Knowledge Discovery with WEKA

30/3/2019 Introduction to WEKA. The Waikato Environment for Knowledge Analysis

The hype for all this AI stuff is getting severely overblown. But that being said predictive algorithms will be an important tool for scientists in any field in the years to come.

Astronomy is no different.

Neural Networks have been used to classify pulsar candidates in this paper:

• V. Morello, E. D. Barr, M. Bailes, C. M. Flynn, E. F. Keane: “SPINN: a straightforward machine learning solution to the pulsar candidate selection problem”, 2014; arXiv:1406.3627. DOI: 10.1093/mnras/stu1188.

Astronomy datasets are notorious for putting the BIG in BIG Data. Not only are the objects being studied are separated from Earth by large distances but also, the true nature of the signals that they produce are magnificently intense.

The paper states that it as the data mounts up in the future, it will no longer be feasible to process it using a typical 'human cogitator' i.e. Grad Students approach to vet the data. That's where the Data Mining and Machine Learning will comes in.

Twinkle Twinkle little Pulsar, But is that what you really are?

So i guess it is important for yours truly to get to grips with it. Thats where WEKA comes in.

WEKA is an an environment that can be used to analyze datasets using prebuilt data mining algorithms.

We can test the same data set with different data mining algorithms to take a measure of their performance. This is done by finding their percentage of correctly classified and their time taken to build model.

The following is a description of the Classifier/ Data Mining Algorithm/ Learner that we use to analyze the attributes/feature and put the data into class.

Data mining’s eclectic nature fostered this inconsistency in naming—the field encompasses contributions from statistics, artificial intelligence (Machine Learning),and database management;each field has chosen different names for the same concept. 

One-R (One Rule

Naive Bayes (probabilistic Classifier with strong independence assumption)

IBK (K Nearest Neighbour)

J48 (unpruned C4.5 decision tree)

Random Forest (Random Decision Forests)

MLP (Multi Layer Perceptron)

SMO (Support Vector Machines)

Data-set	Classifier	Percent correct (%)	Time to Construct (s)
Weather-nominal	One-R	42.8571	0.00
	Naïve Bayes	57.1429	0.00
	IBK	57.1429	0.00
	J48	50.000	0.00
	Random Forest	71.4286	0.02
	MLP	71.4386	0.03
	SMO	64.2857	0.01
Iris	One-R	92.0000	0.01
	Naïve Bayes	96.0000	0.01
	IBK	95.3333	0.00
	J48	96.0000	00.0
	Random Forest	95.3333	0.06
	MLP	97.3333	0.14
	SMO	96.0000	0.04
Pima Diabetes	One-R	65.1042	0.00
	Naïve Bayes	76.3021	0.005
	IBK	70.1823	0.00
	J48 (with missing values)	73.8281	0.02
	J48 (Remove Corrupt Instances) J48 (Padded Corrupt Instances)	74.6228 74.0885	0.01 0.01
	Random Forest	75.7813	0.18
	MLP	75.3906	0.40
	SMO	77.3438	0.03
Soybean	One-R	39.9707	0.01
	Naïve Bayes	92.9722	0.01
	IBK	91.215	0.00
	J48	91.5081	0.04
	Random Forest	92.9722	0.19
	MLP	93.4114	20.00
	SMO	93.8507	0.49

Repeating the model building resulted in faster build time but no changes in accuracy.

Support Vector Machine can be not bad

Matlab DSP with Unit Sample and Unit Step Signal

28/3/2019 Digital Signal Processing Matlab

Seriously i have an assignment tomorrow but here I am doing this signal stuff. Like wtf Life.

Unit Sample and Unit Step Signals are the fundamentals of Signals Processing. Its the first thing you learn before you hit Cyclic Spectroscopy.

How do we generate this?

%Lab 1
%Generation of Unit Sample Sequence
clf;

%Begin
vectorLength = -10:20;
vectorLength2 = -10:7;
vectorLength3 = 1:20;
vectorLength4 = 1:7;

M = 13; %Delayed by M Samples
N = 20;

u = [zeros(1,10) 1 zeros(1,20)];
ud = [zeros(1,M) 1 zeros(1,N - M - 3)];

%Now do Unit Step
s = [ones(1,20)];

%Unit Step Depayed
sd = [ones(1,20 - M)];

%plot unit sample
figure(1)
stem(vectorLength, u)
xlabel('Time index n');
ylabel('Amplitude');
title('Unit Sample Sequence');
axis([-10 20 0 1.2]);

hold on

stem(vectorLength2, ud,'Color',[.204 .64 .100])

%Delayed Unit Sample
figure(2)
stem(vectorLength2, ud, 'Color',[.204 .64 .100])
xlabel('Time index n');
ylabel('Amplitude');
title('Unit Sample Delayed');
axis([-10 20 0 1.2]);

