--- layout: default title: Using Cassandra permalink: /using-cassandra/ redirect_from: - /using_cassandra.html sitemap: priority: 0.7 lastmod: 2015-02-24T00:00:00-00:00 ---
Cassandra is one of the supported databases that can be selected when your application is being generated.
This generator has one limitation:
When Cassandra is selected:
Similar to Liquibase, JHipster provide a tool to apply your CQL migration scripts, with some restrictions:
{timestamp}_{description}.cql
and be placed in the changelog directory: src/main/resources/config/cql/changelog/
Some information on the tool:
src/main/resources/config/cql/changelog/
in the same way we generate Liquibase changelogs for JPAsrc/main/resources/config/cql/changelog/
directory are automically applied to the in memory cluster
schema_version
to store the metadata info
The tool will apply the migration scripts from src/main/resources/config/cql/
in the following order:
create-keyspace.cql
- create the keyspace and the schema_version
table to store the migration metadatacql/changelog/\*.cql
files in alphabetical order
Depending if you are using Docker or not, you have several options to run the migration tool.
If you have started the Cassandra cluster with docker-compose, using the generated app.yml
or cassandra.yml
compose files, the tool has already been run and all cql scripts applied.
After adding a CQL script in the changelog directory, you can relaunch the docker-service responsible to run the migration service again without stopping the cluster:
docker-compose -f src/main/docker/cassandra.yml up <app>-cassandra-migration
With some prerequisites, you can run the tool manually. It could be uselful to familiarize you with the tool to later include it to your deployment pipeline.
export CASSANDRA_CONTACT_POINT=`docker-machine ip default`
To run the tool use this command: src/main/docker/cassandra/scripts/autoMigrate.sh src/main/resources/config/cql/changelog/
If you only want to execute a specific script against your cluster use: src/main/docker/cassandra/scripts/execute-cql.sh src/main/resources/config/cql/changelog/<your script>.cql
On Mac OSx and Windows, Docker containers are not hosted directly but on a VirtualBox VM.
Those, you can not access them in localhost but have to hit the VirtualBox IP.
You can override the Cassandra contact point (localhost by default) with this environment variable:
export SPRING_DATA_CASSANDRA_CONTACTPOINTS=`docker-machine ip default`
Because Cassandra nodes are also hosted in the Virtual machine, the Cassandra Java driver will receive an error when trying to contact them after receiving their address from the contact point.
To workaround this, you can add a routing rule to your routing table, (source).
Assuming the containers running the Cassandra nodes have IP address 172.18.0.x:
sudo route -n add <IP_ADDRESS>/16 `docker-machine ip default`


| | SECTOR scnum |
This is the sector number, it is zero based.
| | NAME sector_name |
This is both a link to the .INF file and a useful reminder.
| | AMBIENT 20 |
Ambient light level in this sector.
Note that this value is used in GROMAS to indicate an amount of red fog, not a light level.
This is a good demonstration of the use of the CMP files.
| | FLOOR TEXTURE 80 -0.38 -0.06 2 |
The TX to apply to the floor of the SC as an index in the TX table.
The following two floats are the X and Z offsets by which the TX must be moved before being mapped.
The third (int) value is unused.
It seems that floor textures must be 64x64, or the game engine does strange things.
| | FLOOR ALTITUDE 0.00 |
The altitude of the floor of this SC. Note that the Y axis goes "down", so higher altitudes have lower values.
| | CEILING TEXTURE 0 0.00 0.00 2 |
| | CEILING ALTITUDE -12.00 |
Same as floor.
| | SECOND ALTITUDE 0.00 |
This is used to indicate a second "floor" altitude in a sector. For instance, a second altitude of 4 will make you "enter into the floor" 4 deep. It will in addition make the sector water like and generate a splashing sound. If you set a negative second altitude, you will be able to walk higher on the sector, provided you also enter the sector higher. This is the way platforms are created (the platform object is only a visual clue).
| | FLAGS 0 0 0 |
Three flags, the second of which is never used in the 14 original levels.
Change various things in the sector. See Sector Flags.
| | LAYER 1 |
The layer on which the SC is (positive, 0 or negative).
This value is used in the game to make different maps corresponding to zones of altitude.
Note that this is only a logical grouping, but is also used by the map in the game.


Anywhere, Anytime.
Pick any location in the world, it could be the Sahara, Yosemite or the Alaskan mountains. Go wild. It can also be anytime, the cold snowy winter or the blazing hot summer, the cool but warm spring and the brown chilly Autumn.
Bring only the essentials
Don't worry if you dont have all the specialist gear, we will tell you what to pack and what you don't have we will provide. If you want you may come only in a suit. You don't have to worry about forgetting anything
It's all about you
You decide where you want to go and what you want to do. We cater for all fitness levels. We will customise the trip to your needs and wants.


--- layout: page title: About header-img: "img/about-bg.png" ---
Nicole M. Lucien is a mom of twins and a teacher turned techie. She spends (too) many hours a day connected to technology and is currently making the transition to tech as a student in Flatiron School.


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Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or quasi-Newton-Raphson optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through resubstitution or cross-validation. The function eztune.cv will compute a cross-validated error rate. The purpose of eztune.cv is to provide a cross-validated accuracy when resubstitution is used for optimization because resubstitution typically produces an accuracy that is too high.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | ada, e1071, GA, gbm, mlbench, parallel, doParallel |
| Published: | 2018-10-14 |
| Author: | Jill Lundell [aut, cre] |
| Maintainer: | Jill Lundell <jflundell at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Citation: | EZtune citation info |
| Materials: | README |
| CRAN checks: | EZtune results |
| Reference manual: | EZtune.pdf |
| Package source: | EZtune_1.0.0.tar.gz |
| Windows binaries: | r-devel: EZtune_1.0.0.zip, r-release: EZtune_1.0.0.zip, r-oldrel: EZtune_1.0.0.zip |
| OS X binaries: | r-release: EZtune_1.0.0.tgz, r-oldrel: EZtune_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=EZtune to link to this page.


All interactions on this demo occur in response to messages routed through a hosted instance of Tavendo WebMQ.
Information on where this is hosted is displayed in the status line at the bottom of the demo box.
Messages can be published from any application or platform that supports HTTP/POST.


We are located in KILIMANI
We deal in all brands of Gas cylinders.
We are located in GONGO R0AD opposite cleanshelf supermarket.
We deal in all brands of Gas cylinders.
We are located in KAWANGWARE next magunas supermarket.
We deal in all brands of Gas cylinders.
We are located in JAMHURI near impala ground.
We deal in all brands of Gas cylinders.
We are located in LOWER KABETE.
We deal in all brands of Gas cylinders.
We are located in KIBRA.
We deal in all brands of Gas cylinders.


