|
Self-DentistryIt's common to see stories passed with tight-lipped seriousness from parents to children on their way out for a fun night, describing all the horrible things that they might do to themselves should they try drugs. Therefore, it strikes an immediate chord when a story turns out to be true.In April of 2002, twenty five year old Samantha Court was found by paramedics on her bed covered in her own blood, 18 of her teeth lying either on the sheets or in a nearby bowl. Suspicion naturally fell on her boyfriend, Jason Morris, who has home at the time. At Morris' court hearing, however, Samantha revealed that she had removed her own teeth with pliers while high on massive amounts of the drug GHB, which induces hallucinations.  "That's when it started choking me." And so she implemented some quick-fix measures that must have gotten rather tedious after the first half-dozen. The Jury, likely shaken by Ms. Court's court appearance, returned a not guilty verdict in two hours, clearing Morris of all charges. The couple, still committed to each other, returned home. Another success story that leaves a smile on your face. |


I know that it's wrong
to even write you this song
You know, yeah it shows
I love you all along
You wanted to go
Look for love a piece of your soul
And here I am,
pretending to be ok
But, I know I'll hear the songs you like
and I know I'll see the stars shining up above
Yeah, I know that tears may break apart
event hough I won't listen to my heart singing...
I know that it is wrong
to even love you....I love you
Ill be your John Lennon,
To your Yoko Ono
Just like Jax Teller
To your Tara
Ill be Shrek and green
To you My Fiona
Gotta, gotta love me now
and don't you wonder
why I want to be closer and closer and closer
Let's wait till the morning sun
and afternoon's delight
Baby let's ride through the night
Ill be your John Lennon,
To your Yoko Ono
Just like Jax Teller
To your Tara
Ill be Shrek and green
To you My Fiona
Money, honey and golden promises
aint what I need to get by
As long as your here, please be near, please be near
Cause I love you so.. and don't you worry.. baby


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Preparation time
less than 30 mins
Cooking time
no cooking required
Serves
Serves 10-12
Dietary
Here is an exercise in chopping meditation for you! There is much to slice, but you end up with a bright tangle of slaw that feeds a crowd and has enough zing in it to bring a table of cold cuts to life.
Shred the cabbage thinly and put into the largest bowl you have. It may be easier to use a huge saucepan as you need to toss all the ingredients together later, and even my largest mixing bowl isn’t big enough.
Slice the spring onions into batons and then cut each baton into thin slices lengthways, so that you, again, have shreds. Add to the cabbage.
Cut the peppers into very thin slices. Add to the cabbage and spring onions.
Finely chop the red chilli, and do the same with the coriander – stalks and leaves. Add the chilli and all but a tablespoon of the chopped coriander to the cabbage bowl.
In a bowl or measuring jug, mix together the pineapple juice, the zest and juice of 1 lime and the juice of half of the second. Sprinkle in the salt, add the sesame oil and maple syrup, and whisk together before pouring over the prepared vegetables. Toss to mix, then leave to stand for at least 15 minutes, and up to 2 hours, before serving. Sprinkle the reserved tablespoonful of chopped coriander over the finished slaw.
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Chopping chillies


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Download Fidelix 0.1.3 (Arthur)
Released June 13, 2020
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ED WOOD A film review by Matthew Springer Copyright 1994 Matthew Springer
Tim Burton has finally found a film to which his unique talents as a filmmaker can be perfectly wed: ED WOOD.
If you've seen and been disappointed by Burton's work in the past as I have, then you'll thrill to this biopic of B-movie director Edward D. Wood, Jr., whose credits include GLEN OR GLENDA? and PLAN 9 FROM OUTER SPACE," widely regarded as some of the worst films of all time. Wood is also known for surrounding himself with people as strange as he himself was, such as former horror film star Bela Lugosi, whose relationship with Wood is at the heart of ED WOOD.
I've often thought of the problems in Burton's past films as a battle between screenplay and director; his gifts are such that they often can stifle an otherwise good script. In this case, I think the script was ideally suited to his directing style, and the marriage between director and screenplay a perfect one. Burton has hit a peak as a director with this film; he invokes emotions in this film that I would have thought him incapable of understanding, let alone conveying on celluloid.
Burton is also aided by an extremely talented team of actors, led by Johnny Depp in a virtuoso performance as the title character and Martin Landau as Lugosi.
Also not to be overlooked are Sarah Jessica Parker as Dolores Fuller, Wood's first wife who in the end leaves him because she is incapable of understanding his friends and lifestyle; Jeffrey Jones as Criswell the Seer, a false psychic who lands roles as narrator in several Wood films; and Bill Murray in an insanely funny, but small role as Bunny Breckenrige, a homosexual actor who longs to become a woman. His small work alone is enough to warrant seeing this film; it's a credit to the cast that a performance that hilariously over-the-top can be lost amidst such other fine work.
In the end, what makes ED WOOD fabulous is the humanity which it reveals in its characters. All of them are obviously freaks, but just when the audience seems about to realize it and begin to laugh too much at them, Burton gives us a glimpse of these people as human beings that brings us right back to square one. This is especially seen in the Wood/Lugosi relationship; it would have been easy to play Lugosi just for laughs (and lines like "Karloff isn't worthy to smell my s---!" certainly make this clear), but Burton chooses instead to develop this father-son relationship to the fullest, with the roles of father and son being reversed by the time Lugosi has died.
All in all, ED WOOD is a must-see. It showcases Burton's abilities as director to the fullest, while still allowing him to expand those abilities in new directions. It features an incredible ensemble of actors who deliver amazing performances; none seem weak or miscast. And it recreates some of the laughingly bad moments in Wood's actual films down to the letter. Whether you're a fan of Wood's unique talents or have never heard of him, you'll get a kick out of this movie.
Matthew Springer Northwestern University, Evanston, IL. USA<EMAIL_ADDRESS>.
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.
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The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Nora Fatehi in Scarlet Red Dress is Nothing Short of Sensuous
Nora Fatehi sizzle in red dress, enchants with her gaze and this pic is a proof.
Canadian dancer, actor and singer Nora Fatehi, has been making headlines ever since she danced for the songs like Dilbar and Ek Toh Kum Zindagani. Also, the actor shared the screen with Vicky Kaushal in the dramatic non-movie song Bada Pachtaoge which went viral.  The beauty looks radiant as her facial features are so apt. While sharing the picture, Nora writes, “Better wake up if you sleep.. #tbt”.
Nora took to the photo and video sharing platform recently and shared a pic wearing a scarlet red dress with Golden hoop earrings. The Moroccan beauty casts a mystic spell in the pic and looks beautiful as always!
Take a look:
Nora Fatehi who is back with an Afro-Latino club number Pepeta, teased her fans with the stills from the song before its release. The song features Nora in pink flowing locks by the beach. The song was released last week and received immense love from fans and followers. This vibrant club number is Nora’s second international song. The music video begins with people enjoying the beach and having a good time.
Nora, who is now a big name in the Hindi Film industry, shot to fame after her song ‘Dilbar’ took the internet by storm. Wearing a sparkling red outfit and flaunting those killer dance moves, the actress soon became the ruler of many hearts.
On the professional front, Nora is gearing up for the release of Remo Dsouza’s dance drama, Street Dancer 3D, the shoot of which recently wrapped up. The movie is slated to release on January 24, 2020.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Buzz Latest News on India.com.
Story Timeline
- 24 Nov 2019 Nora Fatehi in Scarlet Red Dress is Nothing Short of Sensuous
- 25 Oct 2019 Nora Fatehi's Closeup Picture is a Sight For Sore Eyes- Take a Look
- 13 Sep 2019 Nora Fatehi Twerking in THIS Tutorial Video is All You Need to Brush up Your Steps on Pepeta Song
- 09 Sep 2019 Nora Fatehi's Latest Music Video Pepeta Sets Fans Grooving to Afro-Latino Club Number
Comments - Join the Discussion


Keeping it Classy
Week 5
Understanding Ruby Classes
October 17, 2014
Classes are one of the cornerstones of Ruby programs. They help store and access data about common types of items. Classes also let you create and apply your own unique methods on specific items. Because you can create many types of classes and methods within each class, they are one of the most flexible and versatile ways Ruby has to handle storing large amounts of information.
Let’s consider an example. Pretend you own a bike shop, and you need to print out tags for each bike on your sales floor listing the bike brand, model, year, and price. You also need to be able to modify the price easily in case the bike goes on sale. Classes are a perfect way to store and access this information. They let you define a “Bike” class that you can use for all of the many types of bikes you’re selling that you’ll need to tag.
You would start by defining a class called “Bike.” Within that class you would want to create methods to take information on the bike, enter a discount amount, and finally print out a tag to put on each bike.
First you would initialize your new item in the Bike class by requiring information on the bike brand, model, year, and MSRP price. All this information is assigned and stored within the class using an instance variable. By typing an “@” before the variable name, you allow the varialbe to be used in other methods within the same class. There are other types of variables that you can create by typing other symbols before the variable name, including global variables (“$”) and class variables (“@@”). You can read more about these other types of variables in Ruby docs.
Now that we stored the basic information on the bike using the “initialize” method, we need to create methods to access and modify the information. Let’s create a “discount” method that lets you enter a percentage discount on the the original price. In that method, we’ll create code to take a percentage off the original manufacturer’s suggested retail price of the bike:
Now all we need to do is great a “tag” method that prints out a tag will all the pertinent info on the bike. We’ll want to make sure to list all the info on the bike, included the modified sale price.
Once that method is created, we simply create a new class instance called “my_bike,” enter the info for a bike - for example, a 2014 Specialized Stumpsumper - then call our “discount” and “tag” methods.
Then we can post the tag on our bikes and wait for the customers to stream into the shop!


