
df.withColumn("maturity", maturity_udf(df.age))

CanoeFZH

model:
w0: line 0; 1 column;
w: line 1 to 20000187; 1 column
v: line 20000188 to end; 4 columns;
num_factor: 4




user_feature : 

CanoeFZH

import collections
'''
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1330&context=compsci
https://www.quora.com/Where-can-I-find-a-maximum-marginal-relevance-algorithm-in-Python-for-redundancy-removal-in-two-documents

'''
def argmax(keys, f):
    return max(keys, key=f)

CanoeFZH

#include <stdio.h>

int main() {
    printf("%d\n", '0'/(('0'+'0')/'0'));
    printf("%d\n", '0'>>'0'/'0'+0);
    printf("%d\n", -~-~-~-~-~-~0-~-~-~-~-~-~0-~-~-~-~-~-~0-~-~-~-~-~-~0);

    return 0;
}