AlphaZero¶
A little bit harder problem:
- Learn to play complex games given only the rules to the game
- Beat the best human players and their best hand-written programs
- Learning time limit: Less than a day
Typical result:
Game 3 - White: AlphaZero Black: Stockfish
1. d4 Nf6 2. c4 e6 3. Nf3 b6 4. g3 Bb7 5. Bg2 Bb4+ 6. Bd2 Be7 7. Nc3 O-O
8. Qc2 Na6 9. a3 c5 10. d5 exd5 11. Ng5 Nc7 12. h4 h6 13. Nxd5 Ncxd5
14. cxd5 d6 15. a4 Qd7 16. Bc3 Rfe8 17. O-O-O Bd8 18. e4 Ng4
19. Bh3 hxg5 20. f3 f5 21. fxg4 fxg4 22. Bf1 gxh4 23. Bb5 Qf7
24. gxh4 Bf6 25. Rhf1 Rf8 26. Bxf6 gxf6 27. Rf4 Qg7 28. Be2 Qh6
29. Rdf1 g3 30. Qd3 Kh8 31. Qxg3 Rae8 32. Bd3 Bc8 33. Kb1 Rf7
34. Qf2 Bd7 35. h5 Ref8 36. Bc2 Be8 37. Rf3 Re7 38. Rxf6 Qxf6
39. Qxf6+ Rxf6 40. Rxf6 Kg7 41. Rxd6 Bxh5 42. Kc1 Re5 43. a5 bxa5
44. Kd2 Be8 45. Ra6 Rh5 46. Bd3 a4 47. d6 Bf7 48. d7 Rh8 49. e5 1-0
The Approach¶
- MCTS – Monte Carlo Tree Search
- Reinforcment Learning
- Deep convolutional neural networks
The DeepMind team published their results five months ago in the December 2018 Science, “A general reinforcement learning algorithm that masters chess, shogi and Go through self-play”. See: https://deepmind.com/documents/260/alphazero_preprint.pdf
Their self-assessment:
“Our results demonstrate that a general-purpose reinforcement learning algorithm can learn, tabula rasa – without domain-specific human knowledge or data, as evidenced by the same algorithm succeeding in multiple domains – superhuman performance across multiple challenging games.”
Assessment by Garry Kasparov:
“I can’t disguise my satisfaction that it plays with a very dynamic style, much like my own!”
Code You Can Run¶
The advancements from AlphaZero were incorporated in incorporated Leela Chess Zero
Leela Chess is free software. See https://github.com/LeelaChessZero/lc0
It is built using Meson with Python 3 but running primary CUDA code on the GPU.
See the LCBlog blog to get started.
Overview of Monte Carlo Tree Search¶
The Algorithm¶
Here is the outline as summarized in the DeepMind paper:
AlphaZero replaces the handcrafted knowledge and domain-specific augmentations used in traditional game-playing programs with deep neural networks, a general-purpose reinforcement learning algorithm, and a general-purpose tree search algorithm.
Instead of a handcrafted evaluation function and move ordering heuristics, AlphaZero uses a deep neural network (p, v) = fθ(s) with parameters θ. This neural network fθ(s) takes the board position s as an input and outputs a vector of move probabilities p with components pa = Pr(a|s) for each action a, and a scalar value v estimating the expected outcome z of the game from position s, v ≈ E[z|s]. AlphaZero learns these move probabilities and value estimates entirely from self-play; these are then used to guide its search in future games.
Instead of an alpha-beta search with domain-specific enhancements, AlphaZero uses a generalpurpose Monte Carlo tree search (MCTS) algorithm. Each search consists of a series of simulated games of self-play that traverse a tree from root state sroot until a leaf state is reached. Each simulation proceeds by selecting in each state s a move a with low visit count (not previously frequently explored), high move probability and high value (averaged over the leaf states of simulations that selected a from s) according to the current neural network fθ. The search returns a vector π representing a probability distribution over moves, πa = Pr(a|sroot).
The parameters θ of the deep neural network in AlphaZero are trained by reinforcement learning from self-play games, starting from randomly initialized parameters θ. Each game is played by running an MCTS search from the current position sroot = st at turn t, and then selecting a move, at ∼ πt , either proportionally (for exploration) or greedily (for exploitation) with respect to the visit counts at the root state. At the end of the game, the terminal position sT is scored according to the rules of the game to compute the game outcome z: −1 for a loss, 0 for a draw, and +1 for a win. The neural network parameters θ are updated to minimize the error between the predicted outcome vt and the game outcome z, and to maximize the similarity of the policy vector pt to the search probabilities πt. The updated parameters are used in subsequent games of self-play.


