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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

kkweon

import numpy as np
import matplotlib.pyplot as plt

class Filter:
    def __init__(self, filter=None):
        self.filter = filter
        self.filter.predict()

    def get_point(self):
        return self.filter.x[0:2, :].flatten()

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"""Implementation of Figure 5.2 in Artificial Intelligence: A Modern Approach

[MINIMAX Algorithm]
AIMA p.164  Ch.5 Adversarial Search

function Minimax(state) returns a value 
    if terminal-test(state):
        return utility(state)
    if player(state) == MAX:
        return max( Minimax(RESULT(state, action)) for every action in Action(s) )

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""" This file calculates the fitness of 8-queens problem

How to use:

In terminal,
    
    $ python fitness_8_queens.py 24748552

    Input: 24752411
    Output:  22

kkweon

import json
import requests

url = "http://news.naver.com/main/mainNews.nhn"
payloads = {'componentId': 949984, 'page': 1, 'date': '2016-03-13 00:00:00'}
req = requests.get(url, params=payloads)
js = json.loads(req.text)

print(js.keys()) # dict_keys(['itemList', 'dateList', 'pagerInfo', 'currentDate'])
for item in js['itemList']:

kkweon

library(tensorflow)
rm(list=".Random.seed", envir=globalenv()) 
batch.size = 128L
max.step = 500L
print.every = 10L
hidden.dim = 64L
dropout = 0.5

tf$reset_default_graph()
X <- tf$placeholder(tf$int64, shape(NULL, max.seq))

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library(shiny)
library(gsheet)

# Helper Functions
read.list <- function(url = "https://docs.google.com/spreadsheets/d/1aJ2Bv8CCR4OhBoVdsQD16OWON89VwuaLYKDDP-OiTG4") {
    as.data.frame(gsheet::gsheet2tbl(url))
}

read.data <- function(url) {
  data <- as.data.frame(gsheet::gsheet2tbl(url))

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