The Space
This unique mooring is on a spit of land with the canal to one side and lake on the other. The cabin opens out onto a large decked area and garden. Very quiet and peaceful, wake to bird song or spot the odd Munk jack deer grazing by the lake. Swans, moorhens, geese and ducks come to the kitchen window to be fed.
London is on your doorstep. Buses at the end of the Mooring will connect you with the Underground into London or mainline stations to Oxford and beyond. Heathrow airport is 15minutes by car and there are easy connections to Uxbridge. Taxi costs around £15. There is no vehicular access to the mooring...... just a stroll along the canal! The only traffic you will hear will be the occasional canal boat chugging up and down!
If you have a car, there is a free car park at the beginning of the towpath for residents and guests.
There is a second cabin next door.Check it out by typing in the same postcode as for this cabin!
Guest Access
The cabin has a comfortable double bed (vital!) with en suite bathroom. As the cabins are independent of the main houseboat guest can come and go as they please. Continental breakfast on board the main houseboat . Fresh Coffee, fresh orange juice, croissant etc. Please let me know in advance if you would like supper in the evening. This can be arranged for a small fee. Weather permitting this can also be arranged al fresco on the deck. Coffee and tea facilities in the cabin. All linen and towels provided. Unfortunately no T.V. in the room (no space!) but fibre optic broadband connection.... good fast speed! Mobile connection is very good.
Interaction with Guests
I am an artist and work from my studio in the garden. Sometimes I am in London working but prefer to be around the boat! It is all completely easy going and I love to meet new people and chat !
As the boat is a little ways from the car park, I am happy to meet you at the car park with a trolley for luggage should you need it, and to show you the way.
The Neighbourhood
This little mooring is unique and probably the only one like it in the South West of England. We are a community of approximately 22 boats each with our own patch of land and garden. It is rather Bohemian, slightly eccentric, very friendly and we have our fair share of artists and musicians!
In the evening, stroll down the canal, watch the boats go through the lock and drop into a pub for a drink and a meal. Here you are in another world, Once you cross the bridge, you have stepped out of the rat race!
Getting around
Public transport is excellent with many connections available for Central London, Windsor and Oxford. We are 15 minutes by car from Heathrow airport. Bus connections from Heathrow are plenty. If you are planning on using this as a base to go touring, we are 10minutes from the M4 close to the M25,M40 A40 into London. Car parking is available.
Other Things to Note
I live with a very gentle Springer Spaniel dog who loves people and is ball mad. I also have two cats. So if you have an aversion to dogs, or an allergy to cats I am sorry but this will not be good for you!


Kerry student's research into solving oceanic and freshwater dead zones wins top award
by By Digital Desk staffA Co Kerry student has been honoured with a top award for his research into solving oceanic and freshwater dead zones.
Timothy McGrath, a sixth year student from Killorglin Community College will represent Ireland in the United States at the International Science and Engineering Fair 2020 after this win.
Through research Timothy realised that eutrophication, an enrichment of water by nutrient salts, which causes structural changes to the ecosystem, turns huge areas of the ocean into dead zones and affects many countries as a result.
Dead zones are low-oxygen areas in the world's oceans and large lakes, caused by excessive nutrient pollution from human activities.
SciFest CEO, Sheila Porter, was full of praise for the winner, and the standard of entry in the competition.
“The standard of entries this year has been extraordinary. The aim of SciFest has always been to develop a love of STEM and of inquiry-based learning and every year it is refreshing to see how the students of today continue to love and enjoy immersing themselves in science, technology, engineering and maths," she said.
"The students of today are focussed on the future, the future of our environment and what this means for the global climate."


{% extends "home/base.html" %} {% load django_markdown %} {% block content %}
My name is Sarp Orgul and I'm a computer science student at the University of Washington. I am also currently training to become a private pilot. This is a website where I will post information about myself, the projects that I am working on, and other miscellaneous thoughts.
You can find more information about me in my biography and about my work experience in my resume .


Welcome To The Dollhouse Chad'z Rating: ***1/2 (out of 4 = very good) 1996, R, 87 minutes [1 hour, 27 minutes] [drama/satire] starring: Heather Matarazzo (Dawn Wiener), Brendan Sexton Jr. (Brendan McCarthy), Eric Mabius (Steve), Matthew Faber (Mark Wiener), written, produced, and directed by Todd Solondz.
Kids can be so cruel. We'd like to think that in America we live in a land of individuals, and we do for the most part, but never in educational institutions - why is that? Nowhere in the world is the invisible caste system so rigid and strict as that of children in Junior and Senior high school. "Welcome To The Dollhouse" is a different, creative, funny, and at times disturbing story of a girl who isn't accepted at all.
Every school has at least one or two kids who never have and never will fit it to any of the social groups because they are different and do everything wrong. Heather Matarazzo stars as Dawn Wiener, a girl who fits that mold perfe ctly. This is obvious right from the get-go as we see her walk through the cafeteria in a desperate search for a seat where she might be "accepted." Immediately we are given that feeling of cruel fate just from this shot alone, but reality sinks in even more as the cheerleaders ask her if she is a lesbian. We've known Dawn only a few minutes, and already we feel for her and see the world through her eyes. No matter how hard she tries she'll never win.
But the film is not entirely a dismal outlook, it's a serious drama but it has a satirical tone to lighten it up. Reality isn't the same here because everything is portrayed through Dawn's perspective. A bully named Brendan (Sexton) tries to copy her test, but when she tries to turn him in she gets in trouble too. To make matters worse, Brendan continues to taunt her during the punishment without the teacher noticing. You just want to yell at the teacher at tell her to look up for a second so justice might be done...
Dawn's home life is portrayed as just as unfair and satirical as school. Her brother Mark (Faber) is a computer geek who also happens to have Steve (Mabius), the guy all the girls want, in his garage band. But she can't appreciate this because she always distracted by her annoying little sister who can do no wrong in the eyes of her parents.
These relationships often make for funny but sad encounters, such as when Dawn and her sister get into a name-calling argument. Of course her sister tattles and we hear a shouting match between Dawn and her mother in the background. This is a great method of storytelling because it's reminiscent of the times we went to our friend's house and heard them get in trouble with their parents in the other room. The sibling rivalry comes to a boil when Dawn purposely forgets to give her sister a message from her mother about getting a ride home and results in her being kidnapped!
I realize Solondz is trying to make a point through exaggeration and brutal reality, but sometimes it's difficult to tell where the reality ends and the satire begins. For example, when Dawn is constantly harassed by Brendan, to the point where he threatens to "rape" her, it seems a little out of touch because he doesn't know what rape is. Just the fact Dawn agrees to go along with him is unbelievable.
"Welcome To The Dollhouse" is a good depiction of children's caste system and cruel mistreatment of each other without purpose. There's a lot of unfair things about life, it's nice to see a film play upon that universal truth.
Please visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz - over 140 new and old films reviewed in depth, not just blind ratings and capsules. Also, check out The FIRST Shay Astar Web Page @ http://members.aol.com/ChadPolenz/ShayAstar.html
e-mail<EMAIL_ADDRESS>(C)1997 Chad Polenz
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Experience | 8+ Years |
| Currently | Web Application Engineer |
| Javascript |
|
| HTML |
|
| CSS |
|
| Python |
|
| Java |
|
| SQL |
|
| Angular |
|
| Django |
|
| Spring |
|
| Hibernate |
|
| AWS |
|
| Birthday | 27 Feb, 1988 | Nationality | Indian |
| Relationship | Married | Languages | English, Hindi |
| Address | House No. 471, Sector 2B, Vasundhara, Ghaziabad, UP 201012 |
Technology enthusiast having more than 8 years of experience in both frontend and backend technologies. Helping the current company with their user experience. Received appreciation from the clients that their is significant improvement in the usability of the portal. Handled a team of 5 people and delivered solutions in agriculture domain startup. Developed a platform to capture operations data and provide reports to business users. Helped the company to scale their operations by 30 times with the use of technical platform. I can solve complex problems. Wrote a research paper on Effective Blob detection algorithm.


{% extends 'home/base.html' %} {% block content %} {{block.super}}
National Police Service
Ambulance And Emergency Medical Services
St John Ambulance


full-stack development, graphic design and branding by alexander sandberg.
need help? alex@minimaldesign.se


Your browser doesn't support canvas elements.
Your browser still doesn't support canvas elements.
+
-


Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity.
| Version: | 0.2.6 |
| Depends: | R (≥ 3.1.3), methods |
| Imports: | graphics, settings, yaml |
| Suggests: | testthat, knitr, rmarkdown |
| Enhances: | lumberjack |
| Published: | 2018-08-01 |
| Author: | Mark van der Loo [cre, aut], Edwin de Jonge [aut], Paul Hsieh [ctb] |
| Maintainer: | Mark van der Loo <mark.vanderloo at gmail.com> |
| BugReports: | https://github.com/data-cleaning/validate/issues |
| License: | GPL-3 |
| URL: | https://github.com/data-cleaning/validate |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| In views: | OfficialStatistics |
| CRAN checks: | validate results |
| Reference manual: | validate.pdf |
| Vignettes: |
03_Indicators 01_Introduction 02_Rules_in_text_files |
| Package source: | validate_0.2.6.tar.gz |
| Windows binaries: | r-devel: validate_0.2.6.zip, r-release: validate_0.2.6.zip, r-oldrel: validate_0.2.6.zip |
| OS X binaries: | r-release: validate_0.2.6.tgz, r-oldrel: validate_0.2.6.tgz |
| Old sources: | validate archive |
| Reverse depends: | errorlocate, validatetools |
| Reverse imports: | dcmodify, deductive, rspa |
Please use the canonical form https://CRAN.R-project.org/package=validate to link to this page.