[0, 1, 2, 3]
function( attrChoices1, attrChoices2 ) {
att1 = randFromArray( attrChoices1 );
att2 = randFromArray( attrChoices2 );
return [ att1, att2, att1 + " or " + att2, att1 + " and " + att2 ];
}
[ "baseline", MIXER( ["blue"], ["hot"] ), "endline" ]
randFromArray([
["A local store ran a sale on two items, a watch and a shirt. There were ", ["customers who bought a watch", "customers who bought a shirt", "customers who bought a watch or a shirt", "customers who bought a watch and a shirt"], "What was the number of ", "?", false],
["A study group at a nearby high school has ", ["sophomores", "girls", "students who are a sophomore or a girl", "sophomore girls"], "How many ", " are there in the study group?", false],
["In a sample of patients, ", CHOOSE_ATTRIBUTES( ["male", "female"], ["colorblind", "overweight", "underweight", "over sixty", "healthy", "under eighteen", "healthy"] ), "What percentage of the patients are ", "?", true],
["Of the houses in a town, ", CHOOSE_ATTRIBUTES( ["white", "blue", "light green", "solar powered", "near a park"], ["air conditioned", "wooden", "stucco"] ), "What percentage of the houses are ", "?", true],
["A census was recently taken in a certain community, and the results include the following facts about the residents: ", CHOOSE_ATTRIBUTES( ["Hispanic", "Asian", "White", "Black"], ["male", "female", "under 18", "over 65"] ), "What percentage of the residents are ", "?", true],
["A car dealer advertises statistics desribing her inventory. Of the cars on her lot, the advertisement states ", CHOOSE_ATTRIBUTES( ["white", "blue", "light green", "red", "grey", "black"], ["trucks", "minivans", "SUVs", "sports cars", "sedans"] ), "What percentage of the cars are ", "?", true]
])
shuffle( [0, 1, 2, 3] )
( function( usePercentages ) {
var rangemin = usePercentages ? 1 : 2;
var rangemax = usePercentages ? 100 : 10;
var vals = [0, 0, 0, 0];
vals[VARINDX_X] = randRange( rangemin, rangemax );
vals[VARINDX_Y] = randRange( rangemin, rangemax );
vals[VARINDX_X_AND_Y] = randRange( max( rangemin, vals[VARINDX_X]+vals[VARINDX_Y]-100 ), min( vals[VARINDX_X], vals[VARINDX_Y] ) );
vals[VARINDX_X_OR_Y] = vals[VARINDX_X] + vals[VARINDX_Y] - vals[VARINDX_X_AND_Y];
return vals;
} )( USEPERCENTAGES )
( function() {
var optionalPercentage = USEPERCENTAGES ? "% are " : " ";
var qstn = INTRO;
for (var i=0; i < ORDER.length-1; i++) {
qstn += (i===ORDER.length-2) ? " and " : "";
qstn += VARVALS[ORDER[i]] + optionalPercentage + VARDESC[ORDER[i]];
qstn += (i < ORDER.length-2) ? ", " : ". ";
}
qstn += QSTNPRETEXT + VARDESC[ORDER[ORDER.length-1]] + QSTNPOSTTEXT;
return qstn;
} )()
function (innerString) { return enFunc(innerString, USEPERCENTAGES); }
VARVALS[ORDER[ORDER.length-1]]
Remember the addition rule of probability: P(A\cup B) = P(A) + P(B) - P(A\cap B)
Because the denominator of the fraction for each probability is the same, for convenience we can also just use cardinality (the number of items in each category) instead of probability: |A\cup B| = |A|+ |B| - |A\cap B|
Substituting variables, ENFUNC(VARDESC[2]) = ENFUNC(VARDESC[0]) + ENFUNC(VARDESC[1]) - ENFUNC(VARDESC[3])
Rearranging, ENFUNC(VARDESC[0]) = ENFUNC(VARDESC[3]) + ENFUNC(VARDESC[2]) - ENFUNC(VARDESC[1])
Rearranging, ENFUNC(VARDESC[1]) = ENFUNC(VARDESC[3]) + ENFUNC(VARDESC[2]) - ENFUNC(VARDESC[0])
Rearranging, ENFUNC(VARDESC[3]) = ENFUNC(VARDESC[0]) + ENFUNC(VARDESC[1]) - ENFUNC(VARDESC[2])
ENFUNC(VARDESC[0]) = VARVALS[3] + VARVALS[2] - VARVALS[1]
ENFUNC(VARDESC[1]) = VARVALS[3] + VARVALS[2] - VARVALS[0]
ENFUNC(VARDESC[2]) = VARVALS[0] + VARVALS[1] - VARVALS[3]
ENFUNC(VARDESC[3]) = VARVALS[0] + VARVALS[1] - VARVALS[2]
ENFUNC(VARDESC[0]) = VARVALS[3] + VARVALS[2] - VARVALS[1]
ENFUNC(VARDESC[1]) = VARVALS[3] + VARVALS[2] - VARVALS[0]
ENFUNC(VARDESC[2]) = VARVALS[0] + VARVALS[1] - VARVALS[3]
ENFUNC(VARDESC[3]) = VARVALS[0] + VARVALS[1] - VARVALS[2]


When using basic code completion (&shortcut:CodeCompletion;), you don't need to type upper-case letters in CamelHump names. It is enough to type the initial letters of the camel names in lower case, and they will be smartly recognized.


This page lists the different types of runtime user errors that amp-bind can throw.
This paragraph element attempts to bind to "asdf".
This expression is missing a closing parens.
This expression calls `substring` on a number.
This expression contains an invalid [class] result (only string/array/null supported).
null
Expression above references an undefined variable `foo`.
null
Expression above references an undefined member `myState.qux`.
null
Expression above tries to access a boolean member.
null
Expression above tries to access a member of a boolean.
null
Expression above tries to access a null member.
null
Expression above tries to access a member of null.
This paragraph's initial `text` state doesn't match the evaluated expression result.


Copyright © 2009 Marc Worrell Date: 2009-04-08
Behaviours: gen_model.
Authors: Marc Worrell (marc@worrell.nl).
| all_flat/1 | Return a flattened representation of the complete category tree. |
| all_flat/2 | |
| all_flat_meta/1 | |
| boundaries/2 | |
| delete/3 | Delete the category, move referring pages to another category. |
| enumerate/1 | Take a category list and make it into a tree, recalculating the left/right and lvl nrs. |
| get/2 | |
| get_by_name/2 | |
| get_by_parent/2 | |
| get_page_count/2 | |
| get_path/2 | Return the path from a root to the category (excluding the category itself). |
| get_range/2 | |
| get_range_by_name/2 | |
| get_root/1 | |
| id_to_name/2 | |
| image/2 | |
| insert/4 | |
| is_a/2 | Return the list of categories (as atoms) that the category is part of. |
| is_a/3 | Check if the id is within another category. |
| last_modified/2 | Return the last modification date of the category. |
| m_find_value/3 | Fetch the value for the key from a model source. |
| m_to_list/2 | Transform a m_config value to a list, used for template loops. |
| m_value/2 | Transform a model value so that it can be formatted or piped through filters. |
| menu/1 | Return the category tree as a menu resource. |
| move/2 | Move categories to the given positions, no checks (except ACL done). |
| move_before/3 | Move a category in front of another category, resetting the parent of the moved category to the parent of the other category. |
| move_below/3 | Move the category below another category, placing it at the end of the children of that category. |
| move_end/2 | Move the category to the end of all categories, making it a top category in the process. |
| name_to_id/2 | |
| name_to_id_check/2 | |
| ranges/2 | Given a list of category ids, return the list of numeric ranges they cover. |
| renumber/1 | Renumber all categories so that the left/right and level indices are correct. |
| renumber_pivot_task/1 | Resync all ids that have their category nr changed. |
| tree/1 | Return the tree of all categories. |
| tree/2 | Return the tree of all categories below a category id. |
| tree_depth/2 | Return the tree of all categories till a certain depth. |
| tree_depth/3 | Return the tree of all categories below a category id till a certain depth. |
| update_sequence/2 |
all_flat(Context) -> any()
Return a flattened representation of the complete category tree. Can be used for overviews or select boxes. The "meta" categories of predicate, category and group are suppressed.
all_flat(CatId, Context) -> any()
all_flat_meta(Context) -> any()
boundaries(CatId::Id, Context::C) -> {Left, Right}
Delete the category, move referring pages to another category. Fails when the transfer id is not a category.
enumerate(Cats::[Cat]) -> [Sort]
Take a category list and make it into a tree, recalculating the left/right and lvl nrs
get(Name, Context) -> any()
get_by_name(Name, Context) -> any()
get_by_parent(Id, Context) -> any()
get_page_count(Id, Context) -> any()
get_path(Id, Context) -> [CatId]
Return the path from a root to the category (excluding the category itself)
get_range(Id, Context) -> any()
get_range_by_name(Name, Context) -> any()
get_root(Context) -> any()
id_to_name(Name, Context) -> any()
image(Id, Context) -> any()
insert(ParentId, Name, Props, Context) -> any()
is_a(Id::int(), Context) -> atomlist()
Return the list of categories (as atoms) that the category is part of
is_a(Id::int(), Cat, Context) -> atomlist()
Check if the id is within another category.
last_modified(Cat::term(), Context) -> {ok, {{Y, M, D}, {Hour, Min, Sec}}} | {error, Reason}
Return the last modification date of the category. Returns false
m_find_value(Index::Key, M::Source, Context) -> term()
Fetch the value for the key from a model source
m_to_list(M::Source, Context) -> List
Transform a m_config value to a list, used for template loops
m_value(M::Source, Context) -> term()
Transform a model value so that it can be formatted or piped through filters
menu(Context) -> any()
Return the category tree as a menu resource
move(Cats, Context) -> any()
Move categories to the given positions, no checks (except ACL done)
Move a category in front of another category, resetting the parent of the moved category to the parent of the other category.
Move the category below another category, placing it at the end of the children of that category.
move_end(CatId::int(), Context) -> ok | {error, Reason}
Move the category to the end of all categories, making it a top category in the process
name_to_id(Id, Context) -> any()
name_to_id_check(Name, Context) -> any()
ranges(Cat::CatList, Context) -> RangeList
Given a list of category ids, return the list of numeric ranges they cover.
renumber(Context) -> ok
Renumber all categories so that the left/right and level indices are correct.
renumber_pivot_task(Context) -> any()
Resync all ids that have their category nr changed.
tree(Context) -> Tree
Return the tree of all categories
tree(CatId, Context) -> TreeNode
Return the tree of all categories below a category id
tree_depth(Depth, Context) -> Tree
Return the tree of all categories till a certain depth
tree_depth(CatId, Depth, Context) -> TreeNode
Return the tree of all categories below a category id till a certain depth
update_sequence(Ids, Context) -> any()
Generated by EDoc, Apr 11 2012, 20:41:29.


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import uk.gov.hmrc.cataloguefrontend.DateHelper._
@import uk.gov.hmrc.cataloguefrontend.model.Environment
@import uk.gov.hmrc.cataloguefrontend.shuttering.{ShutterEventsView, ShutterStateChangeEvent}
@(events: Seq[ShutterStateChangeEvent], form: Form[_], environments: List[Environment])(implicit messages: Messages, request: Request[_])
@standard_layout("Shutter Events", "shuttering") {
Shutter Events
| @event.serviceName | @event.shutterType.asString | @event.status.value.asString | @ShutterEventsView.reasonFor(event.status) | @event.cause.asString | @event.username | @event.timestamp.asUTCString |


