Ordinary Human

Automobiles

2009-05-19T14:23:00.005-05:00

Yes school is out for a couple of more days, and yes I am going on a rant. For those that think that Obama is doing this wrong and doing that wrong, shut up and give the old man a chance. If you remember when Junebug (GWB) came to office he had clear skies, no wars, moderate taxes, and people were gainfully employed. If your memory does not fail you, when Slick Willie came to office, he had clear skies, no wars, moderate taxes and people were trying to dig out from something or another, and when Pops came to power, we were about to go and kick Sadaam's sorry behind, the new world order was coming into place and the wall was about to fall.
So I say all of that to say, give the man a break, let some of this shake out, work its way out, and for heavens sake, stop bringing up this torture thing, I am sick of hearing about it, do you understand. We did what needed to be done, to do what we had to do, I imagine some of that was going to be payback for them fools aiding in the New York city downtown face lift at the cost of several thousand innocent souls that nobody could get past the lobby in time. And as far as the automobile industry and the current problems that they are having here and now. What did you guys think, gas was going to be cheap forever, and that we all should drive these huge behemoths every where we go. Come on, all they are doing is making the road unsafe for the rest of us. I like my late model sedan, thank you very much.
But anyway, between the unions and the corporate hot shots that thought that we all should purchase an Escalade with 21" wheels (dubs as they call them in one of my old neighborhoods) you fools lack vision. The rank and file really need to pile the two groups of you together and perform a world class action lawsuit on you all for corporate malfeasance and corrupt business practices. They then sent their snot nosed lobbyists to Washington, so they would not fix things like fuel standards and the like.
I think people that drive Hummers are selfish fops trying to make up for social inadequacies by driving something that could over the opposition in the back lot.
Oh and back to that Escalade, why should anyone pay a markup of 75 % for a vehicle that serves no useful purpose except to take up two parking spaces and make it difficult to see around to avoid on coming traffic.
And by the way, gas prices are going to go up, 3$ a gallon painful, you will see it by the end of the summer. You can bet on it.

Final Project

2009-04-29T17:51:00.018-05:00

The Final project that was undertaken by my team and myself consisted of completing some work with the Nero Game that was Created by Neural Networks Research Group, the Computer Science Department at the University of Texas at Austin. In a nutshell, this is a war game that was designed so that a player can train teams of neural network-controlled robots and have them fight each other.
It's not exactly data mining, but it is machine learning, and relates to several of the things we've done in class, so it seems like a decent topic for the presentation.

I did not get to play much with the game, though I have loaded it on a couple of my machines at work, one running Linux and another running Windows, very big powerful machines that crush bits and run simulation software all day long, more about that later.
I was mostly a cheerleader on the sidelines.
After loading the game, it was noticed that there were several instances that the troops needed training, I am running it myself and for some reason on my puny processor and negligent memory size, it runs really slow. Anyway when the team ran it, it was noted that the game crashed rather frequently, and very often the training that was accumulated when it was running, was often lost.
There were some really interesting aspects of the game and the troop training these being, if you have a few troops that just don't seem to want to get with the program, and every program seems to have its outliers and random, non behaving data, you can smite the miss behaving rascals in an effort to cull the herd.

When you train the troops in this case, you set specific goals and weights.

In a manner of speaking this was not unlike a computerized version of the old school yard game capture the flag.
hi game is built using the rtNEAT engine for evolving neural networks in real-time. The basic goal of rtNEAT is to build increasingly complex neural networks to perform increasingly complex tasks, utilizing a genetic algorithm. The technique is not unlike genetic programming, because both strive to evolve structures capable of taking inputs, performing some kind of processing, and producing outputs. While genetic programming evolves program trees, the rtNEAT engine and the accompanying database evolves the neural networks.

Building a neural network can be a bit complex, however the networks themselves are initially simple. When the network is constructed, the object is for the machine to learn from its mistakes and eventually attain the ability to in some ways think for itself. As this network grows and otherwise expands, the data contained therein may become less congruent as there will be several instances of the same behavior, and in this way they mutate and there will be series of unintended consequences.

With this in mind, you need to train the soldiers in Nero in order to attain any level of success, I was not able to train my soldiers very well, the first time out and in reality had very limited success, I will attempt to play with it on another day, just to see what can happen when you are able to apply more power. The basic level of training is not really sufficient to achieve anything but very limited results.

In all I learned a great deal from this experiment and look forward to playing with this program some more.

Number Eight Project

2009-04-25T17:42:00.007-05:00

In working with this exercise I found many, many things along may paths. In redoing it, I had to rebuild the python libraries on my decrepit computer.
The first run of the program revealed to me this:

>>> import newsfeatures
>>> allw,artw,artt= newsfeatures.getarticlewords()
>>> wordmatrix,wordvec= newsfeatures.makematrix(allw,artw)
>>> wordvec[0:10]
['crisis', 'from', 'army', 'after', 'president', 'baghdad', 'they', 'saturday', 'health', 'friday']
>>> artt[1]
u'Rumsfeld: Architect of torture'
>>> wordmatrix[1][0:10]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Then I found my way through to get this

>>> def wordmatrixfeatures(x):
... return [wordvec[w] for w in range(len(x)) if x[w]>0]
File "", line 2
return [wordvec[w] for w in range(len(x)) if x[w]>0]
^
IndentationError: expected an indented block
>>> def wordmatrixfeatures(x):
... return [wordvec[w] for w in range(len(x)) if x[w]>0]
...
>>> wordmatrixfeatures(wordmatrix[0])
['after', 'they', 'said']
>>> import docclass
>>> classifier=docclass.naivebayes(wordmatrixfeatures)
>>> classifier.setdb('newstest.db')
>>> artt[0]
u'Torture planning began in 2001, Senate report reveals'
>>> # Train this as an 'iraq' story
...
>>> classifier.train(wordmatrix[0],'iraq')
>>> artt[1]
u'Rumsfeld: Architect of torture'
>>> #Train this as an 'india' story
...
>>> classifier.train(wordmatrix[0],'india')
>>> artt[2]
u'Obama feels the love at the CIA'
>>> #How is this story classified
...
>>> classifier.classify(wordmatrix[1])
u'iraq'

Then I got this

>>> import clusters
>>> clust=clusters.hcluster(wordmatrix)
>>> clusters.drawdendogram(clust,artt,jpeg='news.jpeg')
Traceback (most recent call last):
File "", line 1, in
AttributeError: 'module' object has no attribute 'drawdendogram'
>>> clusters.drawdendrogram(clust,artt,jpeg='news.jpeg')
>>> from numpy import *
>>> l1=[[1,2,3],[4,5,6]]
>>> l1
[[1, 2, 3], [4, 5, 6]]
>>> m1=matrix[l1]
Traceback (most recent call last):
File "", line 1, in
TypeError: 'type' object is unsubscriptable
>>> m1=matrix(l1)
>>> m1
matrix([[1, 2, 3],
[4, 5, 6]])
>>> m2=matrix([[1,2],[3,4],[5,6]])
>>> m2
matrix([[1, 2],
[3, 4],
[5, 6]])
>>> m1*m2
matrix([[22, 28],
[49, 64]])
>>> shape(m1)
(2, 3)
>>> shape(m2)
(3, 2)
>>> a1=m1.A
>>> a1
array([[1, 2, 3],
[4, 5, 6]])
>>> a2=array([[1,2,3],[1,2,3]])
>>> a1*a2
array([[ 1, 4, 9],
[ 4, 10, 18]])

The Articles Output looks like this
Torture planning began in 2001, Senate report reveals
2.89629172203 ['president', 'after', 'will', 'saturday', 'from', 'said']
2.54603874309 ['they', 'that', 'have', 'could', 'swine', 'said']
1.8468488388 ['they', 'obama', 'have', 'that', 'will', 'diagnose']

Rumsfeld: Architect of torture
0.259813315121 ['saturday', 'could', 'army', 'from', 'obama', 'their']
0.252440004108 ['have', 'from', 'after', 'saturday', 'president', 'dead']
0.19914349176 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

Obama feels the love at the CIA
3.00069691675 ['they', 'obama', 'have', 'that', 'will', 'diagnose']
2.58098325735 ['have', 'president', 'army', 'obama', 'that', 'they']
2.18238750145 ['saturday', 'they', 'were', 'dead', 'said', 'army']

Will gay marriage still work as a Republican wedge issue?
2.02498979116 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.13986676577 ['that', 'obama', 'they', 'dead', 'from', 'saturday']
0.979486170438 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

The prophets of doom
1.34297531817 ['have', 'president', 'army', 'obama', 'that', 'they']
1.00370386661 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
0.958825672077 ['have', 'that', 'could', 'with', 'were', 'into']

"On 9/11, I think they hit the wrong building"
0.857557318577 ['president', 'after', 'will', 'saturday', 'from', 'said']
0.843839354479 ['president', 'from', 'have', 'saturday', 'after', 'friday']
0.79646265001 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

Gay marriage in the Heartland
0.408968950916 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
0.380999489908 ['president', 'from', 'have', 'saturday', 'after', 'friday']
0.371820943158 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

Obama gives a speech and ... Look! A puppy!
0.996906528813 ['obama', 'president', 'have', 'they', 'after', 'saturday']
0.830442513473 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
0.673295439051 ['army', 'saturday', 'president', 'disorder', 'have', 'about']

"But think of the things that were done to Iranians!"
1.06643693648 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
1.02979443527 ['president', 'army', 'they', 'could', 'that', 'said']
0.920321480994 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