Edit a javascript config file located at ./js/config.js
to config the website host, data server host, user main page after login, login page location for those pages required user authentication.
var serverHost = "http://<IP_ADDRESS>/Develop"; var webRoot = "http://<IP_ADDRESS>/Develop"; var requireLoginPage = "login.html"; var afterLoginPage = "main-menu.html";
Edit a php config file located at ./model/config.php
to config the database(MySQL/MariaDB) connection information.
/** * Configuration for: Database Connection * * DB_HOST: database host, usually it's "<IP_ADDRESS>" or "localhost", some servers also need port info * DB_NAME: name of the database. please note: database and database table are not the same thing * DB_USER: user for your database. the user needs to have rights for SELECT, UPDATE, DELETE and INSERT. * DB_PASS: the password of the above user */ define("_DB_HOST", "<IP_ADDRESS>", true); define("_DB_NAME", "acgni308_keithbox", true); define("_DB_USER", "acgni308_kbuser", true); define("_DB_PASS", "Demo-DB3.2", true);
Edit a php file located at ./model/ExcelManager.php
PHPExcel was integrated with three PDF engines that allowed to config which will be use.
Uncomment to pick a one out of three PDF engines (dompdf 0.6.0+, TCPDF 6.2.0+, mpdf 6.1.0+)
// $rendererName = PHPExcel_Settings::PDF_RENDERER_DOMPDF; // $rendererLibrary = 'PDF engine/dompdf/dompdf-0.6.2/'; // local the dompdf enginer folder, PHPExcel 1.8.0+ not support dompdf 0.7.0+ // $rendererLibrary = 'PDF engine/dompdf/dompdf-0.7.0/'; // extremely slow performance // $rendererName = PHPExcel_Settings::PDF_RENDERER_TCPDF; // $rendererLibrary = 'PDF engine/TCPDF/TCPDF-6.2.13/'; $rendererName = PHPExcel_Settings::PDF_RENDERER_MPDF; $rendererLibrary = 'PDF engine/MPDF/mpdf-6.1.3/'; $rendererLibraryPath = BASE_3RD . $rendererLibrary;
error about glyphicons-halflings-regular.woff2 not found
This problem happens because IIS does not know about woff and woff2 file mime types.
Solution 1: Add this lines in your web.config project:
...
Solution 2:


 How about standing in front a desk every once in a while? Click on the link above for more details.
Visit our partner working with us to bring customers ideas for their dorm decorations.


Sweet Free Stuff
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CommentsLove by Melissa Morgan


To add property types and specify values for the property, Locate the blank property type at the bottom of the list (if this is your first property type, it will be the first slot available)
select the property from the dropdown menu and fill in the value.
once you click update, this property and corresponding value will automatically be saved to your product.


From Wikipedia, the free encyclopedia
Henri Cartier-Bresson (August 22, 1908 – August 3, 2004) was a French photographer considered to be the father of photojournalism. He was an early adopter of 35 mm format, and the master of candid photography. He helped develop the street photography or life reportage style that was coined The Decisive Moment that has influenced generations of photographers who followed.


Statistics
All the technical stuff you need to know
The Current Meta
Currently, the card that is used the most overall is the zap. It is currently used by 35% of players in ladder and a huge 43% of players in challenges/tournaments. Another card that is seen in many decks is the Skeleton Army. It's used by 40% of players in ladder and 31% of players in challenges/tournaments. The Hog Rider is currently the top choice for tower destruction, closely followed by the P.E.K.K.A.
Top 3 Challenge decks based on win rate
Top 3 Ladder decks based on win rate
Youtubers
Check out some Clash Royale youtubers for the latest stratergy and gameplays! (click on their names in blue)
How to level up cards faster
Have you found it hard to level up your cards?
Card Upgrades
First, let’s talk about the general mechanic to get you all on the same page. Every chest contains different cards which are drawn by random, but not completely by random. If there’s a new Common or Rare Card available in the game or you just reached a new Arena that unlocks new Cards for you, you might have noticed that you will get these new cards more often than the ones you already have. opening chest clash royale This is because the game automatically balances the amount of all cards you have – this means if you have one card at Level 3 and all other by Level 7, you will get more of the Level 3 type. That doesn’t mean you will only get them, but the odds are a lot higher.
How Many Cards You Need
Let me give you the stripped numbers of how many cards you’ll need in total to max out a certain card: Common Cards (Max Level 13): 9,586 Rare Cards (Max Level 11): 2,586 Epic Cards (Max Level 8): 386 Legendary Cards (Max Level 5): 36 This is the total amount of cards you need to max them out. In addition, you will need to pay between 175,000 to 185,000 in Gold to upgrade the Cards to their maximum Level! Just upgrading costs!
Click on these to see how to use the cards effectively
(Cards updated often)
How much does it cost to max out a Clash Royale account?
The cost varies alot depending on many different things, but if you have just made an account and you havn't played it at all, the cost to max it out would be around $12,000 USD.
Top Clash Royale players of all time
| Rank | Player | Highest Trophies |
|---|---|---|
| #1 | GuaNek™ | 6788 |
| #2 | 梵武神 | 6762 |
| #3 | Dark Light | 6753 |
| #4 | sencer | 6747 |
| #5 | IamBrenChong | 6744 |
Top Clash Royale Clans of all time
| Rank | Clan |
|---|---|
| #1 | Nova eSports |
| #2 | Sandstorm |
| #3 | Crucible Wrath |
| #4 | Mega Stars |
| #5 | 上海 OP Elite |
Upgrading Cards Efficiently
Stick To A Deck
If you switch around decks all the time and always go after the current Meta-Deck, you will never max out your cards. The thing is, you will always have different cards in your deck that you will level up a bit, spend time on requesting it from your clan and pay gold for upgrading it. It’s likely these cards are getting nerfed in the next balancing cycle and useless for the next months. Of course, none of us is using the same deck all the time and if there are very strong cards you should use them – but at least stick to a deck archetype! First of all, find out what deck you like to play most – a beatdown deck, cycle deck with Hog Rider etc. If you play a Cycle Deck with a Hog Ride, you can work on the element cards (like the Hog Rider) or if you play a Golem Deck work on the element cards there. This allows you to focus on these cards and if you switch in and out other cards you will still benefit from the high-level cards you will always have in this deck! Read It! Get More Legendary CardsUse The Shop
Always buy 3 Rare Cards and 3 Common Cards from the shop when new Cards get available. Simply because you will be able to make a profit from it: 3 Rare Cards = 120 Gold in total (20 + 40 + 60 Gold) = 40 Gold in average Donating a Rare Card = 50 Gold 3 Common Cards = 12 Gold in total (2 + 4 + 6 Gold) = 4 Gold in average Donating a Common Card = 5 Gold This will make you earn some small Gold plus it will add Cards to your account for a very low price.Balance Your Deck
I know having a deck with a lot of Epic Cards might be tempting, but you are not able to level them up very fast. It’s no difference if you request Common Cards or Rare Cards, so you don’t need to make a difference there. Requesting The Right Cards Requesting Cards in your clan is the only way to get certain cards on point without having to be lucky when opening a chest or buying them in the shop – in fact, this is the only reliable way to upgrade the cards in the deck. Often times, these cards are on higher level than the other cards so you will see other cards appear in chests more often. Unfortunately, there’s a donation limit on: How much cards you can get per donation request The donation cooldown prevents that you can request cards more often than 3 times per day Due to these limitations (only a few cards and that’s the only way to choose which card you want), it’s extremely important to think very carefully what card you request and also stick to it (if possible).Fun Fact
5 billion years from now, we would have 13 trillion cards in the game if the cards continue being released at the same rate as they are now.
Random text to copy
HAHA!
╭━━┳╮┈╭━━┳━━┳╮╭╮
┃┃┃┃╭╮┃┃┃┃╭╮┃┃┃
┃╰╯┃┃┃┃╰╯┃┃┃┃┃┃
┃╭╮┃╰╯┃╭╮┃╰╯┃╰╯
┃┃┃┃╭╮┃┃┃┃╭╮┃╭╮
╰╯╰┻╯╰┻╯╰┻╯╰╯╰╯
CLASH
╭╮╭┳━━┳╮╭┳━━╮╭╮
┃╭╮┃┃┈┃╭╮┃╭╮┃┃┃┃
┃┃╰┫┃┈┃┃┃┃╰━┫╰╯┃
┃┃╭┫┃╭┫╰╯┣━╮┃╭╮┃
┃╰╯┃╰╯┃╭╮┃╰╯┃┃┃┃
╰━━┻━━┻╯╰┻━━┻╯╰╯


The US-led Campaign against ISIL consists of 40+ member country. Many countries contributed in the campaign by directly striking ISIL positions, others send troops to train local forces, and others send Military aid, Humanitarian aid and some countries contributed by Intelligence informations.
Some fighting Countries intervening in Iraq only, some in Syria only, and others intervening in both (Iraq & Syria).
Some support Countries offered Military aid, some Humanitarian aid, and others Intelligence aid.
(hover over a country to view its contribution in the campaign)
Although the severe striking against ISIL forces/facilities in Iraq & Syria, US-led coalition trying to make local fighters (Iraqi army, Peshmerga, Syrian kurdish militias) get the most benefit from the strikes effect, to gain more Influence on the ground against ISIL forces.
This map is a re-simulation for the strikes showing it's effect on ISIL Influence in Iraq & Syria.
(hover over a city to view its total strikes count)
Strikes vary from city to another.
In this Graph the cities divided by number of strikes, number of targets bombed, and country.
(hover over a circle to view more information)
The large area of ISIL operations in Iraq and Syria lead to a massive numbers/categories of targets that could be bombed by US-led Coalition.
The coalition strikes ISIL to
1) Undermines ISIL's power
2) To support local forces (Iraqi Army, Kurdish militias) which lead to a diversity in types of targets
Although the strikes targeting ISIL facilities/forces in Iraq and Syria mainly, but many civilians died due to the coalition airstrikes.
The numbers are from start of the strikes till 31th March 2015, and the total numbers of civilian casualties may increase.


The Near Room (1996) Oh Dear ! A Scottish attempt at film noir, dreech pictures perhaps.
This film fails on a number of counts. While it is competently filmed and the acting is reasonable it never manages to shake off the "made for TV" feel nor hide the formulaic construction. The sets are dark and industrial, so much so that they never feel real, the characters are underdeveloped rather than mysterious and the dark underside is so contrived that one never has any feeling of true threat or menace.
The script writer should never have given the dialogue describing the "near room" to any character let alone have them describe it twice. It wasn't difficult to get at what the author meant - it was simply unbelievable that any character would utter such pretentious prose.
This is clearly one to avoid, even aficionados of sex and violence will find this as exciting as a chocolate malt drink - dark, distasteful and useful if you have difficulty in getting off to sleep
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Queue Class
Provides a basic queue class
Methods
back
()
Number, String, Array, Object, Bool
This function returns the last item in the queue
Returns:
Number, String, Array, Object, Bool:
element
dequeue
()
Number, String, Array, Object, Bool
This function returns the first item in the array and removes it from the queue.
Returns:
Number, String, Array, Object, Bool:
element
empty
()
This function will clear all items out of the queue.
enqueue
(
-
element
This function adds the passed in element to the end of the queue.
Parameters:
-
element
Number, String, Array, Object, Bool
front
()
Number, String, Array, Object, Bool
This function returns the first item in the queue.
Returns:
Number, String, Array, Object, Bool:
element
length
()
Number
This function returns the current number of items in the queue.
Returns:
Number:
length


Before Change
else:
raise TypeError()
runs = []
for run_ in runs_list:
run = {"run_id": int(run_["oml:run_id"]),
"task_id": int(run_["oml:task_id"]),
"setup_id": int(run_["oml:setup_id"]),
"flow_id": int(run_["oml:flow_id"]),
"uploader": int(run_["oml:uploader"])}
runs.append(run)
runs.sort(key=lambda t: t["run_id"])
return runs
After Change
else:
raise TypeError()
runs = dict()
for run_ in runs_list:
run_id = int(run_["oml:run_id"])
run = {"run_id": run_id,
"task_id": int(run_["oml:task_id"]),
"setup_id": int(run_["oml:setup_id"]),
"flow_id": int(run_["oml:flow_id"]),
"uploader": int(run_["oml:uploader"])}
runs[run_id] = run
return runs


Squid keeps a number of statistics for each cache client (IP
address). You can view them by visiting the cache manager
client_list page. The
ClientInfo data structure is about 240 bytes on
32-bit systems and 300 bytes on 64-bit systems. If you have thousands
of clients, this database can consume a significant amount of memory.
You can disable this directive and free up that memory for other
uses. |
Syntax
|
client_db on|off
| |
Default
|
client_db on
| |
Example
|
client_db off
|
|


{{ bokeh_css }} {{ bokeh_js }}
This dashboard summarize the results from the Big Data Benchmarking project. These charts are generated directly from the CSV file. The charts are explained as follows:
DataTable
displays the raw output from the CSV file.BarChart
displaying the average query execution time for each database based on the concurrency factor.ScatterChart
displays a point for every query comparing the execution time vs the number of rows returned from the query.BarChart
displaying the average query execution time for each database based on the query category.BarChart
displaying the average query execution time for each database based on the table size.BarChart
displaying the average query execution time for each query based on the database.LineChart
displaying the average query execution time for each query based on the database.DataTable
summarizing the average query execution times per database.NOTE: Use the widgets
to control the data in the DataTable. Then click the "Download" button to export the raw data to a CSV file.


