nips nips2011 nips2011-8 nips2011-8-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Asela Gunawardana, Christopher Meek, Puyang Xu
Abstract: We introduce the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams. We describe a closed-form Bayesian approach to learning these models, and describe an importance sampling algorithm for forecasting future events using these models, using a proposal distribution based on Poisson superposition. We then use synthetic data, supercomputer event logs, and web search query logs to illustrate that our learning algorithm can efﬁciently learn nonlinear temporal dependencies, and that our importance sampling algorithm can effectively forecast future events. 1
[1] Simeon M. Berman. Note on extreme values, competing risks and semi-Markov processes. Ann. Math. Stat., 34(3):1104–1106, 1963.
[2] W. Buntine. Theory reﬁnement on Bayesian networks. In UAI, 1991.
[3] David Maxwell Chickering, David Heckerman, and Christopher Meek. A Bayesian approach to learning Bayesian networks with local structure. In UAI, 1997.
[4] D. J. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes: Elementary Theory and Methods, volume I. Springer, 2 edition, 2003.
[5] Thomas Dean and Keiji Kanazawa. Probabilistic temporal reasoning. In AAAI, 1988.
[6] Vanessa Didelez. Graphical models for marked point processes based on local independence. J. Roy. Stat. Soc., Ser. B, 70(1):245–264, 2008.
[7] Yu Fan and Christian R. Shelton. Sampling for approximate inference in continuous time Bayesian networks. In AI & M, 2008.
[8] N. Friedman, I. Nachman, and D. Pe´ r. Using Bayesian networks to analyze expression data. e J. Comp. Bio., 7:601–620, 2000.
[9] Nir Friedman, Kevin Murphy, and Stuart Russell. Learning the structure of dynamic probabilistic networks. In UAI, 1998.
[10] David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, and Carl Kadie. Dependency networks for inference, collaborative ﬁltering, and data visualization. JMLR, 1:49–75, October 2000.
[11] A. A. J. Marley and Hans Colonius. The “horse race” random utility model for choice probabilities and reaction times, and its competing risks interpretation. J. Math. Psych., 36:1–20, 1992.
[12] Uri Nodelman, Christian R. Shelton, and Daphne Koller. Continuous time Bayesian networks. In UAI, 2002.
[13] Uri Nodelman, Christian R. Shelton, and Daphne Koller. Expectation Maximization and complex duration distributions for continuous time Bayesian networks. In UAI, 2005.
[14] Adam Oliner and Jon Stearley. What supercomputers say - an analysis of ﬁve system logs. In IEEE/IFIP Conf. Dep. Sys. Net., 2007.
[15] Shyamsundar Rajaram, Thore Graepel, and Ralf Herbrich. Poisson-networks: A model for structured point processes. In AIStats, 2005.
[16] Aleksandr Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Brock, Rebecca Isaacs, and Reichard Mortier. CT-NOR: Representing and reasoning about events in continuous time. In UAI, 2008.
[17] Aleksandr Simma and Michael I. Jordan. Modeling events with cascades of Poisson processes. In UAI, 2010.
[18] Wilson Truccolo, Uri T. Eden, Matthew R. Gellows, John P. Donoghue, and Emery N. Brown. A point process framework relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol., 93:1074–1089, 2005. 9


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Steve Jobs: The Lost Interview (2012)
谷歌眼镜（Google Project Glass）就像Jobs的作品。你可以认为这是给Geek的。不要忘了，PC机最先也是给Geek的。苹果定位top/down 5%，微软定位90%（big middle）。Our species want betterr, sth beyond. Tribal Globalization.乔布斯可以学禅（Zen），或者伊斯兰。
Writer: Robert X. Cringely
Release year: 2012
In 1995, during the making of his TV series Triumph of the Nerds about the birth of the PC, Bob Cringely did a memorable hour-long interview with Steve Jobs.
It was 10 years since Jobs had left Apple following a bruising struggle with John Sculley, the CEO he had brought into the company. At the time of the interview Jobs was running NeXT, the niche computer company he had founded after leaving Apple.
During the interview, Jobs was at his charismatic best -- witty, outspoken, visionary. In the end, only a part of the interview was used in the series and the rest was thought lost. But recently a VHS copy was found in the series director's garage. Now, cleaned up with modern technology, and put into context by Cringely, the entire interview will be screened in Landmark Theatres.
内容：
In the interview Jobs talks about his pioneering days with Steve Wozniak, when they built a Blue Box and phoned the Pope; how they -- "two guys who didn't know much" -- assembled the first Apple computer and went on to found the Apple company.
Jobs recalls the visits he made to Xerox Palo Alto Research Center and how it inspired the making of the Macintosh, the world's first modern PC, when he was "on a mission from God to save Apple."
He talks frankly and sadly about his enforced departure from Apple and explains what he is doing at NeXT (which he would soon sell to Apple and whose software would then be at the heart of the first iMac's operating system).
Finally in spell-binding terms, he offers his vision of a digital future -- a world of wonderful products created by artists and poets.诗人和艺术家
In 1995, Steve Jobs gave a 70 minute interview to Robert X. Cringley for a Public Broadcasting System [PBS] television series, “Triumph of the Nerds.” The television series included ten minutes from the interview. The rest of the tape was feared lost until the original tape was recently discovered in the director’s garage.
At the time of the interview, Jobs had been 10 years at NeXT, an object-oriented software firm that he had founded. The time–1995–is just before he sells the NeXT software system to Apple, two years before he became interim CEO of Apple. It is five years before he became the permanent CEO of Apple and took it from near-bankruptcy to become one of the biggest and most successful firms in the world. ( Curiously, there is no mention in the interview of Pixar which launched the hugely successful movie, Toy Story, in 1995.)
In business, a lot of things are folklore
In 1975, Steve Jobs together with Steve Wozniak and Mike Markkulla developed the Apple II and announced it at the West Coast Computer Fair, to great acclaim.
实录：
Cringley: So you were 21 years old. You were a big success. You had done all this by the seat of your pants. You didn’t have any particular training in management. How did you learn how to run a company?
 I would always ask why you do things. The answers that I would invariably get are: “Oh, that’s just the way things are done around here.” Nobody knows why they do what they do. Nobody thinks very deeply about things in business. That’s what I found.
I’ll give you an example. When we were building our Apple computers in a garage, we knew exactly what they cost. When we got into a factory in the Apple II days, the accountants had this notion of a standard cost, where you kind of set a standard cost and at the end of the quarter, you would adjust it with a variance. I kept asking: why do we do this? The answer was, “That’s just the way it’s done.”
After about six months of digging into this, I realized that the reason they did this is that they didn’t have good enough controls to know how much it’s going to cost. So you guess. And then you fix your guess at the end of the quarter.  But nobody said it that way.
So later on, when we designed this automated factory for the Macintosh, we were able to get rid of a lot of these antiquated concepts and know exactly what something cost.
So in business a lot of things are folklore. They are done because they were done yesterday. And the day before. What it means is, if you are willing to ask a lot of questions and think about things and work really hard, you can learn business pretty fast. It’s not the hardest thing in the world. It’s not rocket science.
Cringley: The Apple II was a huge success. You went public and you got really rich. What’s it like to get rich? What’s the role of money?
Jobs: Very interesting. I was worth over a million dollars when I was 23. And over ten million dollars when I was 24, and over a hundred million dollars when I was 25. And you know, it wasn’t that important, because I never did it for the money. I think money is a wonderful thing, because it enables you to do things. It enables you to invest in ideas that don’t have a short-term payback. At that time in my life, it was not the most important thing. The most important thing was the company, the people, the products we were making. And what we were going to enable people to do with these products. So I didn’t think about the money a great deal. I never sold any stock. I just believed that the company would do very well over the long term.
Cringley: What’s your passion? What drove you? What drives Steve Jobs?
Jobs: As a kid, I read an article in the Scientific American. It measured the efficiency of locomotion of various species on the planet. Bears. Chimpanzees. Raccoons. Birds. Fish. How many kilo-calories per kilometer did they spend to move? Humans were measured too. And the condor won. It was the most efficient. Humankind came in with an unimpressive showing about a third of the way down the list. But somebody there had the brilliance to test a human riding a bicycle. We blew away the condor. Off the charts.
This really had an impact on me. Humans are tool builders. We build tools that can dramatically amplify our innate human abilities. We ran an ad for this once that the personal computer is the bicycle of the mind. I believe that with every bone in my body.
Of all the inventions of humans, the computer is going to rank near or at the top as history unfolds and we look back. It is the most awesome tool that we have ever invented. I feel incredibly lucky to be at exactly the right place in Silicon Valley, at exactly the right time, historically, where this invention has taken form.
If you set a vector off into space, and you change its direction just a little bit at the beginning, the difference is dramatic when it gets a few miles out in space. If we can nudge it in the right direction, it will be a much better thing. I think we have had a chance to do that a few times. That’s give me and everyone associated with it tremendous satisfaction.
Cringley: How do you know the right direction?
Jobs: (long pause) Ultimately it comes down to taste. It’s a matter of trying to expose yourself to the best things that humans have done. And then try to bring those things into what you are doing. Picasso had a saying: “Good artists copy. Great artists steal.” We have always been shameless about stealing great ideas.
Part of what made the Macintosh great was that the people working on it were musicians, poets, and artists, and zoologists, and historians. They also happened to be the best computer scientists in the world. But if it hadn’t been computer science, these people would have been doing amazing things in other fields. We all brought to this a sort of “liberal arts” air, an attitude that we wanted to pull the best that we saw into this field. You don’t get that if you are very narrow.
我接受了1.5天培训（Seminar），Jobs做的培训。Diving!
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version:1.0<EMAIL_ADDRESS>© retter2012.com