Will Israel attack Iran?
2.74036508398 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
2.22516916854 ['saturday', 'could', 'army', 'from', 'obama', 'their']
1.38606900563 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']

Spare change for news
0.857690974678 ['have', 'that', 'after', 'clinton', 'killed', 'will']
0.83044688537 ['have', 'president', 'army', 'obama', 'that', 'they']
0.539108734458 ['have', 'from', 'after', 'saturday', 'president', 'dead']

"I believe that I did have PTSD"
2.27816442165 ['have', 'that', 'could', 'with', 'were', 'into']
2.13044386154 ['after', 'army', 'from', 'that', 'dead', 'have']
1.92555906988 ['army', 'saturday', 'president', 'disorder', 'have', 'about']

While you were out
1.47406844301 ['president', 'after', 'will', 'saturday', 'from', 'said']
1.43700455911 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
1.15221753414 ['have', 'that', 'after', 'clinton', 'killed', 'will']

What motive does the Army have to misdiagnose PTSD?
2.66830191095 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
2.20466762414 ['have', 'that', 'could', 'with', 'were', 'into']
1.97764037083 ['have', 'president', 'army', 'obama', 'that', 'they']

Tale of the secret Army tape
1.76706962388 ['saturday', 'could', 'army', 'from', 'obama', 'their']
1.4338870048 ['have', 'president', 'army', 'obama', 'that', 'they']
1.4031667344 ['president', 'army', 'they', 'could', 'that', 'said']

The leader of the Pakistani Taliban vows to strike America
0.653117286917 ['president', 'from', 'have', 'saturday', 'after', 'friday']
0.621747350405 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
0.558910114288 ['after', 'army', 'from', 'that', 'dead', 'have']

"I am under a lot of pressure to not diagnose PTSD"
2.02145838231 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
1.41391360291 ['after', 'army', 'from', 'that', 'dead', 'have']
1.40967544236 ['they', 'obama', 'have', 'that', 'will', 'diagnose']

Obama's G-20 confession: "I take responsibility"
2.1595940292 ['they', 'obama', 'have', 'that', 'will', 'diagnose']
2.1027497848 ['obama', 'president', 'have', 'they', 'after', 'saturday']
1.82173856536 ['have', 'president', 'obama', 'will', 'they', 'swine']

A global epidemic of violent crime
0.658709030278 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
0.479698706847 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
0.475228480034 ['president', 'after', 'will', 'saturday', 'from', 'said']

A message war the Republicans won
1.94935330037 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.9255064047 ['that', 'obama', 'they', 'dead', 'from', 'saturday']
1.7881085133 ['have', 'president', 'army', 'obama', 'that', 'they']

Joseph Stiglitz: "It's going to be bad, very bad"
1.21255146115 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
1.03261496002 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
0.955560942691 ['they', 'obama', 'have', 'that', 'will', 'diagnose']

Gated communities of learning
0.373462540004 ['after', 'army', 'from', 'that', 'dead', 'have']
0.328667469302 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
0.292511673097 ['have', 'that', 'could', 'with', 'were', 'into']

Would you buy a used car industry from this man?
1.88763619545 ['president', 'after', 'will', 'saturday', 'from', 'said']
1.41336268932 ['have', 'president', 'obama', 'will', 'they', 'swine']
1.29075480756 ['president', 'army', 'they', 'could', 'that', 'said']

The secret war against American workers
1.39834880291 ['they', 'obama', 'have', 'that', 'will', 'diagnose']
1.21281501467 ['have', 'president', 'army', 'obama', 'that', 'they']
1.12564497429 ['have', 'that', 'could', 'with', 'were', 'into']

Obama goes back to the grass roots
0.752800275116 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
0.715609580515 ['that', 'obama', 'they', 'dead', 'from', 'saturday']
0.680732504079 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

Emergency powers in Mexico to fight flu virus
1.7264204709 ['they', 'that', 'have', 'could', 'swine', 'said']
1.54867427266 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']
1.54616704255 ['president', 'from', 'have', 'saturday', 'after', 'friday']

N. Korea 'reprocessing nuke fuel rods'
0.790633579 ['saturday', 'could', 'army', 'from', 'obama', 'their']
0.742727961256 ['president', 'after', 'will', 'saturday', 'from', 'said']
0.601885662715 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']

ANC scores landslide win in South Africa
0.57452631672 ['president', 'army', 'they', 'could', 'that', 'said']
0.535829759831 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
0.386901916456 ['have', 'president', 'obama', 'will', 'they', 'swine']

Clinton in Baghdad: U.S. committed to Iraq
2.19893788491 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.98993668877 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
1.97743562888 ['have', 'from', 'after', 'saturday', 'president', 'dead']

Professor sought after 3 shot dead in U.S.
1.46428495573 ['president', 'from', 'have', 'saturday', 'after', 'friday']
1.31805642631 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
1.09716934863 ['that', 'obama', 'they', 'dead', 'from', 'saturday']

Jailed journalist on hunger strike in Iran
0.68342677984 ['saturday', 'could', 'army', 'from', 'obama', 'their']
0.380521779594 ['have', 'from', 'after', 'saturday', 'president', 'dead']
0.370494192961 ['army', 'saturday', 'president', 'disorder', 'have', 'about']

Iceland votes in crisis elections
0.853691959209 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
0.814792491531 ['saturday', 'they', 'were', 'dead', 'said', 'army']
0.665176867 ['army', 'saturday', 'president', 'disorder', 'have', 'about']

6 dead in Somali parliament mortar attack
1.19688869102 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']
1.03330410682 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
1.01348844344 ['army', 'saturday', 'president', 'disorder', 'have', 'about']

Actress Bea Arthur dead at 86
0.962799410095 ['saturday', 'they', 'were', 'dead', 'said', 'army']
0.71553576519 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
0.705282096724 ['president', 'after', 'will', 'saturday', 'from', 'said']

Man Utd in stunning comeback
0.602516241654 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
0.361154292344 ['saturday', 'could', 'army', 'from', 'obama', 'their']
0.337729719126 ['that', 'obama', 'they', 'dead', 'from', 'saturday']

Killer swine flu 'could be global pandemic'
2.89298520351 ['they', 'that', 'have', 'could', 'swine', 'said']
2.50987936958 ['have', 'that', 'after', 'clinton', 'killed', 'will']
2.30298306319 ['saturday', 'could', 'army', 'from', 'obama', 'their']

Clinton in Baghdad amid new reports of attacks
1.56153177082 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']
1.4095684496 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.39805816337 ['have', 'president', 'obama', 'will', 'they', 'swine']

5 dead in attack targeting Afghan governor
2.0927632611 ['saturday', 'they', 'were', 'dead', 'said', 'army']
1.39123443056 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
1.31263976594 ['they', 'that', 'have', 'could', 'swine', 'said']

Probe calls for Bangladesh troop deaths
0.44135557955 ['obama', 'president', 'have', 'they', 'after', 'saturday']
0.419138140003 ['from', 'obama', 'said', 'could', 'ptsd', 'after']
0.365893240532 ['after', 'army', 'from', 'that', 'dead', 'have']

Chavez: U.S. still an imperialist empire
2.51232489594 ['president', 'army', 'they', 'could', 'that', 'said']
2.37639356465 ['president', 'dead', 'obama', 'army', 'disorder', 'crisis']
2.17108091494 ['president', 'from', 'have', 'saturday', 'after', 'friday']

U.N. panel freezes assets of N. Korean firms
1.28555507123 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.20457450264 ['have', 'from', 'after', 'saturday', 'president', 'dead']
1.00695501678 ['president', 'from', 'have', 'saturday', 'after', 'friday']

Gridiron rivals to settle 1993 score
0.974111511407 ['have', 'that', 'after', 'clinton', 'killed', 'will']
0.912362945037 ['that', 'obama', 'they', 'dead', 'from', 'saturday']
0.651874822584 ['have', 'that', 'could', 'with', 'were', 'into']

Texas teen gets swine flu; family quarantined
1.28239958832 ['have', 'that', 'could', 'with', 'were', 'into']
1.23990281402 ['that', 'saturday', 'clinton', 'obama', 'health', 'from']
1.19205981734 ['have', 'president', 'obama', 'will', 'they', 'swine']

Data show airplane bird strikes not rare
0.84007251373 ['after', 'army', 'from', 'that', 'dead', 'have']
0.770565974589 ['have', 'that', 'could', 'with', 'were', 'into']
0.517306588055 ['have', 'president', 'army', 'obama', 'that', 'they']

Mother of Craigslist victim 'devastated' by loss
1.39495259601 ['have', 'that', 'after', 'clinton', 'killed', 'will']
1.31991156275 ['president', 'after', 'will', 'saturday', 'from', 'said']
1.26199955582 ['have', 'that', 'could', 'with', 'were', 'into']

Veterans get apology over 'extremist' flap
1.46053467524 ['saturday', 'could', 'army', 'from', 'obama', 'their']
0.975168526335 ['president', 'army', 'they', 'could', 'that', 'said']
0.938887893405 ['have', 'that', 'could', 'with', 'were', 'into']

Anti-doping agency clears Armstrong for Tour
0.966439231655 ['president', 'from', 'have', 'saturday', 'after', 'friday']
0.942747869615 ['president', 'after', 'will', 'saturday', 'from', 'said']
0.598717581258 ['after', 'army', 'from', 'that', 'dead', 'have']