Kotlin User Groups (151)
Europe
- Amsterdam Kotlin User Group, Netherlands
- Athens Kotlin User Group, Greece
- Barcelona, KotlinBCN, Spain
- Belarus Kotlin User Group, Belarus
- Belgium Kotlin User Group, Belgium
- Berlin Kotlin User Group, Germany
- Bosnia Kotlin User Group, Bosnia and Herzegovina
- Bucharest Kotlin User Group, Romania
- Budapest Kotlin User Group, Hungary
- Cologne Kotlin User Group, Germany
- Cosenza Kotlin User Group, Italy
- Croatia Kotlin User Group, Croatia
- Czech Kotlin User Group, Czech Republic
- Dnipro Kotlin User Group, Ukraine
- Dublin Kotliners, Ireland
- Dusseldorf Kotlin User Group, Germany
- Dutch Kotlin User Group, Netherlands
- Hamburg Kotlin User Group, Germany
- Helsinki Kotlin User Group, Finland
- Italy Kotlin User Group, Italy
- Karlsruhe Kotlin User Group, Germany
- Kyiv Kotlin User Group, Ukraine
- London Kotlin, United Kingdom
- Lyon Kotlin User Group, France
- Madrid Kotlin User Group, Spain
- Manchester Kotlin Developers, United Kingdom
- Milano Kotlin User Group, Italy
- Moscow Kotlin User Group, Russia
- Munich Kotlin User Group, Germany
- Paris Kotlin User Group, France
- Rhein-Main Kotlin, Germany
- St. Petersburg Kotlin User Group, Russia
- Serbia Kotlin User Group, Serbia
- Stockholm Kotlin User Group, Sweden
- Swiss Kotlin User Group, Swiss
- Toulouse Kotlin User Group, France
- Utrecht Kotlin User Group, Netherlands
- Uzhgorod Kotlin User Group, Ukraine
- Vienna Kotlin, Austria
- Wroclaw Kotlin, Poland
- Warsaw Kotlin, Poland
- Yorkshire Kotlin Meetup, United Kingdom
Asia
- Ahmedabad Kotlin User Group, India
- Allahabad Kotlin User Group, India
- Azerbaijan Kotlin User Group, Azerbaijan
- Bangladesh Kotlin User Group, Bangladesh
- Bengaluru Kotlin User Group, India
- Bengaluru Kotlin 'BlrKotlin' Meetup , India
- Beijing Kotlin User Group, China
- Bhopal Kotlin User Group, India
- Bhubaneswar Kotlin User Group, India
- Chennai Kotlin User Group, India
- Chengdu Kotlin User Group, China
- Coimbatore Kotlin User Group, India
- Cambodia Kotlin User Group, Cambodia
- Dubai Kotlin User Group, United Arab Emirates
- Hefei Kotlin User Group, China
- Hyderabad Kotlin User Group, India
- Indonesia Kotlin User Group, Indonesia
- Indore Kotlin User Group, India
- Israel Kotlin User Group, Israel
- Istanbul Kotlin User Group, Turkey
- Jalandhar Kotlin User Group, India
- Japan Kotlin User Group, Japan
- Karachi Kotlin User Group, Pakistan
- Kathmandu Kotlin User Group, Nepal
- Kolkata Kotlin User Group, India
- Korean Kotlin User Group, Korea
- Kozhikode Kotlin User Group, India
- Mumbai Kotlin User Group, India
- Mumbai Kotlin for Android, India
- Myanmar Kotlin User Group, Myanmar
- Nepal Kotlin User Group, Nepal
- New Delhi Kotlin User Group, India
- Pakistan KotlinUser Group, Pakistan
- Philippines Kotlin, Philippines
- Pune Kotlin User Group, India
- Saudi Arabia Kotlin User Group, Saudi Arabia
- Shanghai, KotlinThree, China
- Singapore Kotlin User Group, Singapore
- Sri Lanka Kotlin User Group, Sri Lanka
- Syria Kotlin User Group, Syria
- Sultanpur KNIT Kotlin User Group, India
- Taiwan Kotlin User Group, Taiwan
- Tel Aviv Kotlin User Group, Israel
- Thailand Kotlin Developers, Thailand
- Turkey Kotlin User Group, Turkey
- Vellore Kotlin User Group, India
- Vietnam Kotlin User Group, Vietnam
- Vijayawada Kotlin User Group, India
North America
- Albuquerque Kotlin User Group, USA
- Bay Area Kotlin User Group, USA
- Brooklyn (NY) Kotlin User Group, USA
- Cambridge Kotlin Office Hours, USA
- Chicago Kotlin Users Group, USA
- Cincinnati Kotlin User Group, USA
- Columbus Kotlin User Group, USA
- Dallas, Kotlin DFW, USA
- Guadalajara Kotlin User Group, Mexico
- Guatemala Kotlin Meetup, Guatemala
- Mexico, CDMX Kotlin User Group, Mexico
- Mérida Kotlin User Group, Mexico
- New England User Group, USA
- New York Kotlin Meetup, USA
- Norfolk Kotlin User Group, USA
- Orange County Kotlin Meetup, USA
- San Diego Kotlin User Group, USA
- Santa Cruz Kotlin User Group, USA
- South Florida Kotlin User Group, USA
- Toronto Kotlin, Canada
- Twin Cities Kotlin User Group, USA
- Utah Kotlin, USA
- Vancouver Kotlin Meetup, Canada
- Washington DC Kotlin User Group, USA
- West Florida Kotlin User Group, USA
South America
- Brasil Kotlin User Group, Brazil
- Buenos Aires Desarrollo en Android con Kotlin, Argentina
- Cascavel Kotlin Meetup, Brazil
- Chile Kotlin User Group, Chile
- Cochabamba Kotlin User Group, Bolivia
- El Alto Kotlin User Group, Bolivia
- La Paz Kotlin User Group, Bolivia
- Lima Kotlin User Group, Peru
- Peru Kotlin User Group, Peru
- Sao Paulo Kotlin Meetup, Brazil
- Rio de Janeiro, Brazil
Australia/Oceania
- Brisbane Kotlin User Group, Australia
- Sydney Kotlin User Group, Australia
- Wellington Kotlin User Group, New Zealand
Africa
- Abidjan Kotlin User Group, Cote d'Ivoire
- Abuja Kotlin User Group, Nigeria
- Agadir Kotlin User Group, Morocco
- Ado-Ekiti Kotlin User Group, Nigeria
- Angola Kotlin User Group, Angola
- Beira Kotlin User Group, Mozambique
- Brazza Kotlin User Group, Congo
- Cairo Kotlin User Group, Egypt
- Cotonou Kotlin User Group, Benin
- Egypt Kotlin, Egypt
- Fayoum Kotlin User Group, Egypt
- Johannesburg Kotlin User Group, South Africa
- Lagos Kotlin User Group, Nigeria
- Lome Kotlin User Group, Togo
- Minya Kotlin User Group, Egypt
- Nairobi Kotlin User Group, Kenya
- Noun Kotlin User Group, Cameroon
- Ondo Kotlin User Group, Nigeria
- Ouagadougou Kotlin User Group, Burkina Faso
- Rabat Kotlin User Group, Morocco
- Tunisia Kotlin User Group, Tunisia
- Uganda Kotlin User Group, Uganda
If there is no KUG in your area, make a new one.
If you are a leader of active user group and it’s not in a list, please, let us know that there is a Kotlin User Group. We will share the information with others on this site.
Submit a New Kotlin User Group
Guidelines
- User groups provide activities to support professionals interested in a technology
- Try to meet up regularly. It is the best way to increase Kotlin excellence for all members of the user group
- All the events and materials should be public and available for people of any different backgrounds
- All communication should be appropriate for the professional audience
- Follow simple rules to collaborate efficiently: be kind, be someone that other people want to be around, do not insult or bring down other attendees. Collaboration and respect would help you to achieve your personal goals and create proper environment for the fellows.
JetBrains Provides Support
- Kotlin swag for the attendees of your user group;
- Design materials – logos, design, promo materials;
- Spreading the word about your user group in Kotlin official media channels and our network (speakers and regional partners recommendations could be possible);
- JetBrains product licenses to raffle out at your meetups. To receive free licenses, please, fill out the form;
- If you are looking for monetary support or any other type of sponsorships, please contact us at<EMAIL_ADDRESS>If you feel like you need more ideas or advice on user group launch, contact<EMAIL_ADDRESS>Branding
We provide a logo for Kotlin user groups that is specifically designed to be recognizable and convey a reference to Kotlin.
- The Kotlin logo is associated with the language itself and should not be used otherwise to avoid confusion. The same applies to its close derivatives;
- User groups logo also means to reflect that the opinions and actions of the community are independent from the Kotlin team;
- Your opinion doesn't have to agree with ours and we think this is the most benefitial model for a creative and strong community.
So, please use the user group logos and other branding according to these guidelines, which are a part of our community support program
Disclaimer: Kotlin User Groups are not affiliated with JetBrains nor Google. They are independently run organisations.


This library provides a "Search Select" component for use with Angular Material. Behind the scenes, the mat-autocomplete component is used, but the resulting experience is not an auto complete; that is, it does not assist the user and optionally skipping some typing.
Rather, this component is for select controls where the user will search from a (potentially long) list to make a selection. Moreover, this list may be far longer than can fit in the user interface/browser, and might arrive asynchronously from server-side searches.
For a fuller explanation, please see the project README, and its source code.
https://github.com/OasisDigital/angular-material-search-select/blob/master/README.md


One year after arrest, Ghosn seeks to throw out case against him
Legal experts and even the ex-Nissan chief's defense lawyer are doubtful of his chances
by Agence France-PresseSubscribe
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- Glenda Gloria's Take this Tuesday
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{% extends "layout.html" %} {% block page_title %} Prepare home {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Become a childminder Register as self employed {{formData | safe}} Prepare home Prepare home An inspector from Ofsted will come to the place you work, to check they are suitable to care for children in. The inspection will include an interview. Tasks to get premises inspected: read how to prepare your premise for inspection prepare your premises for inspection get inspected recieve certificate of registration {% endblock %}
An inspector from Ofsted will come to the place you work, to check they are suitable to care for children in.
The inspection will include an interview.
Tasks to get premises inspected:


magnetic EuSn surface states Figure electronic topological band crystal Topological bulk structure field Fermi applied Phys. observed {border-style calculations materials quantum perpendicular px;} #ffffff;} energy bands shown single state Structure calculated different new resistivity temperatures using Magnetic properties shows ARPES Insulators Mater. Nat. antiferromagnetic energies material plane Electronic Quantum Rev. Science center layers measured temperature National dispersion ferromagnetic level ordered sample Curie–Weiss Hall Zhang crystals determined diffraction electron magnetoresistance near ordering px; seen structure, {font-family .() Crystal Single State consistent data font-size interactions invariant lattice magnetization meV neutron spin unconventional used (see Band Cava Chem. Eu, GGA Matter Nature WTe Wang antiferromagnetically charge design double; electrons fields material, measurements. parallel performed photon presence spectra spin–orbit strong study surfaces symmetry temperature-dependent (A) (the ./PhysRevB.. ./nature ACS Brillouin Chang Condens. University Wannier basal behavior border-color border-width c-axis. calculations, carried case chemical collected contours coupling fit i.e., incident indicate intensity line low moment number observed. obtained present range red relatively results reveal solid; space states. structure. susceptibility various {font-style {font-weight (B) .). ./PhysRevLett.. ./S ./nphys Bernevig Bruker Calculated Calculations Chemistry EPR Electron Insulator Laboratory Lett. SOC Sn-terminated States Surface United X-ray accessible appear based bold concentration courier; detailed effective effects em; expected experimental feature field-dependent function high in-plane indicates insulator just magnetism material. measurements method orbitals parameter parameters pt;} respectively. result rhombohedral selected small spectral states, supported surface. systems temperatures, temperatures. terminated time-reversal transport valence weight


--- title: About UK GEMINI linkTitle: About menu: main: weight: 10 --- {{< blocks/cover title="About UK GEMINI" image_anchor="bottom" height="min" >}}
UK GEMINI (GEo-spatial Metadata INteroperability Initiative) is a specification for a set of metadata elements for describing geospatial data resources. It has been produced and is maintained by the Standards Committee. It is provided free of charge by the AGI for the benefit of the GI community, under a Creative Commons license.
{{< /blocks/cover >}} {{% blocks/lead %}}The AGI Standards Committee published version 2.3 of UK GEMINI in June 2018.
This revision incorporates material prepared by the UK Location Programme, previously published at data.gov.uk, and material previously published as separate guidance documents. We hope this consolidation will make it easier for you to use. GEMINI has also been updated for change requests from revised INSPIRE guidance and UK users. The AGI is grateful to Defra for funding part of this work, and for several AGI members also contributing.
The existing guidelines on the quality of metadata still apply: Guidelines for UK GEMINI Part 3 Metadata Quality
UK INSPIRE announced their intention to require data publishers to migrate to GEMINI 2.3 by December 2019, in line with the target date from Europe for adopting the current version of their INSPIRE Technical Guidance. However, technical implementations have been delayed. UK INSPIRE, and the data.gov.uk system in particular, will continue to accept GEMINI 2.2 records encoded according to UK Location guidance, at least until then - although they may generate warning messages from the EU portal.
GEMINI 2.3 does not include any changes related to the publication of ISO 19115-1:2014; GEMINI remains based on ISO 19115:2003.
{{% /blocks/lead %}}


Intel® Multi-Buffer Crypto for IPsec Library
Documentation coming soon...
For more information, please visit the
intel-ipsec-mb
GitHub project.


