Review Wizard Status Report for May 2008
News
December 7, 2007 - Forward Library Accepted - Awaiting SVN
December 16 - Unordered Containers Library Accepted - In SVN
December 21 - Factory Library Accepted - Awaiting SVN
January 13, 2008 - Switch Library Accepted Provisionally - Awaiting submission for mini review
January 18 - Singleton Library Rejected - Awaiting resubmission, John Torjo has already volunteered to manage the next review
January 30 - Flyweight Library Accepted - Awaiting SVN
February 13 - Logging Library Rejected - Awaiting resubmission for new review, John Torjo has already resubmitted and Gennadiy Rozental has again volunteered to manage the review
February 27 - Floating Point Utilities Library Accepted - Awaiting SVN
March 14 - Proto Library Accepted - Exists as a component in Xpressive, but not yet as a separate library
April 20 - Egg review completed - Results pending
May 7 - Scope Exit Library Accepted - Awaiting SVN
Older Issues
The binary_int library, accepted in October 2005 has not yet been submitted to SVN. The authors are strongly encouraged to contact the review wizards
The Quantitative Units library, accepted in April 2007 has not yet been submitted to SVN
The Globally Unique Identifier library, accepted provisionally in May 2007 has not yet been submitted for mini-review and full acceptance
The Time Series Library, accepted in August 2007 has not yet been submitted to SVN
The Accumulators library, accepted in February 2007 is in SVN
The Exception library, accepted in October 2007 is in SVN
The Scope Exit review report had not been submitted by the review manager. John Phillips stepped in as substitute review manager and produced a report
For libraries that are still waiting to get into SVN, please get them ready and into the repository. The developers did some great work making the libraries, so don't miss the chance to share that work with others. Also notice that the review process page has been updated with a section on rights and responsibilities of library submitters.
For the Scope Exit review, we would like to publicly apologize to Alexander Nasonov for how long this has languished without a report. The review wizards will work to make sure this doesn't happen any more.
General Announcements
As always, we need experienced review managers. In the past few months there have been a large number of reviews, but the flow of high quality submissions is just as big, so manage reviews if possible and if not please make sure to watch the review schedule and participate. Please take a look at the list of libraries in need of managers and check out their descriptions. In general review managers are active boost participants or library contributors. If you can serve as review manager for any of them, email Ron Garcia or John Phillips, "garcia at cs dot indiana dot edu" and "phillips at mps dot ohio-state dot edu" respectively.
A link to this report will be posted to www.boost.org. If you would like us to make any modifications or additions to this report before we do that, please email Ron or John.
If you're a library author and plan on submitting a library for review in the next 3-6 months, send Ron or John a short description of your library and we'll add it to the Libraries Under Construction below. We know that there are many libraries that are near completion, but we have hard time keeping track all of them. Please keep us informed about your progress.
Review Queue
- Finite State Machines
- Property Map (fast-track)
- Graph (fast-track)
- Lexer
- Thread-Safe Signals
- Boost.Range (Update)
- Shifted Pointer
- DataFlow Signals
- Logging
- Futures (Braddock Gaskill)
- Futures (Anthony Williams)
- Join (Yigong Liu)
- Pimpl (Vladimir Batov)
Finite State Machines
| Author: | Andrey Semashev |
|---|---|
| Review Manager: | Martin Vuille |
| Download: | Boost Sandbox Vault |
| Description: |
The Boost.FSM library is an implementation of FSM (stands for Finite State Machine) programming concept. The main goals of the library are:
|
Property Map (fast-track)
| Author: | Andrew Sutton |
|---|---|
| Review Manager: | Jeremy Siek |
| Download: | http://svn.boost.org/svn/boost/sandbox/graph-v2 |
| Description: |
A number of additions and modifications to the Property Map Library, including:
|
Graph (fast-track)
| Author: | Andrew Sutton |
|---|---|
| Review Manager: | Jeremy Siek |
| Download: | http://svn.boost.org/svn/boost/sandbox/graph-v2 |
| Description: |
A number of additions and modifications to the Graph Library, including:
|
Lexer
| Author: | Ben Hanson |
|---|---|
| Review Manager: | Eric Neibler |
| Download: | Boost Sandbox Vault |
| Description: | A programmable lexical analyser generator inspired by 'flex'. Like flex, it is programmed by the use of regular expressions and outputs a state machine as a number of DFAs utilising equivalence classes for compression. |
Thread-Safe Signals
| Author: | Frank Hess |
|---|---|
| Review Manager: | Need Volunteer |
| Download: | Boost Sandbox Vault |
| Description: | A thread-safe implementation of Boost.Signals that has some interface changes to accommodate thread safety, mostly with respect to automatic connection management. |
Boost.Range (Update)
| Author: | Neil Groves |
|---|---|
| Review Manager: | Needed |
| Download: | Boost Sandbox Vault |
| Description: | A significant update of the range library, including range adapters. |
Shifted Pointer
| Author: | Phil Bouchard |
|---|---|
| Review Manager: | Needed |
| Download: | Boost Sandbox Vault |
| Description: | Smart pointers are in general optimized for a specific resource (memory usage, CPU cycles, user friendliness, ...) depending on what the user need to make the most of. The purpose of this smart pointer is mainly to allocate the reference counter (or owner) and the object itself at the same time so that dynamic memory management is simplified thus accelerated and cheaper on the memory map. |
DataFlow Signals
| Author: | Stjepan Rajko |
|---|---|
| Review Manager: | Needed |
| Download: | http://dancinghacker.com/code/dataflow/ |
| Description: | Dataflow is a generic library for dataflow programming. Dataflow programs can typically be expressed as a graph in which vertices represent components that process data, and edges represent the flow of data between the components. As such, dataflow programs can be easily reconfigured by changing the components and/or the connections. |
Logging
| Author: | John Torjo |
|---|---|
| Review Manager: | Gennadiy Rozental |
| Download: | http://torjo.com/log2/ |
| Description: | Used properly, logging is a very powerful tool. Besides aiding debugging/testing, it can also show you how your application is used. The Boost Logging Library allows just for that, supporting a lot of scenarios, ranging from very simple (dumping all to one destination), to very complex (multiple logs, some enabled/some not, levels, etc). It features a very simple and flexible interface, efficient filtering of messages, thread-safety, formatters and destinations, easy manipulation of logs, finding the best logger/filter classes based on your application's needs, you can define your own macros and much more! |
Futures
| Author: | Braddock Gaskill |
|---|---|
| Review Manager: | Needed |
| Download: | http://braddock.com/~braddock/future/ |
| Description: | The goal of the boost.future library is to provide a definitive future implementation with the best features of the numerous implementations, proposals, and academic papers floating around, in the hopes to avoid multiple incompatible future implementations in libraries of related concepts (coroutines, active objects, asio, etc). This library hopes to explore the combined implementation of the best future concepts. |
Futures
| Author: | Anthony Williams |
|---|---|
| Review Manager: | Needed |
| Download: | http://www.justsoftwaresolutions.co.uk/files/n2561_future.hpp (code) http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2561.html (description) |
| Description: |
This paper proposes a kind of return buffer that takes a value (or an exception) in one (sub-)thread and provides the value in another (controlling) thread. This buffer provides essentially two interfaces:
A typical procedure for working with promises and futures looks like:
Also proposed is a packaged_task that wraps one callable object and provides another one that can be started in its own thread and assigns the return value (or exception) to a return buffer that can be accessed through one of the future classes. With a packaged_task a typical procedure looks like:
|
Notice that we are in the unusual position of having two very different libraries with the same goal in the queue at the same time. The Review Wizards would appreciate a discussion of the best way to hold these two reviews to produce the best possible addition to Boost.
Join
| Author: | Yigong Liu |
|---|---|
| Review Manager: | Needed |
| Download: | http://channel.sourceforge.net/ |
| Description: | Join is an asynchronous, message based C++ concurrency library based on join calculus. It is applicable both to multi-threaded applications and to the orchestration of asynchronous, event-based applications. It follows Comega's design and implementation and builds with Boost facilities. It provides a high level concurrency API with asynchronous methods, synchronous methods, and chords which are "join-patterns" defining the synchronization, asynchrony, and concurrency. |
Pimpl
| Author: | Vladimir Batov |
|---|---|
| Review Manager: | Needed |
| Download: | Boost Sandbox Vault http://www.ddj.com/cpp/205918714 (documentation) |
| Description: | The Pimpl idiom is a simple yet robust technique to minimize coupling via the separation of interface and implementation and then implementation hiding. This library provides a convenient yet flexible and generic deployment technique for the Pimpl idiom. It's seemingly complete and broadly applicable, yet minimal, simple and pleasant to use. |
Libraries under development
Please let us know of any libraries you are currently developing that you intend to submit for review.


{{for activity_type in ACTIVITY_TYPES:}}
{{if activity_type not in ACTIVITY_REVIEWABLE: continue}}
{{for track in ACTIVITY_TRACKS:}}
{{activities_filtered = [act for act in activities if act.type == activity_type and act.track==track]}}
{{if activities_filtered:}}
{{group_id = "%s_%s" % (activity_type, track)}}
{{first = 'in' if first is None else ''}}


Hello, ${data.name}!
${data.referralName} invited you to participate in the To the moon's IСO. This demo stand is designed to demonstrate the capabilities space dashboard. So when you register, you will get free test ETH.
Contact us to order a turn-key solution for your crowdsale:


| chipKIT Sketch Help |
To create a sketch that will run on chipKIT boards, you need to add the chipKIT Core repository for the Arduino IDE. Select File>Preferences (see Figure: Edit Preferences in Arduino IDE) from the Arduino IDE to open the Preferences dialog.Figure: Edit Preferences in Arduino IDEIn the Preferences dialog (Figure: Arduino Preferences Dialog), type to the following URL in the "Additional Boards Manager URLs" field:https://github.com/chipKIT32/chipKIT-core/raw/master/
package_chipkit_index.jsonFigure: Arduino Preferences Dialog
|
Microchip Support
Help Updated: 2017-04-13 14:25:13Z
Help Page: 5
|


Completed files for the David prototype we worked on in Week Three.
N.B. Uses geolocation so must be run over HTTP. We used Prepros.


