notbanker

object Compositional {

  type FunctionOfList[T1] = List[T1] => Option[Double]
  def convertFunctionOfListToFunctionInf[T1](f: FunctionOfList[T1], support: Set[Integer]): (Stream[T1] => Option[Double]) = {
    (xs: Stream[T1]) =>
      {
        val variablesThatMatter = support.toList.map(i => xs(i))
        val fx = f(variablesThatMatter)
        fx

notbanker

ISDA_PERMITTED_CHARS = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
ISDA_PRIME = 59575553

def leiHash( lei, permittedChars = ISDA_PERMITTED_CHARS ):
    """ Shorten 20 character alpha-numeric Legal Entity Identifier into 10 character alpha-numeric "short LEI" that 
        can be used as a Unique Trade Identifier (UTI) prefix. The algorithm is simply the modulo operation lifted from
        integers to the space of alpha-numeric case-insensitive strings. 

         Argument
         --------

notbanker

import math
import func

# These coincide with the choices hardwired into the spreadsheet provided to ISDA, so don't change them
ISDA_PERMITTED_CHARS = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'  # Don't change!
ISDA_PRIME = 59575553


def _avoidReserved( s ):
    """ Avoid clash with reserved namespace used by CFTC """

notbanker

from pymc import Model, MCMC, deterministic, stochastic, Normal, Bernoulli, Beta, distributions
import numpy as np

def twinModelVars( p_identical = 0.5, p_down = 0.01, measurement = 2.5, measurementPrecision = 2.0 ):
    """ Stylized model for twin Down Syndrome likelihood after a blood sample is taken from mother
        containing a mixture of indicators from each foetus

    :param p_identical: (Prior) probability of identical twins versus fraternal (0.1 for population)
    :param p_down:      (Prior) probability of Down Syndrome
    :param measurement: Measured marker value

notbanker

from pymc import Model, MCMC, deterministic, stochastic, Normal, Bernoulli, Beta, distributions
import numpy as np

def twinModelVars( p_identical = 0.5, p_down = 0.01, measurement = 2.5, measurementPrecision = 2.0 ):
    """ Stylized model for twin Down Syndrome likelihood after a blood sample is taken from mother
        containing a mixture of indicators from each foetus

    :param p_identical: (Prior) probability of identical twins versus fraternal (0.1 for population)
    :param p_down:      (Prior) probability of Down Syndrome
    :param measurement: Measured marker value

notbanker

from flask import Flask, request, url_for
from scipy.optimize import bisect
import math
import numpy as np

app = Flask(__name__)
app.secret_key = 'Whatever'

@app.route('/')
def index():

notbanker

lambert_facts = ['Lambert was born the son of a tailor, and was expected by his father to continue in that profession.',
       'Lamberts early fight for his education is a remarkable story---he had to sell drawings and writings to his classmates to buy candles for night study,',
          'Lambert produced fundamental discoveries in cartography, despite being self-educated.',   'Lambert influenced hygrometry, pyrometry, pure mathematics and statistics.',
           'Lambert was more famous as a philosopher than as a mathematician.',   'The Lambert projection is still in use in cartography.',
            'Lambert was the first person to prove that pi is irrational.',   'Proofs that it is impossible to square the circle leaned on insights from Lambert.',
              'Lambert found a series solution to the trinomial equation, by hand calculation.',   'Lamberts technique for solving general series reversion techniques was a forerunner to the Langrange Inversion Theorem.',
                'Lambert discovered an

notbanker

#!/usr/bin/env bash
# Call stockfish engine on mac and return only the evaluation score
# Usage stockfish.sh 'r1b2rk1/4qppp/2pp4/pp6/3Bn3/PB3Q1P/1PP2PP1/3R1RK1' 5 mac 12 1024
# Usage stockfish.sh 'r1b2rk1/4qppp/2pp4/pp6/3Bn3/PB3Q1P/1PP2PP1/3R1RK1' 5 mac 12 1024
# Assumes the stockfish binary is called 'stockfish_'+binary 

fen=$1
seconds=${2:-3}
binary=${3:-mac}
threads=${4:-12}

notbanker

import json
import urllib2
import time
import pandas as pd

def fen_examples( max_users = 3, max_games_per_user=10, sleep_time = 15 ):
    """ Grab some real game positions from the middle of actual games, and the result """
    users = users_in_team( team='coders' )
    records  = list()
    for user in users[ :max_users ]:

notbanker

import subprocess
from lib.common.dominoUtil import running_on_local
from lib.config import PROJECT_PATH
import time
import psutil
import os
import numpy as np

def _recommended_threads():
    return psutil.cpu_count()-2