With Object Relational Mapping, the enhanced XItem classes (see chapter Object meta data) can be automatically loaded from a database or saved back. The majority of the required information to make this happen is already in place. In the following example, the classes have been prepared with meta information. Let's see how they can be automatically preserved in a database.
class Author extends XItem { public $Name; public static function FillMeta(XMeta $m){ $m->Name = XMeta::String(); } } class Book extends XItem { public $Title; public $idAuthor; public $NumberOfPages; public static function FillMeta(XMeta $m){ $m->Title = XMeta::String(); $m->idAuthor = XMeta::ID(); $m->NumberOfPages = XMeta::IntegerOrNull(); } }
First of all, we have to define which database tables are going to be used. This can be done with the SetDBTableName method. Then each field has to be linked to a table column, with the WithDBAlias method. This is optional; if omitted, the field's name is going to be used. It is even possible to use an alias for the inherited id field.
class Author extends XItem { public $Name; public static function FillMeta(XMeta $m){ $m->SetDBTableName('app_authors'); $m->Name = XMeta::String(); } } class Book extends XItem { public $Title; public $idAuthor; public $NumberOfPages; public static function FillMeta(XMeta $m){ $m->SetDBTableName('app_books'); $m->id->WithDBAlias('book_id'); $m->Title = XMeta::String(); $m->idAuthor = XMeta::ID(); $m->NumberOfPages = XMeta::IntegerOrNull()->WithDBAlias('NumPages'); } }
That's all! The classes are now ready. All we need is a connection to the database (see the chapter Setup).
The easiest way to load an object from the database is the Pick function, by passing its id.
$id = Http::$GET['id']->AsID(); $book = Book::Pick( $id ); echo $book->Title;
The Pick function will do a lot of things behind the scene. First of all, it will see in the cache if the object is already loaded and if so, it will save the query to the database. Then it will scan the fields of the meta object and will generate the required sql code. Finally, it will retrieve the columns from the result of the query, it will convert them to the appropriate type and it will assign them to a newly created Book object.
If the book was not found it the database, the function will return null.
Once loaded, the object can be modified by the code and saved back to the database, with the Save method:
$book->Title = 'The old man and the sea'; $book->Save();
Or, it can be deleted, with the Kill method:
$book->Kill();
Once killed, the record from the database and the entry in the cache will be removed. However, the object itself will remain available and can be used:
$book->Kill(); echo 'The book ' . $bool->Title . ' has been deleted';
The Make function creates new instances of an object:
$book = Book::Make(); $book->Title = 'Alice in wonderland'; $book->Save();
The Make function, behind the scenes, will reserve an id and will add an entry in the cache. Therefore, the object is ready to be used directly, even before saving, as the id will not change.
$author = Author::Make(); $author->Name = 'Lewis Carroll'; $book = Book::Make(); $book->Title = 'Alice in wonderland'; $book->idAuthor = $author->id; $author->Save(); $book->Save();
If the object is temporary and you don't want to spend a permanent id for it, then you can use the function Temp. This function will create an object and will assign an id to it, but the id is a temporary one. In addition, the new item will not be stored in the cache. The temporary id is guaranteed to be unique for the duration of the request.
$author = Author::Temp(); $author->Name = 'Lewis Carroll';
Temporary object cannot be saved. If you want to save a temporary object, you have to get a copy with a permanent id.
$author = Author::Temp(); $author->Name = 'Lewis Carroll'; ... $author_copy = $author->Copy(true); $author->Save();
The Pick can also be used for linking objects together, as it is very cheap after the first call. To link a book to its author, it is sufficient to add a simple method in the Book class.
class Book extends XItem { ... public function GetAuthor(){ return Author::Pick( $this->idAuthor ); } ... }
With this method, the author of the book can be found naturally from the code. However, as the Pick function may return null, an extra check must be placed:
$author = $book->GetAuthor(); if (!is_null($author)) echo $author->Name;
As seen in the chapter Object meta data, the XList class holds a list of XItems. Now, the Seek function automatically fills a list from the database.
foreach (Book::Seek() as $book) { echo '
'.$book->Title.'
'; }
The Seek function also does many things behind the scenes. First of all, it acts lazily in the sense that it will not query the database unless really needed. Then, it creates the query but retrieves only the ids of the the objects without loading the objects themselves. Finally, it loads lazily every object needed, with a high possibility to load the object from the cache without hitting the database.
In addition, Seek will take into account the default ordering specified in the meta object. So, if it is defined that the books are to be sorted by title by default, then the generated query will contain an ORDER BY clause.
The lazy approach comes with a cost if it is known that all the objects of the list will be loaded and that no object is already in the cache. This is because it will execute 1+N light queries: one query to retrieve the ids and one query for each of the objects. The alternative approach is to load everything at once in one big query with the cost of reloading data that may exist already in the cache. This is possible with the Aggressively method:
foreach (Book::Seek()->Aggressively() as $book) { echo '
'.$book->Title.'
'; }
It starts to become evident that lists have two distinct states. Either they are ready to be used directly, or there are pending queries the have to be executed before the list is ready to be used. If the list is ready, then it can be accessed as a traditional PHP array, or through the methods of LINQ. If, however, the list is in the pending state, any access to the array will automatically cause the execution of the queries.
While the list rests in the pending state, it is possible to modify the enclosed query. This is done by calling the same LINQ methods. As the list is aware of its state, it is going to translate the calls in a diferrent way according to it.
Let's see an example. The following code will find the first 10 books of 2001 when sorted by title.
$m = Book::Meta(); $list = Book::Seek() ->Where( $m->Year->Eq( 2011 ) ) ->OrderBy( $m->Title ) ->Take( 10 );
The rule of thumb here is that the list will not execute any query, unless it is needed to. So, the call to the function Book::Seek() will create a list with a pending query, but it will not execute the query. Then, the Where() method will add a predicate to that query. The predicate knows how it can be translated to SQL, but for the time being it is not necessary to. Then OrderBy() method will add an ordering instruction and the Take() will add a limit. None of these require the actual execution of the query.
The query is going to be executed when there is access to the items of the list. This happens usually upon iteration with foreach. Until then, the list is open to any manipulation and LINQ methods are translated as modification to the query. Any consequent call will be applied on the objects of the list in memory without hitting the database again.
foreach ($list as $book) { // the SQL query is executed silently before the iteration. echo $book->Title; } foreach ($list->Skip(5) as $book){ // no SQL query will be executed here. LINQ will be executed in memory. echo $book->Title; }
The smart predicates of the the meta model know how to translate themselfs into SQL so that they can postpone and transfer their execution to the database server. If we had used closures, the code would have worked without problem, however the execution time would be longer. Consider the following example, which is the same as the example above with the predicate object have been replaced by a predicate closure:
$m = Book::Meta(); $list = Book::Seek() ->Where( function($book){ return $book->Year == 2011; } ) ->OrderBy( $m->Title ) ->Take( 10 );
The predicate closure does not know how to translate itself in SQL and so it is bound to be executed in memory. That would mean that all books from the database will be first loaded in memory and then be filtered and ordered before the first 10 are taken. Apart from the fact that the loading of all books will take much time, even the queries in memory will take longer than the equivalent in database because the use no precalculated indices.
The following example is the same as the first one, except that this time a closure is used for ordering. This is still slower that the first one, but faster that the second. This is because this time, the filtering predicate can be executed in the database. So, instead of loading every book in memory, only the books of 2011 will be loaded. The sorting, however, will be executed in memory.
$m = Book::Meta(); $list = Book::Seek() ->Where( $m->Year->Eq( 2011 ) ) ->OrderBy( function($book){ return $m->Title; } ) ->Take( 10 );
In general, although both approaches work, it is better to assign the execution of the queries to the database server because it is more optimised. Having said that, there are cases where the use of closures gives a flexibility that cannot be achieved with SQL. For instance, the following code will select the books of 2011 for which there exists a cover as a jpg file in the hard disk, ordered by title.
$m = Book::Meta(); $list = Book::Seek() ->Where( $m->Year->Eq( 2011 ) ) ->OrderBy( $m->Title; ) ->Where( function($book){ return file_exists('covers/'.$book->id->AsHex().'.jpg'); } );
What is nice with the above code is that the form of the query is not changed. What can be executed in the database will be transferred there. The ordering of the calls here is important. Everything will be transferred to the database until something that cannot be transferred is encountered. In this case, the last filter function cannot be executed in the database. This is why, the ordering method is placed before it. Had it been placed after the filter, it would have been executed in memory instead.
The predicates are essentially small elements of code with high reusability value because they can be easily combined together to create complex queries. Therefore, it is wise to structure the code around predicates. The other functions, which will give the final API, can combine predicates to produce the result. This approach have endless possibilities because it is expandable without having to rewrite the basic building blocks.
In the following example, it is agreed that no book is deleted from the database and that searching with a string implies searching in the Title, the Author and the Summary fields of the book. These two facts are coded as predicates, and can be used anywhere in the code to filter books, instead of repeating them. What's more, if, sometime in the future, the definition of what is active changes, then it will be enough to change the predicate without having to touch every book query in the application.
class Book extends XItem { ... // // Predicates // public static function PredIsActive(){ return self::Meta()->DateDeleted->IsNotNull(); } public static function PredBySearchString($search_string){ $m = self::Meta(); $s = '%'.$search_string.'%'; return XPred::Any( $m->Title->Like( $s ) ,$m->Author->Like( $s ) ,$m->Summary->Like( $s ) ); } // // API functions // public static function SeekActive(){ return self::Seek()->Where( static::PredIsActive() ); } public static function SearchActive($search_string){ return self::SeekActive()->Where( self::PredBySearchString($search_string) ); } ... }
It's often the case that a database contain triggers. Those triggers are necessary to maintain referencial integrity which is fine. However, they are usually misused it the sence that part of the application logic and the business intelligence is encoded there. This is simply not the right place for many reasons. First of all, it is easier to maintain the code if the logic is not scattered around. In addition, even if the trigger enforces referencial integrity, the messages returned to the user in case of breaking the integrity are part of the business logic because they should be handled and translated in a message meaningful to him.
For these reasons, Oxygen offers a set of object level events, which are to be executed in PHP. These events include OnInit, OnLoad OnBeforeSave, OnAfterSave, OnBeforeKill and OnBeforeKill. All events can be handled by overriding the protected method with the same name.
OnInit is very often used to provide default values for the objects fields, as PHP dows not allow field initializers to be anything else than scalar literals.
class Book extends XItem { ... protected function OnInit(){ $this->DateCreated = XDateTime::Now(); } ... }
The events on loading and saving can be used to calculate fields derived from other fields, usually for the shake of speed. Finally, the delete events can be used to enforce referencial integrity by throwing exceptions with meaningful messages.