Changing the Honda Reg/Rec
Apart from very poor quality front suspension setups, most Hondas also have another well-known problem - the regulator/rectifier likes to die at very frequent intervals. When it goes, it tends to take other things with it which can make it a very expensive proposition. For almost everyone, they just buy a new Honda item and wire it in - only to have it fail again 35K down the road. This tutorial is to show you how to replace the Honda unit with a non-Honda item: something that won't fail. The general setup is common to all Hondas, but there are some small differences. The photos here are of the conversion done to my VTR1000 and Blackbird, but it should be similar for all Hondas. The VTR photos here were taken by my mate Chris Coote, and the XX pictures come from Scot Mahon.
This is the comparison between the stock Honda-issue r/r on the left and the R1 unit on the right. Notice the complete lack of cooling fins (this generates huge amounts of heat - it produces somewhere around 40amps on the output!). That is one of the primary contributors to the failures - the inability to keep cool, and the resulting heat/cool cycle destroying the semiconductors contained inside.
Note that 2002 and later model blackbirds have a completely different r/r that looks huge. It is mounted on the left side of the subframe rather than the right side for the pre-2002 models. This new item looks really beefy and is far bigger than either the old one or the replacement regulator. I seriously doubt you'd need to replace it.
Knowing when to replace it
Firstly, you need to know what is wrong and why. I wrote a long article on the Blackbird site about How to know if your reg/rec is about to die. In addition, here's a nice photo of the plug going into mine:
It was about to fail big time - notice the black ring around the top left item on the plug on the right. On the left is the new plug to be used.
What to replace it with
There are many options with different rectifiers to replace it with. The first recommendation is to not use anything that comes from Honda.  Their biggest problem is they are just not capable of producing the current required. What you want to do is put on something that can handle the demands. For this task, we chose a reg/rec from a R1. There's heaps of them around in wreckers, they've got nice big cooling fins (unlike the Honda unit, see below) and the number of wires match up. It really doesn't matter what brand and model you get, so long as you have at least the same current rating as the old unit.
This is the fitment on the left side of the subframe on the VTR. The small box on the left side is the indicator relay for the VTR. That will need to be moved somewhere else as the R1 unit is much bigger.
Fitting
Fitting a new reg/rec starts by removing the old one. Chop the leads off about 3cm from the plug. If your wiring is getting crispy, trim longer, but try to cut the old one off as close to the r/r as possible. Next, locate the wiring from the R1 plug. There should be 5 leads coming from it - three white, a red and black.Using a stock connector
To wire up you should match the wires up with the same colour leads. The only difference is that the R1 plug will use white wires, which correspond to the Honda yellow wires. These may be connected in any order - they are just the output of the stator. Note that you must solder the leads together and cover them either with heatshrink or electrical tape. Do not use any sort of crimped endings. There is serious amounts of current going through here and the crimped leads will burn up very quickly.For carbie-fed Hondas, there will only be 5 wires to connect up. Just match colour with colour.
For EFI model Hondas, you will have eight wires, not 5. The DC power leads (red and black) are doubled up for some reason. Just connect the two of them to the corresponding colour lead from the R1 plug. That leaves one other lead, which, according to the Blackbird wiring diagram is blue. This is for sensing starting and if there is another wire (total 9) the other is there for headlight-on sensing. These are there to increase output when the lights are on, and stop charge when starting. They have been proven to NOT help the later bikes with RR failures. You can safely leave this wire unconnected as it is not powered. Just remember to tape over the end and tuck it away so you don't have a loose wire floating around.
When you don't have a connector
If you can't find a connector (the wreckers are unwilling to cut the wiring loom to sell one to you) then it is fine to just put on a bunch of spade connectors directly into the fitting.
When you put the connectors onto the wires, make sure you solder them and put a small amount of heatshrink around each individual connector. To fit the wires to the regulator, put the three yellow wires onto the bottom row - these come from the stator so it doesn't matter which order you put them in. The two power wires go to top row - positive on the left and ground on the right.
Finally, tape everything up so that you can't pull the individual connectors out. I would recomment that you also put a tie-wrap around the wires first before taping, about 3cm away from the solder joints.
Mounting on the bike
Once you have completed the soldering, you need to mount the r/r back on the bike again. Note that the R1 unit is a hell of a lot bigger. It also means the bolt patterns may not line up.
The simplest way to deal with this is to cut the lower of the two bolts off the bike. Just use a single bolt through the top of the unit. Tighten up tight, with the earth lead under it. However, don't use loctite as this will break the electrical contact and your bike won't run for very long at all. If you think the one bolt is not enough, don't worry. We've put a season of racing on it, plus well over 10,000km like that and never had an issue.
For the blackbird, the holes do seem to line up because one of the r/r holes is elongated. If it doesn't quite fit, just take a small circular file to the hole until it does.
Finally tiewrap everything into place. Start the bike up and check the voltage over the battery terminals. You should see between 12.5 and 14.4V depending on revs. If this checks out, fit the bodywork back on the bike and go ride!


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About
Contact Info
Interesting Links
This is
my
caption
Places I Lived
Savannah, GA
Durham, NC
Charlotte, NC


Follow these steps to rename projects:
- Open the project you want to rename.
- In the project panel, hover over the project name and then click the pencil icon that appears.
- Enter the new project name and then click OK.
"Project renamed successfully" displays.


News
December 12, 1999
November 9, 1999
October 24,1999
August 2, 1999
July 28, 1999
May 1st - Toronto, Ontario -- The Opera House Lazy Days
Tour
Interviews
Links
Pics
Home
Rob been taking a break at home in London. He did not appear on Saturday Night Live or the VH1 Fashion
Awards, as far as I know, but Leno was excellent!!! He is performing at two shows in the states:
The Spectrum First Union Corp Center on December 14, 1999 in Philadephia, PA and KIIS FM Jungle Jam at Knotts Berry Farm in Los Angeles, CA on December 17, 1999.
I am just a little upset here! Rob is reportedly frustrated with America and I don't blame him!
Supposedly a body double was used in the new U.S. video for Angels because Rob was too "fat." Since
the beginning he has been marketed as an "ex-boyband member" appealing to the Backstreet Boys, teen crowd when
he has a more original and larger appeal. His past club tour was did not do as well as hoped and EMI has not officially released
Angels! What can be done about this? Well, I think Rob needs a bit of
encouragement for one. Go here to vote for Angels
on MTV's Total Request Live.
Rob is hanging around the states again. Angels has been released and isn't getting played as
much as I would prefer. Rob is going to be on Jay Leno
November 3. More upcoming VCR dates would be Saturday Night Live on November 27 and
the VH1 Fashion Awards on December 3. I don't mean to whine, but this is my niche in cyberspace. Why isn't
Rob playing any West Coast gigs?!! This is driving me crazy!
Would you like to go to Ireland to see Robbie Williams live? Of course you would! Click
here to enter Mtv's online contest. Contest ends August 6th!
Whoa! What happened here? there's a big lapse in time, I know. I apologize, but I went through a
big move and then moved this site around. It looks much better now, don't you think?
So what is new with Rob? His American club tour is over. Click
here for the latest diary dates. I got to see two of Rob's shows, the S.F. and L.A. ones. Did you want
to see a couple of pictures from the L.A. show? Click here.
The next single in America is supposed to be Angels, that's the buzz. But MTV is currently showing
the video for Let Me Entertain You and also his
performance of Angels at the Glastonbury Festival in '98. Try late night/early morning to catch them.
May 23,
1999
Rob
will behosting
120 minutes tonight on MTVwith Beth Orton at midnight.
Millenium
came in atnumber 25 on Rick Dee's weekly countdownthis weekend. Not
bad! Millenium seems to be gaining momentum.
May 14,
1999
Here's
Rob'sU.S. Billboard Chart
Standings and album salesas of May 13, 1999: He's only sold 18,800
albums (not that great so far) and will be ranked #85 on
Billboard. At my local Tower Records, he's #12 in sales. That was
pleasantly surprising. But I'm not worried. I believe his sales
will pick up with the release of other singles (like Angels!).
May 4,
1999
Today's
the day!The
Ego has officially Landed in the U.S.Rob's album came out
today! I'm currently scanning the pictures from the insert to put
up on this site. The cover is great, but I'm sure you've all seen
it by now!
Tonight
he also had a live webcast from New York City House of Blues I was away all
evening unfortunately and missed it. Check and see if the concert
was archived!
Big
fun! An online
store of Rob goodiesopened today!
Rob also did
hisnetwork
television appearance last night on Dave Letterman. This is the first
time I have ever really seen Rob interviewed, but he was
hilarious! He was very excited, and definitely a bit nervous too.
He sang Millenium, of course, changing it up a little in the
bridge to say "Dave Letterman" instead of
"Millenium." He was rumored to be on Saturday Night
Live sometime this month, but since the season is soon ending, it
looks unlikely.
April 25, 1999
Check out
tenative dates for Robbie'sWest Coast Tourdown below in the calendar!
April
22, 1999
Robbie was heard in San
Francisco yesterday on the radio live! I guess he and his
band performed two songs acousticly along with an interview.
Unfortunately, I don't live within receiving signals and missed
this. A friend who is unfamiliar with Robbie said that he came
off kind of "weird." He was making a lot of cracks at
himself, which is normal for Rob, but my friend didn't know what
to make of it. I assume other listeners unfamiliar with him might
have the same reaction. Hmmmm....
Has anyone out
there heard any other radio appearances by Robbie in the states?
I know he did one with Mancow in Chicago a few weeks back. Email me if you have!
April 20, 1999
Here's
a summary of Rob's U.S. tour plans,but for a more
direct source of information on Rob's whereabouts, go to his official website.
May 3rd - Dave Letterman Show
May 4th - NYC -- Bowery Ballroom
May 5th - Philadelphia, PA (venue unknown)
May 6th - Philadelphia, PA -- Theatre of Living Arts
May 7th - Boston, MA --Paradise Rock Club
May 8th - Montreal, Quebec --Le Medley
May 10th - Washington D.C. -- 9.30 Club
May 12th - Atlanta, GA --Cotton Club
May 14th - Chicago, IL --The Metro
May 15th - Detroit, MI --St. Andrews Hall
Note: The below dates are still tenative.
June 2nd - Vancouver, British Columbia -- The Rage
June 4th - Seattle, The Showbox
June 5th -
Portland, La Luna or MTV Movie Awards
June 7th - San Francisco, Bimbos 365 Club
June 9th - Los Angeles, The Mayan
June 10th - Los Angeles (venue unknown at this time).
Most of the tickets for these above shows can be found and
purchased at Ticketmaster online. Just do a search for
Robbie Williams.
This is the tracks listing for Rob's U.S. album, The Ego Has
Landeddue out May 4th.
 He is now bouncing around the continents trying to
promote his album after a much needed rest to recover. His
European tour has been rescheduled for September.
Request
Millenium on MTV!Go to the Total Request Livepage to cast your vote. I've been
seeing the video during MTV late night and once during the day.
So it's getting some airplay.
If you have
any news that I don't know about, tell me!


