coppelia machine learning and analytics coppeliaMLA
coppeliaMLA
/ visGAExamples.R
Created
June 27, 2014 10:52
Examples for visualising the path of a genetic algorithm
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| #Maximize a mixture of multivariate normal distributions | |
| library(mvtnorm) | |
| mnMix<-function(args){ | |
| mean.vec.d1<-rep(0.3,5) | |
| std.vec.d1<-diag(rep(1,5)) | |
| mean.vec.d2<-rep(1,5) | |
| std.vec.d2<-diag(rep(1.5,5)) | |
| mean.vec.d3<-c(1, 5, 2, 1, 0) | |
| std.vec.d3<-diag(rep(0.5, 5)) | |
| if (args[1]<0){ |
coppeliaMLA
/ visGAPath.R
Last active
August 29, 2015 14:03
Visualising the path of a genetic algorithm
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| # *-------------------------------------------------------------------- | |
| # | FUNCTION: visGAPath | |
| # | Function for visualising the path of a genetic algorithmn using | |
| # | principal components analysis | |
| # *-------------------------------------------------------------------- | |
| # | Version |Date |Programmer |Details of Change | |
| # | 01 |18/04/2012|Simon Raper |first version. | |
| # *-------------------------------------------------------------------- | |
| # | INPUTS: func The function to be optimised | |
| # | npar The number of parameters to optimise over |
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| library(reshape2) | |
| #Bagging hierarchical clustering | |
| bagHClust<-function(data, n, k, size, outlier.th) { | |
| clus.bs<-NULL | |
| for (i in 1:n) { |
coppeliaMLA
/ SankeyClusComp
Last active
August 29, 2015 14:03
Generates the data for comparing two clusters using a Sankey diagram
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| clusComp<-function(cl1, cl2, num.clus){ | |
| #Set up object for recording clusters | |
| clus.change<-NULL | |
| ct1<-cutree(cl1, k=num.clus) | |
| add.1<-data.frame(size=rep(1, length(ct1)), ind=names(ct1), cluster=paste0(1, ".", ct1)) | |
| ct2<-cutree(cl2, k=num.clus) | |
| add.2<-data.frame(size=rep(2, length(ct2)), ind=names(ct2), cluster=paste0(2, ".", ct2)) |
coppeliaMLA
/ compCorrMI.R
Created
June 25, 2014 16:00
Look at the relationship between MI and correlation for binary vars (since it's quicker than doing the maths)
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| #Check the relationship between correlation and mutual information for binary vars | |
| store<-NULL | |
| for (i in 1:1000){ | |
| prob.1<-runif(1) | |
| prob.2<-runif(1) | |
| x<-rbinom(10000, 1, prob.1) | |
| y<-rbinom(10000, 1, prob.2) | |
| c<-cor(x,y) | |
| m<-mi.empirical(table(x,y)) | |
| store<-rbind(store, data.frame(c=c, m=m)) |
coppeliaMLA
/ confusion.htm
Created
June 24, 2014 07:52
Exploration of a confusion matrix using tangle.js
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Tangle: a JavaScript library for reactive documents</title> | |
| <link rel="stylesheet" href="http://worrydream.com/Tangle/TangleKit/TangleKit.css" type="text/css"> | |
| <script type="text/javascript" src="http://worrydream.com/Tangle/TangleKit/mootools.js"></script> | |
| <script type="text/javascript" src="http://worrydream.com/Tangle/TangleKit/sprintf.js"></script> | |
| <script type="text/javascript" src="http://worrydream.com/Tangle/TangleKit/BVTouchable.js"></script> |
coppeliaMLA
/ DendToForce.R
Created
June 20, 2014 16:30
Converts a hclust dendrogram into a graph in JSON for input into D3
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| #Run hclust | |
| hc <- hclust(dist(USArrests[1:40,]), "ave") | |
| #Function for extracting nodes and links | |
| extractGraph<-function(hc){ | |
| n<-length(hc$order) | |
| m<-hc$merge | |
| links<-data.frame(source=as.numeric(), target=as.numeric(), value=as.numeric()) |
coppeliaMLA
/ clusterSankey.R
Last active
August 29, 2015 14:02
Visualising cluster stability using a Sankey diagram
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| #Sequence for adding new data | |
| s<-seq(20,50, by=5) | |
| #Set up object for recording clusters | |
| clus.change<-NULL | |
| #Cycle through the clustering solutions | |
| for (i in s){ | |
| hc <- hclust(dist(USArrests[1:i,]), "ave") |
coppeliaMLA
/ binDiff.R
Created
March 21, 2014 08:14
A function that gives the probability mass function for the difference between to binomially distributed random variables
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| modBin<-function(k, n, p){ | |
| if (k<=n) { | |
| return(dbinom(k, n, p)) | |
| } | |
| else { | |
| return(0) | |
| } | |
| } |
coppeliaMLA
/ csvToPipe.py
Created
March 7, 2014 12:50
Another useful bit of code for preparing flat files for Hive. Takes in csvs with double quote text delimiters and outputs pipe delimited files.
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| import os, csv | |
| progDir = '/pathToFolderContainingCSVs/' | |
| for filename in os.listdir(progDir): | |
| if filename != '.DS_Store': | |
| with open(progDir+filename, 'rb') as csvfile: | |
| progReader = csv.reader(csvfile, delimiter=',', quotechar='"') |