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NNFrames
NNEstimator
Scala:
val estimator = NNEstimator(model, criterion)
Python:
estimator = NNEstimator(model, criterion)
NNEstimator
extends org.apache.spark.ml.Estimator
and supports training a BigDL
model with Spark DataFrame data. It can be integrated into a standard Spark ML Pipeline
to allow users to combine the components of BigDL and Spark MLlib.
NNEstimator
supports different feature and label data types through Preprocessing
.
During fit (training), NNEstimator will extract feature and label data from input DataFrame and use
the Preprocessing
to convert data for the model, typically converts the feature and label
to Tensors or converts the (feature, option[Label]) tuple to a BigDL Sample
.
EachPreprocessing
conducts a data conversion step in the preprocessing phase, multiple
Preprocessing
can be combined into a ChainedPreprocessing
. Some pre-defined
Preprocessing
for popular data types like Image, Array or Vector are provided in package
com.intel.analytics.zoo.feature
, while user can also develop customized Preprocessing
.
NNEstimator and NNClassifier also supports setting the caching level for the training data. Options are "DRAM", "PMEM" or "DISK_AND_DRAM". If DISK_AND_DRAM(numSlice) is used, only 1/numSlice data will be loaded into memory during training time. By default, DRAM mode is used and all data are cached in memory.
By default, SeqToTensor
is used to convert an array or Vector to a 1-dimension Tensor.
Using the Preprocessing
allows NNEstimator
to cache only the raw data and decrease the
memory consumption during feature conversion and training, it also enables the model to digest
extra data types that DataFrame does not support currently.
More concrete examples are available in package com.intel.analytics.zoo.examples.nnframes
NNEstimator
can be created with various parameters for different scenarios.
1. NNEstimator(model, criterion)
Takes only model and criterion and use SeqToTensor
as feature and label
Preprocessing
. NNEstimator
will extract the data from feature and label columns (
only Scalar, Array[_] or Vector data type are supported) and convert each feature/label to
1-dimension Tensor. The tensors will be combined into BigDL Sample
and send to model for
training.
2. NNEstimator(model, criterion, featureSize: Array[Int], labelSize: Array[Int])
Takes model, criterion, featureSize(Array of Int) and labelSize(Array of Int). NNEstimator
will extract the data from feature and label columns (only Scalar, Array[_] or Vector data
type are supported) and convert each feature/label to Tensor according to the specified Tensor
size.
3. NNEstimator(model, criterion, featureSize: Array[Array[Int]], labelSize: Array[Int])
This is the interface for multi-input model. It takes model, criterion, featureSize(Array of
Int Array) and labelSize(Array of Int). NNEstimator
will extract the data from feature and label columns (only Scalar, Array[_] or Vector data
type are supported) and convert each feature/label to Tensor according to the specified Tensor
size.
4. NNEstimator(model, criterion, featurePreprocessing: Preprocessing[F, Tensor[T]],
labelPreprocessing: Preprocessing[F, Tensor[T]])
Takes model, criterion, featurePreprocessing and labelPreprocessing. NNEstimator
will extract the data from feature and label columns and convert each feature/label to Tensor
with the featurePreprocessing and labelPreprocessing. This constructor provides more flexibility
in supporting extra data types.
Meanwhile, for advanced use cases (e.g. model with multiple input tensor), NNEstimator
supports:
setSamplePreprocessing(value: Preprocessing[(Any, Option[Any]), Sample[T]])
to directly compose
Sample according to user-specified Preprocessing.
Scala Example:
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.zoo.pipeline.nnframes.NNEstimator
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
val model = Sequential().add(Linear(2, 2))
val criterion = MSECriterion()
val estimator = NNEstimator(model, criterion)
.setLearningRate(0.2)
.setMaxEpoch(40)
val data = sc.parallelize(Seq(
(Array(2.0, 1.0), Array(1.0, 2.0)),
(Array(1.0, 2.0), Array(2.0, 1.0)),
(Array(2.0, 1.0), Array(1.0, 2.0)),
(Array(1.0, 2.0), Array(2.0, 1.0))))
val df = sqlContext.createDataFrame(data).toDF("features", "label")
val nnModel = estimator.fit(df)
nnModel.transform(df).show(false)
Python Example:
from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.util.common import *
from zoo.pipeline.nnframes.nn_classifier import *
from zoo.feature.common import *
data = self.sc.parallelize([
((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0)),
((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0))])
schema = StructType([
StructField("features", ArrayType(DoubleType(), False), False),
StructField("label", ArrayType(DoubleType(), False), False)])
df = self.sqlContext.createDataFrame(data, schema)
model = Sequential().add(Linear(2, 2))
criterion = MSECriterion()
estimator = NNEstimator(model, criterion, SeqToTensor([2]), ArrayToTensor([2]))\
.setBatchSize(4).setLearningRate(0.2).setMaxEpoch(40) \
nnModel = estimator.fit(df)
res = nnModel.transform(df)
Example with multi-inputs Model. This example trains a model with 3 inputs. And users can use VectorAssembler from Spark MLlib to combine different fields. With the specified sizes for each model input, NNEstiamtor and NNClassifer will split the input features data and send tensors to corresponding inputs.
sparkConf = init_spark_conf().setAppName("testNNClassifer").setMaster('local[1]')
sc = init_nncontext(sparkConf)
spark = SparkSession\
.builder\
.getOrCreate()
df = spark.createDataFrame(
[(1, 35, 109.0, Vectors.dense([2.0, 5.0, 0.5, 0.5]), 1.0),
(2, 58, 2998.0, Vectors.dense([4.0, 10.0, 0.5, 0.5]), 2.0),
(3, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 1.0)],
["user", "age", "income", "history", "label"])
assembler = VectorAssembler(
inputCols=["user", "age", "income", "history"],
outputCol="features")
df = assembler.transform(df)
x1 = ZLayer.Input(shape=(1,))
x2 = ZLayer.Input(shape=(2,))
x3 = ZLayer.Input(shape=(2, 2,))
user_embedding = ZLayer.Embedding(5, 10)(x1)
flatten = ZLayer.Flatten()(user_embedding)
dense1 = ZLayer.Dense(2)(x2)
gru = ZLayer.LSTM(4, input_shape=(2, 2))(x3)
merged = ZLayer.merge([flatten, dense1, gru], mode="concat")
zy = ZLayer.Dense(2)(merged)
zmodel = ZModel([x1, x2, x3], zy)
criterion = ZooClassNLLCriterion()
classifier = NNClassifier(zmodel, criterion, [[1], [2], [2, 2]]) \
.setOptimMethod(Adam()) \
.setLearningRate(0.1)\
.setBatchSize(2) \
.setMaxEpoch(10)
nnClassifierModel = classifier.fit(df)
print(nnClassifierModel.getBatchSize())
res = nnClassifierModel.transform(df).collect()
NNModel
Scala:
val nnModel = NNModel(bigDLModel)
Python:
nn_model = NNModel(bigDLModel)
NNModel
extends Spark's ML
Transformer. User can invoke
fit
in NNEstimator
to get a NNModel
, or directly compose a NNModel
from BigDLModel.
It enables users to wrap a pre-trained BigDL Model into a NNModel,
and use it as a transformer in your Spark ML pipeline to predict the results for DataFrame
(DataSet)
.
NNModel
can be created with various parameters for different scenarios.
1. NNModel(model)
Takes only model and use SeqToTensor
as feature Preprocessing. NNModel
will extract the
data from feature column (only Scalar, Array[_] or Vector data type are supported) and
convert each feature to 1-dimension Tensor. The tensors will be sent to model for inference.
2. NNModel(model, featureSize: Array[Int])
Takes model and featureSize(Array of Int). NNModel
will extract the data from feature
column (only Scalar, Array[_] or Vector data type are supported) and convert each feature
to Tensor according to the specified Tensor size. User can also set featureSize as
Array[Array[Int]] for multi-inputs model.
3. NNModel(model, featurePreprocessing: Preprocessing[F, Tensor[T]])
Takes model and featurePreprocessing. NNModel
will extract the data from feature column
and convert each feature to Tensor with the featurePreprocessing. This constructor provides
more flexibility in supporting extra data types.
Meanwhile, for advanced use cases (e.g. model with multiple input tensor), NNModel
supports:
setSamplePreprocessing(value: Preprocessing[Any, Sample[T]])
to directly compose
Sample according to user-specified Preprocessing.
NNClassifier
Scala:
val classifer = NNClassifer(model, criterion)
Python:
classifier = NNClassifer(model, criterion)
NNClassifier
is a specialized NNEstimator
that simplifies the data format for
classification tasks where the label space is discrete. It only supports label column of
DoubleType, and the fitted NNClassifierModel
will have the prediction column of
DoubleType.
model
BigDL module to be optimized in the fit() methodcriterion
the criterion used to compute the loss and the gradient
NNClassifier
can be created with various parameters for different scenarios.
1. NNClassifier(model, criterion)
Takes only model and criterion and use SeqToTensor
as feature and label
Preprocessing. NNClassifier
will extract the data from feature and label columns (
only Scalar, Array[_] or Vector data type are supported) and convert each feature/label to
1-dimension Tensor. The tensors will be combined into BigDL samples and send to model for
training.
2. NNClassifier(model, criterion, featureSize: Array[Int])
Takes model, criterion, featureSize(Array of Int). NNClassifier
will extract the data from feature and label columns and convert each feature to Tensor
according to the specified Tensor size. ScalarToTensor
is used to convert the label column.
User can also set featureSize as Array[Array[Int]] for multi-inputs model.
3. NNClassifier(model, criterion, featurePreprocessing: Preprocessing[F, Tensor[T]])
Takes model, criterion and featurePreprocessing. NNClassifier
will extract the data from feature and label columns and convert each feature to Tensor
with the featurePreprocessing. This constructor provides more flexibility
in supporting extra data types.
Meanwhile, for advanced use cases (e.g. model with multiple input tensor), NNClassifier
supports:
setSamplePreprocessing(value: Preprocessing[(Any, Option[Any]), Sample[T]])
to directly compose
Sample with user-specified Preprocessing.
Scala example:
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.zoo.pipeline.nnframes.NNClassifier
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
val model = Sequential().add(Linear(2, 2))
val criterion = MSECriterion()
val estimator = NNClassifier(model, criterion)
.setLearningRate(0.2)
.setMaxEpoch(40)
val data = sc.parallelize(Seq(
(Array(0.0, 1.0), 1.0),
(Array(1.0, 0.0), 2.0),
(Array(0.0, 1.0), 1.0),
(Array(1.0, 0.0), 2.0)))
val df = sqlContext.createDataFrame(data).toDF("features", "label")
val dlModel = estimator.fit(df)
dlModel.transform(df).show(false)
Python Example:
from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.util.common import *
from bigdl.dlframes.dl_classifier import *
from pyspark.sql.types import *
#Logistic Regression with BigDL layers and Analytics zoo NNClassifier
model = Sequential().add(Linear(2, 2)).add(LogSoftMax())
criterion = ZooClassNLLCriterion()
estimator = NNClassifier(model, criterion, [2]).setBatchSize(4).setMaxEpoch(10)
data = sc.parallelize([
((0.0, 1.0), [1.0]),
((1.0, 0.0), [2.0]),
((0.0, 1.0), [1.0]),
((1.0, 0.0), [2.0])])
schema = StructType([
StructField("features", ArrayType(DoubleType(), False), False),
StructField("label", ArrayType(DoubleType(), False), False)])
df = sqlContext.createDataFrame(data, schema)
dlModel = estimator.fit(df)
dlModel.transform(df).show(False)
NNClassifierModel
Scala:
val nnClassifierModel = NNClassifierModel(model, featureSize)
Python:
nn_classifier_model = NNClassifierModel(model)
NNClassifierModel is a specialized NNModel
for classification tasks.
Both label and prediction column will have the datatype of Double.
NNClassifierModel
can be created with various parameters for different scenarios.
1. NNClassifierModel(model)
Takes only model and use SeqToTensor
as feature Preprocessing. NNClassifierModel
will
extract the data from feature column (only Scalar, Array[_] or Vector data type are supported)
and convert each feature to 1-dimension Tensor. The tensors will be sent to model for inference.
2. NNClassifierModel(model, featureSize: Array[Int])
Takes model and featureSize(Array of Int). NNClassifierModel
will extract the data from feature
column (only Scalar, Array[_] or Vector data type are supported) and convert each feature
to Tensor according to the specified Tensor size. User can also set featureSize as
Array[Array[Int]] for multi-inputs model.
3. NNClassifierModel(model, featurePreprocessing: Preprocessing[F, Tensor[T]])
Takes model and featurePreprocessing. NNClassifierModel
will extract the data from feature
column and convert each feature to Tensor with the featurePreprocessing. This constructor provides
more flexibility in supporting extra data types.
Meanwhile, for advanced use cases (e.g. model with multiple input tensor), NNClassifierModel
supports: setSamplePreprocessing(value: Preprocessing[Any, Sample[T]])
to directly compose
Sample according to user-specified Preprocessing.
Hyperparameter setting
Prior to the commencement of the training process, you can modify the optimization algorithm, batch
size, the epoch number of your training, and learning rate to meet your goal or
NNEstimator
/NNClassifier
will use the default value.
Continue the codes above, NNEstimator and NNClassifier can be set in the same way.
Scala:
//for esitmator
estimator.setBatchSize(4).setMaxEpoch(10).setLearningRate(0.01).setOptimMethod(new Adam())
//for classifier
classifier.setBatchSize(4).setMaxEpoch(10).setLearningRate(0.01).setOptimMethod(new Adam())
Python:
# for esitmator
estimator.setBatchSize(4).setMaxEpoch(10).setLearningRate(0.01).setOptimMethod(Adam())
# for classifier
classifier.setBatchSize(4).setMaxEpoch(10).setLearningRate(0.01).setOptimMethod(Adam())
Prepare the data and start the training process
NNEstimator/NNCLassifer supports training with Spark's DataFrame/DataSet
Suppose df
is the training data, simple call fit
method and let Analytics Zoo train the model
for you.
Scala:
//get a NNClassifierModel
val nnClassifierModel = classifier.fit(df)
Python:
# get a NNClassifierModel
nnClassifierModel = classifier.fit(df)
User may also set validation DataFrame and validation frequency through setValidation
method.
Train summay and validation summary can also be configured to log the training process for
visualization in Tensorboard. See Visualization for the details.
Make prediction on chosen data
Since NNModel
/NNClassifierModel
inherits from Spark's Transformer
abstract class, simply call
transform
method on NNModel
/NNClassifierModel
to make prediction.
Scala:
nnModel.transform(df).show(false)
Python:
nnModel.transform(df).show(false)
For the complete examples of NNFrames, please refer to: Scala examples Python examples
NNImageReader
NNImageReader
is the primary DataFrame-based image loading interface, defining API to read images
into DataFrame.
Scala:
val imageDF = NNImageReader.readImages(imageDirectory, sc)
Python:
image_frame = NNImageReader.readImages(image_path, self.sc)
The output DataFrame contains a sinlge column named "image". The schema of "image" column can be
accessed from com.intel.analytics.zoo.pipeline.nnframes.DLImageSchema.byteSchema
.
Each record in "image" column represents one image record, in the format of
Row(origin, height, width, num of channels, mode, data), where origin contains the URI for the image file,
and data
holds the original file bytes for the image file. mode
represents the OpenCV-compatible
type: CV_8UC3, CV_8UC1 in most cases.
val byteSchema = StructType(
StructField("origin", StringType, true) ::
StructField("height", IntegerType, false) ::
StructField("width", IntegerType, false) ::
StructField("nChannels", IntegerType, false) ::
// OpenCV-compatible type: CV_8UC3, CV_32FC3 in most cases
StructField("mode", IntegerType, false) ::
// Bytes in OpenCV-compatible order: row-wise BGR in most cases
StructField("data", BinaryType, false) :: Nil)
After loading the image, user can compose the preprocess steps with the Preprocessing
defined
in com.intel.analytics.zoo.feature.image
.