{% extends 'layout.html' %} {% block body %}
This is the PoC we, at Tweag, designed for the Connected Car Challenge.


Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
| Version: | 2.3 |
| Depends: | igraph |
| Published: | 2015-01-21 |
| Author: | Giovanni M. Marchetti, Mathias Drton, Kayvan Sadeghi |
| Maintainer: | Giovanni M. Marchetti <giovanni.marchetti at disia.unifi.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | gR |
| CRAN checks: | ggm results |
| Reference manual: | ggm.pdf |
| Package source: | ggm_2.3.tar.gz |
| Windows binaries: | r-devel: ggm_2.3.zip, r-release: ggm_2.3.zip, r-oldrel: ggm_2.3.zip |
| OS X binaries: | r-release: ggm_2.3.tgz, r-oldrel: ggm_2.3.tgz |
| Old sources: | ggm archive |
| Reverse depends: | dynamicGraph |
| Reverse imports: | causaleffect, geneNetBP, osmplotr, pcalg, pcgen, phylopath, qgraph, simPATHy, stablespec, TGS |
Please use the canonical form https://CRAN.R-project.org/package=ggm to link to this page.


LECTURES AND TUTORIALS:
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LABS AND WORKSHOP:
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COURSE NAME:
PROFESSOR:
DAYS:
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Here at Chalk and Sawdust powerlifting gym we cannot stress the importance of "the big 3" enough; the big 3 being the squat, the bench press and the deadlift. These 3 lifts should be the staple of any strength training regime. It is equally as important for high level athletes as it is for the average person trying gain strength or for older people looking to regain control over their body
The squat helps build strength and power in the legs and posterior chain.
The bench press is a great tool for building upper-body strength and mass, particularly the chest and triceps.
The deadlift is almost universally considered the number one exercise for overall physical strength.


My cousin and I decided to trip for the first time the night before a Phish concert. We assumed we would find more drugs, when we'd get there. He and I WERE good friends prior to the trip...
He and I had picked up an eigth of shrooms. We had been told by the person who sold us the shrooms, that you shouldn't take a person who flips out to the hospital unless they are comatose or violent, because the hospital can't speed the drugs out of your system. You must wait it out.
We began the evening with a good start. We didn't drive and we didn't get into any hostile situations, until he 'came down early'.  It hadn't been a problem, but I was acting very oddly. I didn't know who my parents were. I was making no sense. I lost touch with the greater part of reality. No real visual halucinations ('cept tracers), just raw confusion. However, my cousin was the one to panic. He was afraid I was O.D.ing or I was permanently nuts, and he drove me to the hospital.
I was chewing on a Road Atlas in the back of his car, when one of the hospital staff adressed me with the question: 'What did you take?' I replied in terms that made perfect 'sense' to me, '17 hits of acid in 17 days,' and I repeated myself until I had felt I had made myself completey clear. Things pretty much went downhill from there, and as you can probbaly guess, I missed the Phish concert.
My cousin kindly informed the hospital that I was overdosing on shrooms. They led me to the Emergency Room where they tried make me lie down on a table; they were planning to pump my stomach, unbeknownst to me. I resisted. They insisted. A struggle involving swearing, biting, much thrashing 'bout, and a good half dozen people including doctors, security guards, and nurses who forced me down on the table.
They injected me with something, which I am guessing is Thorazine, which stopped almost all of my muscle movements. I couldn't blink, or breath. I was on a lung machine to keep me breathing. All I could do for nine hours was piss and weep. I WAS CONSCIOUS THE ENTIRE TIME! [Stilling tripping balls, too.] I experienced getting my stomach pumped, which may I add, is quite painful and disorienting. I could not force myself into sleep, and any attempt escape the reality before me failed. Only darkness and pain existed.
It took me a few hours to realize that I was actually in a hospital, because I could only hear what was going on around me and see when my mother or a nurse wiped the tears from my eyes. I freaked out periodically, because I strained to move my limbs (or anything at all), which were totally unresponsive.
Eventually (the end of the 9 hour ordeal) I could twitch my arms, which they strapped down, and I fell asleep and woke up in a hospital room. The first questions I asked were, 'Did anything come up from the pump?' 'No.' 'Do I have brain damage?' 'No.' 'Did the mushrooms I took paralyze me?' 'No.' It was off hospital record. I spent 3 days there for an irregular heart beat they noticed while they were monitoring me. The bill totalled $6000, fortunately somehow covered by my 'rents insurence. I was grounded for two weeks! : ] But, forever, there is a gap between my parents and I. My cousin never said he was sorry.
I tripped again, under more supervised circumstances (with experienced trippers and steady rolling friends) to prove to myself that I would have been fine without the hospital's help. I went nuts again, but all went well, I merely learned I have a great susceptability to shrooms, and I have experimented with many other hallucinogens (LSD, anti-histamines (Benadryll/Dramamine), DXM, Estacy, and Morning Glory seeeds.) since then, and had a damn fine time, too. I still trip off shrooms; I have learned to survive the confusion that ensues, plus it is cheaper because I require less. I also learned very strong coping skills. Oh, well.
Er, I mean stronger.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


