shinout

root = true

[*]

end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
indent_style = space

shinout

copyClass = (klass) ->
    newKlass = (new Function("""
        return function (call) {
            return function #{klass.name}() {
                return call(this, arguments)
            };
        };
    """)())(Function.apply.bind(klass))

    for own key, value of klass

shinout

shinout ~ shinout$ npm install strongloop
 
> heapdump@0.2.10 install /Users/shinout/node_modules/strongloop/node_modules/strong-supervisor/node_modules/heapdump
> node-gyp rebuild

  CXX(target) Release/obj.target/heapdump/src/heapdump.o
  CXX(target) Release/obj.target/heapdump/src/platform-posix.o
  SOLINK_MODULE(target) Release/heapdump.node
  SOLINK_MODULE(target) Release/heapdump.node: Finished
 

shinout

N = 100000
READ_SIZE = 100
fs = require "fs"

fd = fs.openSync __filename, "r"

i = 0
console.time "sync"

while i < N

shinout

rt = require "random-tools" # https://github.com/shinout/random-tools

N = 1000000
d = []
i = 0

# data preparation
while i < N
  n = rt.randomInt(256 * 256 * 256 - 1)
  b = new Buffer(3)

shinout

fs = require("fs")
rstream = fs.createReadStream process.argv[2]
wstream = fs.createWriteStream(process.argv[3])

rstream.setEncoding "utf-8"
ended = false
piping = ->
  return if ended
  d = rstream.read()
  return rstream.once "readable", read_write if d is null

shinout

GAP = -1
MISMATCH = -1
MATCH = 1 

class Cell
  constructor: (i,j)->
    @p1 = i 
    @p2 = j 
    @pointers = []
    @score = 0 

shinout

const UP   = 1;
const LEFT = 2;
const UL   = 4;

function nw(s1, s2, op) {
  op = op || {};
  const G = op.G || 2;
  const P = op.P || 1;
  const M = op.M || -1;
  var mat   = {};

shinout

least_square = (dataset)->
  n = dataset.length
  sigma_xy = 0
  sigma_x = 0
  sigma_y = 0
  sigma_xsq = 0

  for data in dataset
    x = data[0]
    y = data[1]

shinout

# 最急降下法
steepest = (f, alpha, x0)->
  alpha = 1/2 * (Math.abs Math.random()) + 0.1 if typeof alpha isnt "number"
  alpha = Math.abs alpha
  x0 = Math.random() if typeof x0 isnt "number"

  fx0 = f(x0)
  x = x0 + 0.01 # for approximating gradient

  loop