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{{ prediction_text }}


This video of the Barge shows the significant damage that resulted from Hurricane Irma in 2017. The elements that are circled in red were damaged or swept into Biscayne Bay by the powerful storm surge and wind. The 3D documentation project helps staff design for resiliency. Fragments from the Barge were salvaged in 2018 and, after restoration, will return to their original locations.


{% extends 'base.html'%} {% import 'macros.html' as macro %} {% block content %}
Welcome to Pitch App where we restore hope and offer insights on how to use pitch in different situations
{{macro.displayPitches(pitches)}}


I guess I am a classic kiwi back yarder, working long hours from the garage and getting the job done. I have been doing this forever. Surfboards and any water craft for that matter have long been a significant part of my life, with some resin odors in the air and foam dust on the floor, I am a happy man. I shaped my first board when I was 13 years old so that must have been around 1990. So much has changed since then but all for the better. Along with shaping came surfboard repairs and then it was more repairs than actual shaping but this is perfectly fine.
Over the years I have shaped many boards and repaired 10 times as many; the nice thing is that there is always a friendly surfer at the other end of each board and conversations on almost every topic have resulted.
Surfboards are my thing. Most surfers know this connection. So let keep out of the water for as little time as possible. Lets keep our boards ready to fire on all four, lets be in the water.
Jeremy


WITNESS LIST Transportation Committee April 3, 2001-2:00P HB 1128 For: Joiner, Gary (Texas Farm Bureau) Registering, but not testifying: On: Linzey, David (Texas Department of Transportation) HB 1457 For: Harold, Barthe (Houston Police Department) Against: Fitzgerald, James (Burlington Northern Santa Fe Railway) McCulley, Hugh (Union Pacific Railroad) Messina, William J. (Universal Maritime Services) Niemand, Walter (Carrier Container Council, Inc.) Registering, but not testifying: For: Findeisen, Les (Texas Motor Transportation Association) Sanders, Stephen (Self and Houston Police Patrolmen's Union) Against: Adams, Don (West Gulf Maritime Association) Morrison, Jim (West Gulf Maritime Association) HB 1939 On: Fulton, David S. (Texas Department of Transportation) HB 2203 For: Rice, Jr., Chuck (Texas Bike Coalition) Stallings, Robin (Self and Texas Bicycle Industry) HB 2522 For: Knox, James M. (Self and Air Lifeline and Angelflight) Koock, Ken (Texas Aviation Association) Scroggins, Norm (Self and Aircraft Owners and Pilots Association) Shahan, Mike (Texas Airport Management Association) Smith, Paul H. (National Business Aviation Association) Against: Dotson, Alfred D. (Self and Robert Mueller Airport Neighborhood Coalition) Ensey, Kathleen (Self) Gieselman, Joe (Travis County) Lehmann, Mary (Self) Paup, Karen (Housing Committee of the Community Development Commission of Austin) Quinn, Suzanne (Austin Film Society/ Austin Studios) Walker, Jim (Mueller Neighborhood Coalition) On: Griffith, Charles (City of Austin) Registering, but not testifying: For: Bennett, William (Self) Deathe, III, Earl P. (Self) Graham, Kenneth R. (Self) Hooper, Jerry (Aircraft Owners and Pilots Association) MacLeod, Barbara (Self) Rogers, Christina (Self) Taylor, Andy (Self) Werkenthin, Conrad (Texas Aviation Association) Whitley, Jennifer D. (Self) Against: Gilles, Janet (Self) Harris, Matthew (Self) Jack, Jeff (Zilker Neighborhood Association) Krivoniak, Rick (Self and Windsor Park Neighborhood Association) Rogoff, Regina (Self) Santana, Sonia (Self and Housing sub-committee of the Community Development Commission) Schuster, Stefan (Self) Sundberg, Barrett (Self) Thomas, Georgia N. R. (Self) On: Fulton, David S. (Self) Looke, William F. (Texas Aerospace Commission) Martin, William J. (Bill) (Self) HB 2616 Registering, but not testifying: On: Russell, Phillip (Texas Department of Transportation/ Texas Turnpike Authority) HB 2667 For: Scoma, Charles (City of North Richland Hills, Mayor) Registering, but not testifying: On: Lopez, Carlos (Texas Department of Transportation) HB 2909 On: Bohuslav, Thomas (Texas Department of Transportation) HB 3323 For: Carter, James (Denton county) Nowels, Micheal (Self) Scott, Rider (Denton county) SB 113 For: Levy, Eric N. (Texas Pediatric Society) Merrifield, John E. (Self) Registering, but not testifying: For: Herzog, Greg (Texas Academy of Family Physicians) Kiser, Kris (Alliance of Automobile Manufacturers) Merrifield, Skylar (Self) Perkins, Vicki (Christus Santa Rosa Children's Hospital) Phipps, Candie (Texas Pediatric Society) Young, Jenny (Texas Medical Association) Against: Joiner, Gary (Texas Farm Bureau) SB 354 For: Schulle, Jr., Gerhardt (Texas Society of Professional Engineers) Stagner, Steve (Consulting Engineers Council) Against: Cosentino, Michael J. (Texas Municipal League) On: Esch, Joseph (City of Sugar Land) Registering, but not testifying: For: Chatron, Michael (AGC - Texas Building Branch) Grace, Cary (City of Houston) Lancaster, David (Texas Society of Architects) Norman, Scott (Consulting Engineers Council of Texas) SCR 25 Registering, but not testifying: For: Findeisen, Les (Texas Motor Transportation Association) Garcia, Joe A. (Texas Border Infrastructure Coalition) Gutierrez, Jr., Hector (City of El Paso)