William M. Coleman, (RPLS, GISP) with over 40 years experience, opened Coleman & Assoc. Land Surveying in 1983. We provide quality professional surveying and mapping services including, but not limited to: construction staking, field design surveys, topographic surveys, boundary surveys and analysis, preparation of easement or right-of-way exhibits, GPS monumentation, and other professional survey services to public and private enterprise. Our well-trained field crews and technicians work under the guidance of an in-house RPLS to bring you an efficiently executed, extraordinary product.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Amet numquam aspernatur eum quasi sapiente nesciunt? Voluptatibus sit, repellat sequi itaque deserunt, dolores in, nesciunt, illum tempora ex quae? Nihil, dolorem!
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam viverra euismod odio, gravida pellentesque urna varius vitae.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Quos quisquam, error quod sed cumque, odio distinctio velit nostrum temporibus necessitatibus et facere atque iure perspiciatis mollitia recusandae vero vel quam!
725 S. Locust St.
Denton, Tx 76201
P:<PHONE_NUMBER>
E<EMAIL_ADDRESS>H: Monday - Friday: 9:00 AM to 5:00 PM


Man Utd 'eyeing £100m Lautaro Martinez transfer' to spearhead attack
The Inter Milan forward has starred for his club so far this season, attracting interest from the Red Devils
by Darren Wells, https://www.mirror.co.uk/authors/darren-wells/Manchester United are reportedly weighing up a move for Inter Milan striker Lautaro Martinez.
The 22-year-old has been in good form for the Nerrazurri so far this season, netting seven times in 13 appearances, while also banging in a hat-trick for Argentina.
United are short of options up front, having sold Romelu Lukaku to the Serie A club in the summer, as well as loaning them Alexis Sanchez.
The Guardian report that United are considering making use of their recent business relations by moving for Martinez.
However they add that United will have to fork out up to £100 million to prise him from the San Siro.
Martinez has been a regular under manager Antonio Conte since the former Chelsea boss arrived in summer.
His form for his club has dramatically improved in this campaign, having fired an average of a goal every two games, compared to one in every four last season.
United boss Ole Gunnar Solskjaer has limited firepower in his ranks, with Marcus Rashford and Anthony Martial the only established strikers.
Youngster Mason Greenwood has also been involved in first-team action, but is still yet to start a game in the Premier League.
RB Salzburg hotshot Erling Braut Haaland is another on United's radar, with the club ready to battle Real Madrid and Juventus for his signature.
The 19-year-old has been banging the goals in for the Austrian outfit, and has teased a move to England.
But Juventus are confident of securing his talents with a £35 million bid, due to their close relations with Haaland's agent Mino Raiola, who is keen for his client to move to Turin.