pyridone
DOI
none
Author
Email
Website
Rights
Program
Gaussian
Computed formula
C
5
H
5
NO
Author formula
C5H5N1O1
Basis
6-31g(d)
Method
becke3lyp
Dipole
-0.5057702 0.0 1.5577061
HF Value
-323.5179124
PointGroup
CS
Gaussian log
Complete CML


| Package | Description |
|---|---|
| opennlp.tools.chunker |
Package related to finding non-recursive syntactic annotation such as noun phrase chunks.
|
| opennlp.tools.doccat |
Package for classifying a document into a category.
|
| opennlp.tools.ml | |
| opennlp.tools.ml.maxent |
Provides main functionality of the maxent package including data structures and
algorithms for parameter estimation.
|
| opennlp.tools.ml.model | |
| opennlp.tools.namefind |
Package related to finding proper names and numeric amounts.
|
| opennlp.tools.parser |
Package containing common code for performing full syntactic parsing.
|
| opennlp.tools.parser.chunking |
Package containing code for performing full syntactic parsing using shift/reduce-style decisions.
|
| opennlp.tools.parser.treeinsert |
Package containing experimental code for performing full syntactic
parsing using attachment decisions.
|
| opennlp.tools.postag |
Package related to part-of-speech tagging.
|
| opennlp.tools.sentdetect |
Package related to identifying sentece boundries.
|
| opennlp.tools.tokenize |
Contains classes related to finding token or words in a string.
|
| opennlp.tools.util |
Package containing utility data structures and algorithms used by multiple other packages.
|
| Modifier and Type | Method and Description |
|---|---|
Event[] |
ChunkSampleSequenceStream.updateContext(Sequence sequence,
AbstractModel model) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
ChunkerEventStream.createEvents(ChunkSample sample) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
DocumentCategorizerEventStream.createEvents(DocumentSample sample) |
| Modifier and Type | Method and Description |
|---|---|
DataIndexer |
AbstractEventTrainer.getDataIndexer(ObjectStream<Event> events) |
MaxentModel |
EventTrainer.train(ObjectStream<Event> events) |
MaxentModel |
AbstractEventTrainer.train(ObjectStream<Event> events) |
| Modifier and Type | Method and Description |
|---|---|
Event |
RealBasicEventStream.read() |
| Modifier and Type | Method and Description |
|---|---|
static GISModel |
GIS.trainModel(ObjectStream<Event> eventStream)
Train a model using the GIS algorithm, assuming 100 iterations and no
cutoff.
|
static GISModel |
GIS.trainModel(ObjectStream<Event> eventStream,
boolean smoothing)
Train a model using the GIS algorithm, assuming 100 iterations and no
cutoff.
|
static GISModel |
GIS.trainModel(ObjectStream<Event> eventStream,
int iterations,
int cutoff)
Train a model using the GIS algorithm.
|
static GISModel |
GIS.trainModel(ObjectStream<Event> eventStream,
int iterations,
int cutoff,
boolean smoothing,
boolean printMessagesWhileTraining)
Train a model using the GIS algorithm.
|
static GISModel |
GIS.trainModel(ObjectStream<Event> eventStream,
int iterations,
int cutoff,
double sigma)
Train a model using the GIS algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
Event[] |
Sequence.getEvents()
Returns the events which make up this sequence.
|
Event |
SequenceStreamEventStream.read() |
Event |
RealValueFileEventStream.read() |
Event |
HashSumEventStream.read() |
Event |
FileEventStream.read() |
Event[] |
SequenceStream.updateContext(Sequence sequence,
AbstractModel model)
Creates a new event array based on the outcomes predicted by the specified parameters
for the specified sequence.
|
| Modifier and Type | Method and Description |
|---|---|
static String |
FileEventStream.toLine(Event event)
Generates a string representing the specified event.
|
| Modifier and Type | Method and Description |
|---|---|
protected List |
OnePassRealValueDataIndexer.index(LinkedList<Event> events,
Map<String,Integer> predicateIndex) |
protected List<ComparableEvent> |
OnePassDataIndexer.index(LinkedList<Event> events,
Map<String,Integer> predicateIndex) |
static MaxentModel |
TrainUtil.train(ObjectStream<Event> events,
Map<String,String> trainParams,
Map<String,String> reportMap)
Deprecated.
Use
TrainerFactory.getEventTrainer(Map, Map) to get an
EventTrainer instead. |
| Constructor and Description |
|---|
Sequence(Event[] events,
T source)
Creates a new sequence made up of the specified events and derived from the
specified source.
|
| Constructor and Description |
|---|
HashSumEventStream(ObjectStream<Event> eventStream) |
OnePassDataIndexer(ObjectStream<Event> eventStream)
One argument constructor for DataIndexer which calls the two argument
constructor assuming no cutoff.
|
OnePassDataIndexer(ObjectStream<Event> eventStream,
int cutoff) |
OnePassDataIndexer(ObjectStream<Event> eventStream,
int cutoff,
boolean sort)
Two argument constructor for DataIndexer.
|
OnePassRealValueDataIndexer(ObjectStream<Event> eventStream,
int cutoff)
Two argument constructor for DataIndexer.
|
OnePassRealValueDataIndexer(ObjectStream<Event> eventStream,
int cutoff,
boolean sort) |
TwoPassDataIndexer(ObjectStream<Event> eventStream)
One argument constructor for DataIndexer which calls the two argument
constructor assuming no cutoff.
|
TwoPassDataIndexer(ObjectStream<Event> eventStream,
int cutoff) |
TwoPassDataIndexer(ObjectStream<Event> eventStream,
int cutoff,
boolean sort)
Two argument constructor for DataIndexer.
|
| Modifier and Type | Method and Description |
|---|---|
Event[] |
NameSampleSequenceStream.updateContext(Sequence sequence,
AbstractModel model) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
NameFinderEventStream.createEvents(NameSample sample) |
static List<Event> |
NameFinderEventStream.generateEvents(String[] sentence,
String[] outcomes,
NameContextGenerator cg) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
AbstractParserEventStream.createEvents(Parse sample) |
| Modifier and Type | Method and Description |
|---|---|
protected abstract void |
AbstractParserEventStream.addParseEvents(List<Event> newEvents,
Parse[] chunks)
Produces all events for the specified sentence chunks
and adds them to the specified list.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
ParserEventStream.addParseEvents(List<Event> parseEvents,
Parse[] chunks)
Adds events for parsing (post tagging and chunking to the specified list of events for the specified parse chunks.
|
static AbstractModel |
Parser.train(ObjectStream<Event> es,
int iterations,
int cut)
Deprecated.
Please do not use anymore, use the ObjectStream train methods instead! This method
will be removed soon.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
ParserEventStream.addParseEvents(List<Event> parseEvents,
Parse[] chunks) |
static AbstractModel |
Parser.train(ObjectStream<Event> es,
int iterations,
int cut)
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
Event[] |
POSSampleSequenceStream.updateContext(Sequence sequence,
AbstractModel model) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
POSSampleEventStream.createEvents(POSSample sample) |
static List<Event> |
POSSampleEventStream.generateEvents(String[] sentence,
String[] tags,
Object[] additionalContext,
POSContextGenerator cg) |
static List<Event> |
POSSampleEventStream.generateEvents(String[] sentence,
String[] tags,
POSContextGenerator cg) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
SDEventStream.createEvents(SentenceSample sample) |
| Modifier and Type | Method and Description |
|---|---|
protected Iterator<Event> |
TokSpanEventStream.createEvents(TokenSample tokenSample)
Adds training events to the event stream for each of the specified tokens.
|
| Modifier and Type | Method and Description |
|---|---|
Event |
EventTraceStream.read() |
Event |
AbstractEventStream.read() |
| Modifier and Type | Method and Description |
|---|---|
protected abstract Iterator<Event> |
AbstractEventStream.createEvents(T sample)
Creates events for the provided sample.
|
| Constructor and Description |
|---|
EventTraceStream(ObjectStream<Event> stream,
Writer writer) |
Copyright © 2015 The Apache Software Foundation. All rights reserved.