We offer a large range of services, from door repairs, wall alterations to full property renovations, extensions, barn conversions and new builds, Oak construction offer a bespoke service provided by professional, craft builders. We have worked with selected developers within North Yorkshire to create small housing developments and renovation projects.


I had taken no supplements or perscription drugs within the past month or so and was ready to explore my thoughts and the effects of weed and salvia laced in a fat blunt. We were at Navarre Beach, Florida on our spring break and were glad to be in the warm sunny weather of Florida. But by nightfall, we were ready to trip.
My friend Adam and his buddy had a fresh half ounce for the whole week along with my other friend David's eighter so we were set.  We had been anticipating smoking the weed, but more so my friends 18g of 20x salvia that he had brought along.
The setting was a big long vast beach around 12:00 am and the air was so crisp and the breeze was soo cool coming in from the ocean with the waves clashing on the shoreline. It made everything seem so perfect and tranquil; even mystical. It was time to toke.
I've smoked straight salvia before three or four times, but never with weed so I was a little hesitant to smoking the blunt. Adam encouraged me to do it aloing with my bro and some other friends and some chicks that were there, so I did. We puffed on the blunt, taking maybe one or two hits and passing it and that fucker burned for a while, so I got a good eleven hits in.
The Experience
At first I felt the onset of the weed was feelin' just like a normal high, but that soon would fade into a whole new feeling for me. We sat in then sand just chillin and chattin with the girls and enjoying ourselves until something really strange took effect. I looked at a light up at the moon and all of the sudden, not expecting a thing, I shoot out of my body towards the moon and the moon turned into this crazy face and this whole movie screen was reflecting on the face of the moon and the segment where ET the extra terrestrial is running in the cornfield towards his ship played out on it. It actually seemed like I had left my body and was watching it as a real life incident.
And then as quick as you could say apple juice I was back into my body sitting on the beach with my friend. I was astonished amazed, and dumfonded all at the same time. It was truly the weirdest thing ever. Im not gonna go into too much detail abou the trip itself, but it was great. Things would sway in my vision slightly, you'd recognize or percieve things from every day life as whole different things, I'd leave my body into really visual and realistic but short trips, some of them in which my friends were involved themselves.(Tripping with me)
Towards the come down I felt warm, secure and at one with myself and God. I'd stare at the black and mysterious abyss of a sky and see really really vivid patterns and colours just moving around in segmented motion, short and sporadic bursts. I was so lost in it all that I wouldn't be able to here noises around me and lost a grasp of reality. It ws such a blissful oblivion and I loved it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


--- layout: post status: publish published: true title: 100th Episode Live Show - On the air! author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 6115 wordpress_url: http://ivyenvy.com/?p=6115 date: '2011-08-13 18:20:08 +0000' date_gmt: '2011-08-13 23:20:08 +0000' categories: - Ivy Envy - Podcast - Live Broadcast tags: - Len and Bob - Live Broadcast - Hamm's - 100th Podcast Episode comments: [] ---
Our shindig for the Ivy Envy Podcast's 100th episode is now live. Click on the "Live Broadcast" tab at the top of the page or go here.
On that page is a video/audio feed from Ustream and an audio feed from Mixlr. There is also a chat at the bottom of the page. This show is dedicated to calls from you, the listeners. You are suffering through this Cubs season along with us, so call in and let us know how you're feeling.
If you're heading out for the night, go to www.mixlr.com on your mobile phone and click on the Ivy Envy show in the list. That will play the broadcast in your built-in audio player on your phone.
Check out the post below to see the prizes that we will be giving away during the show.
If you want to call and talk with us, the phone number is:<PHONE_NUMBER>. If we don't pick up your call, that means we are on the line with someone else and try back in a few minutes.
Turn on the Cubs game, turn down Len and Bob, go to our broadcast page, kick back with a Hamm's and give us a call. Sounds like a great Saturday evening.