Looking at the seed rack, I spotted the Heavenly Blue seeds which I had went into town for that day. I bought four packets, each containing an average of fifty seeds, as I had read two hundred seeds is enough for most people to experience a trip.
I returned home and decided against any form of preperation as it would take too much time. I set out about one hundred seeds onto a plate, and began munching on them.  I expected them to taste worse, so was quite relieved when it was not that bad. I began eating them at about 7:00pm.
Probably as soon as the seeds began to digest, I felt sick. At this point, I had not eaten very many seeds, and was quite sad that I was getting stomach pains so soon. I looked on the seed packet, which said 'Harmful if eaten,' which could mean that they had a layer of poison on them. I forced a few more seeds down, now hating the taste, and feeling increasingly worse. I probably managed to eat about fifty seeds, maybe a few more.
Nothing, I felt nothing...well at least nothing special. I probably felt distanced for about ten minutes, but that would be it. I did not really expect to feel anything, and because of this I began focusing my efforts on getting some cannabis. I managed to buy a little more than a gram of cannabis hashish, and was satisfied that I could get stoned whilst watching the T.V. and listening to music.
On my way to buy the cannabis, I walked the dog. Whilst walking down the street, I seen a friend at the bottom, I was nearer to the top. In what seemed like a second, I was at the bottom, but my friend was no longer there. It would never take me a few seconds to walk down that street, so I became a little confused. I felt distanced again, and began to wonder whether the seeds were working slightly...without making me trip, maybe they were just screwing up time.
I bought the cannabis and returned home. I went to my bedroom and started to hit the pipe. After my first pipe, I felt rather stoned...more stoned than normal. Usually, one pipe would make me feel different, but not stoned. I lay in my bed, watching the T.V. The television was a lot of fun. It was just after 10:00pm. I hit the pipe again, and again and again. I must have had five pipes or so. I continued to watch the television, feeling quite wrecked...but quite different. Things that people were saying on the T.V. began to trigger thoughts,which led to more thoughts and then more thoughts. It seemed that I was able to think about five thoughts at the same time...which would send me into a pit of my own mind, which would then end up with me having to remind myself where I was. These thoughts became stranger and a lot worse. I became a lot more relaxed and sedated. The light was off, all I had was the light from the television.
I looked up into the air, and immediately noticed patterns in the darkness. These patterns were moving a lot, and were very colourful. Instead of watching the televsion, I watched these in complete awe. When I closed my eyes, the patterns were still there, and I was now having what seemed like a hundred thoughts a second. I felt so secure and happy, probably one of the best feelings I have ever had from drugs. I was laughing to myself a lot, and felt like if there was somebody with me I would have talked and talked. I was having to remind myself that I was in my room over and over.
I knew that it was getting very late, and I needed to go to sleep as I had to be up quite early the next morning. I could have layed there forever. I closed my eyes, but there was just too many thoughts and patterns, and I found it quite hard to fall asleep...and even when I did, the sleep wasn't good at all.
I woke up the next day feeling great, very refreshed.
I was very shocked at how the seeds affected me, as I did not eat very many. Was it the cannabis mixing with it? Do I have a bad tolerance to LSA? These things I shall never know, but I shall continue with my exploration with Morning Glory seeds.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


1 write to _lambda
System.Management.Automation (1)
engine\interpreter\LightDelegateCreator.cs (1)
45
_lambda
= lambda;
2 references to _lambda
System.Management.Automation (2)
engine\interpreter\LightDelegateCreator.cs (2)
118
LambdaExpression le =
_lambda
as LambdaExpression;
172
LambdaExpression lambda = (
_lambda
as LambdaExpression); // ?? (LambdaExpression)((LightLambdaExpression)_lambda).Reduce();


#include <ParticleUniverseSphereCollider.h>
Public Member Functions | |
| SphereCollider (void) | |
| virtual | ~SphereCollider (void) |
| const Ogre::Real | getRadius (void) const |
| void | setRadius (const Ogre::Real radius) |
| bool | isInnerCollision (void) const |
| void | setInnerCollision (bool innerCollision) |
| void | calculateDirectionAfterCollision (Particle *particle, Ogre::Vector3 distance, Ogre::Real distanceLength) |
| virtual void | _preProcessParticles (ParticleTechnique *particleTechnique, Ogre::Real timeElapsed) |
| virtual void | _affect (ParticleTechnique *particleTechnique, Particle *particle, Ogre::Real timeElapsed) |
| virtual void | copyAttributesTo (ParticleAffector *affector) |
Static Public Attributes | |
| static const Ogre::Real | DEFAULT_RADIUS = 100.0f |
Protected Attributes | |
| Ogre::Real | mRadius |
| Ogre::Sphere | mSphere |
| Ogre::Vector3 | mPredictedPosition |
| bool | mInnerCollision |
| ParticleUniverse::SphereCollider::SphereCollider | ( | void | ) |
| virtual ParticleUniverse::SphereCollider::~SphereCollider | ( | void | ) | [inline, virtual] |
| const Ogre::Real ParticleUniverse::SphereCollider::getRadius | ( | void | ) | const |
Returns the radius of the sphere
| void ParticleUniverse::SphereCollider::setRadius | ( | const Ogre::Real | radius | ) |
Sets the radius of the sphere
| bool ParticleUniverse::SphereCollider::isInnerCollision | ( | void | ) | const |
Returns indication whether the collision is inside or outside of the box
| void ParticleUniverse::SphereCollider::setInnerCollision | ( | bool | innerCollision | ) |
Set indication whether the collision is inside or outside of the box
| void ParticleUniverse::SphereCollider::calculateDirectionAfterCollision | ( | Particle * | particle, | |
| Ogre::Vector3 | distance, | |||
| Ogre::Real | distanceLength | |||
| ) |
If the particle is on the surface (or just inside the sphere); bounce it Make use of formula R = 2 * (-I dot N) * N + I, where R = the new direction vector I = the old (unit) direction vector before the collision N = the Normal at the collision point
Reset the position (on the sphere), but keep the direction. This doesn't really work good for box-type collisions, because it doesn't take the particle dimensions into account.
| void ParticleUniverse::SphereCollider::_preProcessParticles | ( | ParticleTechnique * | particleTechnique, | |
| Ogre::Real | timeElapsed | |||
| ) | [virtual] |
Perform activities before the individual particles are processed.
Reimplemented from ParticleUniverse::BaseCollider.
Reimplemented in ParticleUniverse::SphereColliderExtern.
| void ParticleUniverse::SphereCollider::_affect | ( | ParticleTechnique * | particleTechnique, | |
| Particle * | particle, | |||
| Ogre::Real | timeElapsed | |||
| ) | [virtual] |
Affect a particle.
| particle | Pointer to a ParticleTechnique to which the particle belongs. | |
| particle | Pointer to a Particle. | |
| timeElapsed | The number of seconds which have elapsed since the last call. |
Implements ParticleUniverse::ParticleAffector.
| void ParticleUniverse::SphereCollider::copyAttributesTo | ( | ParticleAffector * | affector | ) | [virtual] |
Copy attributes to another affector.
Reimplemented from ParticleUniverse::BaseCollider.
const Ogre::Real ParticleUniverse::SphereCollider::DEFAULT_RADIUS = 100.0f [static] |
Ogre::Real ParticleUniverse::SphereCollider::mRadius [protected] |
Ogre::Sphere ParticleUniverse::SphereCollider::mSphere [protected] |
Ogre::Vector3 ParticleUniverse::SphereCollider::mPredictedPosition [protected] |
bool ParticleUniverse::SphereCollider::mInnerCollision [protected] |


TXT
The implementation of TXT CHAT protocol is trying to be informative but not excessive, it should take care of run-time edge cases but not too stable.
ERLANG
CHAT supports RocksDB and Mnesia databases. KVX differs in writing feeds to them so both cases are covered in the example.
CONTRIBUTORS
- 5HT — Namdak Tonpa
- proger — Vlad Ki
- doxtop — Andrii Zadorozhnii
- cryoflamer — Yuri Maslovsky
- qomputer — Igor Kharin


@(params: Map[String, Any]) @uk.gov.hmrc.hmrcemailrenderer.templates.helpers.html.template_main(params, ""){
Dear @params("name"),
As part of the registration for the check and challenge service, you’ve been given a personal ID number. You should keep a note of this number in a safe place as you’ll need it if you want to reset your password.
Your ID number is @params("personId")
You will not need it to sign in to the check and challenge service – you should continue to use your Government Gateway details to sign in.
Valuation Office Agency (VOA)


Created by YaPi on April 16, 2020
Created by YaPi on April 15, 2020
Created by YaPi on March 31, 2020
Created by YaPi on January 2, 2020
Created by YaPi on January 1, 2020
Created by YaPi on December 31, 2019
Created by YaPi on December 30, 2019
Created by YaPi on August 28, 2019
待到秋来九月八 我花开后百花杀


@ Roberto González
@@ Web: [rober.design](https://rober.design)
@@ Portfolio: [rober.design/works](https://rober.design/works)
@@ Email: [hi<EMAIL_ADDRESS># About me
Hello, my name is Rober and I have been a designer for more than 10 years.
I've worked on all kinds of design projects, participating in the entire process, from its ideation to the handoff to the development team.
In recent years I've specialized in working with data and designing dashboards and visualizations for clients like Santander, El Corte Inglés, BBVA or Airbus. At the same time I have been leading design teams and managing projects financially and executively.
I've experience working in agile environments and knowledge of the development pipeline from my background as a developer.
# Experience
// Current
## Principal Product Designer @ Fresh People 🍍
:: Remote
:: May. 2023 → Now
• Principal User Experience, Interaction and User Interface designer.
:: Learn more: [Fresh People](https://www.freshpeople.team/)
// Past
## Lead Product Designer @ Populate Tools
:: Remote
:: Nov. 2021 → May. 2023
• Lead Interaction and User Interface designer.
## Project Leader - Digital Product & IxD Designer @ NTT Data
:: Valladolid, Spain - Madrid, Spain - Remote
:: Mar. 2018 → Oct. 2021
• Experience Design Project Leader.
• Lead Interaction and User Interface designer.
• Data visualization design area expert.
• Design team coordination and project management.
## Partner / CTO / Senior UX / Web Designer @ Vital Innova
:: Valladolid, Spain - Remote
:: Jan. 2016 → Feb. 2018
• UX research, product branding, UI design and front-end development.
• Development team director.
## Lecturer @ MDB, Escuela de Negocios ESDEN
:: Valladolid, Spain
:: 2017
• Professor of the UX subject - Master in Digital Business.
## UX / Web Designer @ Intexdev
:: Cáceres, Spain
:: Nov. 2011 → Jan. 2016
• UX research, UI design and front-end development in high-traffic vertical web portals.
## Web Designer @ Run Software
:: Cáceres, Spain
:: Nov. 2004 → Nov. 2011
• UI design and front-end development for local business webpages.


