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# coding: utf-8
# import scipy.io as io
from scipy import linspace, io
from pylab import *
from cmath import phase
from math import *
 
# Load the .mat files
testData = io.loadmat('BelkinTestData_1.mat')
taggingData = io.loadmat('TaggingInfo_1.mat')

asw456

'''
Convert Yelp Academic Dataset from JSON to CSV

Requires Pandas (https://pypi.python.org/pypi/pandas)

By Paul Butler, No Rights Reserved
'''

import json
import pandas as pd

asw456

function [convf,k,results,xn] = mygaussseidel(A,b,x,niter,tol)

[nrow,~] = size(A);
convf = 0;
results(1,:) = x';
xn = x;

for k = 1:niter
  
   for i = 1:nrow

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L1 cache reference                            0.5 ns
Branch mispredict                             5   ns
L2 cache reference                            7   ns             14x L1 cache
Mutex lock/unlock                            25   ns
Main memory reference                       100   ns             20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy              3,000   ns
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD               150,000   ns    0.15 ms
Read 1 MB sequentially from memory      250,000   ns    0.25 ms
Round trip within same datacenter       500,000   ns    0.5  ms

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function [ L,A,B,C,D ] = mydivdiff( x, f )
% INPUTS: x - Nx1 vector of x values at which data for the function is specified
% f - Nx1 vector of corresponding function values
% OUTPUTS: A - (N-1)x(N-1) table of divided differences

n = size(x,1);
L = zeros(n-1);

L(:,1) = (f(2:n)-f(1:n-1))./(x(2:n)-x(1:n-1));

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function results = mymultinewtfunction(x, func, niter, tol)

for i = 1:niter
    n = max(size(x));
    
    %f = [(log(x(1)) - x(2)) , ((x(1)^2+2*x(1)-24)/25 - x(2))];
    %J = [1/x(1), -1 ; 2*x(1)/25+2/25 , -1];
    f = func(x);
    J = zeros(n);
    

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./community --outfile=graph-cmap.bin --textoutfile=graph-cmap.txt --outgraph=comm-graph-el.bin --min-degree=2 --max-degree=72 --max-num-comm=10 --max-comm-size=30 --scoring=mb --matching=greedy --nsteps=10 output-el.bin


./community --textoutfile=graph-cmap.txt --outgraph=comm-graph-el.bin --min-degree=1 --max-degree=72 --max-num-comm=6 --max-comm-size=1000 --scoring=mb --matching=greedy --nsteps=50 output-el.bin

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%Calls comptrap, doubles n each time
n=1;
old=0;
a=0;
b=1;
i=0;
A=zeros(20,1);
B=zeros(20,1);

global r

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

class Polyomino(object):

    def __init__(self, iterable):
        self.squares = tuple(sorted(iterable))

    def __repr__(self):

asw456

from numpy import *
from scipy.stats import beta


class BetaBandit(object):
    def __init__(self, num_options=2, prior=(1.0,1.0)):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options
        self.prior = prior