## Saturday, September 29, 2012

### experiments with random projections

Vast literature on random projections:
Doing the simplest way possible...

Given best 3 (ish) random projections on the following data, then a 1-D rnn on each projection, how after are you someone else's nearest neighbor?

---| weather |-----------------------

0,    3,    21%,    *********************
1,    2,    14%,    **************
2,    6,    42%,    ******************************************
3,    1,     7%,    *******
4,    2,    14%,    **************

---| autompg |-----------------------

0,    5,      1%,    *
1,    40,    10%,    **********
2,    99,    24%,    ************************
3,    111,   27%,    ***************************
4,    105,   26%,    **************************
5,    31,     7%,    *******
6,    7,      1%,    *

---| china |-----------------------

0,    7,       1%,    *
1,    43,      8%,    ********
2,    131,    26%,    **************************
3,    156,    31%,    *******************************
4,    101,    20%,    ********************
5,    52,     10%,    **********
6,    9,       1%,    *

---| nasa93 |-----------------------

0,    5,     5%,    *****
1,    27,   29%,    *****************************
2,    34,   36%,    ************************************
3,    21,   22%,    **********************
4,    6,     6%,    ******

## Monday, September 24, 2012

### Tukutuku paper

The first set of corrections are here.
The second set of corrections are here.
The final version of the paper after these corrections are here.

## Sunday, September 23, 2012

we're achieving the ICSE'13 results in 7 minutes (as compared with 3 hours)... The only parameter that comes short is spread... but I'm sure we're still performing better than NSGA-II and SPEA2... We haven't yet run those with the new "orderly" mutation operator.

Profiling indicates that we're spending a long time (65%) evaluating the population before sorting and removing worst... This is done a 100 times for our size-100 population... I think if we remove the 5 worst individuals at once we'll be able to shave about half the execution time with little effect on the final results.

So we've got things to try:
1- run more feature models.
2- run more algorithms with orderly mutation.
3- shave time off of IBEA by removing 5 worst.

## Tuesday, September 18, 2012

### Metrics of Interest: Keeping Players in the Game

Period: A period of time (i.e. 1 day, 1 week, 1 month)

Retention: How many players are still playing in period i+1

Churn: 1 - Retention

Stickiness: [Users per Period] / [Users per next Largest Period].  i.e. DAU/MAU (daily vs monthly)

Viral Rate: New Unique User per period / Total Users per period

Research Objectives =
= Maximize Retention
= Minimize Churn
= Maximize Stickiness (shoot for 15-20%)
= For every 1% increase in Churn, Want 2.3% increase in Viral Rate
= = (reference)

--------------------------------------------
pom2 charts: http://i.imgur.com/LQxLw.png

### Prediction Scientific Success

The following is bogus and stupid but oh my it will be visited by 1,000,000 deans:
http://klab.smpp.northwestern.edu/h-index.html

### talks for promise

Top two talks: does size matter and learning to change projects.

## Monday, September 17, 2012

Cluster A3 doubles from the initial round to round 3, indicating that the cluster is a likely candidate for Atrazine aptamers.

Where the clusters contain a combination of the following attributes:

The cluster A3 has all of the attributes in the A cluster as 1 for its centroid.

The concentration of cluster A3 in subsequent rounds varies in a manner that suggests that the system has reached a steady state.  The concentration in round 3 is a reasonable estimate for the final value reached in round 12.

If the concentration of cluster A3 at round 3 was used as an estimate for the concentration of the target cluster, then the non-target clusters, i.e. deadends, are illustrated by the grey section of the chart.

The Bromacil experiment shows a similar trend, with an increase of 1 1/2 from the initial round to round 3.

The experiment is once again dominated by cluster A3.

## pom2:

http://i.imgur.com/IMzQj.png

## games:

http://aimazed2d-web-dev-env.elasticbeanstalk.com/

## cloud pricing:

#### EC2 Service for Web Hosting a container for Game Applications

Cliff notes:
* ~ 10 cents per HOUR of CPU
* ~ 12 cents per GB of data transferred

#### RDS Service for Relational Database Storage

Cliff notes:
* ~ 12 cents per GB of storage

Typically; standard projects can expect to pay ~72\$/month.
Several high-end gaming companies use Amazon Web Services:http://aws.amazon.com/solutions/case-studies/

### Joe Ingram's profiling

The two cases below are not exactly comparable, but the results are intuitive.

Original IBEA with FM43

IBEA with "good mutation"...

### Mobile Phone Feature Model for Z3 (in Python)

from z3 import *

# All features are declared as Boolean variables
Mobile_Phone = Bool('Mobile_Phone')
Calls = Bool('Calls')
Screen = Bool('Screen')
GPS = Bool('GPS')
Media = Bool('Media')
Basic = Bool('Basic')
Color = Bool('Color')
High_res = Bool('High_res')
Camera = Bool('Camera')
MP3 = Bool('MP3')

s = Solver()

# Mandatory features; double implication

# Optional features; child implies parent

# [1,1] group
And(Not(Basic),Color,Not(High_res)) ,
And(Not(Basic),Not(Color),High_res) ) )

# [1,*] group

print s.check()
print s.model()

## Sunday, September 9, 2012

### Predicting the Future of Predictive Modelling

by Tim Menzies

A discussion paper for AISE'12.

It is now well established that predictive models can be generated from the artifacts of software projects. So it is time to ask “what’s next?”.

I suggest that predictive modelling tools can and should be refactored to address the near-term issue of decision systems and the long-term goal of social reasoning.