glinscott

#!/usr/bin/python
import math

def los(n1, n0):
  pi = [1.0] * (n1 + 3)
  pi[0] = 0

  for j in xrange(n0 + 1, 0, -1):
    for i in xrange(0, n1 + 1):
      pi[i + 1] = (pi[i + 1] + pi[i]) * 0.5

glinscott

#!/usr/bin/python
# -*- coding: utf-8 -*-

"""
Usage: clop-cutechess-cli.py CPU_ID SEED [PARAM_NAME PARAM_VALUE]...
Run cutechess-cli with CLOP_PARAM(s).

  CPU_ID        Symbolic name of the CPU or machine that should run the game
  SEED          Running number for the game to be played
  PARAM_NAME    Name of a parameter that's being optimized

glinscott

Script ./clop-cutechess-cli.py
Name BishopPin
IntegerParameter pin_open -100 100
IntegerParameter pin_end -100 100

Processor machine1
Processor machine2
Processor machine3
Replications 2
DrawElo 100

glinscott

#!/usr/bin/python
import time 
import sh
import sys
from multiprocessing import Pool

def get_fens():
  with open('pgn_fen') as f:
    next = False
    for line in f.read().split('\n'):

glinscott

use std::str;

pub struct S2<'self> {
    priv iter: std::str::StrCharIterator<'self>,
}

glinscott

#!/usr/bin/python

import math
import numpy
import subprocess
from scipy.optimize import fmin_cg

bestv = 9999999

def sferror(x):

glinscott

float sdPlane( vec3 p ) {
	return p.y;
}

float sdSphere( in vec3 p, in vec4 s ) {
    return length(p-s.xyz) - s.w;
}

float sdBox( vec3 p, vec3 b )
{

glinscott

import sqlite3
from flask import Flask, jsonify, request

app = Flask(__name__)

# Connect to the database
conn = sqlite3.connect("twitter.db")
cursor = conn.cursor()

# Create the tweets table if it doesn't already exist

glinscott

import sys
import torch

device = "cuda"

"""
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
model_id = "gpt2-large"
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)