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 0000000..221bdbb
Binary files /dev/null and b/project/data/HTRU2/HTRU2.zip differ
diff --git a/project/data/HTRU2/HTRU_2.arff b/project/data/HTRU2/HTRU_2.arff
new file mode 100644
index 0000000..204f122
--- /dev/null
+++ b/project/data/HTRU2/HTRU_2.arff
@@ -0,0 +1 @@
+@relation HTRU_2
@attribute Profile_mean numeric
@attribute Profile_stdev numeric
@attribute Profile_skewness numeric
@attribute Profile_kurtosis numeric
@attribute DM_mean numeric
@attribute DM_stdev numeric
@attribute DM_skewness numeric
@attribute DM_kurtosis numeric
@attribute class {0,1}
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\ No newline at end of file
diff --git a/project/data/HTRU2/HTRU_2.csv b/project/data/HTRU2/HTRU_2.csv
new file mode 100644
index 0000000..7ddd4a6
--- /dev/null
+++ b/project/data/HTRU2/HTRU_2.csv
@@ -0,0 +1 @@
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106.875,47.57132802,0.199440015,0.284964217,3.079431438,20.98445543,8.427474542,78.25936579,0
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126.625,55.72182584,0.002946216,-0.303218137,0.534280936,8.588881866,23.91376131,660.1970352,0
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\ No newline at end of file
diff --git a/project/data/HTRU2/Readme.txt b/project/data/HTRU2/Readme.txt
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+******************************************************************************************
+
+# HTRU2
+
+Author: Rob Lyon, School of Computer Science & Jodrell Bank Centre for Astrophysics,
+		University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL.
+
+Contact:	rob@scienceguyrob.com or robert.lyon@.manchester.ac.uk
+Web:		http://www.scienceguyrob.com or 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.
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+Task
+
+
+Doesn't ha
+
+
+
+
+
+I am not an oracle but usually you don't know what parameters to pass into a classifier - figuring those out is a big deal
+
+
+problem with column 9
+
+
+selecting right features
+
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