From 1559cdd06d295d76b5d9f87ffb15ec76623747a1 Mon Sep 17 00:00:00 2001 From: caes Date: Wed, 19 Apr 2017 02:35:06 -0400 Subject: [PATCH] project files added --- project/anna_program | 23 ++++ project/data/HTRU2/HTRU2.zip | Bin 0 -> 1564074 bytes project/data/HTRU2/HTRU_2.arff | 1 + project/data/HTRU2/HTRU_2.csv | 1 + project/data/HTRU2/Readme.txt | 123 ++++++++++++++++++++++ project/data/HTRU2/__MACOSX/._HTRU_2.arff | Bin 0 -> 171 bytes project/data/HTRU2/__MACOSX/._Readme.txt | Bin 0 -> 171 bytes project/mariia/project/plots.png | Bin 0 -> 34816 bytes project/notes | 17 +++ project/stu1188.pdf | Bin 0 -> 4403755 bytes 10 files changed, 165 insertions(+) create mode 100644 project/anna_program create mode 100644 project/data/HTRU2/HTRU2.zip create mode 100644 project/data/HTRU2/HTRU_2.arff create mode 100644 project/data/HTRU2/HTRU_2.csv create mode 100644 project/data/HTRU2/Readme.txt create mode 100644 project/data/HTRU2/__MACOSX/._HTRU_2.arff create mode 100644 project/data/HTRU2/__MACOSX/._Readme.txt create mode 100644 project/mariia/project/plots.png create mode 100644 project/notes create mode 100644 project/stu1188.pdf diff --git a/project/anna_program b/project/anna_program new file mode 100644 index 0000000..7a19836 --- /dev/null +++ b/project/anna_program @@ -0,0 +1,23 @@ +# Get data from csv +setwd("~/Documents/Classes/CS5821/project/HTRU2") +raw_data = read.csv("HTRU_2.csv") +colnames(raw_data) = c("X1","X2","X3","X4","X5","X6","X7","X8","PLSR") +attach(raw_data) + +# create a new set with equal amounts of both pulsars and not pulsars +ydata=which(PLSR==0) +ndata=which(PLSR==1) +set.seed(11) +data = rbind(sample(ydata,1500),sample(ndata,1500)) + + +# QDA +library(ISLR) +library(MASS) +train=sample(data,2500) +qda.fit = qda(PLSR~X1+X2+X3+X4,data=data,subset=train) +summary(qda.fit) + + + + diff --git a/project/data/HTRU2/HTRU2.zip b/project/data/HTRU2/HTRU2.zip new file mode 100644 index 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http://www.cs.manchester.ac.uk + or alternatively http://www.jb.man.ac.uk +****************************************************************************************** + +1. Overview + + HTRU2 is a data set which describes a sample of pulsar candidates collected during the + High Time Resolution Universe Survey (South) [1]. + + Pulsars are a rare type of Neutron star that produce radio emission detectable here on + Earth. They are of considerable scientific interest as probes of space-time, the inter- + stellar medium, and states of matter (see [2] for more uses). + + As pulsars rotate, their emission beam sweeps across the sky, and when this crosses + our line of sight, produces a detectable pattern of broadband radio emission. As pulsars + rotate rapidly, this pattern repeats periodically. Thus pulsar search involves looking + for periodic radio signals with large radio telescopes. + + Each pulsar produces a slightly different emission pattern, which varies slightly with each + rotation (see [2] for an introduction to pulsar astrophysics to find out why). Thus a + potential signal detection known as a 'candidate', is averaged over many rotations of the + pulsar, as determined by the length of an observation. In the absence of additional info, + each candidate could potentially describe a real pulsar. However in practice almost all + detections are caused by radio frequency interference (RFI) and noise, making legitimate + signals hard to find. + + Machine learning tools are now being used to automatically label pulsar candidates to + facilitate rapid analysis. Classification systems in particular are being widely adopted, + (see [4,5,6,7,8,9]) which treat the candidate data sets as binary classification problems. + Here the legitimate pulsar examples are a minority positive class, and spurious examples + the majority negative class. At present multi-class labels are unavailable, given the + costs associated with data annotation. + + The data set shared here contains 16,259 spurious examples caused by RFI/noise, and 1,639 + real pulsar examples. These examples have all been checked by human annotators. Each + candidate is described by 8 continuous variables. The first four are simple statistics + obtained from the integrated pulse profile (folded profile). This is an array of continuous + variables that describe a longitude-resolved version of the signal that has been averaged + in both time and frequency (see [3] for more details). The remaining four variables are + similarly obtained