Trooper reels in line of Ferraris, Lamborghini
0.0 ['obama', 'into', 'could', 'saturday', 'after', 'baghdad']
0.0 ['army', 'saturday', 'president', 'disorder', 'have', 'about']
0.0 ['have', 'from', 'after', 'saturday', 'president', 'dead']

Three factory workers 'shot dead,' mayor says
1.30439198922 ['after', 'army', 'from', 'that', 'dead', 'have']
1.23860082298 ['saturday', 'they', 'were', 'dead', 'said', 'army']
1.0989853721 ['have', 'that', 'could', 'with', 'were', 'into']

The last of the output from the second portion looks like this:

>>> m1
matrix([[1, 2, 3],
[4, 5, 6]])
>>> m2=matrix([[1,2],[3,4],[5,6]])
>>> m2
matrix([[1, 2],
[3, 4],
[5, 6]])
>>> m1*m2
matrix([[22, 28],
[49, 64]])
>>> shape(m1)
(2, 3)
>>> shape(m2)
(3, 2)
>>> a1=m1.A
>>> a1
array([[1, 2, 3],
[4, 5, 6]])
>>> a2=array([[1,2,3],[1,2,3]])
>>> a1*a2
array([[ 1, 4, 9],
[ 4, 10, 18]])
>>> import nmf
>>> w,h= nmf.factorize(m1*m2,pc=3,iter=100)
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
7622.76240347
>>> w*h
matrix([[ 24.37089834, 25.59837689],
[ 41.82979429, 68.33633783]])
>>> m1*m2
matrix([[22, 28],
[49, 64]])
>>> v=matrix(wordmatrix)
>>> weights,feat=nmf.factorize(v,pc=20,iter=50)
41566.4618877
41566.4618877
41566.4618877
41566.4618877
41566.4618877
>>> reload(newsfeatures)

>>> topp,pn= newsfeatures.showfeatures(weights,feat,artt,wordvec)
>>> reload(newsfeatures)

>>> newsfeatures.showarticles(artt,topp,pn)

Here is my stock market file after I toyed with the ticker symbols

2.18864116109e+18
2.18864116109e+18
2.18864116109e+18
2.18864116109e+18
2.18864116109e+18
[[ 2.37666693e+07 1.34520061e+06 2.62779967e+06 3.11656611e+06
5.67418244e+05 7.01015109e+06 2.92443132e+05 2.52662385e+06
1.11638309e+07 1.01723901e+05 5.19575560e+01 2.24454074e+06
4.68321537e+05 1.39908384e+06 6.70165095e+06 3.67869950e+06
1.36283348e+06]
[ 7.32380312e+06 1.77742373e+06 1.37089810e+05 4.19907883e+06
1.84139062e+06 3.21503878e+06 1.58867082e+06 5.45477728e+05
2.13756184e+07 7.65879459e+04 9.79990523e+04 2.96635135e+06
2.61511881e+06 3.21994135e+06 8.07859461e+06 1.71176781e+07
4.80978463e+06]
[ 7.31448471e+06 1.68610150e+06 2.68834864e+05 4.44721706e+06
8.18098519e+05 7.16104424e+06 2.20732425e+06 1.02730214e+05
2.33750061e+07 1.46506828e+05 9.91507392e+04 2.93492311e+05
2.42360363e+06 2.13632897e+06 1.06336554e+06 1.01049313e+07
4.62805315e+06]
[ 1.16375868e+07 1.51554923e+06 3.09746162e+05 2.40701117e+04
1.39633096e+06 3.24063455e+06 9.18670942e+05 1.69931626e+06
1.09720095e+07 1.38900611e+05 6.57939575e+03 2.50346837e+06
4.94576783e+05 2.62410929e+06 6.73035286e+06 1.67573107e+07
5.55052027e+06]
[ 3.89129521e+04 1.67544745e+06 3.61149806e+06 5.78181352e+05
1.84666719e+06 8.78442435e+06 2.88067850e+05 1.02684638e+06
3.24116653e+07 3.71425867e+04 2.64776382e+04 1.57373825e+06
1.40295786e+06 1.35709632e+06 7.65444330e+06 1.88210697e+07
3.23896403e+06]]
[[ 0.21575133 0.41244662 0.7611537 0.64448275 0.26808093]
[ 0.12021876 0.82897867 0.50603432 0.66638657 0.27620903]
[ 0.81443072 0.61973661 0.0344404 0.59095715 0.68482145]
...,
[ 0.27682508 0.02819674 0.34829965 0.87178094 0.53360722]
[ 0.59688167 0.38735095 0.3671287 0.48936043 0.34759439]
[ 0.21552508 0.52542043 0.44772381 0.17131841 0.57059154]]
Feature 1
(23766669.29600687, 'YHOO')
(11163830.908536306, 'PFE')
(7010151.0916381348, 'CVX')
(6701650.9536335012, 'PG')
(3678699.4954650267, 'XOM')
(3116566.1078192662, 'BP')
(2627799.6681819353, 'BIIB')
(2526623.8459756342, 'D')
(2244540.7368489737, 'DNA')
(1399083.8425632557, 'GOOG')
(1362833.47919346, 'AMGN')
(1345200.6101376782, 'AVP')

[(2.4858386130635974, '2008-05-05'), (2.01164169279535, '2008-06-13'), (1.9833321155276236, '2008-06-12')]

Feature 1
(21375618.441576935, 'PFE')
(17117678.134262577, 'XOM')
(8078594.6123901652, 'PG')
(7323803.1170607973, 'YHOO')
(4809784.6293510608, 'AMGN')
(4199078.8320319587, 'BP')
(3219941.3530922248, 'GOOG')
(3215038.7793839704, 'CVX')
(2966351.3519640886, 'DNA')
(2615118.8114477443, 'EXPE')
(1841390.6153257531, 'CL')
(1777423.7312149594, 'AVP')

[(3.6571803519576398, '2009-01-23'), (2.065513906734485, '2008-10-09'), (1.7966713944385531, '2009-03-06')]

Feature 1
(23375006.123443626, 'PFE')
(10104931.329456437, 'XOM')
(7314484.7133820131, 'YHOO')
(7161044.2435260415, 'CVX')
(4628053.1515141223, 'AMGN')
(4447217.0644083731, 'BP')
(2423603.6282038381, 'EXPE')
(2207324.2490521944, 'LMT')
(2136328.9675750155, 'GOOG')
(1686101.4959016384, 'AVP')
(1063365.5357479909, 'PG')
(818098.51854017749, 'CL')

[(1.7003112043653801, '2009-02-26'), (1.6315850517851636, '2008-10-09'), (1.3317713851210469, '2009-03-19')]

Feature 1
(16757310.711177969, 'XOM')
(11637586.813257042, 'YHOO')
(10972009.485707108, 'PFE')
(6730352.8644809993, 'PG')
(5550520.2650987795, 'AMGN')
(3240634.5484751821, 'CVX')
(2624109.2945769709, 'GOOG')
(2503468.3707587714, 'DNA')
(1699316.2628020733, 'D')
(1515549.228245277, 'AVP')
(1396330.9604749633, 'CL')
(918670.94171835261, 'LMT')

[(2.4853251616509144, '2008-10-06'), (2.0616984621300793, '2008-05-05'), (2.0257449468565722, '2008-11-11')]

Feature 1
(32411665.250238225, 'PFE')
(18821069.746516082, 'XOM')
(8784424.3454179335, 'CVX')
(7654443.2956001945, 'PG')
(3611498.059203208, 'BIIB')
(3238964.0300923302, 'AMGN')
(1846667.188258782, 'CL')
(1675447.4463336298, 'AVP')
(1573738.2528519328, 'DNA')
(1402957.8566456696, 'EXPE')
(1357096.3244690241, 'GOOG')
(1026846.377866357, 'D')

[(1.9466486368672224, '2009-03-05'), (1.7736154551482939, '2009-03-09'), (1.668865393095329, '2008-11-20')]

Portfolio Seven

2009-04-06T07:43:00.008-05:00

These are my results from the Search Engine exercise, I could not figure out how to make the program parse the web page.