Mocking
- Introduction
- Mocking Objects
- Mocking Facades
- Bus Fake
- Event Fake
- HTTP Fake
- Mail Fake
- Notification Fake
- Queue Fake
- Storage Fake
- Interacting With Time
Introduction
When testing Laravel applications, you may wish to "mock" certain aspects of your application so they are not actually executed during a given test. For example, when testing a controller that dispatches an event, you may wish to mock the event listeners so they are not actually executed during the test. This allows you to only test the controller's HTTP response without worrying about the execution of the event listeners since the event listeners can be tested in their own test case.
Laravel provides helpful methods for mocking events, jobs, and other facades out of the box. These helpers primarily provide a convenience layer over Mockery so you do not have to manually make complicated Mockery method calls.
Mocking Objects
When mocking an object that is going to be injected into your application via Laravel's service container, you will need to bind your mocked instance into the container as an instance
binding. This will instruct the container to use your mocked instance of the object instead of constructing the object itself:
use App\Service;
use Mockery;
use Mockery\MockInterface;
public function test_something_can_be_mocked()
{
$this->instance(
Service::class,
Mockery::mock(Service::class, function (MockInterface $mock) {
$mock->shouldReceive('process')->once();
})
);
}
In order to make this more convenient, you may use the mock
method that is provided by Laravel's base test case class. For example, the following example is equivalent to the example above:
use App\Service;
use Mockery\MockInterface;
$mock = $this->mock(Service::class, function (MockInterface $mock) {
$mock->shouldReceive('process')->once();
});
You may use the partialMock
method when you only need to mock a few methods of an object. The methods that are not mocked will be executed normally when called:
use App\Service;
use Mockery\MockInterface;
$mock = $this->partialMock(Service::class, function (MockInterface $mock) {
$mock->shouldReceive('process')->once();
});
Similarly, if you want to spy on an object, Laravel's base test case class offers a spy
method as a convenient wrapper around the Mockery::spy
method. Spies are similar to mocks; however, spies record any interaction between the spy and the code being tested, allowing you to make assertions after the code is executed:
use App\Service;
$spy = $this->spy(Service::class);
// ...
$spy->shouldHaveReceived('process');
Mocking Facades
Unlike traditional static method calls, facades (including real-time facades) may be mocked. This provides a great advantage over traditional static methods and grants you the same testability that you would have if you were using traditional dependency injection. When testing, you may often want to mock a call to a Laravel facade that occurs in one of your controllers. For example, consider the following controller action:
<?php
namespace App\Http\Controllers;
use Illuminate\Support\Facades\Cache;
class UserController extends Controller
{
/**
* Retrieve a list of all users of the application.
*
* @return \Illuminate\Http\Response
*/
public function index()
{
$value = Cache::get('key');
//
}
}
We can mock the call to the Cache
facade by using the shouldReceive
method, which will return an instance of a Mockery mock. Since facades are actually resolved and managed by the Laravel service container, they have much more testability than a typical static class. For example, let's mock our call to the Cache
facade's get
method:
<?php
namespace Tests\Feature;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Cache;
use Tests\TestCase;
class UserControllerTest extends TestCase
{
public function testGetIndex()
{
Cache::shouldReceive('get')
->once()
->with('key')
->andReturn('value');
$response = $this->get('/users');
// ...
}
}
{note} You should not mock the
Request
facade. Instead, pass the input you desire into the HTTP testing methods such asget
andpost
when running your test. Likewise, instead of mocking theConfig
facade, call theConfig::set
method in your tests.
Facade Spies
If you would like to spy on a facade, you may call the spy
method on the corresponding facade. Spies are similar to mocks; however, spies record any interaction between the spy and the code being tested, allowing you to make assertions after the code is executed:
use Illuminate\Support\Facades\Cache;
public function test_values_are_be_stored_in_cache()
{
Cache::spy();
$response = $this->get('/');
$response->assertStatus(200);
Cache::shouldHaveReceived('put')->once()->with('name', 'Taylor', 10);
}
Bus Fake
When testing code that dispatches jobs, you typically want to assert that a given job was dispatched but not actually queue or execute the job. This is because the job's execution can normally be tested in a separate test class.
You may use the Bus
facade's fake
method to prevent jobs from being dispatched to the queue. Then, after executing the code under test, you may inspect which jobs the application attempted to dispatch using the assertDispatched
and assertNotDispatched
methods:
<?php
namespace Tests\Feature;
use App\Jobs\ShipOrder;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Bus;
use Tests\TestCase;
class ExampleTest extends TestCase
{
public function test_orders_can_be_shipped()
{
Bus::fake();
// Perform order shipping...
// Assert that a job was dispatched...
Bus::assertDispatched(ShipOrder::class);
// Assert a job was not dispatched...
Bus::assertNotDispatched(AnotherJob::class);
}
}
You may pass a closure to the assertDispatched
or assertNotDispatched
methods in order to assert that a job was dispatched that passes a given "truth test". If at least one job was dispatched that passes the given truth test then the assertion will be successful. For example, you may wish to assert that a job was dispatched for a specific order:
Bus::assertDispatched(function (ShipOrder $job) use ($order) {
return $job->order->id === $order->id;
});
Job Chains
The Bus
facade's assertChained
method may be used to assert that a chain of jobs was dispatched. The assertChained
method accepts an array of chained jobs as its first argument:
use App\Jobs\RecordShipment;
use App\Jobs\ShipOrder;
use App\Jobs\UpdateInventory;
use Illuminate\Support\Facades\Bus;
Bus::assertChained([
ShipOrder::class,
RecordShipment::class,
UpdateInventory::class
]);
As you can see in the example above, the array of chained jobs may be an array of the job's class names. However, you may also provide an array of actual job instances. When doing so, Laravel will ensure that the job instances are of the same class and have the same property values of the chained jobs dispatched by your application:
Bus::assertChained([
new ShipOrder,
new RecordShipment,
new UpdateInventory,
]);
Job Batches
The Bus
facade's assertBatched
method may be used to assert that a batch of jobs was dispatched. The closure given to the assertBatched
method receives an instance of Illuminate\Bus\PendingBatch
, which may be used to inspect the jobs within the batch:
use Illuminate\Bus\PendingBatch;
use Illuminate\Support\Facades\Bus;
Bus::assertBatched(function (PendingBatch $batch) {
return $batch->name == 'import-csv' &&
$batch->jobs->count() === 10;
});
Event Fake
When testing code that dispatches events, you may wish to instruct Laravel to not actually execute the event's listeners. Using the Event
facade's fake
method, you may prevent listeners from executing, execute the code under test, and then assert which events were dispatched by your application using the assertDispatched
, assertNotDispatched
, and assertNothingDispatched
methods:
<?php
namespace Tests\Feature;
use App\Events\OrderFailedToShip;
use App\Events\OrderShipped;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Event;
use Tests\TestCase;
class ExampleTest extends TestCase
{
/**
* Test order shipping.
*/
public function test_orders_can_be_shipped()
{
Event::fake();
// Perform order shipping...
// Assert that an event was dispatched...
Event::assertDispatched(OrderShipped::class);
// Assert an event was dispatched twice...
Event::assertDispatched(OrderShipped::class, 2);
// Assert an event was not dispatched...
Event::assertNotDispatched(OrderFailedToShip::class);
// Assert that no events were dispatched...
Event::assertNothingDispatched();
}
}
You may pass a closure to the assertDispatched
or assertNotDispatched
methods in order to assert that an event was dispatched that passes a given "truth test". If at least one event was dispatched that passes the given truth test then the assertion will be successful:
Event::assertDispatched(function (OrderShipped $event) use ($order) {
return $event->order->id === $order->id;
});
If you would simply like to assert that an event listener is listening to a given event, you may use the assertListening
method:
Event::assertListening(
OrderShipped::class,
SendShipmentNotification::class
);
{note} After calling
Event::fake()
, no event listeners will be executed. So, if your tests use model factories that rely on events, such as creating a UUID during a model'screating
event, you should callEvent::fake()
after using your factories.
Faking A Subset Of Events
If you only want to fake event listeners for a specific set of events, you may pass them to the fake
or fakeFor
method:
/**
* Test order process.
*/
public function test_orders_can_be_processed()
{
Event::fake([
OrderCreated::class,
]);
$order = Order::factory()->create();
Event::assertDispatched(OrderCreated::class);
// Other events are dispatched as normal...