Using the Pin Object
Each analog and digital pin of the I/O board is represented by a Pin object. The Pin object is the foundation for many of the Breakout IO objects and is also very useful on it’s own. This section describes how to use pins as digital inputs, digital outputs, analog inputs and analog outputs.
Digital Input
Let’s assume you have an Arduino board with a button connected to digital pin 2 and that you have created an instance of the IOBoard object and named it “board”.
In order to use pin 2 as an input, you must first set the pin mode to digital input (DIN):
board.setDigitalPinMode(2, Pin.DIN);
Next you need to get a reference to the Pin object associated with digital pin 2:
var btn = board.getDigitalPin(2);
So in order to use a digital pin as an input, you first need to set the pin mode, then get a reference to the pin:
board.setDigitalPinMode(2, Pin.DIN);
var btn = board.getDigitalPin(2);
Now that we have a reference to the pin we can we can read the pin’s value:
var pinValue = btn.value;
The value of a digital input will be 1 or 0. While we can check the pin value at any time, it’s most useful get the value only when it changes. To do this, you can attach an event listener to the Pin object. In this example we’ll listen for a change event:
btn.addEventListener(PinEvent.CHANGE, onPinChange);
This will call the listener function onPinChange
each time the value of the pin changes. You can
use any name you like for the listener (so onPin2Change, onBtnChange, etc. are also valid names).
Next we define the listener function:
function onPinChange(event) { console.log("pin value = " + event.target.value); }
Notice the function parameter named event
. This is an object that contains data about the event
(in this case a PinEvent).
You can use any name for this parameter (‘evt’ and ‘e’ are other common names). All
event objects have a property named target
that provides a reference to the object that fired the
event. So in this case, event.target
refers to the Pin object representing the button and
therefore event.target.value
refers to the value of the button (either 1 or 0). The code simply
prints the pin value to the console each time the button is pressed.
If you need to refer to the target more than once, it is best to create a variable first such as:
var pin = event.target;
console.log("pin value = " + pin.value);
This is more clear and also makes lookups to properties of pin faster if you need to get or set the value of multiple properties.
We can also get the number
of the pin. This is useful if you have more than one digital input
and want to use a single listener to handle all of the inputs. For example, assume you have also connected
buttons to pins 3 and 4 of the I/O board and added a listener for the CHANGE event to each Pin object. You can
handle all of the changes in a single listener function to save memory:
function onPinChange(event) { var pin = event.target; switch(pin.number) { case 2: doSomething(pin.value); break; case 3: doSomethingElse(pin.value); break; case 4: doAnotherThing(pin.value); break; } }
There are two additional events available for the Pin object. They are PinEvent.FALLING_EDGE and PinEvent.RISING_EDGE. The falling edge event is dispatched / fired when the pin changed value from 1 to 0. The rising edge event is fired when the pin changed value from 0 to 1. If you are only interested in being notified when the value changes from 0 to 1, then simply listen for the rising edge event:
btn.addEventListener(PinEvent.RISING_EDGE, onHigh); function onHigh(event) { // assume the button is wired with a pull-down resistor so a value // of 1 indicates that the button was pressed console.log("button pressed"); }
Now that you have a better understanding of how to use a pin as a digital input, you may be wondering what the difference is between using a Pin object as a digital input and using the Breakout Button object. The Button object is a wrapper around a digital input pin, but it provides a number of useful features such as switch debouncing, sustained press events (fires at the interval specified while the button is held continuously) and setting the pin mode automatically. In most cases, if you are using an actual button (in that it’s a digital input that is physically pressed by a human) as a digital input it is best to use the Button object, but if you have another type of digital input that is not controlled by a physical press (such as a motion sensor that outputs a high or low value when presence is detected), then it may make more sense to use the Pin directly as described in this section.
Now lets look at digital output.
Digital Output
A common digital output that is easy to understand is an LED. Let’s assume an LED is connected to digital pin 9 of an Arduino board.
Like in the digital input example, the first step is to set the pin mode. In this case we want to set pin 9 to digital output (DOUT):
board.setDigitalPinMode(9, Pin.DOUT);
The next step is to get a reference to the pin object:
board.setDigitalPinMode(9, Pin.DOUT);
var led = board.getDigitalPin(9);
Now since we are using a digital output, there is no need to listen for events. We can simply set the value at any time, causing the Led to turn on or off:
// turn the led on led.value = Pin.HIGH; // turn the led off led.value = Pin.LOW;
A cool thing you can do with digital output is attach a Generator. A generator is a type of object that can automatically change (or drive) an output value by applying various waveforms or other equations. Currently the only type of generator in breakout is the Oscillator object. The oscillator uses a waveform to generate an output signal. The possibilities are much more exciting for an analog output (PWM) pin, but you can also use a generator on a digial output to oscillate between 1 and 0 on a set frequency. Here’s an example:
var freq = 1.0; // the frequency in seconds var times = 0; // the number of times to repeat (zero = forever) // create an instance of an Oscillator object that will apply a square wave var blink = new Oscillator(Oscillator.SQUARE, freq, 1, 0, 0, times); // add the Oscillator object to the digital output led.addGenerator(blink); // start the oscillator blink.start();
This code attaches a square wave oscillator, setting the frequency to 1 second, amplitude to 1, offset and
phase each to zero, and a repeat count of zero which will cause the oscillator to run ‘forever’.
Finally the oscillator is started and the Led will blink on for 1 second then off for 1 second repeatedly
until the program execution ends. You can also call blink.stop()
to stop the oscillator.
You can learn more about Oscillators in the Analog Output section of this guide as well as the examples in Breakout/examples/generators/.
Also see the LED object in the Breakout/io/ package. It provides a number of helpful methods that make it easier to use an LED. However note that some of the methods such as fadeIn, fadeOut and fadeTo can only be used if the LED is connected to a PWM pin on the I/O board. You’ll learn more about using PWM pins in the Analog Output section.
Analog Input
Setting a pin as an analog input enables you to read data from hundreds (if not thousands) of different sensors. For this section let’s assume a potentiometer is attached to pin A0 of your I/O board and that you have created an instance of the IOBoard object named “board”.
Unlike the digital input and output examples, we do not have to set the pin mode to use a pin as an analog input, instead we need to enable the analog input pin:
board.enableAnalogPin(0);
By default all analog pins are disabled. This is so the I/O board does not have to send analog data
continuously when it’s not even needed by the user. Therefore it is necessary to enable analog input for
the specific pins you are connecting sensors to. It’s also worth noting that analog data is sent at a
maximum rate of 19 milliseconds (53 times/second). You can lower the interval (sending data less frequently)
using the board.setInterval
property. So board.setInterval = 33;
would cause the
board to send analog data at a rate of 33 milliseconds (or 30 times/second), but you should not set the
sampling interval to a rate faster than 19 milliseconds or you could end up with incomplete data.
The next step is to get a reference to the analog pin:
var sensor = board.getAnalogPin(0);
So to read an analog input we enable the pin then get a reference to the pin object:
board.enableAnalogPin(0);
var sensor = board.getAnalogPin(0);
Now that we have enabled analog input and have a reference to the pin we can read the sensor value:
var sensorValue = sensor.value;
Analog input values are in the range of 0.0 to 1.0. The value is only updated when it changes. While we can read it at any time, it’s best to add a listener to be notified when the value changes:
sensor.addEventListener(PinEvent.CHANGE, onSensorUpdate);
The listener function onSensorUpdate
will be called automatically each time a new analog input
value from pin A0 is received. You can use any name you like for the listener function so onPinChange,
onSensorPinChange, etc are also valid names.
Next you need to implement the listener:
function onSensorUpdate(event) { console.log("sensor value = " + event.target.value); }
This will print the sensor value to the console each time it changes. Note the event
parameter.
This is a reference to the event object (PinEvent). You can call the paramater anything (evt and e are other
common names). Every event object has a property called target
that provides a reference to the
object that dispatched (fired) the event. So in this case target
refers to the analog input pin.
Therefore event.target.value
refers to the value of the analog input pin that fired the event.
There are a number of useful properties that the pin object provides for analog input. First we will use a
variable to store the target so we don’t have to keep referring to it as event.target
:
function onSensorUpdate(event) { var sensorPin = event.target; console.log("sensor value = " + sensorPin.value); }
Now we can explore some of the pin properties:
function onSensorUpdate(event) { var sensorPin = event.target; console.log("sensor value = " + sensorPin.value); // the minimum value sent by the sensor since the application started console.log("min value = " + sensorPin.minimum); // the maximum value sent by the sensor since the application started console.log("max value = " + sensorPin.maximum); // the average of the sensor values since the application started console.log("average value = " + sensorPin.average); // the last (previous) value read console.log("previous value = " + sensorPin.lastValue); }
You may notice if you turn the potentiometer all the way clockwise and then all the way counterclockwise you
will be quickly left with minimim and maximum values of 0.0 and 1.0 respectively. If at any point you want to
reset the minimum, maximum and average values you can call the clear()
method as follows:
sensor.clear();
An analog input pin also has a number
property, but it may not give you the result you expect.
For example if you have an Arduino Uno board and you have connected a sensor to pin A0 and you read the
number
property the result will be 14, not 0. This is because the number property is based on a
count of the total number of analog and digital pins on the I/O board, therefore Analog pin 0 is actually pin
number 14 on an Arduino Uno. There is another property, analogNumber
that returns the analog
number of the pin so for the Arduino Uno, the analog number of pin A0 will be 0. Here’s an example:
console.log("pin number = " + sensorPin.number); // prints 14 for an Arduino Uno console.log("analog number = " + sensorPin.analogNumber); // prints 0 for an Uno
Breakout also includes a number of filters that you can apply to the analog input data. These include a Convolution filter to smooth data, a Scaler filter to scale from one range of data to another using a variety of equations, and a TriggerPoint filter that can be used to set a trigger when specific input values are met. To learn more about using filters, see the Using Filters guide and the examples in Breakout/examples/filters/.
If you are using a potentiometer or other linear analog input, it is often easier to use the Potentiometer object from the Breakout/io/ package rather than using the analog input Pin object directly. The Potentiometer object is a wrapper object for the analog input pin providing similar functionality but is a bit easier to use (you don’t have to worry about enabling the analog pin for example, it’s handled automatically and setting filters is also integrated). You will also find this object used in many of the examples. Also, for a complex example of using analog inputs as well as creating new IO objects for specific sensors, see the implementations of the AnalogAccelerometer and SoftPot objects in Breakout/io/.
Analog Output
Certain pins on an I/O board can be set as analog output to simulate an analog output signal. I say simulate because an actual analog signal is not output, but rather the pin is pulsed on and off rapidly to simulate a specific output voltage. This technique is called Pulse-width modulation (PWM). Most I/O boards will have a few PWM pins. The Arduino Uno has 6 PWM pins: digital pins 3, 5, 6, 9, 10, 11. On many Arduino boards PWM pins are marked with a tilde (~) character. For other I/O boards you may need to refer to the datasheet for the board in order to determine which pins are PWM pins, or you can print the pin capabilities for your I/O board using the following method:
board.reportCapabilities();
Make sure your browser’s console window (search ‘how to view console’ for the browser you
are using if you are not sure how to show the console) is visible before calling the
reportCapabilities()
method. This will print the modes associated with each pin. If you see a pin
number with ‘mode: pwm’ next to it, then it is PWM capable.
Now that you have identified the PWM pins on your I/O board, you can use them to control the speed of a motor or the intensity of an LED, the color of an RGB Led, generate tones if a speaker is attached, etc. For the remainder of this section let’s assume an LED is connected to a PWM pin on your I/O board and that you have created an instance of the IOBoard object and named it “board”.
To use a Pin object as an analog output we first need to set the pin mode to PWM:
board.setDigitalPinMode(11, Pin.PWM);
Next get a reference to the PWM pin:
var led = board.getDigitalPin(11);
Now we can set the intensity (brightness) of the led:
// set the LED to 1/4 intensity (where 1.0 is full intensity)
led.value = 0.25;
Obviously this is more interesting if you can change the value over time. Try creating an on-screen slider to change the value between 0.0 and 1.0, or you can use a potentiometer (or other analog input) to change the value. Here’s an example of controlling the LED intensity using a potentiometer connected to analog pin 0:
board.setDigitalPinMode(11, Pin.PWM); var led = board.getDigitalPin(11); board.enableAnalogPin(0); var pot = board.getAnalogPin(0); pot.addEventListener(PinEvent.CHANGE, onPotChange); function onPotChange(event) { var pin = event.target; // set the LED value equal to the potentiometer value led.value = pin.value; }
You can also attach an Oscillator to an analog output to drive the output value with a waveform. We looked at the Oscillator (which is a type of Generator) briefly in the Digital Output section where we used it to turn an LED on and off. When an LED is connected to a PWM pin, however you can apply a variety of waveforms to the output value. Here’s an example of fading an LED on and off using a sine wave:
var freq = 2.0; // time in seconds var amplitude = 1.0; var offset = 0; var phase = 0; var repeat = 0; // where zero = forever, and 10 = repeat 10 times // create a new sine wave oscillator // this will fade the LED on over a 2 second period and then off over a 2 second // period and repeat this cycle until the oscillator is stopped or the program // execution ends var sinewave = new Oscillator(Oscillator.SIN, freq, amplitude, offset, phase, repeat); // add the oscillator to the PWM pin led.addGenerator(sinewave); // start the oscillator sinewave.start();
Some additional types of waveforms you can apply are: SAW, SQUARE, TRIANGLE, LINEAR, and IMPULSE. To learn more about using Generators, see the examples in Breakout/examples/generators/.
The Breakout/io/ package includes a number of useful objects (‘classes’) such as LED, RGBLED, Servo, and DCMotor that are based on the analog output pin. It’s often easier to use these hardware abstractions rather than the analog Pin object directly. See Breakout/examples/actuators/ for examples. Also take a look at the code for these objects for an example of how to create new IO objects from Pin objects.