| Home | Data Feeds | PMA Feeds |
The Vision Administrator Module performs various Database Administration functions in an interactive Windows environment. You may find it useful to use scripts to perform these same functions in a batch environment. Such sample scripts are described in this document and can serve as a basis for designing your own. These scripts work outside the VAdmin module and allow you to perform multiple functions and/or load multiple files simultaneously, as well as allow you to keep an 'audit trail' of your scripting iterations.
The examples as provided assume certain environment variables have been set to the standards established in PMA Release 1.7. If you are on a prior release, you will need to modify these variables before running your scripts as described in this document:
On Unix:
Modify the general defaults for Vision Database Administrator processing in /localvision/include/DBAVision.env:
On NT:
Modify the System Variables:
If you need additional help setting up these variables, please contact your Insyte consultant.
The server-based software that manages your Vision database is the component known as batchvision. When you use VAdmin or one of the Vision editors, you are communicating with a batchvision process interactively. The batchvision program is also used to execute non-interactive programs. Requests can be submitted to batchvision via Vision script files. A Vision script file is simply a standard text file containing one or more Vision requests. Any number of requests can be included in a Vision script file. Each request is terminated by the ?g command which instructs Vision to begin executing the request.
For example, assume the message displayWeeklyReports has been saved in the Vision database and you want to run this program. You could create the file testfile which contains the following lines:
displayWeeklyReports ; ?gNote that the characters ?g must be the only characters on the line followed by a carriage return. To execute this Vision script file, it can be used as standard input to the batchvision program as shown below for both Unix and NT:
batchvision < testfile > testfile.outThe requests in a Vision Script file can access, create, and manipulate any Vision structures that have been saved in the database or defined in the script.
If you want your Vision script to commit any new or changed information
to the database, you need to start batchvision as the Vision Administrator and include an explicit instruction to update the database
in your Vision script. For example, the following script defines
a new display method for the class Object and saves the method to the
database:
#--- Vision Script to define and save a new method at Object
Object defineMethod: [ | display |
^self whatAmI print ; ^self creationDate print ;
^self code printNL ;
] ;
?g
#--- Now save the results - don't forget the ?g on its own line
Utility updateNetworkWithAnnotation: "define a new display method" ;
?g
Assume this script is saved as the file testupdate. You
can run and save this script on Unix or NT using:
batchvision < testupdate > testupdate.outOnce this script has completed, you will be able to access the new method any time you connect to the Vision database. You must be logged in as the Vision Database Administrator to save the results of this script.
NOTE: On Unix systems, you need to source the following file before you begin your batch processing:
source /localvision/include/DBAVision.env
!!! Warnings !!!
The VAdmin module limits DataFeed loading to one feed at a time. Once you have established some basic procedures for loading your data, you may prefer to create some batch scripts for yourself that perform the same function for multiple feeds at a time.
The Vision method for loading a data file via a data feed requires
that you provide the name of the feed, the name of the file, and
an optional configuration file name.
For example, if you want to
load data using the SecurityMaster feed from the file
/localvision/upload/feeds/secmaster.dat, use the expression:
Interface BatchFeedManager
upload: "SecurityMaster"
usingFile: "/localvision/upload/feeds/secmaster.dat"
withConfig: NA ;
If you have an explicit configuration file to use, replace the
third parameter with the actual file name.
To update the Security
Master via a batch process, do the following:
#-- Load the data Interface BatchFeedManager upload: "SecurityMaster" usingFile: "/localvision/upload/feeds/secmaster.dat" withConfig: NA ; ?g #-- and Update the database - don't forget the ?g on its own line Utility updateNetworkWithAnnotation: "load single secmaster file" ; ?g
Note: On NT systems, if your data files are on a different drive than your script, you need to use the absolute path name.
batchvision < secmaster.vis > secmaster.out
#-- Load the data Interface BatchFeedManager upload: "CompanyMaster" usingFile: "/localvision/upload/feeds/compmaster.dat" withConfig: NA ; ?g Interface BatchFeedManager upload: "SecurityMaster" usingFile: "/localvision/upload/feeds/secmaster.dat" withConfig: NA ; ?g Interface BatchFeedManager upload: "PortfolioMaster" usingFile: "/localvision/upload/feeds/portmaster.dat" withConfig: NA ; ?g Interface BatchFeedManager upload: "PriceFeed" usingFile: "/localvision/upload/feeds/prices.dat" withConfig: NA ; ?g Interface BatchFeedManager upload: "HoldingsFeed" usingFile: "/localvision/upload/feeds/holdings.dat" withConfig: NA ; ?g #-- and Update the database - don't forget the ?g on its own line Utility updateNetworkWithAnnotation: "load daily file" ; ?g
In addition to loading data, the menus in the VAdmin module
provide tools that allow you to define new classes and relationships.
The actual Vision code invoked by VAdmin to perform these functions
is supplied below:
#--------------------
# VAdmin Schema Choice: Create Entity Class
# Creates Entity subclass and associated MasterFeed
# Inputs -
# createEntityClass: name of subclass to create
# from: Entity class to subclass
#--------------------
Interface BatchFeedManager
createEntityClass: "SPIndustry" from: "Industry" ;
#--------------------
# VAdmin Schema Choice: Create DataRecord Class
# Creates DataRecord subclass and EntityExtenderFeed
# Inputs -
# createDataRecordClass: name of subclass to create
# from: DataRecord class to subclass
# linkedTo: Entity class to link to
# via: property to define at linkedTo class
# asTS: should property be a t/s?
#--------------------
Interface BatchFeedManager
createDataRecordClass: "ModelData" from: "DataRecord"
linkedTo: "Company" via: "modelData" asTS: "YES" ;
#------------------------------------------
# VAdmin DataFeeds Choice: PropertySetup
# Define properties for new DataRecord Class
# Inputs -
# classId: name of class/subclass properties are assigned to
# property: name of property
# tsflag: is property a t/s? Y or N
# datatype: class of object returned by this property
PropertySetup updateFromString:
#-- these are tab-delimited
"classId property tsflag datatype
ModelData model1 N Number
ModelData model2 N Number
ModelData model3 N Number
";
#--------------------
# VAdmin Schema Choice: Create Relationship
# Creates MembershipFeed and property tracking relationship
# at the group and member entity classes supplied
# Inputs -
# createRelationship: name of MembershipFeed class to create
# withGroupType: Entity class of Groups
# via: list property defined at group class
# andMemberType: Entity class of Members
# via: property defined at member class
# asTS: should properties be t/s?
#--------------------
Interface BatchFeedManager
createRelationship: "CompanyToSPIndustry"
withGroupType: "SPIndustry" via: "memberList"
andMemberType: "Company" via: "spIndustry" asTS: "YES" ;
You can include these functions in any batch script you submit for update.
Assume that you want to track several model scores about a company over time. You need to:
Interface BatchFeedManager createDataRecordClass: "ModelData" from: "DataRecord" linkedTo: "Company" via: "modelData" asTS: "YES" ; ?g PropertySetup updateFromString: "classId property tsflag datatype ModelData model1 N Number ModelData model2 N Number ModelData model3 N Number " ; ?g Utility updateNetworkWithAnnotation: "Define ModelData class" ; ?gThis creates the class ModelData and defines three properties. Model data for the three model values can be updated using the ModelDataFeed. For example, if you create a file named models.dat, that contains:
id date model1 model2 model3 ibm 9801 1 2 3 ibm 9802 2 2 3 dell 9801 10 12 14you can update it using:
Interface BatchFeedManager upload: "ModelDataFeed" usingFile: "models.dat withConfig: NA ; ?gAccess to the data is provided via the Vision expression:
Named Company IBM modelData do: [ date print: 15 ; model1 print ; model2 print ; model3 printNL ; ] ;
| DBA Function | Description |
|---|---|
| Database Cleanups and Garbage Collection | Performs various cleanup activities that are standard for all installations. |
| Compaction | Physically moves unused information out of the database and temporarily relocates it elsewhere on the disk. |
| Delete Segments | Removes the unused information identified by the compaction process. |
In a starter environment, these maintenance activities are invoked interactively using the VAdmin module as needed. As you move towards a production environment, you may prefer to create a batch process that performs these maintenance tasks.
The adminScripts directory in the localvision directory contains batch scripts that perform these functions. On Unix systems, this directory is accessed via /localvision/adminScripts. On NT systems, this directory is located in c:\localvision by default, but is usually relocated during the installation process. The environment variable LocalVisionRoot refers to the location chosen for the localvision directory on NT.
The following scripts are included in the adminScripts directory:
| Maintenance scripts in /localvision/adminScripts | |
|---|---|
| Script Name | DBA Function |
| garbageCollect.cmd | Database Cleanups and Garbage Collection |
| fullCompact.cmd | Compaction |
| deleteCompactSegments.cmd | Delete Segments |
| batchCleanup.cmd | Includes above 3 functions in one script. |
These scripts can be run directly from the operating system prompt. You must be logged in as the Vision Administrator.
Running Maintenance Scripts in Unix
On Unix systems, you need to source the following file before you start your batchvision session:
source /localvision/include/DBAVision.env
To run a script, type the script name and redirect its output to another file. For example:
/localvision/adminScripts/garbageCollect.cmd > /localvision/logs/gc.yymmdd
Note that if you setup an automated procedure to run the maintenance tools that bypasses the standard login procedure, you should include the following "source" statement in your procedure:
source /localvision/include/DBAVision.envRunning Maintenance Scripts on NT
To run a script, type the script name and redirect its output to another file. For example:
%LocalVisionRoot%adminScripts\garbageCollect.cmd > %LocalVisionRoot%logs\gc.yymmddWhen to Run the Maintenance Scripts
Large production environments typically run the maintenance functions nightly in order to optimize their space utilization. In this type of environment we recommend the steps run in the sequence employed by the batchCleanup.cmd script:
deleteCompactSegments.cmd garbageCollect.cmd fullCompact.cmd
Your environment may not require the maintenance functions to be run as frequently. If you find you only need to run them on a sporadic basis, you will probably want to run the deleteCompactSegments (using the batch script or the VAdmin module) after Compaction to immediately reclaim the space. The following guidelines can be used to determine the optimum frequency for running the maintenance functions: TBD