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XmlHttpRequests that initiate from code executed in an iframe, that is then destroyed, result in an error in FireFox. This test case is used to verify that Closure's IframeIo and XhrLite do not suffer from this problem. See https://bugzilla.mozilla.org/show_bug.cgi?id=369939 .
NOTE(pupius): 14/11/2011 The XhrMonitor code has been removed since the above bug doesn't manifest in any currently supported versions. This test is left in place as a way of verifying the problem doesn't resurface.


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{% extends "gil/base.html" %} {% block title %}{{_("TrueNTH Page Not Found")}}{% endblock %} {% block head %}{{super()}}{% endblock %} {% block main %} {{super()}} {{_("Page Not found")}} {{_("Sorry, the page you requested was not found. It may have been moved.")}} {% endblock %}
{{_("Sorry, the page you requested was not found. It may have been moved.")}}


This page contains introductory videos for the MSc Data Science and Analytics. Students should watch these videos in order to get an idea of the topics they will be studying during the course, and to help them make their optional module selections.
Module Leader: Dr Jianhua Shao
Semester: Spring Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Dr Martin Chorley
Semester: Autumn Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Dr Mathias Treder
Semester: Autumn Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Dr Angelika Kimmig
Semester: Autumn Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Dr Martin Chorley
Semester: Spring Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Professor Omer Rana
Semester: Spring Semester
For module information, please see the Module Handbook and watch the video below
Back to topModule Leader: Dr Padraig Corcoran
Semester: Spring Semester
For module information, please see the Module Handbook and watch the video below
Please note, the below video was recorded by the previous module leader, and some changes may have occured in this module
Back to top


Review the draft agreement before you send
You can review the draft agreement before you send it to the Department of Work and Pensions.
Agreement clauses |
||
|---|---|---|
| What data will be given | {{ data_what }} | Change What data will be given |
| What data will be provided | {{ data_provided }} | Change What data will be provided |
| What the data will be used for | {{ data_use }} | Change What the data will be used for |
| How the data will be accessed | {{ data_how }} | Change How the data will be accessed |
| Who will be responsible for the data access | {{ data['name'] }} <{{ data['email-address'] }}> | Change Who will be responsible for the data access |
| When the data will be acccessed | {{ data_when }} | Change When the data will be accessed |
Now send your request
By submitting this request you are confirming that, to the best of your knowledge, the details you are providing are correct.


---
layout: post
title: 4/11 schoolwork
date: '2014-04-11T22:01:00.003-06:00'
author: Jacob .
tags:
- homeschool-journal
modified_time: '2014-04-11T22:01:51.855-06:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-6733398844341229688
blogger_orig_url: http://www.svlark.com/2014/04/411-schoolwork.html
---
A:
- number bonds
- read on amphibians
- science dictation
- WWE week 21 days 3,4
L:
- puzzles
Norse myths


The video below uses native controls. Reload the page with the permissions policy on/off to see the effect on its controls.
Note: At the moment, you may need to enable the
--enable-picture-in-picture
, --enable-surfaces-for-videos
,
and --enable-experimental-web-platform-features
flags in Chrome 69+
chrome://flags
for this demo to work.
Source: https://mixkit.co/free-stock-video/rain-falling-from-the-roof-on-a-rainy-day-2716/


jMonospace
jQuery plugin to generate lines of monospaced text with any font
Download Files
Non-monospace
The quick brown fox jumped over the lazy dog.
Amazingly few discotheques provide jukeboxes.
jMonospace
The quick brown fox jumped over the lazy dog.
Amazingly few discotheques provide jukeboxes.


Artix Linux - Post-installation
MacBook Pro 9.2
Personal Artix Linux post-installation guide for a MacBook Pro 9.2. This guide is a complement of my Artix Linux post-installation guide and is part of my Linux installation guide.
It will be assumed that this guide is done using an unprivileged user.
Wi-Fi
Broadcom 4331
$ sudo pacman -S linux-headers broadcom-wl-dkms
X Window System
Keyboard and touchpad configuration
Function keys
The behaviour of fn+F<num> is configured by the fnmode
option of the
hid_apple
kernel module. Set the configuration in /etc/modprobe.d/mbp.conf
:
- To disable the fn key, add the following line:
/etc/modprobe.d/mbp.conf
options hid_apple fnmode=0
- To get F<num> by pressing fn+F<num>, add the following
line:
/etc/modprobe.d/mbp.conf
options hid_apple fnmode=1
- To get F<num> by pressing F<num>, add the following line:
/etc/modprobe.d/mbp.conf
options hid_apple fnmode=2
Sound
Configure controller
Add to /etc/modprobe.d/mbp.conf
the following line:
/etc/modprobe.d/mbp.conf
options snd_hda_intel model=intel-mac-auto
Backlight
To prevent ACPI from registering acpi_video0
backlight, set the kernel parameter
acpi_backlight=vendor


Click the following link to go to login section. Click here
Account number of the authority is<PHONE_NUMBER>12G


Assignment #1: PFL067 Statistical NLP
Exploring Entropy and Language Modeling
Instructor: Jan Hajic<EMAIL_ADDRESS>TA: Pavel Pecina<EMAIL_ADDRESS>1. Entropy of a Text
In this experiment, you will determine the conditional entropy of the word distribution in a text given the previous word. To do this, you will first have to compute P(i,j), which is the probability that at any position in the text you will find the word i followed immediately by the word j, and P(j|i), which is the probability that if word i occurs in the text then word j will follow. Given these probabilities, the conditional entropy of the word distribution in a text given the previous word can then be computed as:
The perplexity is then computed simply as
Compute this conditional entropy and perplexity for
TEXTEN1.txt
This file has every word on a separate line. (Punctuation is considered a word, as in many other cases.) The i,j above will also span sentence boundaries, where i is the last word of one sentence and j is the first word of the following sentence (but obviously, there will be a fullstop at the end of most sentences).
Next, you will mess up the text and measure how this alters the conditional entropy. For every character in the text, mess it up with a likelihood of 10%. If a character is chosen to be messed up, map it into a randomly chosen character from the set of characters that appear in the text. Since there is some randomness to the outcome of the experiment, run the experiment 10 times, each time measuring the conditional entropy of the resulting text, and give the min, max, and average entropy from these experiments. Be sure to use srand to reset the random number generator seed each time you run it. Also, be sure each time you are messing up the original text, and not a previously messed up text. Do the same experiment for mess up likelihoods of 5%, 1%, .1%, .01%, and .001%.
Next, for every word in the text, mess it up with a likelihood of 10%. If a word is chosen to be messed up, map it into a randomly chosen word from the set of words that appear in the text. Again run the experiment 10 times, each time measuring the conditional entropy of the resulting text, and give the min, max, and average entropy from these experiments. Do the same experiment for mess up likelihoods of 5%, 1%, .1%, .01%, and .001%.
Now do exactly the same for the file
TEXTCZ1.txt
which contains a similar amount of text in an unknown language (just FYI, that's Czech*)
Tabulate, graph and explain your results. Also try to explain the differences between the two languages. To substantiate your explanations, you might want to tabulate also the basic characteristics of the two texts, such as the word count, number of characters (total, per word), the frequency of the most frequent words, the number of words with frequency 1, etc.
Attach your source code commented in such a way that it is sufficient to read the comments to understand what you have done and how you have done it.
Now assume two languages, L1 and L2 do not share any vocabulary items, and that the conditional entropy as described above of a text T1 in language L1 is E and that the conditional entropy of a text T2 in language L2 is also E. Now make a new text by appending T2 to the end of T1. Will the conditional entropy of this new text be greater than, equal to, or less than E? Explain. [This is a paper-and-pencil exercise of course!]
2. Cross-Entropy and Language Modeling
This task will show you the importance of smoothing for language modeling, and in certain detail it lets you feel its effects.

Here comes the coding: extract word counts from the training data so that you are ready to compute unigram-, bigram- and trigram-based probabilities from them; compute also the uniform probability based on the vocabulary size. Remember (T being the text size, and V the vocabulary size, i.e. the number of types - different word forms found in the training text):
p0(wi) = 1 / V
p1(wi) = c1(wi) / T
p2(wi|wi-1)
= c2(wi-1,wi) /
c1(wi-1)
p3(wi|wi-2,wi-1)
= c3(wi-2,wi-1,wi) /
c2(wi-2,wi-1)
Be careful; remember how to handle correctly the beginning and end of the training data with respect to bigram and trigram counts.
Now compute the four smoothing parameters (i.e. "coefficients", "weights", "lambdas", "interpolation parameters" or whatever, for the trigram, bigram, unigram and uniform distributions) from the heldout data using the EM algorithm. [Then do the same using the training data again: what smoothing coefficients have you got? After answering this question, throw them away!] Remember, the smoothed model has the following form:
ps(wi|wi-2,wi-1) = l0p0(wi)+ l1p1(wi)+ l2p2(wi|wi-1) + l3p3(wi|wi-2,wi-1),
where
l0 + l1 + l2 + l3 = 1.
And finally, compute the cross-entropy of the test data using your newly built, smoothed language model. Now tweak the smoothing parameters in the following way: add 10%, 20%, 30%, ..., 90%, 95% and 99% of the difference between the trigram smoothing parameter and 1.0 to its value, discounting at the same the remaining three parameters proportionally (remember, they have to sum up to 1.0!!). Then set the trigram smoothing parameter to 90%, 80%, 70%, ... 10%, 0% of its value, boosting proportionally the other three parameters, again to sum up to one. Compute the cross-entropy on the test data for all these 22 cases (original + 11 trigram parameter increase + 10 trigram smoothing parameter decrease). Tabulate, graph and explain what you have got. Also, try to explain the differences between the two languages based on similar statistics as in the Task No. 2, plus the "coverage" graph (defined as the percentage of words in the test data which have been seen in the training data).
Attach your source code commented in such a way that it is sufficient to read the comments to understand what you have done and how you have done it.
* If you want to see the accents correctly, select ISO Latin 2 coding (charset=iso-8859-2) for viewing, but your programs obviously will (should) work in any case (supposing they are 8-bit clean). For those linguistically minded & wishing to learn more about the language, look here. We will be using texts in this language often, albeit you will never be required to learn it.)


