suryadutta

for k=1:kmax
	% Run simulation
	[Msamp, Esamp, spin] = jisingAnnealing(N, T(k), num_steps, num_burnin,...
		flip_prop, J, B);

  binSize = 100;
  totalUsed= floor(length(Esamp(num_burnin:end)) / binSize);

  cv_binning = zeros(1,totalUsed); %initialize CV
  chi_binning = zeros(1,totalUsed); %initialize chi

suryadutta

D = csvread('data.dat',5,0);

tempSteps = D(:,1);
tempFactor = 2./tempSteps;

fout = fopen(['energyout.csv'], 'w');
fprintf(fout, ['Temperature, k, K(k), Energy\n']);

k = 2.*(sinh(tempFactor))./((cosh(tempFactor)).^2);

suryadutta

anonymous {
  id: 2,
  title: 'BLUE_EXPONENTIALFUNCTIONS.',
  title_blue: 'Exponential\t\n',
  title_yellow: 'Functions\n',
  background_location: '/images/moduleBackgrounds/Connecticut_Module.png',
  background_name: 'Connecticut Hall\n',
  background_desc: '<p>Completed in 1752, Connecticut Hall is Yale’s oldest standing building. A student dormitory for over 200 years, it was designated a National Historic Landmark in 1965.</p>\n',
  overview: '<p class="text_margin"><p>Grow, Grow Grow, Go, Go, Go! Exponential functions are great for describing things that grow rapidly.  We\'re going to examine how Ben Franklin\'s gift to the cities of Philadelphia and Boston grew over time to get an understanding of exponential functions. We conclude by looking at Malthus\' theories of population growth. Questions we will interact with:</p>\n<ul>\n<li>What\'s an exponential function? (Ok, this seems kind of basic. But it\'s necessary)</li>\n<li>How can we distinguish between an exponential function and a linear function?<

suryadutta

#include "MOptimumFilter.hh"
#include "QEvent.hh"
#include "QEventList.hh"
#include "QRawEvent.hh"
#include "QBaseModule.hh"
#include "QSampleInfo.hh"
#include "QRunDataHandle.hh"
#include "QBaseType.hh"
#include "QBaselineData.hh"
#include "QAveragePulseHandle.hh"

suryadutta

#include "QOptimumFilter.hh"
#include "QError.hh"
#include "QMatrix.hh"
#include "QRealComplexFFT.hh"
#include "QFir.hh"
#include <sstream>

// EXTRA_JITTERS:
// 1: minimum number needed for interpolation
// 2: more point to see if we have a minimum at the boundary (enable nice parabola)

suryadutta

#include "OptimumFilter.hh"
#include "QRealComplexFFT.hh"
#include <iostream>
#include "QMatrix.hh" 

using namespace std; 
using namespace Cuore; 

OptimumFilter::OptimumFilter(): nSigPulses(0),nNoisePS(0){
	//par0=par1=par2=1;

suryadutta

#include "OptimumFilter.hh"
#include "QRealComplexFFT.hh"
#include <iostream>
#include "QMatrix.hh" 

using namespace std; 
using namespace Cuore; 

OptimumFilter::OptimumFilter(): nSigPulses(0),nNoisePS(0){
	//par0=par1=par2=1;

suryadutta

import csv
#get hci output with bash command: hcidump -r hci.log -t > hci.txt   
logs = open('hci.txt','r').read().replace('\n',' ').replace('586524','\n2017')
logs_output = open("hcidata.txt", "w")
logs_output.write(logs)
logs_output.close()
data = open('hcidata.txt','r') #replace with hci output
csv_data = []
skip_header = True
for line in data:

suryadutta

#install.packages('tm')
library(tm)

#install.packages('slam')
library("slam")

#import data
alldata <- read.csv('stackexchange/20161215StatsPostsMerged.csv', header = TRUE, stringsAsFactors = FALSE)

#make corpus

suryadutta

reduceDTM <- function(dtm){
 term_tfidf <- 
    tapply(dtm$v/row_sums(dtm)[dtm$i], dtm$j, mean) *
    log2(nDocs(dtm)/col_sums(dtm > 0))
 
  dtm <- dtm[,term_tfidf >= median(term_tfidf)]
  dtm <- dtm[row_sums(dtm) > 0,]
  
  return(dtm)
}