Three new things I have learnt about myself and my ego as a result of the core learning
-
How to Recognise my Inner Critic
The first thing I learnt was how to recognise my inner critic, the voice in my head that makes me doubt myself and tells me that I can't do something. The most important part about being able to recognise this inner critic is that I am able to stop it in its tracks before I let it derail me. When I recognise the voice telling me I cant do something I am able to remind myself of my Strengths and remind myself that anything is possible and anything is learnable through hardwork, effort and persistance.
-
That EVERYTHING is Learnable
The second thing I learnt is that "Everything is Learnable". The most important thing about learning this is that whenever I want to give up on something and try to convince myself that I am unable to do something I know that this is just a cop out due to knowing that everything is learnable.
-
That I am quite self aware
Going through all these core learnings has shown me how I am quite self aware and am able to be honest with myself and recognise my strengths and weaknesses.
What are the role of values, empathy and self awareness in learning and programming?
The role of values, empathy and self and awareness in learning and programming is that it makes a huge difference in the speed of your learning, the efficiency and the quality. When you know what your strengths and weaknesses are you know where to focus more of your effort, it also helps you to know when tobe easier on yourself when struggling with certain things and when you just need to push through. Knowing your values helps you to find a learning style that is true to yourself.
What surprised me the most about Core Learning?
The biggest surprise for me was how much of the core fundamental learnings I had already learnt and heard about before. It reinforced to me how important the core learnings were anyhow they are so beneficial not just for my coding journey but for my life in general.
The most challenging aspects of the Core Learning for me.
The most challenging aspect of the core learning for me was implementing the practices and habits that I know are good for me and sticking with them. I have known for awhile the amazing benefits of meditating daily but have always struggled to turn this practice into a daily habit that becomes a non negotiable.


PostProcessing
public protocol PostProcessing
The PostProcessing
protocol is used to create post-processes that can be used after the file has been downloaded.
For example, unzip .zip files or create image thumbnails.
The implementation requirements for post-processing are as follows.
For the correct operation, the object that is inherited from PostProcessing
must be Value Types (struct).
Under the supportedFileExtensions parameter, specify a list of file extensions over which post-processing can be performed.
Use the prepare method to get the data you need to start post-processing.
Describe the logic behind post-processing in the process method.
Call onComplete, after the completion of post-processing.
-
A set of file extensions over which post-processing will be performed.
Example: supportedFileExtensions = [
png
,jpg
]Declaration
Swift
var supportedFileExtensions: Set<String> { get }
-
Used to report about the completion of post-processing.
If the post-processing failed, then an
Error
object must be passed.Declaration
Swift
var onComplete: ((_ withError: Error?) -> Void)? { get set }
-
This method passes parameters that may be needed to start post-processing.
Since all parameters are Value Types, you can store the needed parameters globally.
Declaration
Swift
mutating mutating func prepare(fileStorage: FileStorage?, filename: String?, fileExtention: String?, sourceLocation: URL?)
Parameters
fileStorage
Use this parameter if you need to save or delete a file.
filename
Local filename.
fileExtention
File extention.
sourceLocation
The reference to the source file on which post-processing should be performed.
-
The post-processing logic should be described in this method.
The process method is used in
PostProcessingOperation
to start processing.Declaration
Swift
func process()
-
Will be called when operation is canceled.
Declaration
Swift
func cancel() -> Error?
-
name
Extension methodA post process name, used for identification.
Optional value. The default value is equal to the class name.
Declaration
Swift
var name: String { get }


