pearcemc

# context: http://stackoverflow.com/questions/39448808/julia-tcp-server-and-connection

# Use fn to process messages from sock.
# Loop till sock is open and fn returns true.
function processor(fn, sock)
    proc = true
    try
        while proc && ((nb_available(sock) > 0) || isopen(sock))
            proc = fn(sock)
        end

pearcemc

from pylab import *

import scipy.stats as stats

########################################################################################
#
#       QUICK ILLUSTRATION OF USING SCIPY.STATS FOR GAMMA AND INV-GAMMA
#       HOW TO DO SHAPE-RATE (alpha, beta) PARAMETERISATION CORRECTLY
#
########################################################################################

pearcemc

from pylab import *
import numpy

def grid_sq(fr,ncol,bs,npx=2,bval=0):
  Z = fr.shape[0]
  z=0
  vbar = bval*ones((bs[0],npx))
  hbar = bval*ones((npx, (bs[1] + npx)*ncol + npx)) 
  grid = [hbar]
  row = [vbar]

pearcemc

plot.aic <- function(fit, new=T, sd=0.1)
{ # code to plot AIC against order of AR model fitted
  if (new) plot((1:length(fit$aic))-1,fit$aic,type="l",xlab="Order",ylab="AIC") else
  lines((1:length(fit$aic))-1,fit$aic,type="l")
  points(rnorm(1,fit$order,sd=sd),rnorm(1,sd=sd),pch=16,col="red")
}

plot.stat_ic <- function(stat, new=T, sd=0.1)
{ # code to plot AIC against order of AR model fitted
  if (new) plot((1:length(stat))-1,stat,type="l",xlab="Order",ylab="AIC") else

pearcemc

import sys
import time
from pylab import *
import scipy.stats as stats

"""
Script to visualise draws from a two parameter Indian Buffet Process.

usage example:

pearcemc

##################################################################
#
#       APPLYING EM TO A BINOMIAL MIXTURE MODEL
#       matthew@refute.me.uk 
#       Licence: MIT Licence (http://opensource.org/licenses/MIT)
#
##################################################################


###### EM ALGORITHM FUNCTIONS

pearcemc

import numpy as np
import matplotlib.pyplot as plt
from math import exp, sqrt, pi

#the function we're going to map
def gausspdf( x, mu, sd2 ):
  return exp( -1*( (x - mu)**2/(2*sd2)) )/sqrt( 2*pi*sd2 )

#canvas size
x = y = np.arange( 0, 10, 0.1 )

pearcemc

class KNearest:
    """k-nearest neighbour inferer"""
    def __init__(self, ds):
        #set the dataset
        self.ds = ds

    def predict(self, p1, k=1):
        """Given a test point p1, return the modal class of its knearest neighbours"""
        distances = []
        #calculate the distance between the test point and known data points.

pearcemc

class TestPredictor:
    """Iterate a KNearest predictor and return its success rate."""

    def __init__(self, predictor, times=1000):
        #set the KNearest predictor
        self.predictor = predictor 
        #set number of iterations   
        self.times = times

    def test(self, k=1):

pearcemc

import scipy as sp

class PDimClass:
    """
    A p-dimensional class, which takes a list of Normal distribution parameters, 
    each of which defines a dimension
    """
    #a list of normal distribution parameter tuples, for N(mean, sd)
    params = [(0,1), (0,1)]