StreamStats
Seve Badajoz & Thomas Lee
Problem:
Analytics is a too simplistic for mid/high end traffic streamers (~1K+ subscribers)
Solution:
A convenient dashboard that graphs complicated anlytics for these streamers. Only
available for Twitch streamers.
1. Fetch broadcaster data and their associated subscriber info
2. Graphing analytics of the broadcaster based on their subscribers
- Viewership and subscriptions per game
- Leaderboard between subscriptions/viewership per game
- d3 implementation (currently using chart.js)


This icon is desgined to assit you through other organizations that is assiting Autistic people, like yourself. There are several locations located on the map, click on the marker to find a decent amount of information and a website that will further help with your questions.


|
Adbase
Adinf C++ base library V2
|
Directories | |
| directory | Config |
| directory | Head |
| directory | Http |
| directory | Kafka |
| directory | Logging |
| directory | Mc |
| directory | Metrics |
| directory | Net |
| directory | Utility |
Files | |
| file | Config.hpp [code] |
| file | Head.hpp [code] |
| file | Http.hpp [code] |
| file | Kafka.hpp [code] |
| file | Logging.hpp [code] |
| file | Mc.hpp [code] |
| file | Metrics.hpp [code] |
| file | Net.hpp [code] |
| file | Utility.hpp [code] |
| file | Version.hpp [code] |


Maame Serwaa shakes her huge tundra for the camera
by Nana YawIt seems Maame Serwaa just doesn’t only have big breasts, her nyash is also as big as her breasts. Many have not noticed because of her usual pose for the glam.
In a new video sighted online, the Kumawood decided to give a view of her backside by cat-walking and shaking it to the excitement of her followers.
Read also: Has Maame Serwaa now taken over Pamela Odame’s position for the ‘Breastina’ tag? (+Photos)
The 20-year-old actress has grown at a faster rate and it has always thrown fans into doubt as to whether she’s indeed a 20-year-old girl. Well, she is.


Architecture
A brief description of the GSToRE architecture.
View docs
Stable API
Versioned, stable services.
View docs
Developer API
Services listed as Developer may undergo significant changes to the routing or service responses. Use at your own risk.
View docs


My Hobbies
Playing Instruments, Music Theory and Arrangement
ABRSM: Music Theory Grade 5
ABRSM: Piano Grade 4
Violin
Electric Bass
Melodica
My Youtube Channel!
I'm by no means a professional but I thoroughly enjoy covering video game music, especially OSTs from Final Fantasy. Do let me know if you're interested in a collaboration!
Japanese Culture, Language and Media
こんいちは皆さん！日本語を学ぶのが本当難しいだ。でも、頑張ります！
Learning the Japanese language is really quite the challenge, but what is life without a little challenge?


| Class | ActionController::Dispatcher |
| In: |
vendor/rails/actionpack/lib/action_controller/dispatcher.rb
|
| Parent: | Object |
Dispatches requests to the appropriate controller and takes care of reloading the app after each request when Dependencies.load? is true.
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 8 8: def define_dispatcher_callbacks(cache_classes) 9: unless cache_classes 10: # Development mode callbacks 11: before_dispatch :reload_application 12: after_dispatch :cleanup_application 13: end 14: 15: # Common callbacks 16: to_prepare :load_application_controller do 17: begin 18: require_dependency 'application' unless defined?(::ApplicationController) 19: rescue LoadError => error 20: raise unless error.message =~ /application\.rb/ 21: end 22: end 23: 24: if defined?(ActiveRecord) 25: before_dispatch { ActiveRecord::Base.verify_active_connections! } 26: to_prepare(:activerecord_instantiate_observers) { ActiveRecord::Base.instantiate_observers } 27: end 28: 29: after_dispatch :flush_logger if defined?(RAILS_DEFAULT_LOGGER) && RAILS_DEFAULT_LOGGER.respond_to?(:flush) 30: end
Backward-compatible class method takes CGI-specific args. Deprecated in favor of Dispatcher.new(output, request, response).dispatch.
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 34 34: def dispatch(cgi = nil, session_options = CgiRequest::DEFAULT_SESSION_OPTIONS, output = $stdout) 35: new(output).dispatch_cgi(cgi, session_options) 36: end
If the block raises, send status code as a last-ditch response.
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 53 53: def failsafe_response(fallback_output, status, originating_exception = nil) 54: yield 55: rescue Exception => exception 56: begin 57: log_failsafe_exception(status, originating_exception || exception) 58: body = failsafe_response_body(status) 59: fallback_output.write "Status: #{status}\r\nContent-Type: text/html\r\n\r\n#{body}" 60: nil 61: rescue Exception => failsafe_error # Logger or IO errors 62: $stderr.puts "Error during failsafe response: #{failsafe_error}" 63: $stderr.puts "(originally #{originating_exception})" if originating_exception 64: end 65: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 99 99: def initialize(output, request = nil, response = nil) 100: @output, @request, @response = output, request, response 101: end
Add a preparation callback. Preparation callbacks are run before every request in development mode, and before the first request in production mode.
An optional identifier may be supplied for the callback. If provided, to_prepare may be called again with the same identifier to replace the existing callback. Passing an identifier is a suggested practice if the code adding a preparation block may be reloaded.
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 46 46: def to_prepare(identifier = nil, &block) 47: @prepare_dispatch_callbacks ||= ActiveSupport::Callbacks::CallbackChain.new 48: callback = ActiveSupport::Callbacks::Callback.new(:prepare_dispatch, block, :identifier => identifier) 49: @prepare_dispatch_callbacks | callback 50: end
Cleanup the application by clearing out loaded classes so they can be reloaded on the next request without restarting the server.
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 136 136: def cleanup_application 137: ActiveRecord::Base.reset_subclasses if defined?(ActiveRecord) 138: Dependencies.clear 139: ActiveRecord::Base.clear_reloadable_connections! if defined?(ActiveRecord) 140: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 103 103: def dispatch 104: @@guard.synchronize do 105: begin 106: run_callbacks :before_dispatch 107: handle_request 108: rescue Exception => exception 109: failsafe_rescue exception 110: ensure 111: run_callbacks :after_dispatch, :enumerator => :reverse_each 112: end 113: end 114: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 116 116: def dispatch_cgi(cgi, session_options) 117: if cgi ||= self.class.failsafe_response(@output, '400 Bad Request') { CGI.new } 118: @request = CgiRequest.new(cgi, session_options) 119: @response = CgiResponse.new(cgi) 120: dispatch 121: end 122: rescue Exception => exception 123: failsafe_rescue exception 124: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 142 142: def flush_logger 143: RAILS_DEFAULT_LOGGER.flush 144: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 126 126: def reload_application 127: # Run prepare callbacks before every request in development mode 128: run_callbacks :prepare_dispatch 129: 130: Routing::Routes.reload 131: ActionView::TemplateFinder.reload! unless ActionView::Base.cache_template_loading 132: end
# File vendor/rails/actionpack/lib/action_controller/dispatcher.rb, line 152 152: def failsafe_rescue(exception) 153: self.class.failsafe_response(@output, '500 Internal Server Error', exception) do 154: if @controller ||= defined?(::ApplicationController) ? ::ApplicationController : Base 155: @controller.process_with_exception(@request, @response, exception).out(@output) 156: else 157: raise exception 158: end 159: end 160: end


We are currently in a period of rapid technology expansion. When this massive growth takes place, sometimes new concepts and their benefits are difficult to understand. One of the most prominent advancements, and one that is here to stay, is the Sharing Economy.
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By definition from The People Who Share, “The Sharing Economy is a socio-economic ecosystem built around the sharing of human and physical resources.” In short, the Sharing Economy is a network of individuals who re-use resources, and utilize resources that aren’t being used at all.
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The Sharing Economy allows people to easily connect based on resources and needs. This means that the issues of overconsumption and under-utilization can now be overcome through a community marketplace. As a result, the Sharing Economy is economically and environmentally sustainable: a goal for every society in the world.
Listant
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asteroid01 module¶
Description¶
This is the most basic framework of the asteroid game. It establishes a Model-View-Controller (MVC) pattern for coding design. This should make it easier to work on different pieces at once.
Here, the done flag and the time are passed from the model to the view. The controller keeps track of time and check for user input.
The ‘game’ starts when the screen is clicked, and keeps track of the time indefiniately until the ‘a’ button is pressed. Another click will then close the screen.
This is just a simple demonstrantion of how we’re implementing MVC.
-
class
asteroid01.
Controller
¶ Bases:
object
Handles time, user input, and passing data from model to view.
Attributes
model (Model) An instance of the Model class to run the game view (View) An instance of the View class to render the game done (bool) Flag to end the main loop click (grf.Point) The location of the last click by the user key (str) The name of the last key pressed by the user framerate (float) The number of seconds per frame (technically inverse-framerate) start_time (float) Initial start time of game. Used for referencing current length of game. Methods
-
check_user_input
()¶ Record if the user has clicked the mouse or pressed a key
-
run
()¶ Main game loop
-
update
()¶ Updates all portions of the gam (input, model, view, time).
Returns: done : bool
Whether or not the game is finished yet
-
update_time
(loop_start)¶ Checks runtime of loop, sleeps if necessary, and sets model time
Parameters: loop_start : float
A time.time() measurement from the beginning of the loop
-
-
class
asteroid01.
Model
¶ Bases:
object
Contains all of the core game logic
Attributes
width (int) The width of the gameplay space height (int) The height of the gameplay space time (float) The current length of time the game has been played done (bool) Whether or not the game is finished yet observables (dict {str) Keeps track of all the attributes needed for the view Methods
-
set_time
(time)¶
-
update
(click, key)¶ Ends game of ‘a’ is pressed and updates the observables
Returns: done : bool
Whether or not the game is finished yet
-
update_observables
()¶ Recreate observables dict to reflect current state of model
-
-
class
asteroid01.
View
(width, height)¶ Bases:
object
Responsible for rendering the game, given the proper information
Parameters: width : int
The width of the GUI window
height : int
The height of the GUI window
Attributes
win (grf.GraphWin) The GUI window in which the game will be played time_time (grf.Text) Shows the current time played in seconds done_text (grf.Text) Shows whether or not the game is finished observed (dict) All the variables received from the model Methods
-
draw_done
()¶ Update the ‘Done’ label
-
draw_time
()¶ Update the ‘Time’ label
-
initialize
()¶ Draw everything to the screen for the first time
-
update
(observables)¶ Set the observed variables and update everything to the screen
-


Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
| Version: | 1.2.2 |
| Depends: | R (≥ 3.2.2) |
| Imports: | cluster, dplyr (≥ 0.4.3), methods, stats |
| Suggests: | ade4, ggplot2, knitr, Matrix, rmarkdown, testthat, tidytext |
| Published: | 2018-03-05 |
| Author: | Matthias Grenié [aut, cre], Pierre Denelle [aut], Caroline Tucker [aut], François Munoz [ths], Cyrille Violle [ths] |
| Maintainer: | Matthias Grenié <matthias.grenie at gmail.com> |
| BugReports: | https://github.com/Rekyt/funrar/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/Rekyt/funrar |
| NeedsCompilation: | no |
| Citation: | funrar citation info |
| Materials: | README NEWS |
| CRAN checks: | funrar results |
| Reference manual: | funrar.pdf |
| Vignettes: |
How to use 'funrar', an example with plant communities dataset Sparse Marices with 'funrar' |
| Package source: | funrar_1.2.2.tar.gz |
| Windows binaries: | r-devel: funrar_1.2.2.zip, r-release: funrar_1.2.2.zip, r-oldrel: funrar_1.2.2.zip |
| OS X binaries: | r-release: funrar_1.2.2.tgz, r-oldrel: funrar_1.2.2.tgz |
| Old sources: | funrar archive |
Please use the canonical form https://CRAN.R-project.org/package=funrar to link to this page.


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Next: Congruent Lambda-lists for all Methods of a Generic Function, Previous: Introduction to Methods, Up: Generic Functions and Methods
Two methods are said to agree with each other on parameter specializers and qualifiers if the following conditions hold:
Both methods have the same number of required parameters. Suppose the parameter specializers of the two methods are P_{1,1}... P_{1,n} and P_{2,1}... P_{2,n}.
For each 1<= i<= n, P_{1,i} agrees with P_{2,i}. The parameter specializer P_{1,i} agrees with P_{2,i} if P_{1,i} and P_{2,i} are the same class or if P_{1,i}=(eql object_1), P_{2,i}=(eql object_2), and (eql object_1 object_2). Otherwise P_{1,i} and P_{2,i} do not agree.
The two lists of qualifiers are the same under equal.