>>> import urllib2
>>> c=urllib2.urlopen('http://kiwitobes.com/wiki/Programming_language.html')
>>> contents=c.read()
>>> print contents[0:50]
>>
DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Trans
>>> import searchengine
>>> pagelist=['http://kiwitobes.com/wiki/Perl.html']
>>> crawler=searchengine.crawler('')
>>> crawler.crawl(pagelist)
Could not parse page http://kiwitobes.com/wiki/Perl.html
>>> reload(searchengine)

>>> crawler=searchengine.crawler('searchindex.db')
>>> crawler.createindextables()

Traceback (most recent call last):
File "", line 1, in
crawler.createindextables()
File "/home/david/DataMining/searchengine.py", line 130, in createindextables
self.con.execute('create table urllist(url)')
OperationalError: table urllist already exists
>>> reload(searchengine)

>>> crawler=searchengine.crawler('searchindex.db')
>>> pages=\
['http://kiwitobes.com/wiki/Categorical_list_of_programming_languages.html']
>>> crawler.crawl(pages)
Could not parse page http://kiwitobes.com/wiki/Categorical_list_of_programming_languages.html
>>> [row for row in crawler.con.execute(
'select rowid from wordlocation where wordid=1')]
[(1,), (3739,), (6741,), (7464,), (7873,), (8336,), (9045,), (9577,), (10799,), (11348,), (12095,), (12637,), (13306,), (15702,), (16325,), (18417,), (21440,), (25187,), (26249,), (26944,), (28142,), (29541,), (30413,), (32528,), (34351,), (35056,), (35751,), (36973,), (42567,), (43986,), (45637,), (46869,), (47691,), (48448,), (49212,), (51745,), (52175,), (52654,), (53496,), (54077,), (54566,), (55307,), (56141,), (56780,), (57215,), (57662,), (58276,), (61256,), (63007,), (67355,), (70083,), (70588,), (71503,), (73595,), (74053,), (75181,), (76006,), (80408,), (81888,), (83141,), (86014,), (87581,), (90816,), (92139,), (93910,), (95510,), (97160,), (97599,), (98455,), (98947,), (100184,), (100193,), (100539,), (102594,), (103194,), (104902,), (109173,), (109629,), (110863,), (110872,), (111218,), (113273,), (113854,), (114288,), (114732,), (115884,), (116324,), (116924,), (119859,), (120369,), (126569,), (128437,), (128986,), (129603,), (132670,), (133498,), (138813,), (139879,), (140497,), (142522,), (143608,), (145164,), (146484,), (146929,), (147483,), (148797,), (151472,), (152627,), (154988,), (155893,), (157279,), (158086,), (161074,), (165365,), (165835,), (166516,), (167665,), (168335,), (171441,), (171923,), (172601,), (176692,), (177918,), (179133,), (179645,), (180304,), (181484,), (183080,), (183671,), (184680,), (185190,), (185832,), (190213,), (190880,), (191600,), (192711,), (194576,), (195582,), (196071,), (197021,), (197595,), (201888,), (202301,), (205050,), (205536,), (206306,), (208395,), (213581,), (214993,), (215414,), (215895,), (216815,), (217630,), (219928,), (220473,), (222052,), (224401,), (224872,), (225390,), (226119,), (226676,), (227222,), (227676,), (228249,), (231916,), (232861,), (233487,), (234111,), (237838,), (238511,), (242278,), (242902,), (243424,), (244494,), (245260,), (246266,), (247895,), (248407,), (252788,), (253460,), (253886,), (254432,), (256742,), (258764,), (259387,), (260979,), (265214,), (265727,), (266174,), (266852,), (269527,), (270435,), (271502,), (272500,), (273088,), (274326,), (274844,), (276375,), (278964,), (279388,), (280074,), (280574,), (281365,), (282086,), (284649,), (285197,), (285640,), (286674,), (288254,), (288710,), (291532,), (292466,), (294449,), (295792,), (296621,), (297847,), (298779,), (299540,), (302044,), (302699,), (304025,), (304672,), (305314,), (305796,), (306698,), (307186,), (307934,), (310594,), (312383,), (313605,), (314433,), (315356,), (317571,), (319914,), (321398,), (322628,), (324726,), (325296,), (326984,), (327639,), (328513,), (331421,), (331960,), (332418,), (333565,), (334030,), (336537,), (337079,), (346445,), (348395,), (349014,), (350622,), (355601,), (356077,), (358110,), (360833,), (361436,), (362015,), (362430,), (363201,), (364123,), (366583,), (369498,), (370068,), (371058,), (372538,), (373735,), (376402,), (378344,), (378873,), (381350,), (382118,), (382519,), (384311,), (385270,), (387858,), (388349,), (389694,), (392523,), (393661,), (394184,), (394886,), (396562,), (397248,), (398859,), (400068,), (400635,), (401862,), (405465,), (406914,), (407751,), (408696,), (413067,), (413621,), (414161,), (415150,), (416454,), (417118,), (419267,), (420362,), (423651,), (424206,), (426230,), (436789,), (439243,), (439838,), (440606,), (446038,), (447416,), (449883,), (450352,), (451595,), (452830,), (453488,), (454212,), (455276,), (455820,), (459345,), (461041,), (461487,), (461996,), (463216,), (463858,), (466625,), (469350,), (471121,), (471716,), (472690,), (474527,), (475020,), (477171,), (480416,), (482594,), (483283,), (483952,), (484553,), (487483,), (487925,), (489858,), (493533,), (497799,), (498389,), (498928,), (503617,), (505204,), (505623,), (507500,), (508011,), (511208,), (512222,), (512710,), (513499,), (514033,), (514572,), (518517,), (519262,), (519668,), (520392,), (521084,), (525440,), (527661,), (528246,), (529056,), (532682,), (533100,), (533679,), (535687,), (536450,), (536927,), (537586,), (540155,), (540648,), (541793,), (542516,), (544484,), (544926,), (545886,), (551506,), (552195,), (553218,), (553960,), (554431,), (557036,), (558736,), (561861,), (562730,), (565016,), (565637,), (566335,), (568158,), (573799,), (574481,), (578203,), (580066,), (580977,), (581791,), (582250,), (582916,), (584143,), (585205,), (587008,), (587740,), (589971,), (593263,), (594342,), (594878,), (595658,), (596206,), (597368,)]
>>> reload(searchengine)

>>> e=searchengine.searcher('searchindex.db')
>>> e.getmatchrows('functional programming')
select w0.urlid,w0.location,w1.location from wordlocation w0,wordlocation w1 where w0.wordid=144 and w0.urlid=w1.urlid and w1.wordid=19
([(1, 327, 23), (1, 327, 162), (1, 327, 243), (1, 327, 261), (1, 327, 269), (1, 327, 436), (1, 327, 953), (1, 327, 1124), (1, 327, 1164), (1, 327, 1179), (1, 327, 1245), (1, 327, 1257), (1, 327, 1279), (1, 327, 1335), (1, 327, 1386), (1, 327, 1391), (1, 327, 1447), (1, 327, 1450), (1, 327, 1643), (1, 327, 1708), (1, 327, 1721), (1, 327, 1727), (1, 327, 1730), (1, 327, 1755), (1, 327, 1785), (1, 327, 1818), (1, 327, 1897), (1, 327, 1937), (1, 327, 1967), (1, 327, 1989), (1, 327, 2082), (1, 327, 2149), (1, 327, 2159), (1, 327, 2191), (1, 327, 2426), (1, 327, 2493), (1, 327, 2504), (1, 327, 2696), (1, 327, 2801), (1, 327, 2806), (1, 327, 2840), (1, 327, 2895), (1, 327, 3035), (1, 327, 3136), (1, 327, 3212), (1, 327, 3480), (1, 327, 3490), (1, 327, 3505), (1, 327, 3810), (1, 327, 3841), (1, 327, 3844), (1, 327, 3849), (1, 327, 3925), (1, 327, 3928), (1, 327, 4083), (1, 327, 4131), (1, 1631, 23), (1, 1631, 162), (1, 1631, 243), (1, 1631, 261), (1, 1631, 269), (1, 1631, 436), (1, 1631, 953), 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(234, 300, 800), (234, 338, 21), (234, 338, 158), (234, 338, 237), (234, 338, 255), (234, 338, 262), (234, 338, 301), (234, 338, 339), (234, 338, 368), (234, 338, 521), (234, 338, 670), (234, 338, 794), (234, 338, 800), (234, 374, 21), (234, 374, 158), (234, 374, 237), (234, 374, 255), (234, 374, 262), (234, 374, 301), (234, 374, 339), (234, 374, 368), (234, 374, 521), (234, 374, 670), (234, 374, 794), (234, 374, 800), (234, 796, 21), (234, 796, 158), (234, 796, 237), (234, 796, 255), (234, 796, 262), (234, 796, 301), (234, 796, 339), (234, 796, 368), (234, 796, 521), (234, 796, 670), (234, 796, 794), (234, 796, 800), (256, 1075, 22), (256, 1075, 161), (256, 1075, 241), (256, 1075, 261), (256, 1075, 269), (256, 1075, 288), (256, 1075, 373), (256, 1075, 518), (256, 1075, 570), (256, 1075, 692), (256, 1075, 745), (256, 1075, 761), (256, 1075, 846), (256, 1075, 854), (256, 1075, 878), (256, 1075, 996), (256, 1075, 1025), (256, 1075, 1043), (256, 1075, 1054), (256, 1075, 1332), (256, 1075, 1384), (256, 1075, 1835), (256, 1075, 1864), (256, 1075, 1896), (256, 1075, 1974), (256, 1075, 1987), (256, 1075, 2103), (256, 1075, 2105), (298, 386, 353), (298, 386, 378), (298, 386, 387), (298, 386, 584), (298, 386, 609), (298, 386, 618), (298, 386, 763), (298, 617, 353), (298, 617, 378), (298, 617, 387), (298, 617, 584), (298, 617, 609), (298, 617, 618), (298, 617, 763), (9, 2648, 21), (9, 2648, 158), (9, 2648, 237), (9, 2648, 253), (9, 2648, 258), (9, 2648, 352), (9, 2648, 365), (9, 2648, 391), (9, 2648, 444), (9, 2648, 601), (9, 2648, 651), (9, 2648, 728), (9, 2648, 1071), (9, 2648, 1139), (9, 2648, 1149), (9, 2648, 2280), (9, 2648, 2402), (9, 2648, 2425), (9, 2648, 2438), (9, 2648, 2509), (9, 2648, 2526), (9, 2648, 2549), (9, 2648, 2553), (9, 2648, 2643), (9, 2648, 2646), (9, 2648, 2651), (9, 2648, 2654), (9, 2648, 2658)], [144, 19])
>>> reload(searchengine)