$order->update([...]);
}
Scoped Event Fakes
If you only want to fake event listeners for a portion of your test, you may use the fakeFor
method:
<?php
namespace Tests\Feature;
use App\Events\OrderCreated;
use App\Models\Order;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Support\Facades\Event;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Tests\TestCase;
class ExampleTest extends TestCase
{
/**
* Test order process.
*/
public function test_orders_can_be_processed()
{
$order = Event::fakeFor(function () {
$order = Order::factory()->create();
Event::assertDispatched(OrderCreated::class);
return $order;
});
// Events are dispatched as normal and observers will run ...
$order->update([...]);
}
}
HTTP Fake
The Http
facade's fake
method allows you to instruct the HTTP client to return stubbed / dummy responses when requests are made. For more information on faking outgoing HTTP requests, please consult the HTTP Client testing documentation.
Mail Fake
You may use the Mail
facade's fake
method to prevent mail from being sent. Typically, sending mail is unrelated to the code you are actually testing. Most likely, it is sufficient to simply assert that Laravel was instructed to send a given mailable.
After calling the Mail
facade's fake
method, you may then assert that mailables were instructed to be sent to users and even inspect the data the mailables received:
<?php
namespace Tests\Feature;
use App\Mail\OrderShipped;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Mail;
use Tests\TestCase;
class ExampleTest extends TestCase
{
public function test_orders_can_be_shipped()
{
Mail::fake();
// Perform order shipping...
// Assert that no mailables were sent...
Mail::assertNothingSent();
// Assert that a mailable was sent...
Mail::assertSent(OrderShipped::class);
// Assert a mailable was sent twice...
Mail::assertSent(OrderShipped::class, 2);
// Assert a mailable was not sent...
Mail::assertNotSent(AnotherMailable::class);
}
}
If you are queueing mailables for delivery in the background, you should use the assertQueued
method instead of assertSent
:
Mail::assertQueued(OrderShipped::class);
Mail::assertNotQueued(OrderShipped::class);
Mail::assertNothingQueued();
You may pass a closure to the assertSent
or assertNotSent
methods in order to assert that a mailable was sent that passes a given "truth test". If at least one mailable was sent that passes the given truth test then the assertion will be successful:
Mail::assertSent(function (OrderShipped $mail) use ($order) {
return $mail->order->id === $order->id;
});
When calling the Mail
facade's assertion methods, the mailable instance accepted by the provided closure exposes helpful methods for examining the recipients of the mailable:
Mail::assertSent(OrderShipped::class, function ($mail) use ($user) {
return $mail->hasTo($user->email) &&
$mail->hasCc('...') &&
$mail->hasBcc('...');
});
Notification Fake
You may use the Notification
facade's fake
method to prevent notifications from being sent. Typically, sending notifications is unrelated to the code you are actually testing. Most likely, it is sufficient to simply assert that Laravel was instructed to send a given notification.
After calling the Notification
facade's fake
method, you may then assert that notifications were instructed to be sent to users and even inspect the data the notifications received:
<?php
namespace Tests\Feature;
use App\Notifications\OrderShipped;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Notification;
use Tests\TestCase;
class ExampleTest extends TestCase
{
public function test_orders_can_be_shipped()
{
Notification::fake();
// Perform order shipping...
// Assert that no notifications were sent...
Notification::assertNothingSent();
// Assert a notification was sent to the given users...
Notification::assertSentTo(
[$user], OrderShipped::class
);
// Assert a notification was not sent...
Notification::assertNotSentTo(
[$user], AnotherNotification::class
);
}
}
You may pass a closure to the assertSentTo
or assertNotSentTo
methods in order to assert that a notification was sent that passes a given "truth test". If at least one notification was sent that passes the given truth test then the assertion will be successful:
Notification::assertSentTo(
$user,
function (OrderShipped $notification, $channels) use ($order) {
return $notification->order->id === $order->id;
}
);
On-Demand Notifications
If the code you are testing sends on-demand notifications, you will need to assert that the notification was sent to an Illuminate\Notifications\AnonymousNotifiable
instance:
use Illuminate\Notifications\AnonymousNotifiable;
Notification::assertSentTo(
new AnonymousNotifiable, OrderShipped::class
);
By passing a closure as the third argument to the notification assertion methods, you may determine if an on-demand notification was sent to the correct "route" address:
Notification::assertSentTo(
new AnonymousNotifiable,
OrderShipped::class,
function ($notification, $channels, $notifiable) use ($user) {
return $notifiable->routes['mail'] === $user->email;
}
);
Queue Fake
You may use the Queue
facade's fake
method to prevent queued jobs from being pushed to the queue. Most likely, it is sufficient to simply assert that Laravel was instructed to push a given job to the queue since the queued jobs themselves may be tested in another test class.
After calling the Queue
facade's fake
method, you may then assert that the application attempted to push jobs to the queue:
<?php
namespace Tests\Feature;
use App\Jobs\AnotherJob;
use App\Jobs\FinalJob;
use App\Jobs\ShipOrder;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Support\Facades\Queue;
use Tests\TestCase;
class ExampleTest extends TestCase
{
public function test_orders_can_be_shipped()
{
Queue::fake();
// Perform order shipping...
// Assert that no jobs were pushed...
Queue::assertNothingPushed();
// Assert a job was pushed to a given queue...
Queue::assertPushedOn('queue-name', ShipOrder::class);
// Assert a job was pushed twice...
Queue::assertPushed(ShipOrder::class, 2);
// Assert a job was not pushed...
Queue::assertNotPushed(AnotherJob::class);
}
}
You may pass a closure to the assertPushed
or assertNotPushed
methods in order to assert that a job was pushed that passes a given "truth test". If at least one job was pushed that passes the given truth test then the assertion will be successful:
Queue::assertPushed(function (ShipOrder $job) use ($order) {
return $job->order->id === $order->id;
});
Job Chains
The Queue
facade's assertPushedWithChain
and assertPushedWithoutChain
methods may be used to inspect the job chain of a pushed job. The assertPushedWithChain
method accepts the primary job as its first argument and an array of chained jobs as its second argument:
use App\Jobs\RecordShipment;
use App\Jobs\ShipOrder;
use App\Jobs\UpdateInventory;
use Illuminate\Support\Facades\Queue;
Queue::assertPushedWithChain(ShipOrder::class, [
RecordShipment::class,
UpdateInventory::class
]);
As you can see in the example above, the array of chained jobs may be an array of the job's class names. However, you may also provide an array of actual job instances. When doing so, Laravel will ensure that the job instances are of the same class and have the same property values of the chained jobs dispatched by your application:
Queue::assertPushedWithChain(ShipOrder::class, [
new RecordShipment,
new UpdateInventory,
]);
You may use the assertPushedWithoutChain
method to assert that a job was pushed without a chain of jobs:
Queue::assertPushedWithoutChain(ShipOrder::class);
Storage Fake
The Storage
facade's fake
method allows you to easily generate a fake disk that, combined with the file generation utilities of the Illuminate\Http\UploadedFile
class, greatly simplifies the testing of file uploads. For example:
<?php
namespace Tests\Feature;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Foundation\Testing\WithoutMiddleware;
use Illuminate\Http\UploadedFile;
use Illuminate\Support\Facades\Storage;
use Tests\TestCase;
class ExampleTest extends TestCase
{
public function test_albums_can_be_uploaded()
{
Storage::fake('photos');
$response = $this->json('POST', '/photos', [
UploadedFile::fake()->image('photo1.jpg'),
UploadedFile::fake()->image('photo2.jpg')
]);
// Assert one or more files were stored...
Storage::disk('photos')->assertExists('photo1.jpg');
Storage::disk('photos')->assertExists(['photo1.jpg', 'photo2.jpg']);
// Assert one or more files were not stored...
Storage::disk('photos')->assertMissing('missing.jpg');
Storage::disk('photos')->assertMissing(['missing.jpg', 'non-existing.jpg']);
}
}
For more information on testing file uploads, you may consult the HTTP testing documentation's information on file uploads.
{tip} By default, the
fake
method will delete all files in its temporary directory. If you would like to keep these files, you may use the "persistentFake" method instead.
Interacting With Time
When testing, you may occasionally need to modify the time returned by helpers such as now
or Illuminate\Support\Carbon::now()
. Thankfully, Laravel's base feature test class includes helpers that allow you to manipulate the current time:
public function testTimeCanBeManipulated()
{
// Travel into the future...
$this->travel(5)->milliseconds();
$this->travel(5)->seconds();
$this->travel(5)->minutes();
$this->travel(5)->hours();
$this->travel(5)->days();
$this->travel(5)->weeks();
$this->travel(5)->years();
// Travel into the past...
$this->travel(-5)->hours();
// Travel to an explicit time...
$this->travelTo(now()->subHours(6));
// Return back to the present time...
$this->travelBack();
}


