coodoo

module Bar
(
) where

import System.IO
import System.Environment
import Control.Monad
import Data.Maybe
import Data.List
import Control.Monad.Writer

coodoo

big :: Int
big =
  -- (\x -> x - 1) -- 可以
  (-1) -- 不行
  $ length
  $ takeWhile (< 10)
  $ scanl (\acc x -> sqrt x + acc) 0 [1..]

coodoo

const Task = require('data.task')
const { List, Map } = require('immutable-ext')
const { Pair, Sum, Intersection } = require('./monoid')
const { findArtist, relatedArtists } = require('./spotify')

const Bulk = (...xs) => ({
	xs,
	concat: ys => Bulk(...xs.map( (x, idx) => x.concat(ys.xs[idx]) )),
	bulkMap: (...fs) => Bulk(...xs.map((x, idx) => fs[idx](x))),
	toList: _ => xs,

coodoo

/*

- Goal: 
  implmenting something like Pair(a, b) but could accommodate any amount of arguments

- Usage: 
  
  Bulk( Sum(2), Sum(3), Sum(4) )
  .concat(Bulk(Sum(1), Sum(1), Sum(1)))
  

coodoo

const Mapp = val => ({

	val,

	// {a: 'aa', b: 'bb'}
	// f :: v -> value
	map: f => {
		const o = {}
		// let resultArr = Object.keys(val).map((key, idx) => ({ [key]: f(val[key]) })) // 錯的，會變成[{}, {}]
		Object.keys(val).map((key, idx) => o[key] = f(val[key]) )

coodoo

function weight(
	currentWeight = 82,
	currentRatio = 0.23,
	targetRatio = 0.2,
	dailyBurn = 400
) {
	const currentFat = currentWeight * currentRatio
	const currentMuscle = currentWeight - currentFat
	const targetWeight = currentMuscle / (1 - targetRatio)
	const loseFat = currentWeight - targetWeight // kg

coodoo

function weight(
	currentWeight = 82,
	currentRatio = 0.23,
	targetRatio = 0.2,
	dailyBurn = 400,
) {
	const currentFat = currentWeight * currentRatio
	const currentMuscle = currentWeight - currentFat
	const targetWeight = currentMuscle / (1 - targetRatio)
	const loseFat = currentWeight - targetWeight // kg

coodoo

/*
Usage:
import SVGIcon from './Icons.jsx'
<SVGIcon kind='edit' style={{width: 48, height:48, fill:'green'}} />
*/

import React, { Component, PropTypes } from 'react'

// base icon style
const styles = {

coodoo

import hyperarray from 'hyper-array'
import memdb from 'memdb'

const dag = hyperarray( memdb() )

// step 1
// api: array.insert(value, [before], [after], [cb=function (err, entry) {}])
dag.insert( 'abc', null, null, ( err, node ) => {
	conole.log( node )

coodoo

--[[

	- here's a better way to train RNNLM with large dataset, 
		- persist the weights from LookupTable into file 
		- and load it back later to use in sentiment analysis tasks

	- detailed discussion here: https://github.com/torch/nn/issues/747

	- thanks @fmassa for pointing out the direct direction