Let us consider the example of a state-dependent code conversion. As mentioned previously, this type of conversion would occur between JIS, which is a state-dependent multibyte encoding for Japanese characters, and Unicode, which is a wide-character encoding. As usual, we assume that the external device uses multibyte encoding, and the internal processing uses wide-character encoding.
Here is what you must do to implement and use a state-dependent code conversion facet:
Define a new conversion state type if necessary.
Define a new character traits type if necessary, or instantiate the character traits template with the new state type.
Define the code conversion facet.
Instantiate new stream types using the new character traits type.
Imbue a file stream's buffer with a locale that carries the new code conversion facet.
These steps are explained in detail in the following sections.
While parsing or creating a sequence of multibytes in a state-dependent multibyte encoding, the code conversion facet has to maintain a conversion state. This state is by default of type mbstate_t, which is the implementation-dependent state type defined in <cwchar>. If this type does not suffice to keep track of the conversion state, you must provide your own conversion state type that satisfies the requirements of CopyConstructible.
class JISstate_t { /* ... */ };
The conversion state type is part of the character traits. Hence, with a new conversion state type, you need a new character traits type.
If you do not want to rely on a nonstandard and thus non-portable feature of the library, you must define a new character traits type and redefine the necessary types:
struct JIS_char_traits: std::char_traits<wchar_t> { typedef JISstate_t state_type; typedef std::fpos<state_type> pos_type; typedef std::streamoff off_type; };
Just as in the first example, you must define the actual code conversion facet. The steps are basically the same as before, too: define a new class template for the new code conversion type and specialize it. The code would look like this:
template <class internT, class externT, class stateT> class UnicodeJISConversion : public std::codecvt<internT, externT, stateT> {}; class UnicodeJISConversion<wchar_t, char, JISstate_t> : public std::codecvt<wchar_t, char, JISstate_t> { protected: virtual std::codecvt_base::result do_in(JISstate_t& state, const char* from, const char* from_end, const char*& from_next, wchar_t* to, wchar_t* to_limit, wchar_t*& to_next) const; virtual std::codecvt_base::result do_out(JISstate_t& state, const wchar_t* from, const wchar_t* from_end, const wchar_t*& from_next, char* to, char* to_limit, char*& to_next) const; virtual bool do_always_noconv() const throw(){ return false; } virtual int do_encoding() const throw(){ return -1; } };
In this case, the member function do_encoding() has to return -1, which identifies the code conversion as state-dependent. Again, the member functions in() and out() must conform to the error indication policy explained under class codecvt in the Apache C++ Standard Library Reference Guide.
The distinguishing characteristic of a state-independent conversion is that the conversion state argument to in() and out() is used for communication between the file stream buffer and the code conversion facet. The file stream buffer is responsible for creating, maintaining, and deleting the conversion state. At the beginning, the file stream buffer creates a conversion state object that represents the initial conversion state and hands it over to the code conversion facet. The facet modifies it according to the conversion it performs. The file stream buffer receives it and stores it between two subsequent code conversions.
Here is an example of how the new code conversion facet can be used:
typedef std::basic_fstream<wchar_t, JIS_char_traits> JIS_fstream; //1 JIS_fstream inout("/tmp/fil"); UnicodeJISConversion<wchar_t,char,JISstate_t> cvtfac; std::locale cvtloc(std::locale(), &cvtfac); inout.rdbuf()->pubimbue(cvtloc) //2 std::wcout << inout.rdbuf(); //3
| //1 | Our Unicode-JIS code conversion needs a conversion state type different from the default type std::mbstate_t. Since the conversion state type is contained in the character traits, we must create a new file type. |
| //2 | Here the stream buffer's locale is replaced by a copy of the global locale that has a Unicode-JIS code conversion facet. |
| //3 | The content of the JIS encoded file "/tmp/fil" is read, automatically converted to Unicode, and written to std::wcout. |


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]="2"
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
lr = tf.placeholder(tf.float32, name='learning_rate')
return real_input, z_input, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# 28 28 3
x1 = tf.layers.conv2d(images, 64, 3, 2, 'same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
bn1 = tf.layers.batch_normalization(x1, training=True)
leaky1 = tf.maximum(bn1, bn1 * alpha)
dropout1 = tf.layers.dropout(leaky1, name='dropout_1', training=True)
# 14 14 64
x2 = tf.layers.conv2d(dropout1, 128, 3, 2, 'same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
bn2 = tf.layers.batch_normalization(x2, training=True)
leaky2 = tf.maximum(bn2, bn2 * alpha)
dropout2 = tf.layers.dropout(leaky2, name='dropout2', training=True)
# 7 7 128
flat = tf.reshape(dropout2, (-1, 7 * 7 * 128))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return (out, logits)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.2
with tf.variable_scope('generator', reuse=not(is_train)):
x1 = tf.layers.dense(z, 7*7*256)
x1 = tf.reshape(x1, (-1, 7, 7, 256))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(x1, x1*alpha)
x1 = tf.layers.dropout(x1, training=is_train)
x2 = tf.layers.conv2d_transpose(x1, 128, 3, 2, 'same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
# 14 14 256
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(x2, x2*alpha)
x2 = tf.layers.dropout(x2, training=is_train)
x3 = tf.layers.conv2d_transpose(x2, out_channel_dim, 3, 2, 'same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
# 28 28 out_channel_dim
out = tf.tanh(x3)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth = 0.1
input_fake = generator(input_z, out_channel_dim, is_train=True) # What is the training parameter should be?
real_out, real_logits = discriminator(input_real, reuse=False)
fake_out, fake_logits = discriminator(input_fake, reuse=True) # This line has some bugs of scope.
fake_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.zeros_like(fake_logits)))
real_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, labels=tf.ones_like(real_logits)*(1-smooth)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.ones_like(fake_logits)))
return fake_loss+real_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
variables = tf.trainable_variables()
g_var = [var for var in variables if var.name.startswith('generator')]
d_var = [var for var in variables if var.name.startswith('discriminator')]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
g_loss_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_var)
d_loss_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_var)
return d_loss_opt, g_loss_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
%pdb
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
step = 0
sample_z = np.random.uniform(-1, 1, (batch_size, z_dim))
# input
_, width, height, channels = data_shape
input_real, input_z, lr = model_inputs(width, height, channels, z_dim)
# model loss
d_loss, g_loss = model_loss(input_real, input_z, channels)
# model optimizers
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images = batch_images*2
# TODO: Train Model
step += 1
# sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers Run G twice to give it a chance to beat D.
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real:batch_images, lr:learning_rate})
_ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real:batch_images, lr:learning_rate})
_ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real:batch_images, lr:learning_rate})
# Show the loss every 100 steps.
if step % 25 == 0:
print('Step:', step,
'D loss:', d_loss.eval({input_real: batch_images, input_z: batch_z, lr:learning_rate}),
'G loss:', g_loss.eval({input_z: batch_z, lr:learning_rate}))
if step % 400 == 0:
show_generator_output(sess, 9, input_z, channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Pink player: Moving: LEFT, DOWN, RIGHT, UP keys & Kick the ball: L key.
Teal player: Moving: A, S, D, W keys & Kick the ball: F key.
To kick the ball, press the special kick button when you are near it.
Score goals with the ball.