Simple icon:
Flag icon with a label:
Chevron icon with a label on the left side and a size of 2x:
Icon that appears when added to the page (attributes animated and visible combined):
Get in theIcon that appears when clicking the botton (attribute animated and public methods show() / hide()):


{% load i18n %} {% if data.password_change_required %}
{% trans "Your password has expired. Please change it using the form below." %}
Use the following form to change your password.


Speech
Schools Minister Nick Gibb’s speech to the North of England Education Conference.
Thank you Mick for that kind introduction, I’m pleased to be here to talk about “Passion, Potential, Performance: Thinking differently”.
I’m pleased to be back in Leeds where I spent the majority of my secondary school education - at Roundhay High School. My mother also spent a good proportion of her teaching career at Talbot Row Primary School, Roundhay and I’m looking forward to visiting Abbey Grange Church of England Academy later on today.
A few months ago I came up to Batley to celebrate the conversion of the independent Batley Grammar School into one of the first 24 free schools.
The NEEC has a long and distinguished history as a forum for education discussion for well over a century.
The case for comprehensive schools, the first plans for the National Curriculum and the drive towards grant-maintained status are just some of the educational milestones announced at an NEEC.
At last year’s conference, I promised that we would protect school budgets in cash terms at least and devolve as much autonomy as possible to schools and teachers. And, over the last year, that’s what we’ve done.
All our actions have been guided by three overarching goals:
So that schools can take the lead in continuing professional development and leadership training, around 100 outstanding schools have been selected as Teaching Schools. These centres of excellence in teaching practice will give new and experienced teachers an opportunity to learn and develop their professional skills throughout their careers.
We’re giving schools a stronger influence over the content of initial teacher training and the recruitment of trainees, and we’re continuing to ensure that ITT provision focuses on the quality of school placements. We’re prioritising the training of more primary specialist teachers and encouraging ITT providers to offer specialist courses.
In light of research showing that nearly half of serious allegations against school teachers are unsubstantiated, malicious or unfounded, we’ve given teachers a legal right to anonymity from allegations made by pupils, until the point they are charged with a criminal offence. We have also revised guidance to local authorities and schools to speed up the investigation process when a teacher or a member of staff is accused of an offence by a pupil.
And the Education Act (passed in November) further strengthened teachers’ powers to enforce school rules, removing the 24 hours’ notice rule for detentions and allowing Pupil Referral Units the same autonomy and freedoms as schools.
One of the most visible signs that we’ve increased autonomy and put greater trust in the professionalism of teachers is our removal of excessive bureaucracy.
In just one year, under the last Government, the Department produced over 6000 pages of guidance. In one year of this Government, we cut over 6000 pages of guidance.
We’re continuing to shorten guidance in a wide range of areas: for example, slimming down guidance on tackling poor pupil behaviour from over 600 pages to just 50. In total, departmental guidance is being more than halved.
We’ve also revised school admissions and appeals codes to 61 pages rather than 138. Retaining just half of the previous 650 mandatory requirements on admissions authorities, the new codes are fairer and simpler for schools and parents alike.
We have ended the requirement for schools to set statutory performance targets, removed the expectation that every school will complete a self evaluation form, streamlined the inspection framework and clarified that neither the Department nor Ofsted expects to see written lesson plans for every lesson.
And our National Curriculum review is slimming down the curriculum to concentrate on essential knowledge and skills. New programmes of study are being drafted for full public consultation and I hope that many here will participate in that consultation in due course.
In all these areas and more, we are working to free schools and teachers from the burden of excessive and unnecessary bureaucracy.
Over and over again, international research has shown that increased autonomy at school level is reflected in higher standards. As the OECD says: “in countries where schools have greater autonomy over what is taught and how students are assessed, students tend to perform better.”
Of course, one of the most powerful examples of increased freedom for schools is the expansion of the academies programme.
As we start 2012, there are 1529 academies. Over 1300 of these have opened since May 2010. More than a third of all secondary schools are now open or in the process of opening as academies, teaching over one and a quarter million children.
September 2011 also saw the opening of 24 new Free Schools, 4 studio schools, and a University Technical College. 100 new schools are set to open in 2012 or 2013, and early indications show that they will be overwhelmingly located in areas of deprivation or where there is a desperate shortage of school places.
We are delighted that so many schools are taking advantage of the freedoms of academy status; providing opportunities for more children to enjoy an excellent education.
Last summer, an independent assessment of the academies programme by the London School of Economics confirmed that “academy conversion generates… a significant improvement in pupil performance”. Statistics show that academies in some of the most challenging areas of the country are improving their results at twice the speed of non-academy schools.
And according to the LSE assessment, improvements in pupil performance were observed in academies and in their neighbouring schools. The academies programme doesn’t just bring improvements to an individual school, but to schools throughout the system.
We’re currently working hard behind the scenes to tackle policy blockages at local level which are preventing some schools from converting to academy status.
We’re creating an “Academies Work” area on the DfE website, gathering all the online resources on academies and conversion to make it easier for schools to find the information they need.
And as the programme continues to expand, we want to focus even more closely on driving up standards in low-performing schools.
We’ve already set out clear plans to turn round underperforming primary schools. We’re setting tougher floor standards, rising each year, to ensure that all schools continue to improve. The 200 weakest primaries will be converted into Academies, and robust action plans are being prepared in 500 more. If schools aren’t making the right progress, and local authorities don’t have a grip on the issue, we will be able to intervene to secure the best possible result for the children in those schools.
So by expanding the Academies programme, increasing autonomy at school level and improving teacher training, we want to drive up standards in schools right across the country.
We also want to make it clear that we are not prepared to give up on any child.
Children in alternative provision are among the most vulnerable in our education system. Yet despite hard work by dedicated professionals, statistics published for the first time last year show that only 1.4% of children in alternative provision in 2009/10 achieved five or more GCSEs at A*-C including English and Maths, compared to 53.4% in all schools in England.
To drive up standards in alternative provision, we need to increase autonomy, accountability and diversity. From September 2012, outstanding Pupil Referral Units will be able to convert to Academies; and we will invite new providers to establish alternative provision Free Schools, bringing voluntary or private sector expertise to help these vulnerable children.
And we are piloting an approach to exclusions in which the school itself will commission alternative provision for the excluded child and be held to account for the acheivement of that pupil. And Charlie Taylor, the Government’s Expert Adviser on Behaviour, is looking urgently into how we can improve alternative provision - and how we can ensure that another generation is not allowed to fail.
I’d like to take this opportunity to mention two particular priorities for the coming year.
First, reading. One of my greatest pleasures when visiting a good school is listening to children talk with real passion about their favourite books - the characters they love and the stories they tell.
And we’re lucky that some of the most magical and exciting children’s books ever written have been written in the English language - the works of Roald Dahl, Michael Morpurgo and Jacqueline Wilson; Harry Potter and Narnia; the Wind in the Willows and Winnie the Pooh.
By the end of primary school, all children should be able to read and enjoy books like Harry Potter. But too many children can’t enjoy these brilliant books because they haven’t learnt to read properly.
One in six 11-year-olds is still struggling with reading when they leave primary school. One in ten 11-year-old boys has a reading age of seven or below. Secondary schools are forced to provide extra help and catch-up sessions when they should be introducing children to the breadth and depth of the secondary curriculum.
And children who cannot read are more likely to become disengaged and disruptive. A recent report by the Centre for Social Justice showed that between half and three-quarters of children permanently excluded from school display significant literacy problems . As the author said, “many display challenging behaviour to hide the fact that they cannot read.”
Over the last nine years, England has fallen in international reading league tables from seventh to 25th. English 15-year-olds are more than a year behind their peers in Shanghai, Korea and Finland in reading, and at least six months behind Canada, New Zealand, Australia, Hong Kong, Singapore and Japan.
This Government is determined to help all children to read widely and well, and develop a lifelong love of reading.
If children haven’t mastered the basic mechanics of reading they can’t develop their comprehension and understanding, or begin exploring and enjoying all sorts of books and poems.
But with the life-changing skill of turning words on the page into images, information and ideas, we hope that all children can become fluent and enthusiastic readers.
High quality research shows that systematic phonics is the most successful way to teach early reading. Synthetic phonics is equally effective for children of all abilities, from all backgrounds, and for boys and girls alike.
Last summer, we piloted the phonics check for 6-year-olds in around 300 schools around the country. The level they were expected to reach was set by two groups of teachers from the pilot, who independently agreed it was appropriate and challenging.
Only 32% reached the required level, which means that we all need to face up to an uncomfortable fact. Despite the hard work of teachers all over the country, too few children are able to read to a high enough standard.
The levels we currently expect children to reach at Key Stage 1 and Key Stage 2 must not be the limits of our ambition - they should be considered the minimum. Rather than scraping a Level 2 at the end of Key Stage 1, more children can achieve a high Level 2 or even a Level 3.
26 per cent of children already reached level 3 in reading in 2011 - including some schools in the most challenging areas. We want these high expectations to become the norm.
From June, the Year 1 check will help all teachers to ensure that children grasp the basic mechanics of reading. The check will also identify any children who need extra help - and almost half of schools in the pilot said the check identified pupils with reading difficulties of which they were not previously aware.
To support teachers in developing their phonics teaching and ensuring all pupils learn the basics of reading, we are offering match-funding of up to £3000 to help schools buy high quality systematic synthetic phonics resources and training.
From September, a thorough understanding of the teaching of systematic synthetic phonics will be prioritised in teacher training and required for all teachers of early reading.
And phonics and reading are becoming a key part of the new Ofsted inspection framework. For the first time, Ofsted inspectors will focus on the teaching of reading in primary schools and listen to pupils reading aloud, with a particular focus on weaker readers.
But, of course, mastering the mechanics of reading is a necessary but not sufficient condition for being a confident reader. We need to do more to encourage children to read for pleasure and to develop a life-long love of reading.
I remember a few years ago coming back from Finland. In the departure lounge at Helsinki airport it was noticeable how many children and young people were passing the time glued to novels - something not so prevalent at Heathrow and Gatwick.
And a 2009 PISA study shows that almost 40 per cent of pupils in England never read for pleasure - yet the difference in reading ability between these pupils and those who read for just half an hour a day is equivalent to a year’s schooling at age 15. A recent survey by the National Literacy Trust showed that a third of British children do not even own a book.
We are currently developing a national competition to encourage 9-12 year-olds to read voraciously at school and for pleasure at home. Instilling the habit of regular reading at an early age I believe is key to developing a life-long love of reading for pleasure, and we’ll have more to say about that later this year.
2012 is also, of course, the year of the London Olympic and Paralympic Games. I know that this conference has been considering the role of sport in education over the last few days - and the Government is also working hard to make the most of this opportunity.
The advantages of competitive sport are well-known - particularly the benefits for pupils’ health and fitness, social skills and personal development.
Sport teaches young people commitment, dedication, how to work well in a team and how to perform as an individual. Young sportsmen and women quickly learn the importance of fair play - to be magnanimous in victory and gracious in defeat.
Yet only two in five young people currently play regular competitive sport within their own school. Only one in five plays regularly against other schools.
As a result of close collaboration between the Departments for Education, Health and Culture, Media and Sport, and Sport England, the Youth Sport Trust and ParalympicsGB, this year will introduce a new national sporting competition - the School Games.
Building on the excitement and enthusiasm around London 2012, we hope that the School Games will inspire a whole generation of young people to get involved in competitive sport.
There will be opportunities for more competition within and between schools, and at county and district level.
The School Games will culminate in national finals between the country’s best young athletes, and the first of these will take place in May at the Olympic Park. So far, almost 11,000 schools have signed up to take part in this competition.
So by increasing autonomy and reducing bureaucracy at school level, allowing more schools to take advantage of academy freedoms and focusing particularly on reading and school sport, we hope to drive up standards for all children, from all backgrounds.
A PISA study found that England has one of the largest gaps in the world between high and low performing pupils, and a strong relationship between social background and performance. 13.9 per cent of the variance in pupil performance in England can be explained by social background, compared to just 8.3 per cent in Finland and 8.2 per cent in Canada. Yet in countries like Finland, Canada, Japan and Korea, average standards are higher than ours, and achievement gaps are smaller.
A recent report from the OECD also showed that deprived pupils in this country perform significantly less well than deprived pupils in most OECD countries - putting us 39th out of 65 countries. According to PISA, just a quarter of pupils from poor backgrounds are “resilient” in the UK, compared to three-quarters in Shanghai-China and Hong Kong.
To put it another way, research published by the Department for Education last year showed that, if English children performed as well as their peers in Shanghai, 77 per cent would get five GCSEs at A*-C including English and Maths, rather than the 55 per cent that we achieve now. That’s a difference of a fifth of the whole cohort - 100,000 children failing to achieve the qualifications that most employers see as the bare minimum.
And the gap in achievement between children from the wealthiest and poorest backgrounds is still too wide in English schools. As I just said, in 2010, 55 per cent of children achieved five GCSEs at A*-C including English and Maths - but only 31 per cent of pupils on free school meals managed to do the same. And that gap between children from privileged and disadvantaged backgrounds remains stubbornly wide.
International evidence shows us that it is possible for many more young people to achieve more highly than they do now. It is possible to narrow the achievement gap between the richest and the poorest. And this is not an either/or: it is possible to achieve both at once.
By learning from international and domestic evidence, helping the best schools to share excellent practice and supporting schools which are struggling, we want to give every child, from any background, the opportunity to make the most of their talents. Thank you.
Ends
Published: 6 January 2012