from the DM-SNR curve (again see [3] for more details). These are + summarised below: + + 1. Mean of the integrated profile. + 2. Standard deviation of the integrated profile. + 3. Excess kurtosis of the integrated profile. + 4. Skewness of the integrated profile. + 5. Mean of the DM-SNR curve. + 6. Standard deviation of the DM-SNR curve. + 7. Excess kurtosis of the DM-SNR curve. + 8. Skewness of the DM-SNR curve. + + HTRU 2 Summary + + 17,898 total examples. + 1,639 positive examples. + 16,259 negative examples. + + + The data is presented in two formats: CSV and ARFF (used by the WEKA data mining tool). + Candidates are stored in both files in separate rows. Each row lists the variables first, + and the class label is the final entry. The class labels used are 0 (negative) and 1 + (positive). + + Please not that the data contains no positional information or other astronomical details. It is + simply feature data extracted from candidate files using the PulsarFeatureLab tool (see [10]). + +2. Citing our work + + If you use the dataset in your work please cite us using the DOI of the dataset, and the paper: + + R. J. Lyon, B. W. Stappers, S. Cooper, J. M. Brooke, J. D. Knowles, Fifty Years of Pulsar + Candidate Selection: From simple filters to a new principled real-time classification approach + MNRAS, 2016. + +3. Acknowledgements + + This data was obtained with the support of grant EP/I028099/1 for the University of Manchester + Centre for Doctoral Training in Computer Science, from the UK Engineering and Physical Sciences + Research Council (EPSRC). The raw observational data was collected by the High Time Resolution + Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and + managed by the CSIRO. + +4. References + + [1] M.~J. Keith et al., "The High Time Resolution Universe Pulsar Survey - I. System Configuration + and Initial Discoveries",2010, Monthly Notices of the Royal Astronomical Society, vol. 409, + pp. 619-627. DOI: 10.1111/j.1365-2966.2010.17325.x + + [2] D. R. Lorimer and M. Kramer, "Handbook of Pulsar Astronomy", Cambridge University Press, 2005. + + [3] R. J. Lyon, "Why Are Pulsars Hard To Find?", PhD Thesis, University of Manchester, 2015. + + [4] R. J. Lyon et al., "Fifty Years of Pulsar Candidate Selection: From simple filters to a new + principled real-time classification approach", Monthly Notices of the Royal Astronomical Society, + submitted. + + [5] R. P. Eatough et al., "Selection of radio pulsar candidates using artificial neural networks", + Monthly Notices of the Royal Astronomical Society, vol. 407, no. 4, pp. 2443-2450, 2010. + + [6] S. D. Bates et al., "The high time resolution universe pulsar survey vi. an artificial neural + network and timing of 75 pulsars", Monthly Notices of the Royal Astronomical Society, vol. 427, + no. 2, pp. 1052-1065, 2012. + + [7] D. Thornton, "The High Time Resolution Radio Sky", PhD thesis, University of Manchester, + Jodrell Bank Centre for Astrophysics School of Physics and Astronomy, 2013. + + [8] K. J. Lee et al., "PEACE: pulsar evaluation algorithm for candidate extraction a software package + for post-analysis processing of pulsar survey candidates", Monthly Notices of the Royal Astronomical + Society, vol. 433, no. 1, pp. 688-694, 2013. + + [9] V. Morello et al., "SPINN: a straightforward machine learning solution to the pulsar candidate + selection problem", Monthly Notices of the Royal Astronomical Society, vol. 443, no. 2, + pp. 1651-1662, 2014. + + [10] R. J. Lyon, "PulsarFeatureLab", 2015, https://dx.doi.org/10.6084/m9.figshare.1536472.v1. diff --git a/project/data/HTRU2/__MACOSX/._HTRU_2.arff b/project/data/HTRU2/__MACOSX/._HTRU_2.arff new file mode 100644 index 0000000000000000000000000000000000000000..6adb72ec537e12b7d90a1ae5cb0b7773f90043a8 GIT binary patch literal 171 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aU~Fg_^W}KA;dKz1VrNojv*mIP+5?U zIY7*hrkW8UE}op9tCv_%kdvwxl3G#XnwOlPl9`tdR1g~CrfXqsXl!C+Xl`j?0swGa B6-xjB literal 0 HcmV?d00001 diff --git a/project/data/HTRU2/__MACOSX/._Readme.txt b/project/data/HTRU2/__MACOSX/._Readme.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd621b2a8721d40bafad5b53fcca3d1f986c7f49 GIT binary patch literal 171 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aU~Fg_^W}K2;dkJ5(HHS(lG~!`O&m7 uBE-d$^KL)>&NtPPD#j10{!EldE|p%exH literal 0 HcmV?d00001 diff --git a/project/mariia/project/plots.png b/project/mariia/project/plots.png new file mode 100644 index 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