Change Log: 2.087.0
Download D 2.087.0
released Jul 01, 2019
Compiler changes
- A new syntax is available to declare an alias to a function type
- Add Markdown-inspired features to Ddoc
- scope as a type constraint on class declarations is deprecated.
- Usage of this and super as types is obsolete
- Turn deprecation into error for some invalid integer literals.
- fix Issue 16002 - Add is(sym == module) and is(sym == package)
- Local templates can now receive local symbols.
- Windows: The mingw based runtime and platform import libraries can now be selected explicitly
- Floating point types now use a quiet nan as the .init value
- Class allocators and deallocators are now obsolete
- The deprecation phase for access checks is finished
- Initialization of immutable global data from static this is deprecated
- Struct constructors with all-default parameter will now error
- Template alias parameters now match basic types as a conversion.
- 32 Bit Linux now uses XMM registers for float and double rather than the x87.
Runtime changes
Library changes
List of all bug fixes and enhancements in D 2.087.0.
Compiler changes
- A new syntax is available to declare an alias to a function type
It's now possible to declare an alias to a function type using the alias syntax based on the assignment operator. The declaration consists of the return type, followed by parameters and ends with optional (member) function attributes. See the AliasDeclarationY rule for more details.
Example:
alias int OldStyleFunAlias(string); alias NewStyleFunAlias = int(string); int fun(string p){return 0;} static assert(is(typeof(fun) == NewStyleFunAlias));
Note that an advantage compared to the old syntax is that the new allows a shortcut to function template types:
alias SetterProto(T) = void(T t); alias GetterProto(T) = T() const;
- Add Markdown-inspired features to Ddoc
With the -preview=markdown flag to dmd, Ddoc now supports Markdown features like headings, emphasized text, links and more. The full list of features is described in the Ddoc documentation page.
Use the -transition=vmarkdown flag together with the -preview=markdown flag to log all instances of Markdown features as they're processed in your documentation.
- scope as a type constraint on class declarations is deprecated.
scope as a type constraint on class declarations has been deprecated for quite some time. However, starting with this release, the compiler will emit a deprecation warning if used.
scope class C { } // Deprecation: `scope` as a type constraint is deprecated. Use `scope` at the usage site.
Note that this does not apply to structs. Deprecation of scope as a type constraint on structs is still awaiting a decision.
- Usage of this and super as types is obsolete
Prior to this release, this and super could be used as both data or types depending on the context, but was deprecated about a year ago. Starting with this release using this or super as a type will result in a compiler error.
class C { shared(this) x; // Error: Using `this` as a type is obsolete. Use `typeof(this)` instead } class D : C { shared(super) a; // Error: Using `super` as a type is obsolete. Use `typeof(super)` instead super b; // Error: Using `super` as a type is obsolete. Use `typeof(super)` instead }
Use typeof(super) or typeof(this) instead.
class C { shared(typeof(this)) x; } class D : C { shared(typeof(super)) a; typeof(super) b; }
- Turn deprecation into error for some invalid integer literals.
With this release DMD will issue an error when some invalid integer literals like 0b or 0x are used. Example:
auto a = 0b; // Error: `0b` isn't a valid integer literal, use `0b0` instead auto a = 0x; // Error: `0x` isn't a valid integer literal, use `0x0` instead
- fix Issue 16002 - Add is(sym == module) and is(sym == package)
This enhancement adds two new forms of the is(), expression, which determine whether a given symbol represents a module or package.
It also adds __traits(isModule, sym) and __traits(isPackage, sym), which do the same thing.
- Local templates can now receive local symbols.
With this release local and member templates can be instantiated with local symbols.
struct S { private int _m; void exec(alias fun)() { fun(_m); } } unittest { int localVar; void set(int i) { localVar = i; } auto obj = S(10); obj.exec!set(); assert(localVar == 10); }
This was a long-standing limitation that disturbed the metaprograming experience.
See issue 5710.
- Windows: The mingw based runtime and platform import libraries can now be selected explicitly
When specifying -mscrtlib=msvcrt100 explicitly on the command line to dmd, detection of a Visual Studio installation is skipped and the redistributable VC2010 dynamic runtime library and mingw based platform import libraries will be used unconditionally. The LLD linker (provided by the LLVM project) will be invoked instead of the Microsoft linker.
- Floating point types now use a quiet nan as the .init value
Prior to this release, float.init, double.init and real.init used a signalling nan. They now use a quiet nan, which is the same as the corresponding .nan value.
Old behaviour:
double a = double.init, b = double.nan; writefln("%x",*cast(ulong*)&a); // 7ff4000000000000 writefln("%x",*cast(ulong*)&b); // 7ff8000000000000
New behaviour:
double a = double.init, b = double.nan; writefln("%x",*cast(ulong*)&a); // 7ff8000000000000 writefln("%x",*cast(ulong*)&b); // 7ff8000000000000
- Class allocators and deallocators are now obsolete
Starting with this release any use of class allocators or deallocators will result in a compilation error.
See the deprecated features page for more information.
- The deprecation phase for access checks is finished
The -transition=import and -transition=checkimports switches no longer have an effect and are now deprecated. Symbols that are not visible in a particular scope will no longer be found by the compiler.
- Initialization of immutable global data from static this is deprecated
Prior to this release, the following code was possible:
module foo; immutable int bar; static this() { bar = 42; }
However, module constructors (static this) run each time a thread is spawned, and immutable data is implicitly shared, which led to immutable value being overriden every time a new thread was spawned. The simple fix for this is to use shared static this over static this, as the former is only run once per process.
- Struct constructors with all-default parameter will now error
Since 2.070.0, the following code has been giving deprecations:
struct Oops { this (int universe = 42) {} }
It will now error out.
- Template alias parameters now match basic types as a conversion.
With this release, templates with alias parameters can be instantiated with basic types, such as int or void function().
template Example(alias A) { alias Example = A[]; } alias IntArray = Example!int; template MatchType(T) { enum MatchType = "type"; } template MatchType(alias A) { enum MatchType = "alias"; } // alias matches, but type matches take priority. static assert (MatchType!int == "type");
Templates with alias parameters already matched named types, lambdas, and even value literals. Templates with variadic parameters already matched basic types. This led to strange replacements for alias such as template Template(T...) if (T.length == 1). Those replacements are no longer necessary.
As a consequence, in some cases templates will match that did not used to. For instance, a template may have falsely relied on alias parameters being "something callable" vs. type parameters being "the type of something callable", such as
void fun(T...)(T callable) { ... } void fun(alias A)() { ... }
Such overloads may now need to explicitly check whether A is callable in the second case:
void fun(alias A)() if (__traits(compiles, A())) { ... } // shorter, but may miss cases like structs with a static opCall void fun(alias A)() if (!isType!A) { ... }
- 32 Bit Linux now uses XMM registers for float and double rather than the x87.
This should substantially speed up routine float and double processing. SIMD vector operations, however, are still not support on 32 bit Linux code because of issues with 16 byte stack alignment.
This means that generated code will no longer work on older x86 processors that do not have XMM registers. If this is an issue, please file a bug report.
Runtime changes
- Convert FreeBSD's sys/ttycom.h into core.sys.posix.sys.ttycom
In FreeBSD, as in OSX, the tty part of ioctl is defined in sys/ttycom.h which is included by sys/ioctl.h. In druntime, there were OSX part of definitions in ttycom.d, but there were no FreeBSD equivalent. This change implements FreeBSD part of ttycom.d and ioccom.d, and public import core.sys.posix.sys.ttycom in ioctl.d. The OSX users and FreeBSD users can now use full ioctls related to tty by importing core.sys.posix.sys.ioctl, like including sys/ioctl.h in C language. (For example, TIOCGWINSZ ioctl was not provided in FreeBSD.) Since there are only version(OSX) and version(FreeBSD) part in ttycom.d, public import'ing core.sys.posix.sys.ttycom from ioctl.d will make no harm to other platforms.
- GC now marks the heap with multiple threads
The garbage collector now uses available CPU cores to mark the heap faster. This reduces pause times for a collection considerably.
By default, the GC uses all available logical cores of your CPU. This might affect your application if it has threads that are not suspended during the mark phase of the collection. You can configure the number of additional threads used for marking by DRT option parallel to the GC configuration, e.g. by passing --DRT-gcopt=parallel:2 on the command line. A value of 0 disables parallel marking completely.
As usual, you can also embed the configuration into the application by redefining rt_options, e.g.
extern(C) __gshared string[] rt_options = [ "gcopt=parallel:0" ];
- Add the clone and unshare functions to core.sys.linux.sched
clone(2) and unshare(2) are Linux-specific system calls. Bindings have been added to the corresponding D module, as well as the CLONE_* constants that are used as flags to these functions.
Library changes
- Added a table of control characters in ASCII table
There is a new enumerated type std.ascii.ControlChar. It has values for every control character in ASCII table and has a base type of char, allowing for alternative markup of strings with control characters:
import std.ascii, std.conv; with (ControlChar) assert(text("Phobos", us, "Deimos", us, "Tango", rs) == "Phobos\x1FDeimos\x1FTango\x1E");
- Count processors via sched_getaffinity on Linux
On GNU/Linux usable number of processors may be restricted if a process runs in containers. In case it's better to use sched_getaffinity(2).
import std.parallelism; writeln(totalCPUs); // 4 writeln(totalCPUs); // 1: runs on `taskset -c 0`
- Add overload std.algorithm.sorting.schwartzSort!(alias transform, SwapStrategy ss, R)
std.algorithm.sorting.schwartzSort now has an overloaded version that can be used to provide a SwapStrategy without also explicitly specifying the predicate to sort by.
auto s1 = a.schwartzSort!(abs, SwapStrategy.stable); // The old syntax still works. auto s2 = a.schwartzSort!(abs, "a < b", SwapStrategy.stable);
- Phobos is now compiled with -preview=dip1000
Phobos and Druntime are now compiled with -preview=dip1000.
This is a major milestone in the transition to DIP1000. Hence, end users can now start using -preview=dip1000 in their own code bases.
As always, any encountered issue should be reported to the issue tracker.
List of all bug fixes and enhancements in D 2.087.0:
DMD Compiler regressions
- Bugzilla 19661: DMD 2.084.0 SIGSEGV in std.traits.isFunction
- Bugzilla 19758: (0x01 & 0xFF) == 0 by dmd 2.085.0(-m64) on Windows
DMD Compiler bugs
- Bugzilla 711: combining mixins and overriding causes inifite loops
- Bugzilla 1170: Cannot forward reference a type defined in a MixinStatement
- Bugzilla 4923: immutable module variables are modifiable in non-shared module constructors
- Bugzilla 5710: cannot use delegates as parameters to non-global template
- Bugzilla 6541: using synchronized on a templated method crashes the compiler
- Bugzilla 9029: Built-in types treated specially for alias parameters
- Bugzilla 10739: Struct defined by template mixin leads to order-sensitivity of declarations
- Bugzilla 13471: CTFE glitch when executing std.digest.crc.crc32Of() and checking the result with enforce(). (keyword: uninitialized variable)
- Bugzilla 13848: overlapping initialization for r
- Bugzilla 17141: Type Inference Incorrectly Converts Characters to Integers
- Bugzilla 18794: Compiling with -O causes runtime segfault
- Bugzilla 18958: extern(C++) wchar, dchar mangling not correct
- Bugzilla 19234: betterC TypeInfo error when using slice copy on Structs
- Bugzilla 19813: Generated bt instruction seg faults, high 32 bits of register is garbage
- Bugzilla 19814: Nested code blocks in Ddoc break formatting
- Bugzilla 19829: __traits(isSame) returns true for some non-local delegate lambdas even when they are different
- Bugzilla 19870: Generated Copy Constructor disables default construction
- Bugzilla 19890: ICE: Segmentation fault with negative array size
- Bugzilla 19891: Confusing error message for auto ref parameters with default values
- Bugzilla 19893: extern(C++, "ns") should count as module scope for version declarations
- Bugzilla 19905: Floating point .init should be bitwise identical to .nan
- Bugzilla 19920: __trait(parent, ...) broken with extern(C++,"ns") nested in scopes
- Bugzilla 19971: wrong string literals in "cannot pass argument" errors
- Bugzilla 19995: parameter attributes are accepted in parameter-less functions
DMD Compiler enhancements
- Bugzilla 10665: The documentation produced by ddoc should clearly list all public imports of a module
- Bugzilla 16002: Add __traits(isModule) and __traits(isPackage)
- Bugzilla 16020: Allow AliasDeclarationY to express function types
- Bugzilla 19856: [aApplycd2]: foreach (int) doesn't work on BigEndian targets
Phobos regressions
- Bugzilla 17358: [REG 2.074.0] std.stdio.File.lockingTextWriter.put no longer accepts chains of characters
- Bugzilla 19740: Incorrect result of BigInt * BigInt
Phobos bugs
- Bugzilla 11061: std.variant.Variant equality comparison always returns false for static array literals.
- Bugzilla 19226: std.typecons.Nullable(T, T nullValue) doesn't fully handle non-self-equal nullValue
- Bugzilla 19781: etc.c.zlib should be @nogc
- Bugzilla 19836: Excessive probability of UUID collisions in std.uuid.randomUUID
- Bugzilla 19883: Cyclic constructor call for BigInt(dstring)
- Bugzilla 19899: std.bitmanip.bitsSet should accept const arguments
- Bugzilla 19939: std.format %13,3.2f does not count width correctly
Phobos enhancements
- Bugzilla 6657: dotProduct overload for small fixed size arrays
- Bugzilla 13965: More handy schwartzSort
- Bugzilla 19513: Use sched_getaffinity(2) to get the number of CPU cores if available
- Bugzilla 19892: Add CTFE support for std.bitmanip: nativeToBigEndian, bigEndianToNative, littleEndianToNative, nativeToLittleEndian
Druntime bugs
- Bugzilla 19861: core.cpuid reports the wrong number of threads
Contributors to this release (63)
A huge thanks goes to all the awesome people who made this release possible.
- aG0aep6G
- Andrei Alexandrescu
- Atila Neves
- Aurélien Fredouelle
- Basile Burg
- Basile-z
- Bastiaan Veelo
- Ben Jones
- Benjamin L. Merritt
- Cameron Ross
- Chloé
- dan
- Dan Printzell
- David Gileadi
- Dibyendu Majumdar
- Diederik de Groot
- dukc
- Ernesto Castellotti
- Eugen Wissner
- Flying-Toast
- Hiroki Noda
- Hiroo Ono
- hygonsoc
- Iain Buclaw
- Igor Stojković
- Ilya Yaroshenko
- Jacob Carlborg
- Johannes Pfau
- John Colvin
- Jonathan M Davis
- Jonathan Marler
- Joseph Rushton Wakeling
- Leandro Lucarella
- look-at-me
- Manu Evans
- Marco de Wild
- Martin Kinkelin
- Martin Nowak
- Mathias Lang
- Mathis Beer
- MetaLang
- Mike Franklin
- Mike Parker
- Nathan Sashihara
- Nicholas Wilson
- Nick Treleaven
- Per Nordlöw
- Petar Kirov
- Quirin F. Schroll
- Radu Racariu
- Rainer Schuetze
- Razvan Nitu
- Robert Schadek
- Sebastian Wilzbach
- shove
- Simen Kjærås
- stefanos-baziotis
- Steven Schveighoffer
- Tomáš Chaloupka
- Vladimir Panteleev
- Walter Bright
- Zevenberge
- سليمان السهمي (Suleyman Sahmi)