--- title: Adagrad optimizer. type: docs repo: https://github.com/rstudio/keras menu: main: name: Adagrad optimizer. - keras parent: keras-reference aliases: - /reference/keras/optimizer_adagrad.html - /keras/reference/optimizer_adagrad.html - /guide/keras/reference/optimizer_adagrad.html - /tools/tools/keras/reference/optimizer_adagrad.html - /installation/keras/reference/optimizer_adagrad.html - /tutorials/keras/reference/optimizer_adagrad.html - /guide/tools/keras/reference/optimizer_adagrad.html - /deploy/keras/reference/optimizer_adagrad.html - /tools/keras/reference/optimizer_adagrad.html - /tutorials/tools/keras/reference/optimizer_adagrad.html ---
Adagrad optimizer as described in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
optimizer_adagrad(
lr = 0.01,
epsilon = NULL,
decay = 0,
clipnorm = NULL,
clipvalue = NULL
)
| lr | float >= 0. Learning rate. |
| epsilon | float >= 0. Fuzz factor. If |
| decay | float >= 0. Learning rate decay over each update. |
| clipnorm | Gradients will be clipped when their L2 norm exceeds this value. |
| clipvalue | Gradients will be clipped when their absolute value exceeds this value. |
It is recommended to leave the parameters of this optimizer at their default values.
Other optimizers:
optimizer_adadelta()
,
optimizer_adamax()
,
optimizer_adam()
,
optimizer_nadam()
,
optimizer_rmsprop()
,
optimizer_sgd()


% include('head.tpl')
% include('navbar.tpl', page="Music")
% include('hero.tpl', actiontext="Go home?", actionlink="/", text="All songs on this page are stored in a SQL database.
Each song is represented by id, title, artist, album, artwork, and audio preview link.")
% include('songgrid.tpl')
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Accusantium aliquid consequatur deleniti, impedit provident tempora tempore vitae? Culpa illum, ipsum? Start a conversation