>>> e=searchengine.searcher('searchindex.db')
>>> e.query('functional programming')
select w0.urlid,w0.location,w1.location from wordlocation w0,wordlocation w1 where w0.wordid=144 and w0.urlid=w1.urlid and w1.wordid=19

Traceback (most recent call last):
File "", line 1, in
e.query('functional programming')
File "/home/david/DataMining/searchengine.py", line 235, in query
scores=self.getscoredlist(rows,wordids)
File "/home/david/DataMining/searchengine.py", line 222, in getscoredlist
(5.0,self.nnscore(rows,wordids))]
File "/home/david/DataMining/searchengine.py", line 304, in nnscore
nnres=mynet.getresult(wordids,urlids)
File "nn.py", line 113, in getresult
self.setupnetwork(wordids,urlids)
File "nn.py", line 75, in setupnetwork
self.hiddenids=self.getallhiddenids(wordids,urlids)
File "nn.py", line 64, in getallhiddenids
'select toid from wordhidden where fromid=%d' % wordid)
OperationalError: no such table: wordhidden
>>> reload(searchengine)

>>> e=searchengine.searcher('searchindex.db')
>>> e.query('functional programming')
select w0.urlid,w0.location,w1.location from wordlocation w0,wordlocation w1 where w0.wordid=144 and w0.urlid=w1.urlid and w1.wordid=19

Traceback (most recent call last):
File "", line 1, in
e.query('functional programming')
File "/home/david/DataMining/searchengine.py", line 235, in query
scores=self.getscoredlist(rows,wordids)
File "/home/david/DataMining/searchengine.py", line 222, in getscoredlist
(5.0,self.nnscore(rows,wordids))]
File "/home/david/DataMining/searchengine.py", line 304, in nnscore
nnres=mynet.getresult(wordids,urlids)
File "nn.py", line 113, in getresult
self.setupnetwork(wordids,urlids)
File "nn.py", line 75, in setupnetwork
self.hiddenids=self.getallhiddenids(wordids,urlids)
File "nn.py", line 64, in getallhiddenids
'select toid from wordhidden where fromid=%d' % wordid)
OperationalError: no such table: wordhidden
>>> reload(searchengine)

>>> crawler=searchengine.crawler('searchindex.db')
>>> crawler.calculatepagerank()
Iteration 0
Iteration 1
Iteration 2
Iteration 3
Iteration 4
Iteration 5
Iteration 6
Iteration 7
Iteration 8
Iteration 9
Iteration 10
Iteration 11
Iteration 12
Iteration 13
Iteration 14
Iteration 15
Iteration 16
Iteration 17
Iteration 18
Iteration 19
>>> cur=crawler.con.execute('select * from pagerank order by score desc')
>>> for i in range(3): print cur.next()

(438, 2.5285160000000002)
(2, 1.1614640000000001)
(543, 1.064252)
>>> import nn
>>> mynet=nn.searchnet('nn.db')
>>> mynet.maketables()
>>> wWorld,wRiver,wBank =101,102,103
>>> uWorldBank,uRiver,uEarth =201,202,203
>>> mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,UEarth])

Traceback (most recent call last):
File "", line 1, in
mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,UEarth])
NameError: name 'UEarth' is not defined
>>> mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,uEarth])
>>> for c in mynet.con.execute('select * from wordhidden'): print c

(101, 1, 0.5)
(103, 1, 0.5)
>>> for c in mynet.con.execute('select * from hiddenurl') : print c

(1, 201, 0.10000000000000001)
(1, 202, 0.10000000000000001)
(1, 203, 0.10000000000000001)
>>> reload(nn)

>>> mynet=nn.searchnet('nn.db')
>>> mynet.getresult([wWorld,wBank],[uWorldBank,uRiver,uEarth])
[0.076012508375416149, 0.076012508375416149, 0.076012508375416149]
>>> reload nn
SyntaxError: invalid syntax
>>> reload(nn)

>>> mynet=nn.searchnet('nn.db')
>>> mynet.trainquery([wWorld,wBank],[uWorldBank,uRiver,uEarth],uWorldBank)
>>> mynet.getresult({wWorld,wBank],[uWorldBank,uRiver,uEarth])
SyntaxError: invalid syntax
>>> mynet.getresult([wWorld,wBank],[uWorldBank,uRiver,uEarth])
[0.33506324671253318, 0.055127057492088002, 0.055127057492088002]
>>> allurls=[uWorld,uRiver,uEarth]

Traceback (most recent call last):
File "", line 1, in
allurls=[uWorld,uRiver,uEarth]
NameError: name 'uWorld' is not defined
>>> allurls=[uWorldBank,uRiver,uEarth]
>>> for i in range(30):
mynet.trainquery([wWorld,wBank],allurls,uWorldBank)
mynet.trainquery([wRiver,wBank],allurls,uRiver)
mynet.trainquery([wWorld],allurls,uEarth)

>>> mynet.getresult([wWorld,wBank],allurls)
[0.86154797717394405, 0.01107121517146442, 0.015725794221216588]
>>> mynet.getresult9[wRiver,wBank],allurls)
SyntaxError: invalid syntax
>>> mynet.getresult([wRiver,wBank],allurls)
[-0.030344006191459796, 0.88298149804489123, 0.005509562270886237]
>>> mynet.getresult([wBank],allurls)
[0.86540476120703236, -0.00067859116915910554, -0.85191567250806755]
>>>
End Game

I used idle to streamline the output and make for a better capture of input and output.

Data Mining the Fifth part

2009-02-28T13:51:00.020-05:00

In my journey to find and use some interesting data visualizations, I stumbled across this one first, A Data representation of all of the Top level domain country codes. If you may notice that the United States is not the largest one, by far.

I found this one while searching for something else entirely, while trying to bring up Moby on my ipod.
This Le Grande Content Data Visualization can be fun.

There is a google gadget that you can add to your home page located here , I'm working on getting some data that it can read and display, the first thing that I tried basically blew up in my face and displayed absolutely nothing.

Here is the speech given the other night by President Obama on the telly in front of congress with the word displays. Which words showed up how often.

Another word visualization regarding the cost of non resident tuition at several state universities.

I found this one very interesting, this concerns job cut warnings in the state of California, for this year. This was actually sort of an eyeful

I jumped over to Gap minder and found several of those that we had seen in class, I found some of those here.....Here I mean

Then I found this growth of Target in relation to its major competitor Wal-Mart, and how it seems to locate opportunity and managed an expansion.

Data Mining Section 4

2009-02-21T20:45:00.013-05:00

After loading the code in the book, played with the data sets that were recommended. What I had difficulty with was making something display once I had everything in place. I am running Linux and I am trying to decipher the README in an effort. Ok, here is the deal, the imaging library don't work on my machine.
After fiddling with some code, I found an image library that works on my box
and somewhere in the mix, I managed to find this output.

import clusters
>>> blognames,words,data=clusters.readfile('blogdata.txt')
>>> clust=clusters.hcluster(data)
>>> reload(clusters)

>>> clusters.printclust(clust,labels=blognames)
-
-
we make money not art
-
Giga Omni Media, Inc.
-
-
-
The Unofficial Apple Weblog (TUAW)
-
Download Squad
-
Autoblog
-
Joystiq
Engadget
-
TMZ.com
Celebrity gossip juicy celebrity rumors Hollywood gossip blog from Perez Hilton
-
Creating Passionate Users
-
gapingvoid: "cartoons drawn on the back of business cards"
-
SimpleBits
-
-
-
kottke.org
plasticbag.org
-
SpikedHumor - Today's Videos and Pictures
Oilman
-
-
blog maverick
-
Copyblogger
-
-
Captain's Quarters
-
Wonkette
-
Eschaton
-
Talking Points Memo
-
The Daily Dish | By Andrew Sullivan
-
Instapundit
Think Progress
-
Seth's Blog
-
-
-
ongoing
-
TreeHugger
-
Wired Top Stories
-
-
Topix.net Weblog
-
Micro Persuasion
-
Publishing 2.0
-
Scobleizer -- Tech geek blogger
-
-
BuzzMachine
Matt Cutts: Gadgets, Google, and SEO
-
Gawker: Valleywag
-
-
The Viral Garden
-
Mashable!
-
ReadWriteWeb
-
The Official Google Blog
-
John Battelle's Searchblog
-
Search Engine Watch Blog
-
Google Blogoscoped
-
Google Operating System
Search Engine Roundtable
-
Bloggers Blog: Blogging the Blogsphere
TechCrunch
-
-
Bloglines | News
-
-
A Consuming Experience (full feed)
-
-
Schneier on Security
-
Gizmodo
Lifehacker
-
Techdirt
-
-
Michelle Malkin
-
-
MAKE Magazine
-
ScienceBlogs : Combined Feed
Pharyngula
-
The Superficial - Because You're Ugly
Slashdot
-
-
MetaFilter
-
-
Crooks and Liars
-
-
Power Line
-
Little Green Footballs
-
Daily Kos
NewsBusters.org - Exposing Liberal Media Bias
-
Gothamist
-
Deadspin
Gawker
-
Derek Powazek
-
Neil Gaiman's Journal
-
Signal vs. Noise
-
43 Folders
Stepcase Lifehack
-
Kotaku
Boing Boing
-
Sifry's Alerts
Jeremy Zawodny's blog
-
456 Berea Street
-
mezzoblue
Joel on Software
-
PaulStamatiou.com
-
Shoemoney - Skills To Pay The Bills
ProBlogger Blog Tips
-
flagrantdisregard
WIL WHEATON dot NET: in exile
-
Joho the Blog
Joi Ito's Web
-
-
The Full Feed from HuffingtonPost.com
-
GoFugYourself
Cool Hunting
-
Steve Pavlina's Personal Development Blog
-
Quick Online Tips
-
Online Marketing Report
How to Change the World
>>>

I have since found the handles, and the rest of my output looks like this

Iteration 3
>>> [blognames[r] for r in kclust[0]]
['Joystiq', 'Download Squad', 'Engadget', 'The Unofficial Apple Weblog (TUAW)', 'Autoblog']

and

[blognames[r] for r in kclust[1]]
['we make money not art', "Neil Gaiman's Journal", 'Online Marketing Report', "Sifry's Alerts", 'Cool Hunting', 'The Full Feed from HuffingtonPost.com', "Joi Ito's Web", 'Bloglines | News']

Data Mining Assignment 3

2009-02-07T15:58:00.003-05:00

In the absence of my team mates, I have managed to finish coding the recommendation system. My biggest issue was the lack of credible data source.
I also lack some understanding of the utilization of APIs. I defer to my team mates,
what I have figured out from coding on my own, which I will post a little later is in python, it is pretty straight forward, I just don't have a pretty front end to hang on it right now.