Lorde finally revealed why she won't be releasing new music soon, and the reason will break your heart
Cape Town – Lorde – who last released an album in 2017 titled Melodrama - has written a lengthy note to subscribers of her newsletter about the status of her next album and why it won't be coming as soon as fans would have hoped.
The reason for the delay, the singer, revealed was the death of her beloved dog, Pearl. In the note the creative also explained her extended silence on social networking platforms.
The 22-year-old explained her relative silence over the past few years by writing: "When we first met, I would write little notes for you online, and they would form a big map that you would use to chart the constellations in my work.
Then she went on to explain that her dog, Pearl, who she adopted in 2018, died last month. She wrote: "Pearl had two cardiac arrests about an hour apart, and after the second one, he died…I was holding him when he went, and I know he knew that I was there. But this loss has been indescribably painful, and a light that was turned on for me has gone out."
Then, the talented New Zealander, asked for patience so that she can deal with her loss and find a way forward.
She said in part: "So I'm asking for your patience, as 2020 comes around and you start to wonder where the next record is. I have lost my boy, and I need some time to see the good again, to finish making this for you. It won't be the same work— as anyone who has felt loss can understand, there's a door that opens that you step through, and everything is different on the other side. But when this great loss crystallises inside me, and my chest rebuilds around it, hopefully, I'll be able to finish up, and share it with you, and we'll all grow together, as we always do."
READ THE FULL NOTE FROM THE NEWSLETTER HERE:
Melodrama was nominated for the Grammy Award for Album of the Year in 2018 and was received with widespread critical acclaim.
Compiled by Alex Isaacs.
(Photo: Getty Images)


public class NeuralNetwork
extends java.lang.Object
This project was created for the ROB[SP17] course in an attempt to better differentiate colors when using the E-puck camera.
Note:The external libraries used are JAMA, which provides basic linear algebra functions and apache-commons-cli for the CLI argument parser. In this project, JAMA is mostly used to store information in matrices, which makes the calculations more readable. (Initially, I planned to implement a batch learning version, but I lost the overview when implementing the back-propagation algorithm). In a batch version, Matrix operations enables much more elegant code.
Network type: FFANN (Feed forward artificial neural network)
Learning Algorithm: SGD (Stochastic gradient descent)
Learning type: Supervised
Note: It is not advised to train in simulation or trying to test in simulation.
| Modifier and Type | Field and Description |
|---|---|
static int |
BIAS
Represents the networks bias value
(Every neuron has an additional bias input
with the value of
BIAS ) |
private java.lang.String[] |
colorNames
Contains color definition in the testing set
|
private java.io.BufferedWriter |
err_writer
Represents the writer used to print
network error statistics.
|
private double |
finalTrainingErr
Represents the final training classification error
at the end of the program run
|
private double |
finalValidationErr
Represents the final validation classification error
at the end of the program run
|
private HiddenLayer |
hiddenLayer1
Represents the networks first hidden layer
|
private HiddenLayer |
hiddenLayer2
Represents the networks second hidden layer
|
private InputLayer |
inputLayer
Represents the networks input layer
|
private int |
nbOfColors
Describes how many colors are to be classified (nb of output neurons)
|
private OutputLayer |
outputLayer
Represents the networks output layer
|
private Jama.Matrix |
testingInput
Represents matrices containing the testing data.
|
private Jama.Matrix |
testingOutput
Represents matrices containing the testing data.
|
private Jama.Matrix |
trainingInput
Represents the training data sets.
|
private Jama.Matrix |
trainingOutput
Represents the training data sets.
|
private Jama.Matrix |
validationInput
Represents matrices containing the validation data.
|
private Jama.Matrix |
validationOutput
Represents matrices containing the validation data.
|
private boolean |
writeStats
Write network error statistics to stats/err_writer.csv
if set to true (-s flag)
|
| Constructor and Description |
|---|
NeuralNetwork(int nbOfHiddenNeurons,
int nbOfColors)
Create a neural network with the following topology:
Input Layer: 3 input nodes (R/G/B) + bias
2 hidden Layer with a variable amount of neurons (-n flag)
Output Layer: variable amount of neurons (-c flag)
|
| Modifier and Type | Method and Description |
|---|---|
double |
getFinalTrainingErr() |
double |
getFinalValidationErr() |
void |
init(int nbOfEpochs)
Trains, validates and tests the neural network.
|
void |
parseDataSets(java.lang.String trainingSet,
java.lang.String validationSet,
java.lang.String testingSet)
Parses a csv file, which is in following format:
r,g,b;x1,x2,x3,...,xn With one of the xi set to one. |
private void |
printProgress(int nbOfEpochs,
int currentEpoch)
Print the current training progress percentage
|
double[] |
query(boolean normalized,
double red,
double green,
double blue)
Queries the neural network using following parameters
|
void |
setWriteStats(boolean writeStats)
Setter for
writeStats |
private void |
test()
Tests the network performance under realistic circumstances.
|
private void |
train()
Feeds a the training set through the neural network,
then back-propagates the error and adjusts the weights.
|
private void |
validate()
Validates the network output by using a validation set
|
void |
write_C_source(java.lang.String filename)
Prints out the query formula for the neural network in a c file that can then be compiled
with the other project files and can be run independently from the java program.
|
private void |
write_statistics(int currentEpoch,
int nbOfEpochs)
Writes network error statistics to stats/err_writer.csv
(percentage of false classifications)
|
public static final int BIAS
BIAS
)private java.io.BufferedWriter err_writer
private double finalTrainingErr
private double finalValidationErr
private InputLayer inputLayer
private HiddenLayer hiddenLayer1
private HiddenLayer hiddenLayer2
private OutputLayer outputLayer
private Jama.Matrix trainingInput
parseDataSets(String, String, String)
private Jama.Matrix trainingOutput
parseDataSets(String, String, String)
private Jama.Matrix validationInput
private Jama.Matrix validationOutput
private Jama.Matrix testingInput
private Jama.Matrix testingOutput
private int nbOfColors
private boolean writeStats
private java.lang.String[] colorNames
public NeuralNetwork(int nbOfHiddenNeurons, int nbOfColors)
nbOfHiddenNeurons
- specifies the number of hidden layer neuronsnbOfColors
- specifies how many colors are to be recognizedpublic double[] query(boolean normalized, double red, double green, double blue)
normalized
- indicates whether the passed values are already normalizedred
- the red rgb value [0-255]green
- the green rgb value [0-255]blue
- the blue rgb value [0-255]parseDataSets(String, String, String)
)public void init(int nbOfEpochs) throws java.lang.Exception
nbOfEpochs
- number of cycles that the training matrix is
to be fed through the network.java.lang.Exception
- if the training input matrix is null.train()
,
validate()
,
test()
,
write_statistics(int, int)
private void printProgress(int nbOfEpochs, int currentEpoch)
nbOfEpochs
- total number of epochscurrentEpoch
- elapsed epochsprivate void test()
Testing takes place at the very end of the program, only if a testing set is provided. A training set consists of data not present in training and validation sets. The data present in the testing set is passed through the network. The result gives an indication of the overall network performance.
private void write_statistics(int currentEpoch, int nbOfEpochs) throws java.io.IOException
currentEpoch
- the current epochnbOfEpochs
- total number of epochsjava.io.IOException
- on IO exception when accessing stats/error_stats.csvprivate void validate()
Validation only takes place if a validation set is provided. A validation set consists of data not present in the training set. When used in conjunction with the (-s) flag, allows to compare the training and validation error. If the number of epochs is to high or if the amount of neurons in the hidden layers is to high, overfitting will occur (the validation error will increase while the training error decreases). This loss of generalisation can be avoided by observing the network statistics and reducing the number of epochs and/or hidden layer neurons.
Note that no backpropagation takes place.
init(int)
private void train()
init(int)
public void parseDataSets(java.lang.String trainingSet, java.lang.String validationSet, java.lang.String testingSet) throws java.lang.Exception
r,g,b;x1,x2,x3,...,xn
xi
set to one. Where r
: red value [0-255]g
: green value [0-255]b
: blue value [0-255]x1
: set to 1 if color is redx2
: set to 1 if color is greenx3
: set to 1 if color is bluexi
can be interchanged, this will result
in a change of the output vector write_C_source(String)
)
The csv values are then inserted into the following MatricestrainingInput
trainingOutput
validationInput
validationOutput
testingInput
testingOutput
trainingSet
- csv file path of the training setvalidationSet
- csv file path of the validation set (optional)testingSet
- csv file path of the testing set (optional)java.io.IOException
- if an IO exception occursjava.lang.Exception
- if the csv file has an incorrect format.public void write_C_source(java.lang.String filename)
filename
- the c source file to write to.public void setWriteStats(boolean writeStats)
writeStats
writeStats
- set to true if error stats are to be written to
stats/err_writer.csvpublic double getFinalTrainingErr()
public double getFinalValidationErr()