Object spaces are used to organize functional components of the Vision database. They correspond to physical directories in the localvision/network branch of the file system. By default, classes, instances, and data will be stored in object space 3. This is fine for prototyping and small systems. If you plan to load a large amount of historical data from multiple sources and update on a regular basis, multiple object spaces are normally appropriate. The primary reason to do this is that it will allow the compaction process to operate more efficiently and effectively. Most users and applications will not need to know about the object space structure in order to use the database since messages encapsulate the navigations for you.
We recommend creating a separate object space for each data source that loads as a unit on a regular basis. For example, Barra data should be physically stored in its own object space. Note that your initial installation includes object spaces that have been pre-allocated for pricing and holdings so there is no need to create separate object spaces for these sources initially.
The script installSpace.cmd is available in the /localvision/adminScripts directory and is used to create new object spaces. The script requires that you provide a code that identifies the object space and an option type that indicates the role of the object space. The type can be supplied as one of DB, DBA, or User. If omitted, the type is assumed to be DB. DB object spaces are used to store instances and property values for one or more classes. DBA object spaces are used to store tools to manage various aspects of the database. User object spaces are used to store private data for a specific user.
For example, to install a new object space for storing model data, run the following command:
/localvision/adminScripts/installSpace.cmd Models DBOn NT:
%LocalVisionRoot%adminScripts\installSpace.cmd Models DB
Once you have created your new Models object space you can access it
via:
Environment DB Models
NOTE: If you rollback through a space installation, you will need to manually delete the associated physical sub-directory under localvision/network. If you are unsure of what the number of the object space to be deleted is, refer to the NDF.JOURNAL file in the localvision/network directory.
By default, new Entity classes are created in object space 3 and new DataRecord classes are created in object space 6. The instances of a class and the property values associated with each of these instances are stored in the object space in which the class is originally installed.
We recommend creating a separate object space for each data source
that loads as a unit on a regular basis. For example, your Model
data could be physically stored in its own object space.
You need to specify
the object space location of a new class prior to its creation using the
following expression:
#--- Set the object space of the next class created to
#-- the space define to hold the Models
Interface BatchFeedManager setObjectSpaceTo: Environment DB Models;
You can use the object space name or the object space number as the
parameter to setObjectSpaceTo:.
You must precede each Entity and DataRecord subclass
creation request with the setObjectSpaceTo: expression if you want
the new class set up in a specific object space. For example:
Interface BatchFeedManager
setObjectSpaceTo: Environment DB Models;
Interface BatchFeedManager
createEntityClass: "ModelType" from: "Classification" ;
Interface BatchFeedManager
setObjectSpaceTo: Environment DB Models;
Interface BatchFeedManager
createDataRecordClass: "ModelData" from: "DataRecord"
linkedTo: "Company" via: "modelData" asTS: "YES" ;
This request will create the new entity class ModelType and
the new data record class ModelData in the object space named
Environment DB Models if the classes do not already exist.
Note that this approach will not
recreate the classes if they already exist
and it will not relocate existing classes to the object space
specified.
By default, the instances of a class and the property values associated with these instances are stored in the object space in which the class was originally installed. For DataRecord subclasses that will store a large number of instances over time, we recommend that data be physically stored in a new storage structure in its own object space.
Once you have created the space, you will
need to define a "new store" for the class you wish to store in
that space. For example, to create a new store for ModelData
in the Models object space, create a Vision script that
contains the following:
#-- Create a new store for ModelData in Models object space
ModelData createNewStoreAt: Environment DB Models ;
?g
Once you have saved this script, any new instances created via
the ModelDataFeed will be stored in the Models object space.
Note that instances and data that existed prior to the creation of the new store will continue to reside in its original location. Normally, you would create the new object space and store immediately after creating the new DataRecord subclass and prior to updating any actual data.
If you use a DataFeed to update your data, the information will
automatically get placed in the last "store" created. If you are
writing your own Vision methods to load data directly, you will need
to modify any code that creates new instances so it is in the form:
^global ModelData currentStore createInstance: "xxx" ;
The subclasses of the Bridge class are used to manage the protocol for connecting an entity to one or more DataRecord instances. It is useful when there are complex relationships that need to be managed and you do not want to overburden the entity class with all the required information. An instance of a Bridge subclass is normally referenced via the entity instance with which it is associated.
To create a subclass of Bridge, you provide the Entity
class with which the new class is associated, the name of the property
that establishes the connection between an instance of entity's class
and an instance of the bridge's class, and an indication whether the
relationship should vary over time.
For example, you may want
to create a bridge class called FundamentalBridge which
links to separate data record classes that track annual, quarterly,
and monthly fundamental data.
To create the FundamentalBridge class and associate
it with the Company entity via the fixed property
fundamentalData, do the following:
#--------------------
# Create Bridge Class
# Creates Bridge subclass and establishes relationship
# with existing Entity subclass
# Inputs -
# createBridgeClass: name of subclass to create
# from: Bridge class to subclass
# linkedTo: Entity class to link to
# via: property to define at linkedTo class
# asTS: should property be a t/s?
#--------------------
Interface BatchFeedManager
createBridgeClass: "FundamentalBridge" from: "Bridge"
linkedTo: "Company" via: "fundamentalData" asTS: "NO" ;
You can create and attach the different data records to this class
using:
Interface BatchFeedManager
createDataRecordClass: "AnnualData" from: "DataRecord"
linkedTo: "FundamentalBridge" via: "annual" asTS: "YES" ;
Interface BatchFeedManager
createDataRecordClass: "QuarterlyData" from: "DataRecord"
linkedTo: "FundamentalBridge" via: "quarterly" asTS: "YES" ;
The PropertySetup feed can be used to define updateable properties
for the AnnualData and QuarterData classes and the
AnnualDataFeed and QuarterlyDataFeed classes can be used
to update these properties for specific companies on specific dates.
Once these classes have been established, you can access the specific
fundamental data record using:
Named Company IBM fundamentalData annual #insert property name
or
Named Company IBM fundamentalData quarterly #insert property name
A LocalEntity is an abstract subclass of Bridge designed to provide special protocol for a subset of instances of a specific entity class to enable private saves. LocalEntity subclasses can be created in a way that allows them to connect with their underlying entity dynamically. As a result, these bridge classes, and their related data record classes can be stored in a way that allows them to be updated privately.
For example, suppose the Equity Research group wants to update information about a subset of companies without requiring the intervention of the global database administrator. A separate object space can be created for this group. The ERCompany bridge class and one or more data record classes can be created in this space. Data associated with this bridge class can then be updated privately.
Here are the steps involved with setting up a private space that can be updated using data feeds:
/localvision/adminScripts/installSpace.cmd MySpace
Interface BatchFeedManager setObjectSpaceTo: Environment DB MySpace ; Interface BatchFeedManager createBridgeClass: "MyCompany" from: "LocalEntity" linkedTo: "Company" via: "myCompany" asTS: "Method" ;
Interface BatchFeedManager setObjectSpaceTo: Environment DB MySpace ; Interface BatchFeedManager createDataRecordClass: "AnalystRating" from: "DataRecord" linkedTo: "MyCompany" via: "rating" asTS: "Y" ; Interface BatchFeedManager setObjectSpaceTo: Environment DB MySpace ; Interface BatchFeedManager createDataRecordClass: "CompanyAnnualOverride" from: "DataRecord" linkedTo: "MyCompany" via: "companyAnnualOverride" asTS: "Y" ;
Environment DB MySpace define: 'Entity' toBe: CoreWorkspace Entity newPrototype ; Environment DB MySpace define: 'Named' toBe: CoreWorkspace Named newPrototype ;This code must be saved by the Vision Administrator.
Interface BatchFeedManager setObjectSpaceTo: Environment DB MySpace ; Interface BatchFeedManager createEntityClass: "MyIndustry" from: "Entity" ;
Note that private updates should not run when general vision database maintenance is in progress. This includes garbage collection and compaction.
The subclasses of the EstimateBridge class are used to manage
the relationship between an entity or bridge and
data that can be estimated for multiple time periods.
To create a subclass of Bridge, you provide the Entity
or Bridge
class with which the new class is associated, the name of the property
that establishes the connection between an instance of this class
and an instance of the estimate bridge's class, the type of
estimate record, and the periodicity of the estimate.
For example, you may want to create an estimate bridge for
tracking annual company eps estimates made by an analyst as well
as consensus estimate provided by a source such as I/B/E/S:
#--------------------
# Create EstimateBridge Class
# Inputs -
# createEstimateBridgeClass: name of subclass to create
# from: EstimateBridge class to subclass
# linkedTo: Entity or Bridge class to link to
# via: t/s property to define at linkedTo class
# withEstimateRecordClass: subclass of EstimateRecord
# andFreq: periodicity of estimates
#--------------------
Interface BatchFeedManager
createEstimateBridgeClass: "EpsABridge" from: "EstimateBridge"
linkedTo: "Company" via: "analystEpsEst"
withEstimateRecordClass: "AnalystEstimateRecord"
andFreq: 12 monthEnds ;
Interface BatchFeedManager
createEstimateBridgeClass: "IbesEpsABridge" from: "EstimateBridge"
linkedTo: "Company" via: "ibesEpsEst"
withEstimateRecordClass: "ConsensusEstimateRecord"
andFreq: 12 monthEnds ;
Once the estimate bridges have been created you can use the appropriate feed to update the database using the upload:usingFile:withConfig: message.
Access to estimate structures and additional examples are described elsewhere.
| Copyright © 1999 Innovative Systems Techniques, Inc. | Updated December 31, 1999 |