Data Mining more deeper

2009-01-31T01:39:00.013-05:00

In this exercise, I have found the limits of expression in the realm of data, within the first part and I can see some utility of the Weka (pronounced like Mecca) and watching the data dance about on the screen, proves to be more than a little interesting.
However using the tool and fiddling around with the data (not real sure which data set I should be using) I discovered some tendencies and patterns, that could prove useful in the long run. I would have to use this with some familiar data, just so I could better understand what is coming from this. (Something like, needing to see the answer before asking the question.)

Using the Heart data-- and J48
I got something like this

=== Run information ===
http://www.blogger.com/posts.g?blogID=3679037668869916100
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: cleveland-14-heart-disease
Instances: 303
Attributes: 14
age
sex
cp
trestbps
chol
fbs
restecg
thalach
exang
oldpeak
slope
ca
thal
num
Test mode: 10-fold cross-validation

=== Classifier model (full training set) ===

J48 pruned tree
------------------

thal = fixed_defect
| ca <= 0
| | exang = no: <50 (5.06)
| | exang = yes: >50_1 (3.06/1.0)
| ca > 0: >50_1 (10.0)
thal = normal
| ca <= 0: <50 (117.21/12.55)
| ca > 0
| | cp = typ_angina
| | | trestbps <= 138: >50_1 (4.0/1.0)
| | | trestbps > 138: <50 (3.0)
| | cp = asympt: >50_1 (20.0/3.0)
| | cp = non_anginal: <50 (13.9/1.0)
| | cp = atyp_angina
| | | restecg = left_vent_hyper
| | | | exang = no: >50_1 (3.0)
| | | | exang = yes: <50 (2.0)
| | | restecg = normal: <50 (4.0)
| | | restecg = st_t_wave_abnormality: <50 (0.0)
thal = reversable_defect
| cp = typ_angina
| | chol <= 229: <50 (3.0)
| | chol > 229
| | | age <= 48: >50_1 (2.0)
| | | age > 48: <50 (3.0/1.0)
| cp = asympt
| | oldpeak <= 0.6
| | | restecg = left_vent_hyper: >50_1 (8.0/1.0)
| | | restecg = normal
| | | | trestbps <= 136
| | | | | ca <= 0: <50 (4.0)
| | | | | ca > 0
| | | | | | thalach <= 151: <50 (2.0)
| | | | | | thalach > 151: >50_1 (3.0)
| | | | trestbps > 136: >50_1 (4.0)
| | | restecg = st_t_wave_abnormality: >50_1 (0.0)
| | oldpeak > 0.6: >50_1 (57.39)
| cp = non_anginal
| | slope = up: <50 (7.39/1.0)
| | slope = flat
| | | ca <= 0
| | | | trestbps <= 122: <50 (3.0)
| | | | trestbps > 122: >50_1 (3.0)
| | | ca > 0: >50_1 (8.0/1.0)
| | slope = down: <50 (1.0)
| cp = atyp_angina
| | ca <= 0
| | | oldpeak <= 0.1: <50 (4.0)
| | | oldpeak > 0.1: >50_1 (2.75/0.75)
| | ca > 0: >50_1 (2.25/0.25)

Number of Leaves : 30

Size of the tree : 51

Time taken to build model: 0.14 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances 235 77.5578 %
Incorrectly Classified Instances 68 22.4422 %
Kappa statistic 0.5443
Mean absolute error 0.1044
Root mean squared error 0.2725
Relative absolute error 52.0476 %
Root relative squared error 86.5075 %
Total Number of Instances 303

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.83 0.29 0.774 0.83 0.801 0.809 <50
0.71 0.17 0.778 0.71 0.742 0.809 >50_1
0 0 0 0 0 ? >50_2
0 0 0 0 0 ? >50_3
0 0 0 0 0 ? >50_4
Weighted Avg. 0.776 0.235 0.776 0.776 0.774 0.809

=== Confusion Matrix ===

a b c d e <-- classified as
137 28 0 0 0 | a = <50
40 98 0 0 0 | b = >50_1
0 0 0 0 0 | c = >50_2
0 0 0 0 0 | d = >50_3
0 0 0 0 0 | e = >50_4

The other output looks like this

=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: cleveland-14-heart-disease
Instances: 303
Attributes: 14
age
sex
cp
trestbps
chol
fbs
restecg
thalach
exang
oldpeak
slope
ca
thal
num
Test mode: evaluate on training data

=== Classifier model (full training set) ===

J48 pruned tree
------------------

thal = fixed_defect
| ca <= 0
| | exang = no: <50 (5.06)
| | exang = yes: >50_1 (3.06/1.0)
| ca > 0: >50_1 (10.0)
thal = normal
| ca <= 0: <50 (117.21/12.55)
| ca > 0
| | cp = typ_angina
| | | trestbps <= 138: >50_1 (4.0/1.0)
| | | trestbps > 138: <50 (3.0)
| | cp = asympt: >50_1 (20.0/3.0)
| | cp = non_anginal: <50 (13.9/1.0)
| | cp = atyp_angina
| | | restecg = left_vent_hyper
| | | | exang = no: >50_1 (3.0)
| | | | exang = yes: <50 (2.0)
| | | restecg = normal: <50 (4.0)
| | | restecg = st_t_wave_abnormality: <50 (0.0)
thal = reversable_defect
| cp = typ_angina
| | chol <= 229: <50 (3.0)
| | chol > 229
| | | age <= 48: >50_1 (2.0)
| | | age > 48: <50 (3.0/1.0)
| cp = asympt
| | oldpeak <= 0.6
| | | restecg = left_vent_hyper: >50_1 (8.0/1.0)
| | | restecg = normal
| | | | trestbps <= 136
| | | | | ca <= 0: <50 (4.0)
| | | | | ca > 0
| | | | | | thalach <= 151: <50 (2.0)
| | | | | | thalach > 151: >50_1 (3.0)
| | | | trestbps > 136: >50_1 (4.0)
| | | restecg = st_t_wave_abnormality: >50_1 (0.0)
| | oldpeak > 0.6: >50_1 (57.39)
| cp = non_anginal
| | slope = up: <50 (7.39/1.0)
| | slope = flat
| | | ca <= 0
| | | | trestbps <= 122: <50 (3.0)
| | | | trestbps > 122: >50_1 (3.0)
| | | ca > 0: >50_1 (8.0/1.0)
| | slope = down: <50 (1.0)
| cp = atyp_angina
| | ca <= 0
| | | oldpeak <= 0.1: <50 (4.0)
| | | oldpeak > 0.1: >50_1 (2.75/0.75)
| | ca > 0: >50_1 (2.25/0.25)

Number of Leaves : 30

Size of the tree : 51

Time taken to build model: 0.02 seconds

=== Evaluation on training set ===
=== Summary ===

Correctly Classified Instances 279 92.0792 %
Incorrectly Classified Instances 24 7.9208 %
Kappa statistic 0.8396
K&B Relative Info Score 21518.6331 %
K&B Information Score 232.2202 bits 0.7664 bits/instance
Class complexity | order 0 305.5409 bits 1.0084 bits/instance
Class complexity | scheme 99.2382 bits 0.3275 bits/instance
Complexity improvement (Sf) 206.3027 bits 0.6809 bits/instance
Mean absolute error 0.0532
Root mean squared error 0.1624
Relative absolute error 26.5595 %
Root relative squared error 51.542 %
Total Number of Instances 303

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.952 0.116 0.908 0.952 0.929 0.952 <50
0.884 0.048 0.938 0.884 0.91 0.952 >50_1
0 0 0 0 0 ? >50_2
0 0 0 0 0 ? >50_3
0 0 0 0 0 ? >50_4
Weighted Avg. 0.921 0.085 0.922 0.921 0.921 0.952

=== Confusion Matrix ===

a b c d e <-- classified as
157 8 0 0 0 | a = <50
16 122 0 0 0 | b = >50_1
0 0 0 0 0 | c = >50_2
0 0 0 0 0 | d = >50_3
0 0 0 0 0 | e = >50_4