Machine learning is a field of Artificial Intelligence that has been gaining a lot of prominence in the current era. It is specifically to do with building systems that are able to learn by themselves without having to be programmed. Deep learning is a subset of techniques of machine learning. Deep learning allows multiple processing layers to breakdown the given data into smaller parts and learn the representations of these data. Deep learning is the state of the art in areas such as speech recognition, natural language understanding, visual object recognition, etc. Convolutional neural networks have brought many breakthroughs in areas such as processing images, pictures and speech, whereas Recurrent networks have been extremely successful in areas such as processing text and speech.
Lessons from History
QA is a well researched area from the point of NLP (Natural Language Processing) research. QA has mostly been used to develop intricate dialogue systems such as chat-bots and other systems that mimic human interaction. Traditionally most of these systems use the tried methods of parsing, part-of-speech tagging, etc that come from the domain of NLP research. While there is absolutely nothing wrong with these techniques, they do have their limitations.
W.A. Woods et al. shows how we can use NLP as a front end for extracting information from a given query and then translate that into a logical query which can then then be converted into a database query language that can be passed into the underlying database management system. In addition to that there needs to be a lexicon that functions as an admissible vocabulary of the knowledge base so that it is possible to filter out unnecessary terminology. The knowledge base is processed to an ontology that breaks it down into classes, relations and functions. Natural Language Database Interfaces (NLDBIS) are database systems that allow users to access stored data using natural language requests. Some popular commercial systems are IBM’s LanguageAccess and Q&A from Symantec.
Information retrieval (IR) is another technique that has been used to address the problem of QA. With IR systems pay attention to the organisation, representation and storage of information artifacts such that when a user makes a query the system is able to to return a document or a collection of artifacts that relate to the query. Recent advances in OCR and other text scanning techniques have meant that it is possible to retrieve passages of text rather than entire documents. However IR is still widely seen as from the document retrieval domain rather than from the QA domain.
Template based question answering is another technique that has been used for QA
Template based question answering is another technique that has been used for QA and is currently being used by the START system which has answered over a million questions since 1993. START uses natural language annotations to match questions to candidate answers. An annotation will have the structure of ‘subject-relationship-object’ and when a user asks a question, the question will be matched to all the available annotation entries at the word level (using synonyms, IS-A, etc) and the structure level. When a successful match is found, the annotation will point to an information segment which will be returned as the answer.
When new information resources are incorporated into the SMART system, the natural language annotations have to be composed manually. START uses Omnibase as the underlying database system to store information and when the annotation match is found, the database query must be used to retrieve the information. While this system has been relatively successful, it requires a lot of preprocessing which must be done manually.
Our Approach
Our literature survey has found that the QA domain has an active community of researchers and many different approaches have been tried to tackle this problem. While the problem of QA is a very old one, the origins of the problem can be traced back as far as the 1960’s, using our access to cheaper and better computational power and newer techniques in data processing we believe we can attempt to solve this issue using a different set of tactics. This will be explained in the next section, the research gap.


{% extends 'base.html' %} {% block content %} Exceed allowed login attempts! You have tried more than {{ failure_limit }} times to login. You can try again after {{ cooloff_time }}our. {% for message in messages %} {% endfor %} {% endblock %}


About Me
I'm Sallam Thabet a Creative Full Stack Developer
I'm a full stack developer with experience in operational development and continuous improvement. I have
a background in Industrial and Manufacturing Engineering as well as project management.
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Specialty: Full Stack Developer
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Address: Toronto
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Phone: +1 (514) 000 - 000
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Email<EMAIL_ADDRESS>-
Background: Industrial Engineer
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Workdays: Mon - Fri
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WorkHours: 9:00 - 5:00
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al;skdjf;Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been
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and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with
the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop
publishing software like Aldus PageMaker including versions of Lorem Ipsum.
ITen Associates
f;Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been
the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type
and scrambled it to make a type specimen book. It has survived not only five centuries, but also the
leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with
the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop
publishing software like Aldus PageMaker including versions of Lorem Ipsum.