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| Name Of the Courses |
Course Code |
Instructor |
| Software Engineering |
CS-308 |
Atul Gupta |
| Computer Networks |
CS-305 |
Vinod Kumar Jain |
| Operating Systems |
CS-306 |
Manish Kumar Bajpai |
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| Days |
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10-11 AM |
11-12 AM |
12-01 PM |
2:30-3:30 PM |
3:30-5:30 PM |
| Monday |
September |
jhlilk22 |
No |
No |
No |
No |
| Monday |
January |
jamieh |
Yes |
No |
No |
No |
| Monday |
May 11, |
jilsew |
Yes |
No |
No |
No |
| Days |
9-10 AM |
10-11 AM |
11-12 AM |
12-01 PM |
2:30-3:30 PM |
3:30-5:30 PM |
| Monday |
September |
jhlilk22 |
No |
No |
No |
No |
| Monday |
January |
jamieh |
Yes |
No |
No |
No |
| Monday |
May 11, |
jilsew |
Yes |
No |
No |
No |
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Gaining Access
Inviting Friends
Warranty
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100 Hollywood Lane, Hollywood, CA 90210
800-100-3333
Avenue Restaurant & Bar is a traditional European cuisine venue, serving the best dishes and drinks from across the range of European cuisines. A definitive neighborhood joint in Davis Square, Avenue offers a laid-back dining experience in a casual setting to guests from all walks of life. Honest food and honest drinks set the foundation of our place's menu!