The Parameter class is the implementation of a resource’s attributes of parameter kind. The Parameter class is also the base class for {Puppet::Property}, and is used to describe meta-parameters (parameters that apply to all resource types). A Parameter (in contrast to a Property) has a single value where a property has both a current and a wanted value. The Parameter class methods are used to configure and create an instance of Parameter that represents one particular attribute data type; its valid value(s), and conversion to/from internal form.
The intention is that a new parameter is created by using the DSL method {Puppet::Type.newparam}, or {Puppet::Type.newmetaparam} if the parameter should be applicable to all resource types.
A Parameter that does not specify and valid values (via {newvalues}) accepts any value.
@see Puppet::Type @see Puppet::Property @api public
Makes the given `name` an alias for the given `other` name. Or said differently, the valid value `other` can now also be referred to via the given `name`. Aliasing may affect how the parameter’s value is serialized/stored (it may store the `other` value instead of the alias). @api public @dsl type
# File lib/puppet/parameter.rb, line 274 def aliasvalue(name, other) @value_collection.aliasvalue(name, other) end
Defines how the `default` value of a parameter is computed. The computation of the parameter’s default value is defined by providing a value or a block. A default of `nil` can not be used. @overload defaultto(value)
Defines the default value with a literal value @param value [Object] the literal value to use as the default value
@overload defaultto({|| … })
Defines that the default value is produced by the given block. The given block should produce the default value.
@raise [Puppet::DevError] if value is nil, and no block is given. @return [void] @see Parameter.default @dsl type @api public
# File lib/puppet/parameter.rb, line 99 def defaultto(value = nil, &block) if block define_method(:default, &block) else if value.nil? raise Puppet::DevError, "Either a default value or block must be provided" end define_method(:default) do value end end end
Sets the documentation for this parameter. @param str [String] The documentation string to set @return [String] the given `str` parameter @see doc @dsl type @api public
# File lib/puppet/parameter.rb, line 154 def desc(str) @doc = str end
Produces a documentation string. If an enumeration of _valid values_ has been defined, it is appended to the documentation for this parameter specified with the {desc} method. @return [String] Returns a documentation string. @api public
# File lib/puppet/parameter.rb, line 117 def doc @doc ||= "" unless defined?(@addeddocvals) @doc += value_collection.doc if f = self.required_features @doc += " Requires features #{f.flatten.collect { |f| f.to_s }.join(" ")}." end @addeddocvals = true end @doc end
Produces a String with the value formatted for display to a human. When the parameter value is a:
**single valued parameter value** the result is produced on the form `‘value’` where value is the string form of the parameter’s value.
*Array* the list of values is enclosed in `[]`, and each produced value is separated by a comma.
*Hash* value is output with keys in sorted order enclosed in `{}` with each entry formatted on the form `‘k’ => v` where `k` is the key in string form and v is the value of the key. Entries are comma separated.
For both Array and Hash this method is called recursively to format contained values. @note this method does not protect against infinite structures.
@return [String] The formatted value in string form.
# File lib/puppet/parameter.rb, line 541 def self.format_value_for_display(value) if value.is_a? Array formatted_values = value.collect {|value| format_value_for_display(value)}.join(', ') "[#{formatted_values}]" elsif value.is_a? Hash # Sorting the hash keys for display is largely for having stable # output to test against, but also helps when scanning for hash # keys, since they will be in ASCIIbetical order. hash = value.keys.sort {|a,b| a.to_s <=> b.to_s}.collect do |k| "'#{k}' => #{format_value_for_display(value[k])}" end.join(', ') "{#{hash}}" else "'#{value}'" end end
Initializes the instance variables. Clears the internal value collection (set of allowed values). @return [void] @api private
# File lib/puppet/parameter.rb, line 163 def initvars @value_collection = ValueCollection.new end
Sets a marker indicating that this parameter is the namevar (unique identifier) of the type where the parameter is contained. This also makes the parameter a required value. The marker can not be unset once it has been set. @return [void] @dsl type @api public
# File lib/puppet/parameter.rb, line 202 def isnamevar @isnamevar = true @required = true end
@return [Boolean] Returns whether this parameter is the namevar or not. @api public
# File lib/puppet/parameter.rb, line 210 def isnamevar? @isnamevar end
Sets a marker indicating that this parameter is required. Once set, it is not possible to make a parameter optional. @return [void] @dsl type @api public
# File lib/puppet/parameter.rb, line 220 def isrequired @required = true end
@overload munge {|| … } Defines an optional method used to convert the parameter value from DSL/string form to an internal form. If a munge method is not defined, the DSL/string value is used as is. @note This adds a method with the name `unsafe_munge` in the created parameter class. Later this method is
called in a context where exceptions will be rescued and handled.
@dsl type @api public
# File lib/puppet/parameter.rb, line 175 def munge(&block) # I need to wrap the unsafe version in begin/rescue parameterments, # but if I directly call the block then it gets bound to the # class's context, not the instance's, thus the two methods, # instead of just one. define_method(:unsafe_munge, &block) end
Initializes the parameter with a required resource reference and optional attribute settings. The option `:resource` must be specified or an exception is raised. Any additional options passed are used to initialize the attributes of this parameter by treating each key in the `options` hash as the name of the attribute to set, and the value as the value to set. @param options [Hash{Symbol => Object]] Options, where `resource` is required @option options [Puppet::Resource] :resource The resource this parameter holds a value for. Required. @raise [Puppet::DevError] If resource is not specified in the options hash. @api public @note A parameter should be created via the DSL method {Puppet::Type::newparam}
# File lib/puppet/parameter.rb, line 329 def initialize(options = {}) options = symbolize_options(options) if resource = options[:resource] self.resource = resource options.delete(:resource) else raise Puppet::DevError, "No resource set for #{self.class.name}" end set_options(options) end
Defines valid values for the parameter (enumeration or regular expressions). The set of valid values for the parameter can be limited to a (mix of) literal values and regular expression patterns. @note Each call to this method adds to the set of valid values @param names [Symbol, Regexp] The set of valid literal values and/or patterns for the parameter. @return [void] @dsl type @api public
# File lib/puppet/parameter.rb, line 263 def newvalues(*names) @value_collection.newvalues(*names) end
Removes the `default` method if defined. Has no effect if the default method is not defined. This method is intended to be used in a DSL scenario where a parameter inherits from a parameter with a default value that is not wanted in the derived parameter (otherwise, simply do not define a default value method).
@return [void] @see desc @api public @dsl type
# File lib/puppet/parameter.rb, line 143 def nodefault undef_method :default if public_method_defined? :default end
Creates instance (proxy) methods that delegates to a class method with the same name. @api private
# File lib/puppet/parameter.rb, line 282 def self.proxymethods(*values) values.each { |val| define_method(val) do self.class.send(val) end } end
Returns whether this parameter is required or not. A parameter is required if a call has been made to the DSL method {isrequired}. @return [Boolean] Returns whether this parameter is required or not. @api public
# File lib/puppet/parameter.rb, line 239 def required? @required end
@comment This method is not picked up by yard as it has a different signature than
expected for an attribute (varargs). Instead, this method is documented as an overload of the attribute required_features. (Not ideal, but better than nothing).
@todo If this text appears in documentation - see comment in source and makes corrections - it means
that an issue in yardoc has been fixed.
# File lib/puppet/parameter.rb, line 230 def required_features=(*args) @required_features = args.flatten.collect { |a| a.to_s.downcase.intern } end
@overload unmunge {|| … } Defines an optional method used to convert the parameter value to DSL/string form from an internal form. If an `unmunge` method is not defined, the internal form is used. @see munge @note This adds a method with the name `unmunge` in the created parameter class. @dsl type @api public
# File lib/puppet/parameter.rb, line 191 def unmunge(&block) define_method(:unmunge, &block) end
@overload validate {|| … } Defines an optional method that is used to validate the parameter’s value. Validation should raise appropriate exceptions, the return value of the given block is ignored. @return [void] @dsl type @api public
# File lib/puppet/parameter.rb, line 250 def validate(&block) define_method(:unsafe_validate, &block) end
Writes the given `msg` to the log with the loglevel indicated by the associated resource’s `loglevel` parameter. @todo is loglevel a metaparameter? it is looked up with `resource` @return [void] @api public
# File lib/puppet/parameter.rb, line 346 def log(msg) send_log(resource[:loglevel], msg) end
@return [Boolean] Returns whether this parameter is a meta-parameter or not.
# File lib/puppet/parameter.rb, line 351 def metaparam? self.class.metaparam end
Munges the value to internal form. This implementation of `munge` provides exception handling around the specified munging of this parameter. @note This method should not be overridden. Use the DSL method {munge} to define a munging method
if required.
@param value [Object] the DSL value to munge @return [Object] the munged (internal) value
# File lib/puppet/parameter.rb, line 415 def munge(value) begin ret = unsafe_munge(value) rescue Puppet::Error => detail Puppet.debug "Reraising #{detail}" raise rescue => detail raise Puppet::DevError, "Munging failed for value #{value.inspect} in class #{self.name}: #{detail}", detail.backtrace end ret end
@!attribute [r] name @return [Symbol] The parameter’s name as given when it was created. @note Since a Parameter defines the name at the class level, each Parameter class must be
unique within a type's inheritance chain.
@comment each parameter class must define the name method, and parameter
instances do not change that name this implicitly means that a given object can only have one parameter instance of a given parameter class
# File lib/puppet/parameter.rb, line 363 def name self.class.name end
@return [Boolean] Returns true if this parameter, the associated resource, or overall puppet mode is `noop`. @todo How is noop mode set for a parameter? Is this of value in DSL to inhibit a parameter?
# File lib/puppet/parameter.rb, line 370 def noop @noop ||= false tmp = @noop || self.resource.noop || Puppet[:noop] || false #debug "noop is #{tmp}" tmp end
@todo Original comment = _return the full path to us, for logging and rollback; not currently
used_ This is difficult to figure out (if it is used or not as calls are certainly made to "pathbuilder" method is several places, not just sure if it is this implementation or not.
@api private
# File lib/puppet/parameter.rb, line 382 def pathbuilder if @resource return<EMAIL_ADDRESS>self.name] else return [self.name] end end
@return [Puppet::Provider] Returns the provider of the associated resource. @todo The original comment says = _“Retrieve the resource’s provider.
Some types don't have providers, in which case we return the resource object itself."_ This does not seem to be true, the default implementation that sets this value may be {Puppet::Type.provider=} which always gets either the name of a provider or an instance of one.
# File lib/puppet/parameter.rb, line 496 def provider @resource.provider end
Sets the associated resource to nil. @todo Why - what is the intent/purpose of this? @return [nil]
# File lib/puppet/parameter.rb, line 464 def remove @resource = nil end
@return [String] The name of the parameter in string form.
# File lib/puppet/parameter.rb, line 519 def to_s name.to_s end
Unmunges the value by transforming it from internal form to DSL form. This is the default implementation of `unmunge` that simply returns the value without processing. The DSL method {unmunge} should be used to define an overriding method if required. @return [Object] the unmunged value
# File lib/puppet/parameter.rb, line 404 def unmunge(value) value end
This is the default implementation of `munge` that simply produces the value (if it is valid). The DSL method {munge} should be used to define an overriding method if munging is required.
@api private
# File lib/puppet/parameter.rb, line 395 def unsafe_munge(value) self.class.value_collection.munge(value) end
This is the default implementation of `validate` that may be overridden by the DSL method {validate}. If no valid values have been defined, the given value is accepted, else it is validated against the literal values (enumerator) and/or patterns defined by calling {newvalues}.
@param value [Object] the value to check for validity @raise [ArgumentError] if the value is not valid @return [void] @api private
# File lib/puppet/parameter.rb, line 436 def unsafe_validate(value) self.class.value_collection.validate(value) end
Performs validation of the given value against the rules defined by this parameter. @return [void] @todo Better description of when the various exceptions are raised.ArgumentError is rescued and
changed into Puppet::Error.
@raise [ArgumentError, TypeError, Puppet::DevError, Puppet::Error] under various conditions A protected validation method that only ever raises useful exceptions. @api public
# File lib/puppet/parameter.rb, line 448 def validate(value) begin unsafe_validate(value) rescue ArgumentError => detail fail detail.to_s rescue Puppet::Error, TypeError raise rescue => detail raise Puppet::DevError, "Validate method failed for class #{self.name}: #{detail}", detail.backtrace end end
@return [Object] Gets the value of this parameter after performing any specified unmunging.
# File lib/puppet/parameter.rb, line 469 def value unmunge(@value) unless @value.nil? end
Sets the given value as the value of this parameter. @todo This original comment _“All of the checking should possibly be
late-binding (e.g., users might not exist when the value is assigned but might when it is asked for)."_ does not seem to be correct, the implementation calls both validate an munge on the given value, so no late binding.
The given value is validated and then munged (if munging has been specified). The result is store as the value of this arameter. @return [Object] The given `value` after munging. @raise (see validate)
# File lib/puppet/parameter.rb, line 484 def value=(value) validate(value) @value = munge(value) end
Generated with the Darkfish Rdoc Generator 2.


|
ReactPhysics3D
0.7.1
Physics engine library
|
This class represents the contact solver that is used to solve rigid bodies contacts. More...
#include <ContactSolver.h>
Public Member Functions | |
| ContactSolver (MemoryManager &memoryManager, const WorldSettings &worldSettings) | |
| Constructor. | |
| ~ContactSolver ()=default | |
| Destructor. | |
| void | init (Island **islands, uint nbIslands, decimal timeStep) |
| Initialize the contact constraints. | |
| void | initializeForIsland (Island *island) |
| Initialize the constraint solver for a given island. | |
| void | setSplitVelocitiesArrays (Vector3 *splitLinearVelocities, Vector3 *splitAngularVelocities) |
| Set the split velocities arrays. | |
| void | setConstrainedVelocitiesArrays (Vector3 *constrainedLinearVelocities, Vector3 *constrainedAngularVelocities) |
| Set the constrained velocities arrays. | |
| void | storeImpulses () |
| Store the computed impulses to use them to warm start the solver at the next iteration. | |
| void | solve () |
| Solve the contacts. | |
| bool | isSplitImpulseActive () const |
| Return true if the split impulses position correction technique is used for contacts. | |
| void | setIsSplitImpulseActive (bool isActive) |
| Activate or Deactivate the split impulses for contacts. | |
This class represents the contact solver that is used to solve rigid bodies contacts.
The constraint solver is based on the "Sequential Impulse" technique described by Erin Catto in his GDC slides (http://code.google.com/p/box2d/downloads/list).
A constraint between two bodies is represented by a function C(x) which is equal to zero when the constraint is satisfied. The condition C(x)=0 describes a valid position and the condition dC(x)/dt=0 describes a valid velocity. We have dC(x)/dt = Jv + b = 0 where J is the Jacobian matrix of the constraint, v is a vector that contains the velocity of both bodies and b is the constraint bias. We are looking for a force F_c that will act on the bodies to keep the constraint satisfied. Note that from the virtual work principle, we have F_c = J^t * lambda where J^t is the transpose of the Jacobian matrix and lambda is a Lagrange multiplier. Therefore, finding the force F_c is equivalent to finding the Lagrange multiplier lambda.
An impulse P = F * dt where F is a force and dt is the timestep. We can apply impulses a body to change its velocity. The idea of the Sequential Impulse technique is to apply impulses to bodies of each constraints in order to keep the constraint satisfied.
— Step 1 —
First, we integrate the applied force F_a acting of each rigid body (like gravity, ...) and we obtain some new velocities v2' that tends to violate the constraints.
v2' = v1 + dt * M^-1 * F_a
where M is a matrix that contains mass and inertia tensor information.
— Step 2 —
During the second step, we iterate over all the constraints for a certain number of iterations and for each constraint we compute the impulse to apply to the bodies needed so that the new velocity of the bodies satisfy Jv + b = 0. From the Newton law, we know that M * deltaV = P_c where M is the mass of the body, deltaV is the difference of velocity and P_c is the constraint impulse to apply to the body. Therefore, we have v2 = v2' + M^-1 * P_c. For each constraint, we can compute the Lagrange multiplier lambda using : lambda = -m_c (Jv2' + b) where m_c = 1 / (J * M^-1 * J^t). Now that we have the Lagrange multiplier lambda, we can compute the impulse P_c = J^t * lambda * dt to apply to the bodies to satisfy the constraint.
— Step 3 —
In the third step, we integrate the new position x2 of the bodies using the new velocities v2 computed in the second step with : x2 = x1 + dt * v2.
Note that in the following code (as it is also explained in the slides from Erin Catto), the value lambda is not only the lagrange multiplier but is the multiplication of the Lagrange multiplier with the timestep dt. Therefore, in the following code, when we use lambda, we mean (lambda * dt).
We are using the accumulated impulse technique that is also described in the slides from Erin Catto.
We are also using warm starting. The idea is to warm start the solver at the beginning of each step by applying the last impulstes for the constraints that we already existing at the previous step. This allows the iterative solver to converge faster towards the solution.
For contact constraints, we are also using split impulses so that the position correction that uses Baumgarte stabilization does not change the momentum of the bodies.
There are two ways to apply the friction constraints. Either the friction constraints are applied at each contact point or they are applied only at the center of the contact manifold between two bodies. If we solve the friction constraints at each contact point, we need two constraints (two tangential friction directions) and if we solve the friction constraints at the center of the contact manifold, we need two constraints for tangential friction but also another twist friction constraint to prevent spin of the body around the contact manifold center.