{% extends '_base.html' %} {% block title %}Choose Trello board{% endblock %} {% block main %}
Select the board that you use to track the progress of stories that you’re working on.


Nehemiah 11:4
And at Jerusalem dwelt certain of the children of Judah, and of the children of Benjamin.


Fundamentals of
Data Analytics and
Predictions
Regularization
Xi (Rossi) LUO
Department of Biostatistics and Data Science
School of Public Health
The University of Texas Health Science Center at Houston
Outline
- Linear Predictors
- Ridge Regression
- Lasso
- Elastic Net
Linear Predictors
- Input: $X=x_1, \dots, x_p$
- Output: $Y$
- The linear regression model is $Y=\beta_0+\sum_{j=1}^{p}X_j\beta_j+\epsilon$
- To find least squares solution, want to minimize $E(y-f(x))^2$
- Approximated by the empirical loss $RSS(\beta)=\sum_{i=1}^{i=N}(y_i-f(x_i))^2$
- Has closed-form solution $\hat \beta=(X'X)^{-1}X'y$
Linear Model With Many Predictors —1
- Even though we have a linear model that seems to fit the data well, we may still have some
problems.
- If we have many predictors, the prediction accuracy may not be optimal.
- If we have many predictors, interpretation may be difficult
Linear Model With Many Predictors —2
- Two possible solutions:
- Use a subset of the predictors
- Shrink some of the coefficients toward zero
Subset (Variable) Selection
- Consider a case where the true model is $Y=\beta_0+\beta_1X_1+\beta_2X_2+\epsilon$ and $X_1$ and
$X_2$ are almost perfectly correlated (co-linear)
- If we remove $X_2$ from the model, we can approximate the model by:
$Y=\beta_0+(\beta_1+\beta_2)X_1+\epsilon$ and get a good estimate of $Y$ estimating 2 parameters
instead of 3
- Estimate will be a bit biased but we may lower the variance considerably.
Simulated Example
- Consider fitting various subset models to a simulated example where the true model is a linear
model with 10 predictors and we observe 15 outcomes
- Select a number of covariates to include in the model, then look at all possible combinations of
covariates and pick one with the smallest RSS
- Apply to a training and test set
Simulated Example
Prostate Data Example —1
- Outcome: prostate specific antigen (PSA)
- Covariates:
- lcavol(log cancer volume)
- lweight(log prostate weight)
- Age
- agelbph
- log(benign prostatic hyperplasia amount)
- svi(seminal vesicle invasion)
- lcp(log capsular penetration)
- gleasonscore
- % gleasonscores 4 or 5.
Prostate Data Example —2
Subset (Variable) Selection Summary —1
- Pros:
- Easy to interpret
- Variable is either in or out
- Improved prediction error over the full model
- Cons:
- Has high variance
- How to choose the number of covariates to include?
- The choice of one covariate over an another can sometimes be a very arbitrary decision
(especially with correlated predictors)
Subset (Variable) Selection Summary —2
- We can think of the subset selection procedure as one that shrinks some of the coefficients to
0. What if we do this in a smoother way?
- Instead, can shrink the regression coefficients in a continuous manner (instead of multiplying
each coefficient by 0 or 1, can multiply by a number between 0 and 1
- Methods include ridge regression, the lasso, least angle regression, and elastic nets
Ridge Regression —1
- Recall that the least squares fitting procedure estimates $\beta_0, \beta_1, \dots, \beta_p$
using the values that minimize
$RRS(\beta)=\sum_{i=1}^{n}(y_i-\beta_0-\sum_{j=1}^{p}\beta_jx_{ij})^2$
- In contrast, the ridge regression coefficient estimate $\hat\beta^R$ are the values that
minimize
$\sum_{i=1}^{n}(y_i-\beta_0-\sum_{j=1}^{p}\beta_jx_{ij})^2+\lambda\sum_{j=1}^{p}\beta_j^2=RSS+\lambda\sum_{j=1}^{p}\beta_j^2$
where $\lambda\ge0$ is a tuning parameter, to be determined separately
Ridge Regression —2
- As with least squares, ridge regression seeks coefficient estimates that fit the data well, by
making the RSS small
- However, the second term, $\lambda\sum_{j=1}^{p}\beta_j^2$ called a shrinkage
penalty, is small when $\beta_1, \dots, \beta_p$ are close to zero, and so it has
the effect of shrinking the estimates of $\beta_j$ towards zero
- The tuning parameter $\lambda$ serves to control the relative impact of these two terms on the
regression coefficient estimates
- Selecting a good value for $\lambda$ is critical, cross-validation is used for this
Credit Data Example
Scaling Predictors
- The standard least squares coefficient estimates are scale equivalent;
multiplying $X_j$ by a constant $c$ simply leads to a scaling of the least squares coefficient
estimates by a factor of $\frac{1}{c}$. In other words, regardless of how the jthpredictor is
scaled, $X_j\hat\beta_j$ will remain the same
- In contrast, the ridge regression coefficient estimates can change substantially when
multiplying a given predictor by a constant, due to the sum of squared coefficients term in the
penalty part of the ridge regression objective function
- Therefore, it is best to apply ridge regression after standardizing the
predictors, using the formula
$\tilde{x}_{ij}=\frac{x_{ij}}{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{ij}-\bar{x}_j)^2}}$
Why Ridge Regression Better Than Least Squares
-
Simulated data with $n=50$ observations and $p=45$ predictors, all having nonzero
coefficients.
-
Squared bias (black), variance (green) and test mean square error (purple) for the ridge
regression on a simulated dataset as a function of $\lambda$ and $\frac{\lVert
\hat{\beta}_\lambda^R \rVert_2}{\lVert \hat{\beta} \rVert_2 }$
-
The horizontal dashed line indicates the minimum possible MSE
-
The purple crosses indicate the ridge regression models for which the MSE is smallest
Comments on Ridge Regression
- History
- Andrey Tikhonov (Russian 1906-1993): Tikhonov regularization or Tikhonov-Phillips
regularization for ill-posed problem
- Arthur E. Hoerl(1962, 1970): Ridge regression
- Note that this helps problem when have many correlated variables since in a linear regression
model, the coefficients can exhibit high variance
- However, ridge regression includes ALL $p$ predictors (unlike subset selection)
The Lasso —1
- Ridge regression does have one obvious disadvantage: unlike subset selection, which will
generally select models that involve just a subset of the variables, ridge regression will
include all p predictors in the final model
- The Lasso is a relatively recent alternative to ridge regression that overcomes
this disadvantage. The lasso coefficients, $\hat{\beta}_\lambda^L$ minimize the quantity
$\sum_{i=1}^{n}(y_i-\beta_0-\sum_{j=1}^{p}\beta_jx_{ij})^2+\lambda \sum_{j=1}^{p}\mid\beta_j\mid
=RSS+\lambda\sum_{j=1}^{p}\mid\beta_j\mid$
- In statistical parlance, the lasso uses an $ℓ_1$ (pronounced “ell 1”) penalty instead of an
$ℓ_2$ penalty. The $ℓ_1$ norm of a coefficient vector $\beta$ is given by $\lVert \beta \rVert
_1=\sum\mid\beta_j\mid$
The Lasso —2
- As with ridge regression, the lasso shrinks the coefficient estimates towards zero
- However, in the case of lasso, the $ℓ_1$ penalty has the effect of forcing some of the
coefficient estimates to be exactly zero when the tuning parameter $\lambda$ is sufficiently
large
- Hence, much like best subset selection, the lasso performs variable
selection.
- We say that lasso yields sparse models, that is, models that involve only a
subset of the variables
- As in ridge regression, selecting a good values of $\lambda$ for the lasso is critical;
cross-validation is again the method of choice
Example: Credit Dataset
The Variable Selection Property of the Lasso -1
- Why is it that the lasso, unlike ridge regression, results in coefficient estimates that are
exactly equal to zero?
The Variable Selection Property of the Lasso —2
- Why is it that the lasso, unlike ridge regression, results in coefficient estimates that are
exactly equal to zero?
- One can show that the lasso and ridge regression coefficient estimates solve the problems
Lasso: $$\underset{\beta}{\text{minimize}}\sum_{i=1}^{n}(y_i-\beta_0-\sum_{j=1}^{p}\beta_jx_{ij})^2
\quad
\text{subject to}\sum_{j=1}^{p}\mid\beta_j\mid\le s$$
Ridge: $$\underset{\beta}{\text{minimize}}\sum_{i=1}^{n}(y_i-\beta_0-\sum_{j=1}^{p}\beta_jx_{ij})^2
\quad
\text{subject to}\sum_{j=1}^{p}\beta_j^2\le s$$
Comparing Ridge and Lasso
Comments on Ridge and Lasso: Ridge
- Ridge in $\mathbb{R} ^2$: $\lambda\sum\beta_j^2$, $\beta_1^2+\beta_2^2 \le s$
Comments on Ridge and Lasso: Lasso
- Lasso in $\mathbb{R} ^2$: $\lambda\sum\mid\beta_j\mid$, $\mid\beta_1\mid+\mid\beta_2\mid \le s$
Comparing Lasso and Ridge Regression —1
- Left plot: Plots of squared bias (black), variance (green), and test MSE (purple)for the lasso
on simulated data set
- Right plot: Comparison of squared bias, variance and test MSE between lasso (solid) and ridge
(dashed). Both are plotted against their $R^2$ on the training data, as a form of indexing.
- The crosses in both plots indicate the lasso model which the MSE is smallest
Comparing Lasso and Ridge Regression —2
- Left plot: Plots of squared bias (black), variance (green), and test MSE (purple)for the lasso
on simulated data set. The simulated data is similar, except that now only two predictors are
related to response
- Right plot: Comparison of squared bias, variance and test MSE between lasso (solid) and ridge
(dashed). Both are plotted against their $R^2$ on the training data
- The crosses in both plots indicate the lasso model which the MSE is smallest
Conclusions
- These two examples illustrate that neither ridge regression nor the lasso will universally
dominate the other
- In general, one might expect the lasso to perform better when the response is a function of only
a relatively small number of predictors
- However, the number of predictors that is related to the response is never know a priori for
real data sets
- A technique such as cross-validation can be used in order to determine which approach is better
on a particular data set
Selecting Tuning Parameters for Ridge Regression and Lasso
- As with subset selection, for ridge regression and lasso we require a method to determine which
of the models under consideration is best
- We require a method selecting a value for the tuning parameter $\lambda$ or equivalently, the
value of the constraint $s$
- Cross-validation provides a simple way to tackle this problem. We choose a grid of $\lambda$
values, and compute the cross-validation error rate for each value of $\lambda$
- We then select the tuning parameter value for which the cross-validation error is smallest
- Finally, the model is re-fit using all of the available observations and the selected value of
the tuning parameter
Credit Data Example
- Left: Cross-validation error that result from applying ridge regression to the credit dataset
with various values of $\lambda$
- Right: The coefficient estimates as a function of $\lambda$.
- The vertical dashed lines indicate the value of $\lambda$ selected by cross validation
Simulated Data Example
- Left: Ten-fold cross-validation MSE for the lasso, applied to the sparse simulated dataset
- Right: The corresponding lasso coefficient estimates are displayed.
- The vertical dashed line lines indicate the lasso fit for which the cross-validation error is
smallest
Generalize Lasso and Ridge
- We can generalize the constraints:
$\hat\beta=\underset{\beta}{arg \,
min}\{\sum_{i=1}^{N}(y_i-\beta_0-\sum_{j=1}^{p}x_{ij}\beta_j)^2+\lambda\sum_{j=1}^{p}\mid\beta_j\mid^q\}$
- Note:
- if $q=1\Rightarrow$ lasso
- And if $q=2\Rightarrow$ ridge regression
- Can compromise with $1\lt q\lt 2$ but we won't get the lasso property of being able to set some
$\beta$'s to 0.
- In R, can use the Lars function
Elastic Net
$\lambda\sum_{j=1}^{p}(\alpha\beta_j^2+(1-\alpha)\mid\beta_j\mid)$
- Weighted sum of lasso and ridge regression penalties
- Selects variable like the lasso
- Shrinks together the coefficient of correlated predictors like ridge regression
- In R,
enet
function in elasticnet
or glmnet
from
glmnet
library (note $\alpha$ may be scaled by different constants in packages)
Dimensionality Reduction and PCA
Details of Dimension Reduction Methods —1
- Let $Z_1, Z_2, \dots, Z_M$ represent $M \lt p$ linear combinationsof our original $p$
predictors. That is $Z_m=\sum_{j=1}^{p}\phi_{mj}X_j$; for some constants $\phi_{m1}, \dots,
\phi_{mp}$ (1)
- We can then fit the linear regression model using ordinary least squares model
$y_i=\theta_0+\sum_{m=1}^{M}\theta_mz_{im}+\epsilon_i, i=1, \dots, n$ (2)
- Note in the second model, the regression coefficients are given by $\theta_0, \theta_1, \dots,
\theta_M$. If the constants $\phi_{m1}, \dots, \phi_{mp}$ are chosen wisely, then such dimension
reduction approaches can often outperform OLS regression
Details of Dimension Reduction Methods—2
- Note from equation 1 in slide 2
$\sum_{m=1}^{M}\theta_mz_{im}=\sum_{m=1}^{M}\theta_m\sum_{j=1}^{p}\phi_{mj}x_{ij}$
$=\sum_{j=1}^{p}\sum_{m=1}^{M}\theta_m\phi_{mj}x_{ij}=\sum_{j=1}^{p}\beta_jx_{ij}$
where $\beta_j=\sum_{m=1}^{M}\theta_m\phi_{mj}$ (3)
- Hence model (2) can be thought of as a special case of the original linear regression
model
- Dimension reduction serves to constrain the estimated $\beta_j$ coefficients, since now
they must take the form (3)
- Can win in the bias-variance tradeoff
Principal Components Regression
- Here we apply principal components analysis (PCA) to define the linear combinations of the
predictors, for use in our regression
- The first principal component is that (normalized) linear combination of the variables with the
largest variance
- The second principal component has largest variance, subject to being uncorrelated with the
first. This continues on $\dots$
- Hence with many correlated original variables, we replace them with a small set of principal
components that capture their joint variation
Pictures of PCA —1
- The population size and ad spending for 100 different cities are shown as purple circles.
- The green solid line indicates the first principal component and the blue dashed line indicates
the second principal component
Pictures of PCA —2
- Left: The first principal component, chosen to minimize the sum of the squared perpendicular
distances to each point, is shown in green.
- Right: The left-hand panel has been rotated so that the first principal component lies on the
x-axis
Pictures of PCA: $z_{i1}$ & $z_{i2}$
- Plots of the first principal component scores $z_{i1}$ versus population and ad spending
- The relationships are strong
- Plots of the first principal component scores $z_{i2}$ versus population and ad spending
- The relationships are weak
Application to Principal Components Regression
- PCR was applied to two simulated data sets. The black, green and purple lines correspond to
squared bias, variance, and test mean squared error, respectively
- Left: All 45 predictors are relevant
- Right: Only 2 predicators are relevant
Choosing the Number of Directions $M$
- Left: PCR standardized coefficient estimates on the Credit data set for different values of $M$
- Right: The 10-fold cross validation MSE obtained using PCR, as a function of $M$
Partial Least Squares —1
- PCR identifies linear combinations, or directions, that best represent the predictors $X_1, X_2,
\dots, X_p$
- These directions are identified in an unsupervised way, since the response Y is
not used to help determine the principal component directions
- That is, the response does not supervise the identification of the principal
components
- Consequently, PCR suffers from a potentially serious drawback: there is no guarantee that the
directions that best explain the predicators will also be the best directions to use for
predicting the response
Partial Least Squares —2
- Like PCR, Partial Least Squares (PLS) is a dimension reduction method, which
first identifies a new set of features $Z_1, Z_2, \dots, Z_M$ that are linear combinations of
the original features, and then fits a linear model via OLS using M new features
- Unlike PCR, PLS identifies these new features in supervised way, that is, it makes use of the
response Yin order to identify new features that not only approximate the old features well, but
also that are related to the response
- The PLS approach attempts to find directions that help explain both the response and the
predictors
Details of Partial Least Squares
- After standardizing the $p$ predictors, PLS computes the first $Z_1$ by setting each $\phi_{1j}$
in (Dimensionality Reduction and PCA equation 1) equal to the coefficient from the simple
linear regression of $Y$ onto $X_j$
- One can show that this coefficient is proportional to the correlation between $Y$ and $X_j$
- Hence in computing $Z_1=\sum_{j=1}^{p}\phi_{1j}X_j$, PLS places the highest weight on the
variables that are strongly related to the response
- Subsequent directions are found by taking residuals and then repeating the above prescription
Summary
- Model selection methods are an essential tool for data analysis, especially for big datasets
involving many predictors
- Research into methods that give sparsity, such as the lassois an especially hot area