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STREET FIGHTER A film review by Andy Eddy Copyright 1994 Andy Eddy
December 10, 1994, v1.0 by Andy Eddy (vidgames@netcom.com), Senior Editor, GamePro magazine
[According to its author, this file may go through revisions; the latest version will be kept on ftp.netcom.com, in pub/vi/vidgames/faqs as sfmovie.txt. -Moderator]
Intro
Whether you like fighting games or not--for that matter, whether you're an avid video-game player or not--it had to catch your attention when it was announced that Capcom (the company behind the Street Fighter series of video games) was financing the making of a movie revolving around the Street Fighter characters.
It became an even bigger story when it was announced that Steven deSouza would be writing and directing it. It would be his first motion picture directing credit, though he's well known for writing such action films as BEVERLY HILLS COP, COMMANDO, DIE HARD, 48 HOURS and THE FLINTSTONES (only kidding). He's also directed television projects, such as "Tales from the Crypt"; I'll tie that in later.
After deSouza's participation was decided on, the cast started coming together. The first big name was Jean-Claude Van Damme, who would play the part of Guile--for a reported $7 million. He was followed up by such names as Ming-Na Wen (THE JOY LUCK CLUB) as Chun-Li and Kylie Minogue (Australian star, best known in the U.S. for her remake of the song, "The Locomotion.") as Cammy. The villain M. Bison would be played by versatile actor Raul Julia (THE ADDAMS FAMILY and KISS OF THE SPIDER WOMAN) in what would be his final film, as he would die a short time before the movie's official release of December 23, 1994.
The ambitious filming schedule would give deSouza and crew about eight months to complete the film in time for the very firm pre-Christmas release. The majority of the filming started in June 1994 (I'll confirm the shooting schedule in a later revision), in Thailand, followed by a stint in Australia; Vancouver was listed in the credits also, so I'll find out where that fit in the shooting. The movie would offer a lot of action and fist-fighting, as the Van Damme connection made apparent, though it was stipulated by Capcom that STREET FIGHTER: THE MOVIE have a PG-13 rating, to make the film accessible to the teenage audience that made Capcom rich on the game market.
The Movie Review
First, a disclaimer: If there's one thing I hate in a movie review, it's when the reviewer talks as if everyone reading it saw the movie with him. I'm not going to talk much about the story and its outcome because there are some twists that deSouza's put in to keep the viewer off balance.
What I will say, in summary, is that Guile is a military leader with the "good guys" Allied army. He's trying to thwart a plan by Bison to use peace-loving hostages (lots of them sporting Red Cross symbols on their clothes) as pawns to get a $20 billion ransom. This cash will be used to build his growing empire.
To fill out the extensive cast of characters (I'll formally list the cast in a future release of the FAQ/review), Cammy is Guile's right-hand ... uh, person, as is T. Hawk; Zangief and Dee Jay fill a similar role for Bison; Dhalsim is a doctor held captive to accomplish Bison's dastardly deeds in the lab; Chun-Li is a TV journalist, who has Honda and Balrog as her crew; Sagat and Vega are local crime scum, doing dirty deeds (though not dirt cheap) with Bison; Ryu and Ken are hustlers who get caught up in the action. A new character (due to make an appearance in a future Street Fighter game) is Captain Sawada, who saw minor action in the film as one of Guile's troop leaders and is reasonably forgettable.
I saw the movie on December 10, 1994, in the first "public" showing of the movie with Capcom employees, press like myself and other invited guests. They called it the first premiere of the film. As best as I can tell, it was a finished print. I've been to working-print screenings before, and they usually lack credits, music, special effects and often are quite scratched. Two weeks before release, this looked to be the version that will hit theaters.
The cast was pretty well chosen, something that was in debate by many Street Fighter purists. There was the moment that after a fight, Van Damme/Guile's bicep came into the full view to show his American flag tattoo, an interesting twist given his very strong accent. Instances like this are few and far between. As with most action films, there is a strong suspension of belief, and STREET FIGHTER: THE MOVIE is no different. However, given the abilities of the game's characters (throwing fireballs, extended helicopter kicks and flaming attacks, let alone the Hulk-like appearance of Blanka), deSouza kept it pretty much within believability, using poetic license to enable certain characters to do the tricks from the game. Again, I'll leave surprises for you to discover when you see the film; you can thank me later.
The creepiest aspect is Raul Julia's portrayal as M. Bison. He comes across as a very evil and believable baddie, with all the flamboyance the character calls for. When I first saw stills of him, I was amazed at how much weight he had lost to achieve the gaunt facial features. Little did I know, it would appear that Julia was mortally ill with the cancer that would claim his life a few months later. There's a creepy moment in the film toward the end, but it doesn't last long (don't ask me to explain ... you'll know it when you see it). The fact that Julia died *after* the movie was finished means there are no awkward moments due to his death, like those faced by the producers of THE CROW (Brandon Lee died during filming, but rewriting and creative use of computer graphics helped them around it) or, even worse, BRAINSTORM (Natalie Wood died during filming, which made the final product quite jumpy). His character still graces the screen as a powerhouse in sheer strength and fighting skill, and Julia carries the film with his acting. The movie is dedicated to him with a "To Raul--Vaya Con Dios" at the start of the closing credits.
[Trivia: Edward Pressman, who was a producer for STREET FIGHTER: THE MOVIE, also produced THE CROW. I'm sure he doesn't want to be remembered for films that have actors die before the movie's release, but I'm afraid he's stuck with the stigma now.]
Contrary to that is the portrayal of Guile by Van Damme. Not to say that his acting is bad, but he really isn't required to do that much besides act like a tough, capable military man and do his trademark martial-arts combat. He does well at both--aside from the previously noted goofiness associated with his being an American--but the movie doesn't center around his character as it does Julia's Bison. Adding to that is the fact that there are a lot of Street Fighter characters that need attention in the movie, which spreads screen time pretty thin, Van Damme included.
The fighting in the movie, a staple of de Souza films, is energetic and very believable. No pro-wrestling theatrics here, but honest-to-goodness pounding back and forth, with some exceptions (for instance, Zangief and Honda crashing through the floor was a bit overdone). A familiar face to martial-arts fans as one of Bison's underlings (in a nonspeaking role) and in the credits as the fighting coach is Benny "The Jet" Urquidez. The former champion does his magic making average actors look like black belts.
deSouza is also known for the sense of humor he injects in his screenplays, and STREET FIGHTER: THE MOVIE is no different. Best of all, it's not grins that are targeted at Street Fighter fanatics. There were times when, because we were in a crowd of Capcom employees and their families, certain things in the movie were rewarded with cheers and giggles from the audience. Your reaction in those cases, depending on how much of a gamer you are, may not be the same. I will give away one for the sake of example: After Bison steps onto the platform that holds the control panel for his vast array of weaponry, the camera comes around for a close-up and we see it's the joystick-and-button cluster from the Super Street Fighter II arcade game. The majority of the time, the gags are accessible to all viewers, though potentially subtle (like the give-and-take between Balrog and Honda when they've been captured).
That's the stuff that makes the movie more than just a video-game movie. We've all seen how producers have tried to squeeze out video-game-related films like "The Wizard" (with Fred Savage), "Super Mario Bros." (with Dennis Hopper and Bob Hoskins) and the most recent "Double Dragon" (with Robert Patrick and Alyssa Milano). They usually end up as a competition of product placement and/or very forced. I didn't get that feeling with "Street Fighter: The Movie". While there are a few moments of sloppiness in the script, they aren't too bad. There's lots of action, a decent plot vehicle to bring the game's characters on-screen and (unlike some movies these days) not too much commercialism beyond the basic premise of a Street Fighter movie. (After a visit to the set, by the way, I have similar hopes about the Mortal Kombat movie that's slated for a spring 1995 release. I haven't seen the script however, which would be a telling factor.)
Now the negatives, and I'll just hit them as bullet points:
~ deSouza's background as a TV director hurts him here, in my opinion, because he shoots too many scenes, to use Wayne Campbell's phrase, in "extreme close-up." Fight scenes are more imposing from this perspective, but it's also harder to see what's going on. It could have been used to mask the fighting skills of such actors as Minogue, but I'd suspect that it's just deSouza's eye for the shot, because it happens in other non-action scenes too. I would guess that "Street Fighter: The Movie" will play well as a video as a result, but we'll have to wait.
~ Some scenes are also too dark and musty for easy viewing. While this can be attributed to the sinister lair of M. Bison, other times it appears to be just poor cinematography.
~ Similarly, there are times when the audio isn't sharp enough to get a character's line. This makes it hard for actors with an accent, like Van Damme and to a lesser extent Minogue, to always come through clearly.
~ Here's a rehash and further comment: While not miscast, Van Damme isn't the greatest choice for a character that's an American military hero with a U.S. flag tattoo. Jeff Speakman is a capable fighter, right? Hey, even Chuck Norris could have been made up with Guile's haircut. Well, maybe not. As I said, it didn't bother me as much, but I think a better selection could have been made. Some purists will no doubt think Van Damme laughable.
~Also, the casting of the role of Cammy could have been improved. Not to say that Kylie Minogue is bad, but when I co-interviewed deSouza before shooting began, he was saying how he wanted a lithe British actress with martial arts skills to play Cammy with integrity. Then Minogue was announced. I suspect her marquee value overseas was a strong reason for the choice, but they could have played much more on the part and the action if a more capable choice was made. Hey, how about Cynthia Rothrock? If Van Damme can play an American soldier, Rothrock--a veteran in the male-dominated genre of martial-arts films--could probably have played a British import. I guess that's why I wasn't chosen as casting director, eh?
Summary
I'll admit that I'm not a big fighting game fan, and as a result not a big Street Fighter fan. With that in mind, I'm probably not the best judge of the success this movie has as an extention of the game. Perhaps fanatics will be disappointed. In a nutshell, though, I wasn't disappointed in STREET FIGHTER: THE MOVIE as action-film entertainment. In fact, someone I know who's is really into fighting games also said he thought it was good, which I take as a qualification that de Souza did his job in bringing the game to the big screen. What's left to be seen is whether Capcom can pull in about $50 million in box-office receipts it needs to break even.
-- Andy Eddy, Senior Editor, GamePro Magazine The views expressed in this text aren't necessarily those of my employer
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