SKILL
Section: Linux User's Manual (1)Updated: March 12, 1999
Index Return to Main Contents
NAME
skill, snice - send a signal or report process statusSYNOPSIS
skill [signal to send] [options] process selection criteria snice [new priority] [options] process selection criteria
DESCRIPTION
These tools are probably obsolete and unportable. The command syntax is poorly defined. Consider using the killall, pkill, and pgrep commands instead.The default signal for skill is TERM. Use -l or -L to list available signals. Particularly useful signals include HUP, INT, KILL, STOP, CONT, and 0. Alternate signals may be specified in three ways: -9 -SIGKILL -KILL.
The default priority for snice is +4. (snice +4 ...) Priority numbers range from +20 (slowest) to -20 (fastest). Negative priority numbers are restricted to administrative users.
GENERAL OPTIONS
| -f | fast mode | This is not currently useful. |
| -i | interactive use |
You will be asked to approve each action.
|
| -v | verbose output |
Display information about selected processes.
|
| -w | warnings enabled | This is not currently useful. |
| -n | no action | This only displays the process ID. |
| -V | show version | Displays version of program. |
PROCESS SELECTION OPTIONS
Selection criteria can be: terminal, user, pid, command. The options below may be used to ensure correct interpretation. Do not blame Albert for this interesting interface.| -t | The next argument is a terminal (tty or pty). |
| -u | The next argument is a username. |
| -p | The next argument is a process ID number. |
| -c | The next argument is a command name. |
SIGNALS
The signals listed below may be available for use with skill. When known, numbers and default behavior are shown.| Name | Num | Action | Description |
| 0 | 0 | n/a | exit code indicates if a signal may be sent |
| ALRM | 14 | exit | |
| HUP | 1 | exit | |
| INT | 2 | exit | |
| KILL | 9 | exit | this signal may not be blocked |
| PIPE | 13 | exit | |
| POLL | exit | ||
| PROF | exit | ||
| TERM | 15 | exit | |
| USR1 | exit | ||
| USR2 | exit | ||
| VTALRM | exit | ||
| STKFLT | exit | may not be implemented | |
| PWR | ignore | may exit on some systems | |
| WINCH | ignore | ||
| CHLD | ignore | ||
| URG | ignore | ||
| TSTP | stop | may interact with the shell | |
| TTIN | stop | may interact with the shell | |
| TTOU | stop | may interact with the shell | |
| STOP | stop | this signal may not be blocked | |
| CONT | restart | continue if stopped, otherwise ignore | |
| ABRT | 6 | core | |
| FPE | 8 | core | |
| ILL | 4 | core | |
| QUIT | 3 | core | |
| SEGV | 11 | core | |
| TRAP | 5 | core | |
| SYS | core | may not be implemented | |
| EMT | core | may not be implemented | |
| BUS | core | core dump may fail | |
| XCPU | core | core dump may fail | |
| XFSZ | core | core dump may fail |
EXAMPLES
| Command | Description |
| snice seti crack +7 | Slow down seti and crack |
| skill -KILL -v /dev/pts/* | Kill users on new-style PTY devices |
| skill -STOP viro lm davem | Stop 3 users |
| snice -17 root bash | Give priority to root's shell |
SEE ALSO
killall(1) pkill(1) kill(1) renice(1) nice(1) signal(7) kill(2)STANDARDS
No standards apply.AUTHOR
Albert Cahalan<EMAIL_ADDRESS>wrote skill and snice in 1999 as a replacement for a non-free version, and is the current maintainer of the procps collection. Please send bug reports to<EMAIL_ADDRESS>Index
- NAME
- SYNOPSIS
- DESCRIPTION
- GENERAL OPTIONS
- PROCESS SELECTION OPTIONS
- SIGNALS
- EXAMPLES
- SEE ALSO
- STANDARDS
- AUTHOR
This document was created by man2html, using the manual pages.
Time: 05:29:10 GMT, December 24, 2015


{% extends 'base.html' %} {% block title %}Enrolled Courses{% endblock title %} {% block header_text %}Enrolled Courses{% endblock header_text %} {% block content %}
| Course Name | Session | Start Time | End Time |
| {{ course.course.name }} | {{ course.timeblock.name }} | {{ course.timeblock.start_time|date:"h:i A" }} | {{ course.timeblock.end_time|date:"h:i A" }} |


Interactive rebase in Sourcetree
The Official Blog is concise and sufficient. And I put a screenshot here to make it easier to understand.


-- Linda Bulk and Robert Brouwer
“Every idea starts with a problem. Linda and I were doing commercial photography and dreamt about getting an image from the sky without the cost of a helicopter.”


Please use this tool when calling our IS Support Center at 4-8080
Memory Information
Available
Total
--
--
Networking Information
Name
Address
Storage Information
ID
Type
Capacity
CPU Information
--
CPU #
Idle (ms)
Kernel (ms)
User (ms)
Total (ms)
Display Information
ID
Name
Mirroring Source
Primary
Internal
Enabled
Work Area
DPI X:Y
Authorize
This application section allows only authorized personnel use.
Any unauthorized attempts are logged and monitored.
Device ID:
OU:
Model:
S/N:
NIC:
Wifi:
OS:
User:
Location:
Notes:
PATCH
New OU:
MOVE
* restart required
Asset ID:
...
IS Field Support tech


Akan Names
Akan names are derived from Ghanian culture.
Frequently in Ghana, children are given their first name as a 'day name'
which corresponds to the day in the week they were born.
Here are Ghanian day names.
Century
Year
Month
Day
male
Female
SEND
cancel


| (BS) BAHAMAS |
| Subdivision | 31 districts (en) / district (fr) |
| List Source | BET 1996; http://www.geonames.de/coubs.html ; http://www.statoids.com/ubs.html ; http://laws.bahamas.gov.bs/statutes/statute_CHAPTER_37.html#Ch37s3 ; http://www.bahamas.gov.bs/bahamasweb2/home.nsf/vContentW/A5FB7665F3E4341306256F0000763055 |
| Code Source | ISO/TC 46/WG 2 Secretariat (*) |
| Country | Subdivision | Name | Level |
| BS | AK | Acklins | District |
| BS | BI | Bimini | District |
| BS | BP | Black Point | District |
| BS | BY | Berry Islands | District |
| BS | CE | Central Eleuthera | District |
| BS | CI | Cat Island | District |
| BS | CK | Crooked Island and Long Cay | District |
| BS | CO | Central Abaco | District |
| BS | CS | Central Andros | District |
| BS | EG | East Grand Bahama | District |
| BS | EX | Exuma | District |
| BS | FC | Fresh Creek | District |
| BS | FP | City of Freeport | District |
| BS | GC | Grand Cay | District |
| BS | HI | Harbour Island | District |
| BS | HT | Hope Town | District |
| BS | IN | Inagua | District |
| BS | LI | Long Island | District |
| BS | MC | Mangrove Cay | District |
| BS | MG | Mayaguana | District |
| BS | MI | Moores Island | District |
| BS | NE | North Eleuthera | District |
| BS | NO | North Abaco | District |
| BS | NS | North Andros | District |
| BS | RC | Rum Cay | District |
| BS | RI | Ragged Island | District |
| BS | SA | South Andros | District |
| BS | SE | South Eleuthera | District |
| BS | SO | South Abaco | District |
| BS | SS | San Salvador | District |
| BS | SW | Spanish Wells | District |
| BS | WG | West Grand Bahama | District |
© UNECE | Terms of use | Privacy notice | Site map | Contact us |


Skribble it
Enjoy The Game
- Wait for your party to join the game
- The first one join will start the game
- Wait until your turn start and when you get the word you have to start drawing , you have a 60 second
, and don't use the chat
- The first one get 10 points will win
- Then the game will End


Getting Down with HTML Lessons
Elements
Block-level Elements
Inline Elements
Object Elements
Special Characters
Lists
Ordered
Unordered
Definition
Tables
Decimal, Binary, Octal, and Hex
My Table
Links
My Favorite Websites
Images
Gallery I
Gallery II


Nike released a shoe for 'everyday heroes' like doctors, nurses and home health aides
by CNN NewsourceNike's new shoe was not created for athletes such as basketball players or tennis players, but for a different crowd -- medical workers.
Nike Air Zoom Pulse is an athletic shoe designed for those in the medical profession. Nike said in a news release that they had "everyday heroes" specifically in mind.
The company went to OHSU Doernbecher Children's Hospital in Portland, Oregon, to study those in the profession. They took into account the challenges of those on the job-- including long hours on their feet and liquid spills -- and the comfort needed for long shifts.
The shoe is designed like a clog, an industry favorite, but is just made more athletic, Nike said. "The fit, cushioning and traction systems work together to secure the foot in all hospital conditions."
The shoe has a laceless upper and an elastic strap keeps the heel securely contained. Nike said this makes the shoe easy to get on with one hand. It has a durable, full-rubber outsole with a sole that provides traction even when liquid or water is present.