What would you like?
Breakfast & Brunch
Casual Lunch
Boojie Dinner
Hangover Grubs
Thai
Italian
Tapas
Japanese
Mexican
Desserts
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Breweries
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Radius to search
5 miles
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15 miles
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Submit
SPIN
Congratulations!
You are now entering the world of Hangry No Mas!
Play again!


You can set up multiple ways to receive notifications when checks in your account change state. Doing so is useful for several reasons:
Each notification method ("integration") belongs to a project: if you want to use a notification method in multiple projects, you will need to set it up in each project separately.
In the web interface, the "Checks" page shows a visual overview of which alerting methods are active for each check. You can click the icons to toggle them on and off:
You can also toggle the integrations by clicking the "ON" / "OFF" labels in individual check's details pages:
SITE_NAME sets a quota on the maximum number of SMS, WhatsApp, and phone-call notifications an account can send per given month. The specific limit depends on the account's billing plan. The quota automatically resets at the start of each month. The "unused" sends from one month do not carry over to the next month.
When an account exceeds its monthly limit, SITE_NAME will:
If you want to receive repeated notifications for as long as a particular check is down, you have a few different options:
SITE_NAME sends periodic email reports, either monthly at the start of each month, or weekly every Monday. Use them to make sure all checks have their expected state, and nothing has "fallen through the cracks."
The reports list checks from all your projects, grouped by project. For each check, they show:
You can opt-out from receiving the the reports in the Account Settings › Email Reports page or by clicking the "Unsubscribe" link in the email report's footer.


| var x, y, buttonNumber, buttonUpDown : int) |
The parameter motion must be one of "up", "down", "updown" or "downup". If an event of the type requested is in the queue, Mouse.ButtonWait returns instantly. If there isn't such an event, Mouse.ButtonWait waits until there is one and then returns (much like getch handles keystrokes).
In "single-button mode" (where the mouse is treated like a one-button mouse), a "down" event occurs whenever all the buttons are up and a button is pressed. An "up" event takes place when the last button is released so that no buttons remain pressed.
In "multi-button mode", a "down" event occurs whenever any button is pressed, and an "up" event occurs whenever any button is released.
The parameters x and y are set to the position of the mouse cursor when the button was pressed. The parameter buttonnumber is set to 1 when in "single-button mode". In "multi-button mode", it is set to 1 if the left button was pressed, 2 if the middle button was pressed, and 3 if the right button was pressed. The parameter buttonupdown is set to 1, if a button was pressed and 0 if a button was released.
var x, y, btnNumber, btnUpDown, buttons : int
var nx, ny : int
loop
Mouse.ButtonWait ("down", x, y, btnNumber, btnUpDown)
nx := x
ny := y
loop
Draw.Line (x, y, nx, ny, 0) % Erase previous line
exit when Mouse.ButtonMoved ("up")
Mouse.Where (nx, ny, buttons)
Draw.Line (x, y, nx, ny, 1) % Draw line to position
end loop
Mouse.ButtonWait ("up", nx, ny, btnNumber, btnUpDown)
Draw.Line (x, y, nx, ny, 2) % Draw line to final position
end loop
var ch : string (1)
var x, y, btnNum, btnUpDown : int
loop
if hasch then
getch (ch)
Text.Locate (1, 1)
put "The character entered is a: ", ch
end if
if Mouse.ButtonMoved ("down") then
Mouse.ButtonWait ("down", x, y, btnNum, btnUpDown)
Text.Locate (1, 1)
put "The button was clicked at position: ", x, ", ",y
end if
end loop
This means that you can only call the function by calling Mouse.ButtonWait, not by calling ButtonWait.


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The most direct way to contribute is to join us on GitHub, and either:
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This afternoon Apple announced their earnings for the fourth quarter of their 2018 fiscal year, and in what can only be described as a very Apple-esque quarter, revenue was...
Read moreNubia, a ZTE brand, this week introduced its new range-topping smartphone outfitted with front and rear displays and Qualcomm’s high-end SoC. The new Nubia X features a rare 93.6%...
Read moreToday at Apple’s Special Event in New York, among other things such as the a new release of a new Macbook Air and a new Mac Mini, Apple has...
Read moreToday OnePlus has revealed its half-generation refresh for 2018: The OnePlus 6T. This summer we thoroughly reviewed the OnePlus 6 and I was extremely impressed – ending up as...
Read moreWhen Razer announced its Razer Phone as a 'gaming smartphone', a sizeable number scoffed at the idea - how can it be a gaming smartphone if everyone has the...
Read moreEveryone is always interested in the next budget 'high-end killer' smartfone. In recent memory we've seen a number of Asian smartphone companies do it, with Flagship-like specifications always below...
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CS 111 - Introduction to Computer Science I
Spring 2017
Course Information
Course Information Page
Syllabus
Guide to CS1 Success
Homework Assignments
Readings
(4th Hour Requirement:
MyProgrammingLab
exercises for all assigned readings of Chapters 1-10 for the first 10 weeks of the semester)
Homework #1
Homework #2
Homework #3
Homework #4
Homework #5
Homework #6
Homework #7
Homework #8
Homework #9
Homework #10
Homework #11
Homework #12
Homework #13
Homework #14
Online Resources
Text Home Page (author)
,
(Pearson)
Java SE API Documentation
Lecture Examples
,
Summary
,
printf quick reference
Help with Lock Files
("I can't start Firefox/Eclipse!")
Java Problets
"Hello, world!" Eclipse walkthrough
Levels of Java
Getting Eclipse on your own computer
Expected lifetime earnings based on terminal degree and field
Computing careers market data
,
"Code to riches" graph
Math majors: Don't Skip the Code