| Home | Trees | Indices | Help |
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ARDRegressionScikitsLearnNode Bayesian ARD regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Fit the weights of a regression model, using an ARD prior. The weights of
the regression model are assumed to be in Gaussian distributions.
Also estimate the parameters lambda (precisions of the distributions of the
weights) and alpha (precision of the distribution of the noise).
The estimation is done by an iterative procedures (Evidence Maximization)
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AdaptiveCutoffNode Node which uses the data history during training to learn cutoff values. |
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BayesianRidgeScikitsLearnNode Bayesian ridge regression This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.BayesianRidge class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Fit a Bayesian ridge model and optimize the regularization parameters
lambda (precision of the weights) and alpha (precision of the noise).
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BinarizerScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.preprocessing.sparse.Binarizer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
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CCAScikitsLearnNode CCA Canonical Correlation Analysis. |
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CCIPCANode Candid-Covariance free Incremental Principal Component Analysis (CCIPCA) extracts the principal components from the input data incrementally. |
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CCIPCAWhiteningNode Incrementally updates whitening vectors for the input data using CCIPCA. |
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Convolution2DNode Convolve input data with filter banks. |
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CountVectorizerScikitsLearnNode Convert a collection of raw documents to a matrix of token counts This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.CountVectorizer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
This implementation produces a sparse representation of the counts using
scipy.sparse.coo_matrix.
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CuBICANode Perform Independent Component Analysis using the CuBICA algorithm. |
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CutoffNode Node to cut off values at specified bounds. |
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DiscreteHopfieldClassifier Node for simulating a simple discrete Hopfield model |
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ElasticNetCVScikitsLearnNode Elastic Net model with iterative fitting along a regularization path This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.ElasticNetCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
The best model is selected by cross-validation.
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ElasticNetScikitsLearnNode Linear Model trained with L1 and L2 prior as regularizer This node has been automatically generated by wrapping the ``scikits.learn.linear_model.coordinate_descent.ElasticNet`` class from the ``sklearn`` library. |
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EtaComputerNode Compute the eta values of the normalized training data. |
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FANode Perform Factor Analysis. |
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FDANode Perform a (generalized) Fisher Discriminant Analysis of its input. It is a supervised node that implements FDA using a generalized eigenvalue approach. |
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FastICANode Perform Independent Component Analysis using the FastICA algorithm. |
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FastICAScikitsLearnNode FastICA; a fast algorithm for Independent Component Analysis This node has been automatically generated by wrapping the scikits.learn.decomposition.fastica_.FastICA class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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GMMHMMScikitsLearnNode Hidden Markov Model with Gaussin mixture emissions This node has been automatically generated by wrapping the scikits.learn.hmm.GMMHMM class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Attributes
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GMMScikitsLearnNode Gaussian Mixture Model This node has been automatically generated by wrapping the scikits.learn.mixture.GMM class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Representation of a Gaussian mixture model probability distribution.
This class allows for easy evaluation of, sampling from, and
maximum-likelihood estimation of the parameters of a GMM distribution.
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GNBScikitsLearnNode Gaussian Naive Bayes (GNB) This node has been automatically generated by wrapping the scikits.learn.naive_bayes.GNB class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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GSFANode This node implements "Graph-Based SFA (GSFA)", which is the main component of hierarchical GSFA (HGSFA). |
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GaussianClassifier Perform a supervised Gaussian classification. |
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GaussianHMMScikitsLearnNode Hidden Markov Model with Gaussian emissions This node has been automatically generated by wrapping the scikits.learn.hmm.GaussianHMM class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Representation of a hidden Markov model probability distribution.
This class allows for easy evaluation of, sampling from, and
maximum-likelihood estimation of the parameters of a HMM.
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GaussianProcessScikitsLearnNode The Gaussian Process model class. This node has been automatically generated by wrapping the scikits.learn.gaussian_process.gaussian_process.GaussianProcess class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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GeneralExpansionNode Expands the input samples by applying to them one or more functions provided. |
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| GenericUnivariateSelectScikitsLearnNode | |||
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GrowingNeuralGasExpansionNode Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas. |
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GrowingNeuralGasNode Learn the topological structure of the input data by building a corresponding graph approximation. |
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HLLENode Perform a Hessian Locally Linear Embedding analysis on the data. |
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HistogramNode Node which stores a history of the data during its training phase. |
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HitParadeNode Collect the first n local maxima and minima of the training signal
which are separated by a minimum gap d .
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ICANode ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis. |
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ISFANode Perform Independent Slow Feature Analysis on the input data. |
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IdentityNode Execute returns the input data and the node is not trainable. |
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IncSFANode Incremental Slow Feature Analysis (IncSFA) extracts the slowly varying components from the input data incrementally. |
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JADENode Perform Independent Component Analysis using the JADE algorithm. |
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KMeansClassifier Employs K-Means Clustering for a given number of centroids. |
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KNNClassifier K-Nearest-Neighbour Classifier. |
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KernelCentererScikitsLearnNode Centers a kernel. This is equivalent to centering phi(X) with This node has been automatically generated by wrapping the scikits.learn.preprocessing.KernelCenterer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
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KernelPCAScikitsLearnNode Kernel Principal component analysis (KPCA) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.KernelPCA`` class from the ``sklearn`` library. |
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LARSScikitsLearnNode Least Angle Regression model a.k.a. LAR This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LARS class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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LDAScikitsLearnNode Linear Discriminant Analysis (LDA) This node has been automatically generated by wrapping the scikits.learn.lda.LDA class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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LLENode Perform a Locally Linear Embedding analysis on the data. |
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LabelBinarizerScikitsLearnNode Binarize labels in a one-vs-all fashion. This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Several regression and binary classification algorithms are available in the
scikit. A simple way to extend these algorithms to the multi-class
classification case is to use the so-called one-vs-all scheme.
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LassoCVScikitsLearnNode Lasso linear model with iterative fitting along a regularization path This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.LassoCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
The best model is selected by cross-validation.
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LassoLARSScikitsLearnNode Lasso model fit with Least Angle Regression a.k.a. LARS This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LassoLARS class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
It is a Linear Model trained with an L1 prior as regularizer.
lasso).
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LassoScikitsLearnNode Linear Model trained with L1 prior as regularizer (aka the Lasso) This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.Lasso class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Technically the Lasso model is optimizing the same objective function as
the Elastic Net with rho=1.0 (no L2 penalty).
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| LengthNormalizerScikitsLearnNode | |||
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LinearModelCVScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.linear_model.coordinate_descent.LinearModelCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
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LinearRegressionNode Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that the linear combination
y_i = b_0 + b_1 x_1 + ... b_N x_N , for i = 1 ... M , minimizes
the sum of squared error given the training x 's and y 's.
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LinearRegressionScikitsLearnNode Ordinary least squares Linear Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.base.LinearRegression class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Attributes
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LinearSVCScikitsLearnNode Linear Support Vector Classification, Sparse Version This node has been automatically generated by wrapping the scikits.learn.svm.sparse.classes.LinearSVC class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Similar to SVC with parameter kernel='linear', but uses internally
liblinear rather than libsvm, so it has more flexibility in the
choice of penalties and loss functions and should be faster for
huge datasets.
|
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LogisticRegressionScikitsLearnNode Logistic Regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.logistic.LogisticRegression class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Implements L1 and L2 regularized logistic regression.
|
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MCANode Minor Component Analysis (MCA) extracts minor components (dual of principal components) from the input data incrementally. |
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MultinomialHMMScikitsLearnNode Hidden Markov Model with multinomial (discrete) emissions This node has been automatically generated by wrapping the scikits.learn.hmm.MultinomialHMM class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Attributes
|
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NIPALSNode Perform Principal Component Analysis using the NIPALS algorithm. |
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NMFScikitsLearnNode Non-Negative matrix factorization by Projected Gradient (NMF) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.nmf.NMF`` class from the ``sklearn`` library. |
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NearestMeanClassifier Nearest-Mean classifier. |
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NeighborsClassifierScikitsLearnNode Classifier implementing k-Nearest Neighbor Algorithm. This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsClassifier class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
|||
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NeighborsRegressorScikitsLearnNode Regression based on k-Nearest Neighbor Algorithm This node has been automatically generated by wrapping the scikits.learn.neighbors.NeighborsRegressor class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
The target is predicted by local interpolation of the targets
associated of the k-Nearest Neighbors in the training set.
|
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NeuralGasNode Learn the topological structure of the input data by building a corresponding graph approximation (original Neural Gas algorithm). |
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NoiseNode Inject multiplicative or additive noise into the input data. |
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NormalNoiseNode Special version of NoiseNode for Gaussian additive noise. |
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NormalizeNode Make input signal meanfree and unit variance. |
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NormalizerScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.preprocessing.Normalizer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
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NormalizingRecursiveExpansionNode Recursively computable (orthogonal) expansions and a trainable transformation to the domain of the expansions. |
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|
NuSVCScikitsLearnNode NuSVC for sparse matrices (csr). |
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NuSVRScikitsLearnNode NuSVR for sparse matrices (csr) This node has been automatically generated by wrapping the ``scikits.learn.svm.sparse.classes.NuSVR`` class from the ``sklearn`` library. |
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OneClassSVMScikitsLearnNode Unsupervised Outliers Detection. This node has been automatically generated by wrapping the scikits.learn.svm.classes.OneClassSVM class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Estimate the support of a high-dimensional distribution.
|
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OnlineCenteringNode OnlineCenteringNode centers the input data, that is, subtracts the arithmetic mean (average) from the input data. This is an online learnable node. |
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OnlineTimeDiffNode Compute the discrete time derivative of the input using backward difference approximation: |
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PCANode Filter the input data through the most significatives of its principal components. |
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PCAScikitsLearnNode Principal component analysis (PCA) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.PCA`` class from the ``sklearn`` library. |
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PLSCanonicalScikitsLearnNode PLS canonical. |
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PLSRegressionScikitsLearnNode PLS regression (Also known PLS2 or PLS in case of one dimensional response). |
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PLSSVDScikitsLearnNode Partial Least Square SVD This node has been automatically generated by wrapping the ``scikits.learn.pls.PLSSVD`` class from the ``sklearn`` library. |
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PerceptronClassifier A simple perceptron with input_dim input nodes. |
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PolynomialExpansionNode Perform expansion in a polynomial space. |
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ProbabilisticPCAScikitsLearnNode Additional layer on top of PCA that adds a probabilistic evaluation This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.ProbabilisticPCA`` class from the ``sklearn`` library. |
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ProjectedGradientNMFScikitsLearnNode Non-Negative matrix factorization by Projected Gradient (NMF) This node has been automatically generated by wrapping the ``scikits.learn.decomposition.nmf.ProjectedGradientNMF`` class from the ``sklearn`` library. |
|||
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QDAScikitsLearnNode Quadratic Discriminant Analysis (QDA) This node has been automatically generated by wrapping the scikits.learn.qda.QDA class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
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QuadraticExpansionNode Perform expansion in the space formed by all linear and quadratic monomials. QuadraticExpansionNode() is equivalent to a PolynomialExpansionNode(2) |
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RBFExpansionNode Expand input space with Gaussian Radial Basis Functions (RBFs). |
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RBMNode Restricted Boltzmann Machine node. An RBM is an undirected probabilistic network with binary variables. The graph is bipartite into observed (visible) and hidden (latent) variables. By default, the execute method returns the probability of one of the hiden variables being equal to 1 given the input. Use the sample_v method to sample from the observed variables given a setting of the hidden variables, and sample_h to do the opposite. The energy method can be used to compute the energy of a given setting of all variables. |
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RBMWithLabelsNode Restricted Boltzmann Machine with softmax labels. An RBM is an undirected probabilistic network with binary variables. In this case, the node is partitioned into a set of observed (visible) variables, a set of hidden (latent) variables, and a set of label variables (also observed), only one of which is active at any time. The node is able to learn associations between the visible variables and the labels. By default, the execute method returns the probability of one of the hiden variables being equal to 1 given the input. Use the sample_v method to sample from the observed variables (visible and labels) given a setting of the hidden variables, and sample_h to do the opposite. The energy method can be used to compute the energy of a given setting of all variables. |
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RFECVScikitsLearnNode Feature ranking with Recursive feature elimination and cross validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
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RFEScikitsLearnNode Feature ranking with Recursive feature elimination This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFE class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
|||
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RandomizedPCAScikitsLearnNode Principal component analysis (PCA) using randomized SVD This node has been automatically generated by wrapping the ``scikits.learn.decomposition.pca.RandomizedPCA`` class from the ``sklearn`` library. |
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RecursiveExpansionNode Recursively computable (orthogonal) expansions. |
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RidgeCVScikitsLearnNode Ridge regression with built-in cross-validation. This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.RidgeCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation. Currently, only the n_features >
n_samples case is handled efficiently.
|
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| RidgeClassifierCVScikitsLearnNode | |||
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RidgeClassifierScikitsLearnNode Classifier using Ridge regression This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.RidgeClassifier class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
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RidgeScikitsLearnNode Ridge regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.ridge.Ridge class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
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SFA2Node Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components. |
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SFANode Extract the slowly varying components from the input data. |
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SGDClassifierScikitsLearnNode Linear model fitted by minimizing a regularized empirical loss with SGD. |
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SGDRegressorScikitsLearnNode Linear model fitted by minimizing a regularized empirical loss with SGD This node has been automatically generated by wrapping the scikits.learn.linear_model.sparse.stochastic_gradient.SGDRegressor class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
|
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SVCScikitsLearnNode C-Support Vector Classification. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
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SVRScikitsLearnNode epsilon-Support Vector Regression. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVR class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
The free parameters in the model are C and epsilon.
|
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ScalerScikitsLearnNode Object to standardize a dataset This node has been automatically generated by wrapping the scikits.learn.preprocessing.Scaler class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
It centers the dataset and optionaly scales to fix the variance to 1 for
each feature
|
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|
SelectFdrScikitsLearnNode Filter : Select the p-values corresponding to an estimated false This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFdr class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
|||
|
SelectFprScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFpr class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
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SelectFweScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectFwe class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
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SelectKBestScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectKBest class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
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SelectPercentileScikitsLearnNode This node has been automatically generated by wrapping the scikits.learn.feature_selection.univariate_selection.SelectPercentile class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
|
|||
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SignumClassifier This classifier node classifies as 1 if the sum of the data points
is positive and as -1 if the data point is negative.
|
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SimpleMarkovClassifier A simple version of a Markov classifier. |
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| SparseBaseLibLinearScikitsLearnNode | |||
| SparseBaseLibSVMScikitsLearnNode | |||
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TDSEPNode Perform Independent Component Analysis using the TDSEP algorithm. |
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TfidfTransformerScikitsLearnNode Transform a count matrix to a TF or TF-IDF representation This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.TfidfTransformer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
TF means term-frequency while TF-IDF means term-frequency times inverse
document-frequency:
|
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TimeDelayNode Copy delayed version of the input signal on the space dimensions. |
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TimeDelaySlidingWindowNode TimeDelaySlidingWindowNode is an alternative to TimeDelayNode which should be used for online learning/execution. Whereas the TimeDelayNode works in a batch manner, for online application a sliding window is necessary which yields only one row per call. |
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TimeFramesNode Copy delayed version of the input signal on the space dimensions. |
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VartimeSFANode Extract the slowly varying components from the input data. This node can be understood as a generalization of the SFANode that allows non-constant time increments between samples. |
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VectorizerScikitsLearnNode Convert a collection of raw documents to a matrix This node has been automatically generated by wrapping the scikits.learn.feature_extraction.text.Vectorizer class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Equivalent to CountVectorizer followed by TfidfTransformer.
|
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|
WardAgglomerationScikitsLearnNode Feature agglomeration based on Ward hierarchical clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.
Parameters
|
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WhiteningNode Whiten the input data by filtering it through the most significant of its principal components. |
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XSFANode Perform Non-linear Blind Source Separation using Slow Feature Analysis. This node is designed to iteratively extract statistically independent sources from (in principle) arbitrary invertible nonlinear mixtures. The method relies on temporal correlations in the sources and consists of a combination of nonlinear SFA and a projection algorithm. More details can be found in the reference given below (once it's published). |
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_OneDimensionalHitParade Class to produce hit-parades (i.e., a list of the locally largest and smallest values) out of a one-dimensional time-series. |
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iGSFANode This node implements "information-preserving graph-based SFA (iGSFA)", which is the main component of hierarchical iGSFA (HiGSFA). |
|
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| int |
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__package__ =
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nvariables after
a polynomial expansion of degree degree .
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__package__
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| Home | Trees | Indices | Help |
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|---|
| Generated by Epydoc 3.0.1-MDP on Mon Apr 27 21:56:15 2020 | http://epydoc.sourceforge.net |