What I am looking for with the final set. The bookmark recommendations are sketchy at best

Data Mining

2009-01-21T23:04:00.007-05:00

This journey into the world of python-- I am beginning to understand python, the mining portion I more less understand. Trying to figure out how to match up an question with an answer.
I found these answers to the aforementioned

from recommendations import critics
>>> critics ['Lisa Rose']['Lady in the Water']
2.5
>>> criticx ['Toby']['Snakes on a Plane']=4.5

NameError: name 'criticx' is not defined
>>> critics ['Toby']['Snakes on a Plane']=4.5
>>> critics ['Toby']
{'Snakes on a Plane': 4.5, 'You, Me, and Dupree': 1.0, 'Superman Returns': 4.0}

from math import sqrt
>>> sqrt (pow(4.5-4,2)+pow(1-2,2))
1.1180339887498949
>>> 1/(1+sqrt(pow(4.5-4,2)+pow(1-2,2)))
0.47213595499957939

(3.3477895267131013, 'The Night Listener'), (2.8325499182641614, 'Lady in the Water'), (2.5309807037655649, 'Just My Luck')]

(3.457128694491423, 'The Night Listener'), (2.7785840038149234, 'Lady in the Water'), (2.5502787061890753, 'Just My Luck')]

[(0, 'You, Me, and Dupree'), (0, 'The Night Listener'), (0, 'Snakes on a Plane'), (0, 'Lady in the Water'), (0, 'Just My Luck')]

These are my answers, I feel there is much more to learn about this, and will prove to a useful skill set in the years to come.

Zack is Home

2008-05-13T13:18:00.004-05:00

Hi, I know, I know its been a few days. Zack is home finally and beginning to get used to being at the house. Yes, he's had an accident or two. Shhhhh, don't tell his mother, she'll get all cross with me again. He's happy, he's learning new stuff, like where to hide in the family room so that he won't get stepped on. (a useful skill for when his biped brothers and sisters come home) He is still a little frightened of doors, thanks to whomever had him before he went to the shelter. Now that Zack is home, I don't spend my evenings on pet finder, I actually do some real house work. Like the vegetable garden I put in over the weekend, and the row of flowers I added along side the driveway. Ooops that goes in my other blog. With pictures. Hey honey, would you take those pictures for me. The sun is out and the flowers are beginning to bloom. Zack does like bananas, and everybody that knows me, knows that a dog is not my dog, until he eats bananas. Just ask my mother, she'll tell you the story of me and the bananas and all I could eat for a couple of months. And no, I don't climb trees.

Dog days

2008-04-18T09:08:00.002-05:00

Well its official now, he's here, he's home with me and he's still trying to figure out what he is allowed to do, it'll be a while. Zack came home this past Saturday with my wife and I. He's very quiet, very, very quiet. In fact he made no vocal noises until Monday when he whimpered at one of my neighbor's dogs

Still More dog days

2008-04-09T19:02:00.005-05:00

This past weekend was a wash, no dog yet, lots of stress, and now I'm way behind on a bunch of stuff. God willing this weekend will be better. We should be united with Zack and he and I should be happy and chill. When he comes to the house I'll get some pix of him in the garden and about the house.

Jumping Ship

2008-04-04T15:21:00.005-05:00

Brothers and Sisters, I really think that it is time for us to examine ourselves, and our political parties and make some cold hard decisions about where we want to go as individuals. With the blinding ascent of Barack Obama in the presidential race and the dirty nasty tricks, the strange innuendos that Bill and Hillary are speaking, and all of this sideways looking and this so called liberal bias. Sooooo, its great that he is a candidate until he decides to run against so called party royalty,hmmmmm sounds like a double standard to me. It really, really does. Lets look at a few interesting tidbits. Fact the political party that was in the White House when some of our ancestors(please note, not all of us have 5 -10 generations in this country) were enslaved and the man that signed the Emancipation Proclamation was a republican. The Democrats at that juncture were interested in keeping black folk enslaved. The court that over saw Brown versus board of education-Topeka, KS was republican. The group of folks that we stauchly anti intergration were none other than Strom Thurmond and his dixie crats (Democrats) who opposed any and all mixing of the races. The Democrats have brought you many, many years of welfare with no reform in exchange for your vote, we don't need a hand out, we just need a hand up, and a fair and level playing field.

No more dog days

2008-03-28T10:42:00.005-05:00

Here goes, I'm going to start this early and work long, I found my puppy, he is about a year or so old, he's little and he's cute. And yes he is going to help me write in here from time to time. I'm posting his picture here next to mine (the duck is me) and Zack, yes that's his name is the mugshot next to mine. And yes to my fans I'll even write a poem for him.

Global Warming

2008-03-19T12:46:00.006-05:00

Global Warming is caused by politicians worldwide making spurious promises to convince us the people, that they are really going to fix that stupid pot hole on main street that has been broken for the past three years. I am convinced that we lack the political will to resolve any problem in the country amicably. We have an opportunity to make the world a better place, by reducing our dependence on fossil fuels, by using a bus or train to transport folks, goods and some services. We have had the entrepreneurial drive to solve the problem at least three or four times over, but still we pay 112$ a barrel for oil, which really should not bother anyone, but it does. My car gets 30 miles to a gallon of gas, but thats mostly because I can drive on the highway and don't stop until I have to. There are a thousand cars that pass me each day with one person in it. Here are a few tips to reduce your fossil fuel footprint, don't worry about anyone else, just worry about you and yours.

1. Check the pressure on the tires in your car, did you know that its written on the door jamb in your car, next to the drivers seat. ( No the 35 psi the tire guy puts in is dead wrong and it burns gas, and costs YOU money)

2. I was told to drive the speed limit, that will save a significant amount of fuel and your money.

3. Make every trip away from the homestead count, no more running out to buy milk and then back and then out to the post office.

4. Plan your trips so that you can increase the number of right turns that you make, most places have right on red laws now, so you can turn on red if the traffic permits.

5. Walk once in a while, ride a bicycle,

6.TIPS ON PUMPING GAS
I don't know what you guys are paying for gasoline.... but here in the east we are also paying higher, up to $3.50 per gallon. Only buy or fill up your car or truck in the early morning when the ground temperature is still cold.

Remember that all service stations have their storage tanks buried below ground. The colder the ground the more dense the gasoline, when it gets warmer gasoline expands, so buying in the afternoon or in the evening....your gallon is not exactly a gallon. In the petroleum business, the specific gravity and the temperature of the gasoline, diesel and jet fuel, ethanol and other petroleum products plays an important role.

A 1-degree rise in temperature is a big deal for this business. But the service stations do not have temperature compensation at the pumps. When you're filling up do not squeeze the trigger of the nozzle to a fast mode. If you look you will see that the trigger has three (3)stages: low, middle, and high. In slow mode you should be pumping on low speed, thereby minimizing the vapors that are created while you are pumping.
All hoses at the pump have a vapour return. If you are pumping on the fast rate, some other liquid that goes to your tank becomes vapour.
Those vapours are being sucked up and back into the underground storage tank so you're getting less worth for your money. One of the most important tips is to fill up when your gas tank is HALF FULL or HALF EMPTY. The reason for this is, the more gas you have in your tank the less air occupying its empty space. Gasoline evaporates faster than you can imagine. Gasoline storage tanks have an internal floating roof. This roof serves as zero clearance between the gas and the atmosphere, so it minimizes the evaporation.
Another reminder, if there is a gasoline truck pumping into the storage tanks when you stop to buy gas, DO NOT fill up-most likely the gasoline is being stirred up as the gas is being delivered, and you might pick up some of the dirt that normally settles on the bottom. Hope this will help you get the most value for your money.
DO SHARE THESE TIPS WITH OTHERS!
WHERE TO BUY USA GAS, THIS IS VERY IMPORTANT TO KNOW. READ ON Gas rationing in the 80's worked even though we grumbled about it. It might even be good for us! The Saudis are boycotting American goods. We should return the favour.

An interesting thought is to boycott their GAS.
Every time you fill up the car, you can avoid putting more money into The coffers of Saudi Arabia. Just buy from gas companies that don't import their oil from the Saudis.
Nothing is more frustrating than the feeling that every time I fill-up the tank, I am sending my money to people who are trying to kill me, my family, and my friends.
I thought it might be interesting for you to know which oil companies are the best to buy gas from and which major companies import Middle Eastern oil.
These companies import Middle Eastern oil:

Shell........................... 205,742,000 barrels
Chevron/Texaco........ 144,332,000 barrels
Exxon/Mobil.............. 130,082,000 barrels
Marathon/Speedway.. 117,740,000 barrels
Amoco.......................... ..62,231,000 barrels
Citgo gas is from South America, from a Dictator who hates Americans.
If you do the math at $30/barrel, these imports amount to over $18 BILLION! (oil is now $90 - $100 a barrel Here are some large companies that do not import Middle Eastern oil:
Sunoco................0 barrels
Conoco..................0 barrel s
Sinclair.................0 barrels
BP/Phillips.............0 barrels
Hess.....................0 barrels
ARC0.....................0 barrels
If you go to Sunoco.com, you will get a list of the station locations near you.
All of this information is available from the Department of Energy and each is required to state where they get their oil and how much they are importing.
But to have an impact, we need to reach literally millions of gas buyers. It's really simple to do.
Pass it on if you like

More dog days

2008-03-19T11:59:00.003-05:00

Here goes, more on the dog saga. I searched high and low on the internet, apparently its the new efficient way to look for a new pooch, I've taken to look here because like many of my fellow Americans, my days are very full, the roads around me are clogged with more of my fellow Americans who can't get anywhere fast except to drive there.(Thanks to the politicians and the petroleum and automobile companies that fund them) Anyway while I digress, here is the crux of my complaint; All that I want to get is a dog, almost any dog will do, he just needs to be small, so that he does not and will not walk my wife(even though I have my doubts) He needs not be very lazy, the humans in the house cover that just fine. He needs to not shed when the Lord above announces a permanent season change.