%Delayed Unit Sample
%Equivalence Principle
figure(3)
stem(vectorLength3, s)
xlabel('Time index n');
ylabel('Amplitude');
title('Unit Step Sequence');
axis([-10 20 0 1.2]);

%Delayed Unit Step
figure(4)
stem(vectorLength4, sd,  'Color',[.103 .62 .100])
xlabel('Time index n');
ylabel('Amplitude');
title('Unit Step Sequence Delayed');
axis([-10 20 0 1.2]);

One Month Retrospective on New Zealand

24/3/2019 One Month Retrospective of New Zealand Experience

Today is the 24^th of March 2019. Today marks the 1-month Anniversary that I have made landfall in Aotearoa. For the 3^rd time in my life I am separated from the nation of my Birth for the duration of at least one Month. The first time being during my childhood in America, the 2^nd during my trip to Europe in 2013, and now I begin a 2-year journey on what I hope is the first step towards becoming a Professional Astronomer by completing a two-year Master’s degree under the Institute of Radio Astronomy and Space Research at Auckland University of Technology in New Zealand.

Welcome to Auckland

For the longest time since the heydays of my youth I have been captivated by the stars (I enjoyed Science Fiction immeasurably and I loved watching Science Documentaries especially on space and the Universe) and although it took a few further steps of activation (and a considerable amount of patience in between) before I realized that a life involved in the astrophysical Sciences would be a workable noble goal totally within my reach I have now finally gotten around to applying myself to that cause of becoming a Radio Astronomer. It truly is the most awesome job in the Universe.

'The Institute', playing a vital role in southern sky observations

Since I am now one month (to the day) into that journey to understand the stars, I figure it would be a good time to write a retrospective of some of my experiences.

First of All; Astronomy is everything that I dreamed of and more. It feels so right when we get down to it. It is a wonderful are of study full of incredible phenomenon that defy conventional human understanding of what nature can be. I love it. To study Astronomy is to study nature at its most extreme, its most violent, its most raw. And to put your mind in those places is to open your mind to incredible forces and circumstances that humble your being. What new wonders may we discover further down this path? What new phenomena undreamt of in our age may we blaze the trail for future generations of Astronomers to unlock?

Well that’s why we’re here isn’t it?

We now operate under the tutelage of Dr. Willem van Straten of ‘The Institute’. Dr. Willem’s work revolves around Pulsar Astronomy and Pulsar Timing Arrays for the application of Gravitational Wave Detection (the results of which are still inconclusive but has played an effective role in Galaxy Mapping). Our present mission is to investigate the dispersive effect of the Interstellar Media (ISM) in the propagation of Electromagnetic Radiation (light) through space. The technique we are looking to apply to investigate the ISM is Cyclic Spectroscopy. It is variation of Spectroscopy that when applied to periodic signals can rid of as the DM noise. I can’t complain really, it’s a place to start.

I begin my journey by studying Pulsar Astronomy.

The rest of my studies involve courses in currently involve courses in signal processing, machine learning and computational mathematics and statistics. These are also powerful fields of knowledge that are valuable to the progress of humanity, however they are not Astronomy. They are not my undying passion. So, some of those old sentiments persist. “Why do we need to study this if it’s not going to be directly involved with Astronomy?”

Well, I guess we have to be patient and take the best of whatever comes our way.

All in all, Auckland is a small city such that I have already grown tired of its main thoroughfare called Queen Street. Albert Park was such a disappointment, so I alternate between a few of the city parks. Auckland is not the capital of New Zealand, but it is the most populated city. The Capital is Wellington which I hope to visit soon. As of writing this I have visited Hamilton and Raglan. Hamilton is a small city that lies two hours south by bus from Auckland and is home to the University of Waikato, that is where they developed WEKA, a framework for machine learning and data analysis.

 

 

I was told that New Zealand is host to incredible natural landscapes. All of which must be sought after outside of Auckland. I am bounded so far by the North Island. I must break free and see more.

 

Within 3 weeks of my stay here there was a terrorist attack on a mosque. 50 people died. That was not cool. I guess this is the world we live in now. The great challenge during Carl Sagan's time was nuclear weapons. Now it is the looming shadow of terrorism and ethnic suspicion. It's funny because the night before the attack the motion at the debate club was 'THW suppress the name, race, gender and religion of the perpetrator of a terrorist attack'. Oh the irony. 

Within the first month I have, hiked, played airsoft, visited the local GW, camped, and danced at a festival (hats off to Boom Shangkar). It’s been a great first month. The things that I hope to do during my stay here would be to climb ever higher mountains (Mt. Cook is somewhere on that list), walk through some deep forests, and if I am lucky, learn how to ride a motorbike, and if I am even luckier get a chance to own one (Café racers come to mind). I was told by a wise doctor that I should look to what things may come. Don't look back and don't be held back.