See the earth, from the space, anywhere.
EarthVR supports R1 gamepad control.
Axis 0 & 1: Rotating about the earth
B5
⏎ +
Axis 1: Zooming
B3 (sometimes
A on the pad): Faster time
B2 (sometimes
C): Slower time
B4
◯: Toggling HUD
When using a PC, the VR effect will not work. You can simply use your mouse and keyboard to navigate.
Left mouse button: Rotating about axis
© Celestial Phineas @ ZJU
MIT License


Closes a dynamic data exchange (DDE) channel previously opened with the INITIATE function. Use TERMINATE to close a channel after you have finished communicating with another application.
Syntax
TERMINATE(channel_num)
Important Microsoft Excel for the Macintosh requires system software version 7.0 or later for this function.
Channel_num is the number returned by a previously run INITIATE function. Channel_num identifies a DDE channel to close.
If TERMINATE is not successful, it returns the #VALUE! error value.
Related Functions
EXEC Starts another application
INITIATE Opens a channel to another application
Return to index


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Nodal / IN 9 Selects node of engaged segment
Engage a segment on a node by pressing A or Q - Q.
Press 9 to add only the node of the engaged segment.
The node coincident with the cursor is added to the set.
Nodal / OUT 9 UN-Selects node of engaged segment
Engage a segment on a node by pressing A or Q - Q.
Press 9 to REMOVE only the node of the engaged segment.
The node coincident with the cursor is removed from the set.
This operation permits you to be precise in defining the node trap. Only one node at a time is processed. The cursor position and the blinking segment tell you exactly which node you are working with.


1 write to _context
System.Management.Automation (1)
engine\DscResourceSearcher.cs (1)
25
_context
= context;
1 reference to _context
System.Management.Automation (1)
engine\DscResourceSearcher.cs (1)
147
_context


-->
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|
Panda3D
|
Public Member Functions | |
| def | __init__ (self, packageDesc, patchMaker, xpackage=None) |
| def | getBaseKey (self) |
| def | getCurrentKey (self) |
| def | getGenericKey (self, fileSpec) |
| def | getTopKey (self) |
| def | readDescFile (self, doProcessing=False) |
| def | writeDescFile (self) |
This is a particular package. This contains all of the information needed to reconstruct the package's desc file.
| def __init__ | ( | self, | |
| packageDesc, | |||
| patchMaker, | |||
xpackage = None |
|||
| ) |
| def getBaseKey | ( | self | ) |
Returns the key to locate the "base" or oldest version of this package.
| def getCurrentKey | ( | self | ) |
Returns the key to locate the current version of this package.
| def getGenericKey | ( | self, | |
| fileSpec | |||
| ) |
Returns the key that has the indicated hash.
| def getTopKey | ( | self | ) |
Returns the key to locate the "top" or newest version of this package.
| def readDescFile | ( | self, | |
doProcessing = False |
|||
| ) |
Reads the existing package.xml file and stores it in this class for later rewriting. if doProcessing is true, it may massage the file and the directory contents in preparation for building patches. Returns true on success, false on failure.
| def writeDescFile | ( | self | ) |
Rewrites the desc file with the new patch information.
| anyChanges |
| baseFile |
| basePv |
| compressedFilename |
| contentsDocPackage |
| currentFile |
| currentPv |
| doc |
| hostUrl |
| packageDesc |
| packageDir |
| packageName |
| patches |
| patchMaker |
| patchVersion |
| platform |
| topFile |
| topPv |
| version |