Character Corner: 36 Christian Ways to Reduce Stress

2008-03-19T07:22:00.001-05:00

Character Corner: 36 Christian Ways to Reduce Stress

The Revolution, My revolution

2008-03-16T18:49:00.001-05:00

The revolution has come,

it has gone,

it was televised

and for the most part,

many of us were left behind.

There will be no instant replay,

Jesse Jackson still

is a has been

a not really

and a never will be.

The revolution

was here and

I believe brother

you got left behind.

The revolution will not

come again,

Its message

was

do for yourself

it matters not

what your neighbor says

he won't pay your bills.

There will be no replay

on the late news

the late, late show

or the morning show.

Take up your shoes

find and make your way in the world.

This is not the time.

All those promises

where have they got you today

All those prayers

got your courage up.

The revolution will come again

when you awaken it in yourself.

Light Fusiion

2008-03-16T18:20:00.005-05:00

Sound, light, flashing neon
drumbeats, whistles
pencils clattering on the
side walk.
The world rushing
by.

Let the music in
let the music out
the streets, the alley
the world below.
As the streets rush
by.

The rhythm
and the noise
with the dogs
barking in the sky
above.
Sound, light, drums
crashing----
time stands
still
The music in your head,
it is there.
close your eyes.....

Just Listen

Dog Days

2008-03-13T11:28:00.008-05:00

Could someone please tell me why it is so hard to adopt a dog that I find appealing. All that I want is a simple little dog that won't grow too big, so that he has a nice disposition, so he won't walk my wife, when I am either too lazy or not at home to walk him. You can adopt a child with less pain, you can adopt a road, you can adopt a school. All of these are much less painful than this. I saw puppy in the next state up, he is a Jack Russell Terrier and something mix, the reason behind this later. He has 18 applications, I'm not going to drive 100 miles with gas prices as silly as they are and come home empty handed. Not going to work, all that I want is a simple JRT and something mix, that looks like he has some sense.
I've almost always had a dog in my house since I've been on this earth, and having one is as natural as breathing. You get up in the morning, wash your back, put some clothes on, take the doggy out, maybe you drink some coffee on the trip down the block, maybe not. You take him to the doggy doc when he is sick, you treat him with courtesy and respect not unlike your children or your spouse, not saying that you should treat your spouse like a dog, even though some folks do and most of them seem to have zero luck on Saturday night with getting that itch re scratched by the same person over and over. But I'm not going there today, not the time or the place. So can someone, anyone, please tell me why it is soooo hard to adopt a little puppy.

Tax and Spend

2008-02-19T08:39:00.007-05:00

Its that time of year again folks, taxes, taxes and more taxes. Some of us are fortunate enough to live in tax friendly locales, some of us, myself included do not. Why is it that our elected representatives see fit to provide for themselves and many of our much richer brethren a litany of loopholes and breaks, while paying lip service to the many of our much poorer brethren while leaving the remainder of us scratching our heads in bewilderment. I don't make that much money, my numbers are well below the average American tax payer, however what I seem to be paying for seems to be much higher. Like most folks, I really wonder where my money is being spent or wasted and why it won't stop, we are in fact educated and some what rational people, or at least that is what I like to think. We have conflicts in which our young men and women are engaged in and while we have the technological superiority with which we should prevail, we seem to be ham fisted in many facets of execution to achieve that end. We should never have dumb people in our schools, we have more computers per capita than almost everyone. No one in this country should ever, ever go to bed hungry, go without being able to visit a doctor or dentist, and yet we do. We have the best country in the world in which to excel and one of the worse for taking care of our down trodden, we seem to encourage laziness and some aspects of failure far too often, we praise and worship entertainers, athletes, folks who require limited education and really don't produce anything we can't do without. Folks like doctors, engineers, teachers, military leaders ( yes them too, for an enlightened educated military is a really good way to project power at low cost) and even I'll say it lawyers, despite most of my misgivings about that line of work. But these are the people we need to glorify, these are the people that will allow us to remain competitive in the world today and tomorrow, we don't need another rock star, another stripper wanna be and please no more bad actors. I will sit down some time soon and write about how to fix the government once and for all.

My Old School,

2008-02-14T20:19:00.002-05:00

NPR: Alternative 'Commie' High Mellows with Time

For those that think that nothing good comes from an alternative school, check this link out, the problem with schools in America is not bigger, but make them smaller, why do you think that home schooled children are doing so well, parents care, people want thier children to learn, I want to retire and have somebody around to be a viable part of the work force.

New Year

2008-02-05T15:14:00.001-05:00

Yes I know, its the beginning of February and its well after the new year, to all greetings and welcome to the new year. Those of you who might read this from time to time expecting that I might come up with something new, well I have something new. I am planning to split some of the work of this one blog into other areas. That's merely speculative right now. My post today is to help out some folks with some finance stuff.

It goes like this; the more money that you save now, means that there money around sooner and bigger so that you will stop having to go to work when you are much older. There are several reasons for this focus. It is much more enjoyable to go to work when you want to, not when you have to. I am not going to recommend a specific stock, I'll share with you some of the things I have purchased, and why. I'll also provide some information about links to find these places so that you can read and learn for your self. The only advice I am allowed and qualified to give you is this; Read everything, the big print, the little print with the big words, everything. Make sure that you understand the expense structure of purchase, there are fees that you will not recover unless the stock that you purchase increases in value over time. I don't day trade, I have a regular day job that keeps me idle enough. I don't really have a broker per se, I do all of my own research, thats right ask questions follow leads on the phone, over the internet, ask your friends to explain stuff, ask them why they own stock in XYZ company.

Here goes, I do own a significant amount of utility stock, why, simple. If you can read this page you need to thank four people; the teacher (parents too) that taught you how to read, the electric company, that brings the electricity to power the computer, the telephone or cable company that brings the network packets to your front door, and depending upon your locale the gas company or the local geek if your'e not him/her that enabled that computer to run smoothly. Folks infrastructure is important, many of you can not do your job in whole or in part because of infrastructure. You need lights, you need your beer, you need the fresh milk for the baby, you need that and this, face it, if it was not for the product the power company produces, we might well live in a cave. Power companies with few exceptions pay good strong dividends, now my brother says I own too many, I think at last count there were nine different stocks.

I can name three that you can get into for next to nothing and they are consistent.

Dominion Resources There are three reasons, they are solid, strong, they provide electricity to alot of very important people, they have paid a substantial dividend since before my wife was born, and she's no spring chicken either.

Southern Company This company was the 800 pound gorilla in the power industry, at least it was when bought into them, anyway they supply power to Georgia, Alabama, part of Mississippi, and North West Florida.

SCANA These folks are into both gas and electric, real big in South Carolina, one of the reasons why I own them is simple world domination I wanted a piece of every light company any place that my family or myself could possibly live, some out of spite, some out of bragging rights. There are a good stock, thats the only reason I can think of right now.

This Christmas

2007-12-12T14:05:00.000-05:00

This Christmas,
Yes, this Christmas
I'll but everybody books
a tape or a CD here
and there.
As we all need to learn,
to grow,
to change our direction,

This Christmas,
we can all give thanks for the years
behind us,
the years
before us.

Ah this Christmas,
I'll shop,
at home and at work.
I'll think,
I'll pray and I'll smile.

I'll be giving thanks for
the blessings that
I have,
the smiles you bring.

And this Christmas,
I'll cheer at the money,
I save
by not going to the stores,
waiting in traffic.

So this Christmas,
you can tell me what's
on your reading list,
your play list,
or your wish list.

Your book only comes in
one size,
the one that fits you.

This Christmas, I hope you smile
again.
I wish you much happiness,
I wish you well.

Come On People Book Review

2007-12-02T17:44:00.000-05:00

I recently finished two books that I will speak about briefly here. The first that I read is Bill Cosby's Come on People. I really liked the book, and it did touch on many very good points, and for some reason, I would be remiss to think that many of the people that need to read this book, may not, either because they have no interest in saving themselves or the rest of us, or that they actually have no time. My parents were very much against any type of electronic surrogate parenting. While he covered a great deal of information and presented a lot of talking points all of which he could have spent much more time diving into in order to give a more complete picture. I'm not going to fault him too much, all that he said and a little bit more needed to be said. We do need leave this mentality of we are all victims, which is why we can not succeed, the man is keeping us down, its not my fault, and worst of all the subscription to the we need reparations for all things done wrong to us to, more affirmative action and set asides industrial complex. The shakedown artists come and find me too. I think that not enough people will read it, and those that need to hear the message, might not could get to it, as they are following one or more of the false prophets. Remember that line in scriptures, I believe it applies to everyone regardless of where your faith and outlook actually falls. Matthew 7:15 Beware of false prophets, which come to you in sheeps clothing, but inwardly they are ravening wolves. (KJV)
By and large, Dr Cosby presented many, many topics with some really simple outcomes, instead of waiting, why not do something constructive.