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task copyPoems(type: Copy) {
from 'text-files'
into 'build/poems'
}
task taskX << {
println 'taskX'
}
task taskY << {
println 'taskY'
}
taskX.dependsOn taskY
apply plugin: 'java'
#Other common plugins: android, jetty, maven +++
com.google.guava
guava
16.0.1
compile 'com.google.guava:guava:16.0.1'
# Or
compile group:'com.google.guava' name: 'guava' version: '16.0.1'
application/
└── build.gradle # Contains build script
└── gradlew # Downloads gradle
└── wrapper
├── gradle-wrapper.jar
└── gradle-wrapper.properties
# empty build.gradle
# build.gradle
apply plugin:'java'
group = 'no.gradlejavazone.samples'
version = '1.0.0'
description = "Samples"
sourceCompatibility = 1.8
targetCompatibility = 1.8
repositories {
maven { url "http://repo.maven.apache.org/maven2" }
}
dependencies {
compile group: 'org.hibernate', name: 'hibernate', version:'3.1.0'
compile 'org.hibernate:hibernate-core:3.6.7'
}
gradle init --type java-library
Table 23.4. Java plugin - default project layout
| Directory | Meaning | |
src/main/java
|
Production Java source | |
src/main/resources
|
Production resources | |
src/test/java
|
Test Java source | |
src/test/resources
|
Test resources | |
src/
|
Java source for the given source set | |
src/
|
Resources for the given source set |
jar {
manifest {
attributes 'Main-Class': 'no.weel.javazone.HelloWorld'
}
}
shadowJar {
mergeServiceFiles {
exclude 'META-INF/*.*SA'
}
}
Minimal wrapper to download and use specified Gradle version.
./gradlew tasks # Downloads gradle and runs command to show tasks
$ ./gradlew tasks # Lists all tasks available
$ ./gradlew jettyRun # Builds and run on jetty
$ ./gradlew jettyRunWar # Builds and run war on jetty
$ ./gradlew build
# Add to gradle.properties in folder ~/.gradle
org.gradle.daemon=true
or start with
gradle --daemon
allprojects {
tasks.withType(JavaCompile) {
options.incremental = true
}
}
#For specific task compileJava
compileJava.options.encoding = 'UTF-8'
# or for all compile tasks
apply plugin: 'java'
tasks.withType(Compile) {
options.encoding = 'UTF-8'
}
apply plugin: 'java'
apply plugin: 'maven'
group = 'com.gradleware.samples'
uploadArchives {
repositories.mavenDeployer {
repository(url: "https://mavenrepository.com")
}
}
$ gradle --profile build
def helloName = "JavaZone"
int personAge = 40
task hello << {
println "Hello ${helloName}. I believe you are ${personAge} years of age."
}
“I’ve known people who have not mastered their tools who are good programmers, but not a tool master who remained a mediocre programmer.” – Kent Beck


In the experiment you just completed, you rated a variety of foods according to how many nutrients you think they have and how healthy and tasty you think they are. Finally, you had to make choices between all the food items and a “reference” food, according to which you would rather eat. The “reference” food was an item you rated as neutral on both taste and health. We are interested in whether people choose foods because they are healthy or because they are tasty, and whether people know the nutritional content of the foods we pictured.
This study’s goal is to gather information about food pictures to help us study people who suffer from eating disorders, such as Anorexia Nervosa (AN). AN is defined as persistent restriction of energy intake leading to significantly low body weight. AN patients consistently choose low-fat, low-calorie food, and one theory for this behavior is that patients with AN have extreme self-control regarding food choices. Another explanation for this behavior is that low-fat food choice is a habit, which does not require self-control. To conduct studies of how persons with anorexia nervosa choose food, we use pictures of food like the ones you saw in this study. Understanding how people without anorexia nervosa rate those pictures helps us in such AN studies.


MAGNOLIA (New Line) Starring: Tom Cruise, Philip Baker Hall, Philip Seymour Hoffman, Julianne Moore, Jeremy Blackman, John C. Reilly, Melora Walters, William H. Macy, Jason Robards, Melinda Dillon. Screenplay: Paul Thomas Anderson. Producers: Joanne Sellar and Paul Thomas Anderson. Director: Paul Thomas Anderson.  Running Time: 179 minutes. Reviewed by Scott Renshaw.
At one pivotal point in MAGNOLIA -- one of several pivotal points in Paul Thomas Anderson's epic of human connection and disconnection -- a woman sits alone in her apartment, singing along to a song. At first it seems she's singing along to her stereo, until Anderson cuts to another character singing the same song. He does this half a dozen more times, capturing every one of his principal characters accompanying the haunting original Aimee Mann composition "Wise Up." As the characters struggle with moments of painful realization, they become individual voices in a mournful chorus looking for forgiveness, reconciliation and redemption.
That sequence in MAGNOLIA is one of the year's best pieces of film-making, and I could name a dozen more moments from the film that will be sticking in my memory. If Anderson had pulled all those pieces of film-making together into a stronger whole, MAGNOLIA might have been the year's best film, instead of just a very good one. It's certainly an ambitious work, following many characters through one rainy day in the San Fernando Valley. Earl Partridge (Jason Robards) is dying of cancer, inspiring his young wife Linda (Julianne Moore) to guilt-ridden thoughts and inspiring Earl's caretaker Phil (Philip Seymour Hoffman) to seek out Earl's estranged son, self-help guru Frank T. J. Mackey (Tom Cruise). Game show host Jimmy Gator (Philip Baker Hall) is also dying of cancer and also has an estranged child, a coke-addicted daughter named Claudia (Melora Walters) who becomes the object of affection for good-natured cop Jim Kurring (John C. Reilly). Meanwhile, quiz show whiz kid Stanley (Jeremy Blackman) tries to get through another show, while has-been whiz kid Donnie Smith (William H. Macy) tries to get through another day.
Anderson sets out to connect his stories thematically with a wild prologue involving three unrelated tales of coincidence and chance. In a way, the whole thing is little more than an extended attempt to justify the film's most surreal device (more on which later). It's also undeniably compelling, charged by Anderson's kitchen sink visual style. And for three hours, that's what MAGNOLIA does: It delivers thrilling scene after thrilling scene which, by all rights, should simply feel self-indulgent. Tom Cruise is positively electrifying as the motivational speaker who teaches a course in male sexual conquest called "Search and Destroy" (it's the best performance of his career until he once again makes the mistake of crying on screen). Reilly is wonderful, both in his awkward flirtations with the hopped-up Claudia and in his trite straight-arrow response to a young would-be rapper.
When Anderson nails these characters at their most vulnerable and conflicted, he creates sensational scenes for actors. MAGNOLIA plays like a cross between a symphony and a jazz jam, with solo showcases building toward splendidly edited crescendos. The only thing missing is an anchor, something that ties Anderson's multiple story lines together in a way that makes them greater than the sum of their parts. It shouldn't have been that difficult considering the parallels Anderson sets up (present day whiz kid and past whiz kid, the two cancer patients and their angry progeny). And yes, sins of the fathers play a role in most of the characters' lives. It's obvious Anderson wants us to feel a connection. I just don't think he pulls it off, giving more attention to the individual trespasses and recriminations than to the links between them.
There's sure to be much talk about Anderson's most audacious attempt to pull his story together, a cloudburst in which something besides rain falls from the sky in a torrent. I'm not about to guess what Anderson was thinking by loosing a Biblical plague upon the San Fernando Valley, nor will I pretend the credulity-stretching trick doesn't feel more like a grand gag than a dramatic fulcrum. There's also something about it that just plain works -- maybe just as damn-the-torpedoes film-making, maybe as acknowledgement of how primal forces can throttle you into re-evaluating your life. That, in a nutshell, is the wonderful frustration of MAGNOLIA. Part of you wants to expose Anderson as a philosophical poseur, and the other half can't stop watching everything he throws at the screen.
On the Renshaw scale of 0 to 10 surreal magnolias: 8.
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