[model] element index
Package indexes
root
default
exceptions
view
All elements
a
c
d
g
r
s
t
_
_
top
__construct
Column::__construct()
in Column.class.php
Creates a new Column.
__construct
Cell::__construct()
in Cell.class.php
Creates a new Cell.
a
top
addCell
Row::addCell()
in Row.class.php
Adds a new cell to this row.
addColumn
DataTable::addColumn()
in DataTable.class.php
Adds a new column definition to the table.
addRow
DataTable::addRow()
in DataTable.class.php
Adds a new data-row to the table.
addRowsAssocArray
DataTable::addRowsAssocArray()
in DataTable.class.php
Fills the DataTable by reading an associative array.
c
top
Cell
Cell
in Cell.class.php
A Cell stores the data for one cell in the
DataTable
.
Column
Column
in Column.class.php
A Column stores the definition of one column of the
DataTable
.
Cell.class.php
Cell.class.php
in Cell.class.php
Column.class.php
Column.class.php
in Column.class.php
d
top
DataTable
DataTable
in DataTable.class.php
A DataTable represents the data that will be visualized in a chart.
DataTable.class.php
DataTable.class.php
in DataTable.class.php
g
top
getCells
Row::getCells()
in Row.class.php
Gets all cells that have been added to this row.
getCellsCount
Row::getCellsCount()
in Row.class.php
Gets the number of cells that have been added to this row.
getColumns
DataTable::getColumns()
in DataTable.class.php
Gets all columns that have been added to the DataTable.
getColumnsCount
DataTable::getColumnsCount()
in DataTable.class.php
Gets the number of columns that have been added to the DataTable.
getFormatted
Cell::getFormatted()
in Cell.class.php
Gets the cell's formatted value.
getId
Column::getId()
in Column.class.php
Gets the column's ID.
getLabel
Column::getLabel()
in Column.class.php
Gets the column's label.
getProperties
Column::getProperties()
in Column.class.php
Gets the properties that have been added to the column.
getProperties
Cell::getProperties()
in Cell.class.php
Gets the cell's properties.
getRows
DataTable::getRows()
in DataTable.class.php
Gets all rows that have been added to the DataTable.
getRowsCount
DataTable::getRowsCount()
in DataTable.class.php
Gets the number of rows that have been added to the DataTable.
getType
Column::getType()
in Column.class.php
Gets the data type of all
Cell
s added in this column.
getValue
Cell::getValue()
in Cell.class.php
Gets the cell's value.
r
top
Row.class.php
Row.class.php
in Row.class.php
Row
Row
in Row.class.php
A Row represents a data entry (a row) of the
DataTable
.
s
top
setFormatted
Cell::setFormatted()
in Cell.class.php
Sets the cell's formatted value.
setId
Column::setId()
in Column.class.php
Set's the column's ID.
setLabel
Column::setLabel()
in Column.class.php
Set's the columns label.
setProperties
Column::setProperties()
in Column.class.php
Set's the columns properties.
setProperties
Cell::setProperties()
in Cell.class.php
Sets the cell's properties.
setType
Column::setType()
in Column.class.php
Sets the column's type.
setValue
Cell::setValue()
in Cell.class.php
Sets the cell's value.
t
top
toJson
Row::toJson()
in Row.class.php
Converts this object into a JSON-object.
toJson
DataTable::toJson()
in DataTable.class.php
Converts this object into a JSON-object.
toJson
Column::toJson()
in Column.class.php
Converts this object into a JSON-object.
toJson
Cell::toJson()
in Cell.class.php
Converts this object into a JSON-object.
TYPE_BOOLEAN
Column::TYPE_BOOLEAN
in Column.class.php
TYPE_DATE
Column::TYPE_DATE
in Column.class.php
TYPE_DATETIME
Column::TYPE_DATETIME
in Column.class.php
TYPE_NUMBER
Column::TYPE_NUMBER
in Column.class.php
TYPE_STRING
Column::TYPE_STRING
in Column.class.php
TYPE_TIMEOFDAY
Column::TYPE_TIMEOFDAY
in Column.class.php
a
c
d
g
r
s
t
_


--- title: Class AnonymousRecordNameChangedListener description: AnonymousRecordNameChangedListener navLabel: AnonymousRecordNameChangedListener category: interface body_class: dark ---
Notified whenever the anonymous record changes
recordName
- The new recordNameanonymousRecord
- The anonymous
Record which name changed


Usually, scripts are kept in a project as source files and compiled by Unity whenever the source changes. However, it is also possible to compile a script to a dynamically linked library (DLL) using an external compiler. The resulting DLL can then be added to the project and the classes it contains can be attached to objects just like normal scripts.
It is generally much easier to work with scripts than DLLs in Unity. However, you may have access to third party Mono code which is supplied in the form of a DLL. When developing your own code, you may be able to use compilers not supported by Unity (F#, for example) by compiling the code to a DLL and adding it to your Unity project. Also, you may want to supply Unity code without the source (for an Asset Store product, say) and a DLL is an easy way to do this.
To create a DLL, you will first need a suitable compiler. Not all compilers that produce .NET code are guaranteed to work with Unity, so it may be wise to test the compiler with some available code before doing significant work with it. If the DLL contains no code that depends on the Unity API then you can simply compile it to a DLL using the appropriate compiler options. If you do want to use the Unity API then you will need to make Unity’s own DLLs available to the compiler. On a Mac, these are contained in the application bundle (you can see the internal structure of the bundle by using the Show Package Contents command from the contextual menu; right click or ctrl-click the Unity application):-
The path to the Unity DLLs will typically be
/Applications/Unity/Unity.app/Contents/Frameworks/Managed/
…and the two DLLs are called UnityEngine.dll and UnityEditor.dll.
On Windows, the DLLs can be found in the folders that accompany the Unity application. The path will typically be
C:\Program Files\Unity\Editor\Data\Managed
…while the names of the DLLs are the same as for Mac OS.
The exact options for compiling the DLL will vary depending on the compiler used. As an example, the command line for the Mono C# compiler, mcs, might look like this on Mac OS:-
mcs -r:/Applications/Unity/Unity.app/Contents/Frameworks/Managed/UnityEngine.dll -target:library ClassesForDLL.cs
Here, the -r option specifies a path to a library to be included in the build, in this case the UnityEngine library. The -target option specifies which type of build is required; the word “library” is used to select a DLL build. Finally, the name of the source file to compile is ClassesForDLL.cs (it is assumed that this file is in the current working folder, but you could specify the file using a full path if necessary). Assuming all goes well, the resulting DLL file will appear shortly in the same folder as the source file.
Once compiled, the DLL file can simply be dragged into the Unity project like any other asset. The DLL asset has a foldout triangle which can be used to reveal the separate classes inside the library. Classes that derive from MonoBehaviour can be dragged onto Game Objects like ordinary scripts. Non-MonoBehaviour classes can be used directly from other scripts in the usual way.
This section explains how to build and integrate a simple DLL example with MonoDevelop and Visual Studio and also how to prepare a debugging session for the DLL.
First, open MonoDevelop or Visual Studio and create a new project. In MonoDevelop, you do this by selecting File > New > Solution and then choosing C# > Library. In Visual Studio, you should select File > New > Project and then choose Visual C# > Class Library.
You then need to fill out the information for the new library:
Next, you should add references to the Unity DLLs. In MonoDevelop, you should open the contextual menu For References in the Solution Browser and choose Edit References. Then, choose the option .Net Assembly tab > File System > select file. In Visual Studio, open the contextual menu For References in the Solution Explorer and choose Add Reference. Then, choose the option Browse > Browse > select file.
At this stage, you will have the option to select the required DLL file. On Mac OSX, the file can be found at
Applications/Unity.app/Contents/Frameworks/Managed/UnityEngine.dll
…while on Windows, the path is
Program Files\Unity\Editor\Data\Managed\UnityEngine.dll
For this example, rename the class to “MyUtilities” in the Solution browser and replace its code with the following:
using System;
using UnityEngine;
namespace DLLTest {
public class MyUtilities {
public int c;
public void AddValues(int a, int b) {
c = a + b;
}
public static int GenerateRandom(int min, int max) {
System.Random rand = new System.Random();
return rand.Next(min, max);
}
}
}
With the code in place, build the project to generate the DLL file along with its debug symbols.
For this example, create a new project in Unity and copy the built file <project folder>/bin/Debug/DLLTest.dll
into the Assets folder. Then, create a C# script called “Test” in Assets, and replace its contents with the following code:
using UnityEngine;
using System.Collections;
using DLLTest;
public class Test : MonoBehaviour {
void Start () {
MyUtilities utils = new MyUtilities();
utils.AddValues(2, 3);
print("2 + 3 = " + utils.c);
}
void Update () {
print(MyUtilities.GenerateRandom(0, 100));
}
}
When you attach this script to an object in the scene and press Play, you will see the output of the code from the DLL in the Console window.
Firstly, you should prepare the debug symbols for the DLL. In MonoDevelop, copy the built file <project folder>/bin/Debug/DLLTest.dll.mdb
into the Assets/Plugins folder. In Visual Studio, execute
Program Files\Unity\Editor\Data\Mono\lib\mono\2.0\pdb2mdb.exe
in the command prompt, passing <project folder>\bin\Debug\DLLTest.pdb
as a parameter. Then, copy the converted file <project folder>\bin\Debug\DLLTest.dll.mdb
into Assets/Plugins.
Next, open the “Test” script in MonoDevelop. Make sure the Unity debugger is enabled from the Tools menu (Windows) or MonoDevelop-Unity menu (MacOS). The option you need from this menu is Add-in Manager > Installed tab > Unity > select Mono Soft Debugger Support for Unity > Enable.
With this setup completed, you can debug code that uses the DLL in Unity in the usual way. See the Scripting Tools section for further information about debugging.