 

On top of all of that, the craft continues. I wish to learn to write better, be more composed in my thoughts, and learn to be a better speaker, all of which are traits that will help me in Science Communication (my chosen stagecraft). To do that I must read more (science fiction and the great books), practice debate, and be persistent in learning skills that will involve experiences that may be bitter and humbling at the same time. But that is the reality of it. Coming up on the 11^th of April will be our first fight. We ride for PATW where we will deliver a 10-minute presentation on Cyclic Spectroscopy and its applications for mankind. The YouTube stories will keep coming out. There are many more stories left to tell.

This is just the beginning.

Sincerely,
Afiq Abdul Hamid

Hypothesis Testing the Dark Forest Theory (It's more like a hypothesis really)

I have a really great professor that often swears in class. He's an Englishman so he often takes to saying words like ass and cock. Its not a problem, in fact it is his many quirks that make his teaching enjoyable.

Plus it would make for a really good drinking game. Take a shot for every 'ass' or 'cock'.

He teaches Computational Mathematics and Statistics. His name Robin Hankin and he's a pretty smart cookie. He went to the same school as Sir Isaac Newton.

Full Power Robin. He's about to undergo apotheosis into a being of absolute statistics. Which if you know anything about Statistics means Unity.

He recently went full circle and completed a course section on Hypothesis Testing introducing the concepts of Type I error and Type II error. He introduced the concept of the 'alternative Hypothesis' where we would compare the Null Hypothesis against the Alternative Hypothesis.

Type 1 error: Rejecting the Null given that the Null is true
Type 2 error: Failing to Reject the Null given that the Null is false

He used this example in the case of sentencing a man to jail the case is 'statistically' broken down as follows:

Ho = The Accused man is innocent (By default Innocent until proven guilty)

Ha = The man is guilty (In the weight of the Evidence(p-value))

So he used this example to Explain that if a Type I error occurs that's a case of sentencing innocent man going to jail, and that a Type II error occurs, that's a case of letting a guilty man go Free.

Both are undesirable outcomes. Both are unfortunate occurrences with respect to the justice system.

Here's how we illustrate the Regions that the Errors would Exist in:

Concepts:
**********************************************************

a = the probability of getting a type 1 error
b = probability of getting a type 2 error

power = 1 - b

**********************************************************

So while in class I got to thinking about Alien Civilizations within the context of  Hypothesis testing while also examining the Dark Forest Theory as a solution to Fermi's Paradox. Made popular by Ciu Lixin's novel  'The Dark Forest'. The Dark Forest talks about an emerging interstellar conflict between humans and extraterrestrials called the Trisolarans.

10/10 Amazeballs and would blow your mind

There was a paragraph from the book that had chilled me to the bone the moment I encountered it.

"The universe is a dark forest. Every civilization is an armed hunter stalking through the trees like a ghost, gently pushing aside branches that block the path and trying to tread without sound. Even breathing is done with care. The hunter has to be careful, because everywhere in the forest are stealthy hunters like him. If he finds another life—another hunter, angel, or a demon, a delicate infant to tottering old man, a fairy or demigod—there's only one thing he can do: open fire and eliminate them."

The Dark Forest Theory implies the Universe is filled with hostile civilizations and that communicating our presents in the cosmos would mean suicide.

Now let the Dark Forest be our null Hypothesis:

Ho = The Alien Civilizations are inherently Hostile. Contacting them would be suicide. (Grimdark Universe)

what would an alternative (Ha) to Dark Forest be?

Ha = Alien Civilizations are not Hostile and that there is a vibrant galactic community of civilizations working towards Survival. (Federation of Planets)

so in Hype testing we have something called a p value defined as:

p = The probability given that the null is true of finding and observation or an observation more extreme.

if the the p value falls inside or our of a critical region of significance value (a) that determines whether we reject our null hypothesis or fail to reject our null hype. 

so if the p value were inside the critical region of significance value (a = 0.05) of the Dark Forest Null Hypothesis this would mean we reject an inherently hostile Universe for one that is friendly to communication between civilizations. Referring to our above definition of the Type 1 Error:

Type 1 Error: Rejecting the idea that the Universe is hostile. Basically the probability of encountering a friendly civilization, a culture eager to break bread with us. (more Federation of Planets, less Imperium of Man) in a Universe that is actually hostile.

our Type 2 Error: (failure to reject the Null in a situation it is false) would imply; Encountering a hostile civilization in a Universe that is actually Friendly.

That would be unfortunate wouldn't it. In all that vastness, in all that obscurity. In a universe that is actually friendly towards intelligent life, we discover a civilization that would wipe us out in an instant. In statistics we can do something to reduce the number of type 2 Errors we make. We can collect more data. I guess SETI searches may have a function beyond wasting tax payer money after all.

We come in peace to liberate you from your hubris.

Sincerely
Afiq Abdul Hamid