WITNESS LIST Border and International Affairs Committee March 30, 2005-8:00A HB 486 - Committee Substitute (Merritt) For: Brown, Will (AARP Texas) Caldwell, Jim (Self and Texas Silver-Haired Legislature) Higgins, Carlos (Self and Texas Silver-Haired Legislature) O'Connor, Caroline (Texas State Employees Union) Riddering, Hannah (Texas National Organization for Women) Against: Nath, Rohan (Self) Nation, Brenda (American Council of Life Insurers) Wall, Matt (Texas Hospital Association) Registering, but not testifying: For: Flores, Rebecca (United Farm Workers) Krause, Wayne (Texas Civil Rights Project) Yanez-Correa, Ana (National League of United Latin American Citizens) Against: Thompson, Jay (TX Association of Life and Health Insurers and Association of Fire and Casualty Companies of Texas) On: Bates, Joan (Department of State Health Services) Bingham, Bill (Texas Department of Insurance) HB 805 - Committee Substitute (Moreno) Registering, but not testifying: For: Figueroa, Luis (Mexican America Legal Defense and Educational Fund) Henson, Scott (Self and American Civil Liberties Union of Texas) Yanez-Correa, Ana (National LULAC) HB 2420 For: Gilbert, Don (Valley Baptist Health System) Springfield, James G. (Self) On: Sanchez, Eduardo (Department of State Health Services) Registering, but not testifying: For: Crowe, Michael (El Paso County Hospital District) Manley, James O. "Jim" (El Paso County Judge Dolores Briones) Sibley, David (City of Harlingen)


Ban and timeout
The CLEARCHAT Twitch command contains information about users being banned or given a timeout.
Messages with fewer than 2 parameters are a real CLEARCHAT
event where the chatroom was emptied. Messages with 3 parameters and a banDuration
are only a temporary ban, and otherwise a permanent one.
twitch.chat.extend('CLEARCHAT', function (msg) {
let command;
if (msg.params.length < 2) {
command = 'clear';
} else if (msg.tags.banDuration === null) {
command = 'ban';
} else {
command = 'timeout';
}
return { command };
});
twitch.chat.on('clear', function (msg, channel) {
console.log(`Chat in ${channel} has been cleared by a moderator!`);
});
twitch.chat.on('ban', function (msg, channel, user) {
const { banReason } = msg.tags;
console.log(`${user} has been banned from ${channel}.`);
console.log(`Reason: "${banReason}" permanently.`);
});
twitch.chat.on('timeout', function (msg, channel, user) {
const { banReason, banDuration } = msg.tags;
console.log(`${user} has been given a timeout from ${channel}.`);
console.log(`Reason: "${banReason}" for ${banDuration} seconds.`);
});


|
||||||||||
| 上一个 下一个 | 框架 无框架 |
| 使用 com.sun.tools.javac.v8.code 的软件包 | |
|---|---|
| com.sun.tools.javac.v8 | |
| com.sun.tools.javac.v8.code | |
| com.sun.tools.javac.v8.comp | |
| com.sun.tools.javac.v8.tree |
| com.sun.tools.javac.v8 使用的 com.sun.tools.javac.v8.code 中的类 | |
|---|---|
| ClassReader.SourceCompleter
|
|
| Flags
Access flags and other modifiers for Java classes and members. |
|
| Kinds
Internal symbol kinds, which distinguish between elements of different subclasses of Symbol. |
|
| Symbol.ClassSymbol
A class for class symbols |
|
| Symbol.CompletionFailure
|
|
| TypeTags
An interface for type tag values, which distinguish between different sorts of types. |
| com.sun.tools.javac.v8.code 使用的 com.sun.tools.javac.v8.code 中的类 | |
|---|---|
| ByteCodes
Bytecode instruction codes, as well as typecodes used as instruction modifiers. |
|
| ClassFile
Generic Java classfiles have one additional attribute for classes, methods and fields: "Signature" (u4 attr-length, u2 signature-index) A signature gives the full Java type of a method or field. |
|
| ClassReader
This class provides operations to read a classfile into an internal representation. |
|
| ClassReader.BadClassFile
Error Diagnoses |
|
| ClassReader.SourceCompleter
|
|
| ClassWriter
This class provides operations to map an internal symbol table graph rooted in a ClassSymbol into a classfile. |
|
| ClassWriter.PoolOverflow
Thrown when the constant pool is over full. |
|
| ClassWriter.StringOverflow
|
|
| Code
An internal structure that corresponds to the code attribute of methods in a classfile. |
|
| Code.Chain
A chain represents a list of unresolved jumps. |
|
| CRTable
This class contains the CharacterRangeTable for some method and the hashtable for mapping trees or lists of trees to their ending positions. |
|
| CRTFlags
The CharacterRangeTable flags indicating type of an entry. |
|
| Flags
Access flags and other modifiers for Java classes and members. |
|
| Kinds
Internal symbol kinds, which distinguish between elements of different subclasses of Symbol. |
|
| Pool
An internal structure that corresponds to the constant pool of a classfile. |
|
| Scope
A scope represents an area of visibility in a Java program. |
|
| Scope.Entry
A class for scope entries. |
|
| Source
The source language version accepted. |
|
| Symbol
Root class for Java symbols. |
|
| Symbol.ClassSymbol
A class for class symbols |
|
| Symbol.Completer
Symbol completer interface. |
|
| Symbol.CompletionFailure
|
|
| Symbol.MethodSymbol
A class for method symbols. |
|
| Symbol.OperatorSymbol
A class for predefined operators. |
|
| Symbol.PackageSymbol
A class for package symbols |
|
| Symbol.TypeSymbol
A class for type symbols. |
|
| Symbol.VarSymbol
A class for variable symbols |
|
| Symtab
A class that defines all predefined constants and operators as well as special classes such as java.lang.Object, which need to be known to the compiler. |
|
| Target
The classfile version target. |
|
| Type
This class represents GJ types. |
|
| Type.ClassType
|
|
| Type.Mapping
An abstract class for mappings from types to types |
|
| Type.MethodType
|
|
| TypeTags
An interface for type tag values, which distinguish between different sorts of types. |
| com.sun.tools.javac.v8.comp 使用的 com.sun.tools.javac.v8.code 中的类 | |
|---|---|
| ByteCodes
Bytecode instruction codes, as well as typecodes used as instruction modifiers. |
|
| Code
An internal structure that corresponds to the code attribute of methods in a classfile. |
|
| CRTFlags
The CharacterRangeTable flags indicating type of an entry. |
|
| Flags
Access flags and other modifiers for Java classes and members. |
|
| Kinds
Internal symbol kinds, which distinguish between elements of different subclasses of Symbol. |
|
| Pool
An internal structure that corresponds to the constant pool of a classfile. |
|
| Scope
A scope represents an area of visibility in a Java program. |
|
| Symbol
Root class for Java symbols. |
|
| Symbol.ClassSymbol
A class for class symbols |
|
| Symbol.CompletionFailure
|
|
| Symbol.VarSymbol
A class for variable symbols |
|
| Symtab
A class that defines all predefined constants and operators as well as special classes such as java.lang.Object, which need to be known to the compiler. |
|
| Type
This class represents GJ types. |
|
| TypeTags
An interface for type tag values, which distinguish between different sorts of types. |
| com.sun.tools.javac.v8.tree 使用的 com.sun.tools.javac.v8.code 中的类 | |
|---|---|
| Flags
Access flags and other modifiers for Java classes and members. |
|
| Kinds
Internal symbol kinds, which distinguish between elements of different subclasses of Symbol. |
|
| Scope
A scope represents an area of visibility in a Java program. |
|
| Symbol
Root class for Java symbols. |
|
| Symbol.ClassSymbol
A class for class symbols |
|
| Symbol.MethodSymbol
A class for method symbols. |
|
| Symbol.PackageSymbol
A class for package symbols |
|
| Symbol.TypeSymbol
A class for type symbols. |
|
| Symbol.VarSymbol
A class for variable symbols |
|
| Type
This class represents GJ types. |
|
| TypeTags
An interface for type tag values, which distinguish between different sorts of types. |
|
||||||||||
| 上一个 下一个 | 框架 无框架 |


Covers the basics
You get basic funcionality such as opening from file , url and saving the playlists, you can add a new channel or delete one, you also get the ability to test the channel to see if the stream works. You can double click the video to go fullscreen. Eddy is unfortunately windows only right now.


