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Greg Borenstein atduskgreg

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  • Graph the rest of the games
  • For (174) graph from black's POV two sets of centipawn scores (for the same moves): => high search depth vs low search depth => we're looking for "false hope"
  • Python script that starts from an empty game, plays through best move for black and white and logs the centipawn score for each played move (graph that) (ruby code for reference for basic architecture: https://gist.github.com/atduskgreg/8951065)
class LoopingFile {
String[] lines;
int currentLine = -1;
LoopingFile( String nameOfFile){
lines = loadStrings(nameOfFile);
}
int currentLineNum(){
return currentLine;
@atduskgreg
atduskgreg / 20140228_maurice_agenda.md
Created February 28, 2014 05:10
Maurice Meeting Agenda 2/28/2014
  • Meet Shannon
  • Shannon update on automation progress
  • Twitch! Chess network stream of world championship match: http://www.twitch.tv/chessnetwork/b/481728593
  • We should use Twitch to prototype (when we're ready). April? May?
  • Parallels with Philip Tan's work on Starcraft
  • Discussion of time in chess
* Produce a PGN log of each game that we play (save as a file)
* Find a PGN "validator"
* Also, log the info output from the engine (annotated with PGN move number)
* Convert to fsm

"A lot of my interest in Apocalypse Now was carried over into Star Wars. I figured I couldn’t make that film because it was about the Vietnam War, so I would essentially deal with some of the same interesting concepts that I was going to use and convert them into space fantasy, so you’d have essentially a large technological empire going after a small group of freedom fighters or human beings."

-- George Lucas, quoted in The Making of Star Wars

Webserver that responds to
GET http://localhost/boomerang?f=asdfasdfasd/asdfasdfasdf/asdfasdfasfasdf/ b - -
=> csv with centipawn time series from white's pov
import gab.opencv.*;
import processing.video.*;
import java.awt.Rectangle;
int minBlobArea = 500;
float smoothAmount = 0.8; // 1.0 is never move, 0.0 is don't smooth
OpenCV opencv;
Capture cam;
p = []
lastMouse = false;
function setup(){
resetDrawing();
}
function draw(){
if(mouseIsPressed()){
p[p.length-1].push({x: mouseX, y: mouseY});
lastMouse = true;
# -*- coding: utf-8 -*-
# 1. Generate 100 points x uniformly distributed between 0 and 1, and let y = 2+3x+ζ,
# where ζ is a Gaussian random variable with a standard deviation of 0.5. Use an
# SVD to fit y = a + bx to this data set, finding a and b.
import numpy as np
# x is uniformly distributed
x = np.random.random(100)