Tape is a collection of queue-related classes for Android and Java by Square, Inc.
QueueFile
is a lightning-fast, transactional, file-based FIFO. Addition and removal from an instance is an O(1) operation and is atomic. Writes are synchronous; data will be written to disk before an operation returns. The underlying file is structured to survive process and even system crashes and if an I/O exception is thrown during a mutating change, the change is aborted.
An ObjectQueue
represents an ordering of arbitrary objects which can be backed either by the filesystem (via QueueFile
) or in memory only.
Available via Maven or as a downloadable .jar
.
<dependency>
<groupId>com.squareup.tape2</groupId>
<artifactId>tape</artifactId>
<version>(insert latest)</version>
</dependency>
If you would like to contribute code to Tape you can do so through GitHub by forking the repository and sending a pull request.
When submitting code, please make every effort to follow existing conventions and style in order to keep the code as readable as possible. Please also make sure your code compiles by running mvn clean verify
. Checkstyle failures during compilation indicate errors in your style and can be viewed in the target/checkstyle-result.xml
file.
Before your code can be accepted into the project you must also sign the Individual Contributor License Agreement (CLA).
Copyright 2012 Square, Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


Using the Exercises
This course is a little bit different than most of my other courses. There are no exercise files, You can find the all code for this course on Codepen.
To get the code simply go to raybo.org/slides_boot5new.
This will take you to a slideshow with all of the links you'll need for the course.
At the bottom of the slideshow you'l find a navigation toolbar. You can hit the t key to show or hide the toolbar.
If you hit the m key, you'll see an index of all of the slides for the course.
At the beginning of each slide, there is an overlay with a link to each of the slides. If you're watching the video, you can jump to that video's slides at that URL.
To move through the slides you can use the arrow keys.
The slides are fully interactive, so you can click on buttons or copy any code you see on screen.
If you click inside a text area and get stuck inside an editable item, just double click towards the top of the slides.
You also have access to a micro site with all of my notes for this course, Just open up the slide index by hitting the m key and then click on notes.
You can navigate to each of my notes on the left navigation and on the right go through each of the sections on slides.
If you want to go back to a slide view click on the icon to the right of the index.
A lof of the slides have links to Codepen demos where you can follow along with me, copy the code or try it yourself.


Please enjoy this {{vm.writingSample.genre}} written by user {{vm.writingSample.author}}, using the word or phrase "{{vm.writingSample.random}}":
{{vm.writingSample.content}}


--- layout: post title: How long before HURD dies? tags: - Software Development status: publish type: post published: true meta: {} --- The GNU Hurd - GNU Project - Free Software Foundation (FSF)
Although the POSIX interface is provided, some additional interfaces like POSIX shared memory or semaphores are still under development. All this applies to the current development version, and not to the last release (0.2). We encourage everybody who is interested to try out the latest development version, and send feedback to the Hurd developers.Hurd has been under development sinse about 1990, surely it's time to do the decent thing and put it down (or is it already dead)?
Powered by ScribeFire.


Although snorting clon. Is to me naughty, I could not resist doing it... It is a fine sat. night and I took the insufflated clon. , about 5 mins ago. Naughty. I just had to know.
To be utterly fair clonazepam is a very helpful drug and has helped me significantly with panic disorder/anxiety. I personally think also that pregabalin (lyrica) could well be a useful aid in the fight to recover from substance dependency. Its also helped my meralgia p. For which it was prescribed. It (pregabalin) with occasional usage of clonazepam as required has changed my life positively and the potent mood stabilisation of pregabalin is a real life saver. Its basically dumb of me to snort this amazing med, and I never will again, out of principal. It is the most effective benzo (and Iíve had em all) for real anxiety, long lasting, paralysing and nightmarish.
Pregabalin+clonazepam cured my agoraphobia and I could for the first time ever face the flower man and buy my girlfreind some flowers. I have noticed a lot of positives about this combo. I am starting a full time sound engineerring exam... On the basis of the empathic way which I had-2mg clonazepam, 150 pregabalin. As I had done a mediocre test score it was up to me to keep on talking. I also used 20mg vinpocetine that time, for proper brain functioning. Although pregabalin takes up tp 2 weeks to kick in, its an amazingly efective painkiller/antidepressant. Anyways I got in to college, they liked me. Cool. +ten mins... . Melting nicely... Typing difficult... Mmmmmm. I have a pile of it powdered up next to the computer. If only I could find that e.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Main orx include, execution convenience helpers, freely modifiable by users
Local include file
Kernel include file
Plugin include file
Utils (tools) include file
| #define __orxEXTERN__ /* Not compiling orx library */ |
Definition at line 48 of file orx.h.
Definition at line 46 of file orx.h.
Orx default basic event handler
- Parameters
-
- Returns
- orxSTATUS_SUCCESS if handled / orxSTATUS_FAILURE otherwise
Definition at line 74 of file orx.h.
| static orxINLINE void orx_Execute |
( |
orxU32 |
_u32NbParams, |
|
|
orxSTRING |
_azParams[], |
|
|
const orxMODULE_INIT_FUNCTION |
_pfnInit, |
|
|
const orxMODULE_RUN_FUNCTION |
_pfnRun, |
|
|
const orxMODULE_EXIT_FUNCTION |
_pfnExit |
|
) |
| |
|
static |
Orx main execution function
- Parameters
-
| [in] | _u32NbParams | Main function parameters number (argc) |
| [in] | _azParams | Main function parameter list (argv) |
| [in] | _pfnInit | Main init function (should init all the main stuff and register the main event handler to override the default one) |
| [in] | _pfnRun | Main run function (will be called once per frame, should return orxSTATUS_SUCCESS to continue processing) |
| [in] | _pfnExit | Main exit function (should clean all the main stuff) |
Definition at line 256 of file orx.h.
| static void orxFASTCALL orx_MainSetup |
( |
| ) |
|
|
static |
Default main setup (module dependencies)
Definition at line 91 of file orx.h.
Should stop execution by default event handling?
Definition at line 63 of file orx.h.


{% extends 'core.html' %} {% block header %}
/etc/udev/rules.d/50-switch.rules
SUBSYSTEM=="usb", ATTR{idVendor}=="0955", MODE="0664", GROUP="plugdev"
Fusee Launcher ported to JavaScript using WebUSB.
Source can be found on GitLab under the MIT license (or by hitting view source, there is no backend!).
Ported from fusee-launcher.
Thanks to ktemkin and ReSwitched for Fusée Gelée and a ton of other things!
Web exploit code developed by atlas44.


Robert Carlyle gives foul-mouthed summary of his Brexit views
Robert Carlyle has given a foul-mouthed summary of his views on Brexit.
The Scottish actor, who has now made his home in Canada, said the division caused by Brexit is “very sad”.
He is concerned that friends and family can be split by their opinions on the referendum.
The star of Trainspotting and the upcoming BBC series The War Of The Worlds said he believes Brexit is a “f****** disaster”.
Speaking to Radio Times magazine, he added: “People are entitled to their opinion.
“Whether they voted remain or whether they voted to leave, that’s up to them, their opinion’s valid.
“But maybe it’s interesting for people to see how the world views this all. It seems like a shocking thing to be happening.
“There are friends back home who have fallen out over this, families have fallen out, and I find it hard to believe that some political thing could cause such unrest. I don’t know. It’s very sad.”
Carlyle currently resides in Vancouver, Canada, and is focused on projects that earn money for his family, rather than taking independent film work in the UK.
Carlyle stars in The War Of The Worlds, a female-led update based on the HG Wells novel, which is coming to the BBC.
The full interview with Carlyle is available in Radio Times magazine, out now.