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AMP TESTCASES
IMAGE
Basic Static Image tests
Static Image Roadblocking Test
Label Exclusion
Image layouts ALL
Image Layout Container
Image Layout Fill
Image Layout Fixed
Image Layout Fixed Height With Width
Image Layout Fixed Height
Image Layouts No Display
Image layouts Responsive
Native
Native Fixed
Native Fluid
Native Layout Container
Native Layout Fill
Native Layout Fixed
Native Layout Fixed Height
Native Layouts No Display
Native layouts Responsive
HTML5
Static HTML5
Rubicon trafficked via DFP
Taboola Fixed
Taboola Fluid
Third Party Networks
AdX Type Lineitem
Video
AMP video tests
Audience
DFP - Static Image - Audience
DFP - Krux - Audience


Home
Gallery
Tracks
Blog
Other
Old Website
8th Grade Element Project
9th Grade TTF Project
About Me
Progressive Orchestra No.2
Made with Apple Garageband.
Progressive Orchestra No.1
Made with Apple Garageband.


Music
University of Miami
Madurai Kamaraj University
Dance
Maharajah's Government College of Music and Dance
Madras Music Academy
Art
Government College of Fine Arts
University of West London
Falmouth University
Others
Contemporary Music and Performance


Tutorial 3
Bora Seo
This dataset has various parameters related to health condition to predict whether the patient is likely to get stroke.
**Data source:
Stroke Prediction Dataset (Kaggle)
**Data with 'N/A' or 'Unknown' were excluded.
Filter Selection


I used what I had learnt from the mindfulness chapter and had a mindful conversation with my girlfriend. Often I am distracted by my surroundings or thinking of future tasks that I need to work on, so this really helped me be in the present while I was speaking with her. I felt this incredible clarity in thought, and could understand exactly what she was saying. I was able to effectively express my own feelings. My girlfriend loved having a “mindful conversation” with me - she said she felt that she had my complete attention.
Practicing mindfulness through training my insula (area in the brain that is involved in emotional intelligence) by intentionally focusing on one part of the body at a time. I tried this technique after watching the video chapter, finding myself very calm and focused after doing so.


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".
%matplotlib inline
import helper
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle as pkl
import problem_unittests as tests
import tensorflow as tf
import warnings
from distutils.version import LooseVersion
from glob import glob
from matplotlib import pyplot
print('All imports successful, ready to rip!')
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 16
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
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 = 40
"""
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
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# 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)
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)
"""
image = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = 'input_real')
z = tf.placeholder(tf.float32, (None, z_dim), name = 'input_z')
lr = tf.placeholder(tf.float32, name = 'learning_rate_t')
return image, z, 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).
D_NAME = 'discriminator'
def discriminator(images, reuse = False, alpha = .2):
"""
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)
"""
with tf.variable_scope(D_NAME, reuse = reuse):
x1 = tf.layers.conv2d(images, 64, 5, strides = 2, padding = 'same')
relu1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d(relu1, 128, 5, strides = 2, padding = 'same')
# Training is always true because we are only interested in using the generator.
# If need be, can set to false if we need to use this for inference
bn2 = tf.layers.batch_normalization(x2, training = True)
relu2 = tf.maximum(alpha * bn2, bn2)
x3 = tf.layers.conv2d(relu2, 256, 5, strides = 2, padding = 'same')
bn3 = tf.layers.batch_normalization(x3, training = True)
relu3 = tf.maximum(alpha * bn3, bn3)
flattened = tf.reshape(relu3, (-1, 4 * 4 * 256))
logits = tf.layers.dense(flattened, 1)
activation_output = tf.sigmoid(logits)
return activation_output, 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.
G_NAME = 'generator'
def generator(z, out_channel_dim, is_train = True, alpha = .2):
"""
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
"""
with tf.variable_scope(G_NAME, reuse = not is_train):
x1 = tf.layers.dense(z, 7 * 7 * 512)
x1 = tf.reshape(x1, (-1, 7, 7, 512))
bn1 = tf.layers.batch_normalization(x1, training = is_train)
relu1 = tf.maximum(alpha * bn1, bn1)
# 2x2x512
x2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides = 2, padding = 'same')
bn2 = tf.layers.batch_normalization(x2, training = is_train)
relu2 = tf.maximum(alpha * bn2, bn2)
# 7x7x256
x3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides = 1, padding = 'same')
bn3 = tf.layers.batch_normalization(x3, training = is_train)
relu3 = tf.maximum(alpha * bn3, bn3)
# 14x14x128
logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides = 2, padding = 'same')
# 28x28xout_channel_dim
output_activation = tf.tanh(logits)
return output_activation
"""
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, alpha = .2,
# Shamelessly copied from https://github.com/openai/improved-gan/blob/9ff96a7e9e5ac4346796985ddbb9af3239c6eed1/imagenet/model.py
label_smoothing = .1):
"""
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)
"""
g_model = generator(input_z, out_channel_dim, is_train = True)
d_model_real, d_logits_real = discriminator(input_real, alpha = alpha)
d_model_fake, d_logits_fake = discriminator(g_model, reuse = True, alpha = alpha)
d_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real,
labels = tf.ones_like(d_model_real) * (1. - label_smoothing) )
# Per https://arxiv.org/pdf/1606.03498.pdf, do not apply label smoothign to negative examples
d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
labels = tf.zeros_like(d_model_fake) + label_smoothing)
g_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
labels = tf.ones_like(d_model_fake))
g_loss = tf.reduce_mean(g_loss)
d_loss_real = tf.reduce_mean(d_loss_real)
d_loss_fake = tf.reduce_mean(d_loss_fake)
d_loss = d_loss_real + d_loss_fake
return d_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)
"""
t_vars = tf.trainable_variables()
d_vars = [tf_var for tf_var in t_vars if tf_var.name.startswith(D_NAME)]
g_vars = [tf_var for tf_var in t_vars if tf_var.name.startswith(G_NAME)]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate, beta1 = beta1)
d_train_op = optimizer.minimize(d_loss, var_list = d_vars)
g_train_op = optimizer.minimize(g_loss, var_list = g_vars)
return d_train_op, g_train_op
"""
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
"""
def show_generator_output(sess, input_z, out_channel_dim, image_mode, example_z):
"""
Show example output for the generator
:param sess: TensorFlow session
: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")
:param example_z: A fixed, but random, set of inputs (rather than generating from scratch each time)
"""
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.figure(figsize=(8, 8))
pyplot.imshow(images_grid, cmap=cmap, aspect='equal')
pyplot.show()
return samples
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.
def model_inputs_from_shape(data_shape, z_dim):
# image_width, image_height, image_channels, z_dim
return model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
def train(epoch_count, batch_size, z_dim, learning_rate, beta1,
get_batches, data_shape, data_image_mode,
print_every = 10, show_every = 100,
num_images_to_sample = 18,
train_discriminator_every_N_steps = 2):
"""
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")
"""
out_channel_dim = data_shape[3]
input_real, input_z, lr_tensor = model_inputs_from_shape(data_shape, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
d_train_op, g_train_op = model_opt(d_loss, g_loss, lr_tensor, beta1)
saver = tf.train.Saver()
# We would prefer to use the same 'randomized' input so we can compare output over training time...
sample_z = np.random.normal(-1, 1, size=(num_images_to_sample, z_dim)) # use normal per https://github.com/soumith/ganhacks
samples, losses = [], []
steps = 0
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):
steps += 1
batch_images *= 2 # per https://discussions.udacity.com/t/need-help-with-final-project-discrimnator-loss-much-lower-than-the-generator-one/400443
batch_z = np.random.normal(-1, 1, size = (batch_size, z_dim)) # use normal per https://github.com/soumith/ganhacks
sess.run(d_train_op, feed_dict = {input_real: batch_images, input_z: batch_z, lr_tensor: learning_rate})
# original GAN paper mentioned to run g a few more times than d.
for _ in range(train_discriminator_every_N_steps):
sess.run(g_train_op, feed_dict = {input_real: batch_images, input_z: batch_z, lr_tensor: learning_rate})
if steps % print_every == 0:
train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
train_loss_g = g_loss.eval({input_z: batch_z})
print('Step {}, Epoch {}/{}'.format(steps, epoch_i + 1, epoch_count),
'Discriminator Loss: {:.4f}...'.format(train_loss_d),
'Generator Loss: {:.4f}'.format(train_loss_g))
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
gen_samples = show_generator_output(sess, input_z,
out_channel_dim, data_image_mode, sample_z)
samples.append(gen_samples)
saver.save(sess, './checkpoints/generator.ckpt')
with open('samples.pkl', 'wb') as f:
pkl.dump(samples, f)
return losses, samples
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 = 0.0002
beta1 = .4
"""
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')))
losses = []
samples = []
with tf.Graph().as_default():
losses, samples = train(epochs, batch_size, z_dim, learning_rate, beta1,
mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
print('all done!')
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
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 = 64
z_dim = 100
learning_rate = 0.0002
beta1 = .33
"""
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():
losses, samples = train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode
#train_discriminator_every_N_steps = 2
)
print('all done!')
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
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.


In the legend below, click the box symbols to toggle the
display of the line charts and click the circles to toggle
the display of the box plots and scatter plots. If a color is
visible in the symbol, the corresponding plot elements are
displayed, otherwise they are hidden.
The box plots in the upper figure represent the distribution
of the selected alpha diversity metric for each group of
samples at each even sampling depth. The lower and upper
whiskers of the box plot are the 9th and 91st percentiles of
the distribution (respectively), while the lower and upper
extents of the box are the 25th and 75th percentiles of the
distribution (respectively). The horizontal bar through the
middle of the box is the median of the dstribution
(i.e., the 50th percentile). Outlier points of these
distributions are not shown.
The line chart in the upper figure connects the median
values of the alpha diversity metric distribution
across the sampling depths.
If a sampling depth is higher than the number of sequences
in a sample, that sample will not be included in the
rarefaction plot at that sampling depth. The line chart in
the lower figure illustrates the number of samples in each
group (i.e., the sample size for each box plot) at each
sampling depth.