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Below are the 22 home automation designs we had originally brainstormed, but deemed not right for a low-fidelity prototype. This decision was made with following justifications:
| Lights On/off for whole house and room. The idea here is to group each light by room, which allows whole rooms to be turned on/off at once. However, individual lights can be added to a grouping (burglar deterrent one light on in each room). Why not selected?: This was similar to the fan control design that was one of the 3 chosen. | |
| Home screen, blueprint view. Home screen will show all the home automation for a home, with respect to where it is in the house. Why not selected?: The blueprint view was covered in the fan prototype. | |
| Home screen, list view. Home screen will show all of the home automation tasks that have been configured. Not right for prototype as more of a summary view than specific design. Why not selected?: The list view is effectively a sub-feature in the other, chosen designs. | |
| Security device authorization. Ensures that devices cannot be added or removed without authorization, also prevents any unwanted changes to automated activities. (Change the alarm from 6:30 am to 4:30 am!) Why not selected?: This design idea offered little user interaction beyond physically connecting devices. | |
| TV usage. Monitors what you are watching on which subscription service. Makes sure you are not wasting money on unused services! Why not selected?: This overlapped thematically with the energy usage prototype which made the final 3. | |
| Auto laundry/bathroom fan. Automatically turns on laundry room fan when dryer is running, and bathroom fan when shower is running. Why not selected?: This was similar to the fan control design that was one of the 3 chosen. | |
| Mail Notification. Receive a digital notification when physical mail is delivered. Why not selected?: This design idea offered little user interaction. | |
| Water savings. Integrates sprinkler system with weather reports, to ensure you don’t waste water on days it will rain. Why not selected?: Timing feature covered in routine editor prototype. | |
| Basement Water Alarm. Receive a notification if water is present in the basement. Targeted towards homeowners with those pesky leaky basements! Why not selected?: This design idea offered little user interaction. | |
| Midnight snack lighting. Automatically turn on night lights and set the brightness level to an eye friendly level for the midnight trips to the fridge. Why not selected?: Timing feature covered in routine editor prototype. | |
| Music/ sound in occupied rooms. Why not selected?: This design idea offered little user interaction. | |
| Movie/TV lights. Integrate lighting with ‘play’ and ‘pause’. Avoid using your phone as flashlight once the movie has ended. Why not selected?: This would be difficult to prototype without the visceral feedback of actual lighting changes. | |
| Trash day. Don’t sit awake in bed wondering if you brought the trash out to the street or forgot! Why not selected?: This design idea offered little user interaction. | |
| Window weather. Receive notifications if you left windows open with a storm approaching in the forecast. Why not selected?: This design idea offered little user interaction. | |
| Window weather 2. Receive a notification if the temperature outside drops below the temperature inside, to turn off the air and enjoy the weather! Why not selected?: This design idea offered little user interaction. | |
| Lock doors. Doors lock automatically if your smartphone has left the house but the doors were left unlocked. Why not selected?: This design idea offered little user interaction. | |
| Moisture sensors/ air quality. Receive reports of any moisture or changes in air quality in the house. Why not selected?: This design idea offered little user interaction. | |
| Proximity sensors. Receive notifications when family members are arriving home. Are the kids going to make curfew? Why not selected?: Blueprint view covered in one of the final 3 prototypes. | |
| Window curtains, blueprint view. Control which window curtains are open or closed. Use independently or integrate within other home automation tasks, such as energy savings. Why not selected?: Blueprint view covered in one of the final 3 prototypes. | |
| Usage analytics to produce reports or insights based on consumed telemetry from managed devices part of the home automation network. Use independently or integrate within other home automation tasks, such as energy savings. | |
| List view of managed devices in home automation network. Control which sensors report on or off state. Functionality can be used to determine the state of an appliance. Use independently or integrate within other home automation tasks, such as energy savings. | |
| Home blueprint view. Control which sensors report open or closed state. Functionality can be used to determine the state of an entry point. Use independently or integrate within other home automation tasks, such as energy savings. |