three.js
- platformer demo. cubemap by
Jochum Skoglund
.
Use arrow keys to look around, WASD to move and SPACE to jump.


Group Id: 42
home
Home
trending_up
Trending
history
Corona Analysis
location_ons
Maps
groups
About us
Covid-19 Sentimental Analysis
Toggle navigation
{{ map|safe }}


We know the secret of your success
Use a past defeat as a motivator. Remind yourself you have nowhere to go except up as you have already been at the bottom.


Many Congress leaders join JD(S) in K.R. Pet
by Special CorrespondentMany Congress workers joined the Janata Dal (Secular) in K.R. Pet on Tuesday promising that they would work for the victory of B.L. Devaraj in the byelections to K.R. Pet Assembly segment on December 5.
Former Ministers H.D. Revanna and C.S. Puttaraju welcomed them by offering them JD(S) flags. Prominent among those who quit the Congress is M.B. Harish, former president, Mandya Milk Union Ltd. (MANMUL). He was a hardcore loyalist of former Minister and Congress leader N. Cheluvarayaswamy.
According to him, the Congress nominee in the elections, K.B. Chandrashekar, had worked against him in the recently held elections to the MANMUL and that had prompted him to leave the Congress.


[JDF-1] Listing untrue differences between xmlwriter 2.1 and 2.2 | |
| Status: | Resolved |
| Project: | JarDiff |
| Component/s: | |
| Affects Version/s: | |
| Fix Version/s: |
| Type: | Bug | Priority: | Major |
| Reporter: | Henri Yandell | Assignee: | Antony Riley |
| Resolution: | Won't Fix | ||
| Environment: |
| Description |
| The diff report has lots of diffs for AbstractXmlWriter; a class that doesn't seem to have changed: http://dist.osjava.org/releases/multidoc-jnr/xmlwriter/diff-report-2.2_2.1.html Based on jars: http://dist.osjava.org/maven/xmlwriter/jars/xmlwriter-2.1.jar and http://dist.osjava.org/maven/xmlwriter/jars/xmlwriter-2.2.jar |
| Comment by antony [ Mon, 13 Feb 2006 09:30:54 -0800 (PST) ] |
| This is due to a compiler bug. Whatever compiler was used to compile xmlwriter 2.1 did not mark the methods it inserted as synthetic in AbstractXmlWriter. If the compiler doesn't insert the correct information at compile time, there is no way this can be reconstructed by jardiff. |


Contents
This chapter is informative. UAs are not required to implement the properties of this chapter in order to conform to CSS 2.1.
We expect that in a future level of CSS there will be new properties and values defined for speech output. Therefore CSS 2.1 reserves the 'speech' media type (see chapter 7, "Media types"), but does not yet define which properties do or do not apply to it.
The properties in this appendix apply to a media type 'aural', that was introduced in CSS2. The type 'aural' is now deprecated.
This means that a style sheet such as
@media speech { body { voice-family: Paul } }
is valid, but that its meaning is not defined by CSS 2.1, while
@media aural { body { voice-family: Paul } }
is deprecated, but defined by this appendix.
The aural rendering of a document, already commonly used by the blind and print-impaired communities, combines speech synthesis and "auditory icons." Often such aural presentation occurs by converting the document to plain text and feeding this to a screen reader -- software or hardware that simply reads all the characters on the screen. This results in less effective presentation than would be the case if the document structure were retained. Style sheet properties for aural presentation may be used together with visual properties (mixed media) or as an aural alternative to visual presentation.
Besides the obvious accessibility advantages, there are other large markets for listening to information, including in-car use, industrial and medical documentation systems (intranets), home entertainment, and to help users learning to read or who have difficulty reading.
When using aural properties, the canvas consists of a three-dimensional physical space (sound surrounds) and a temporal space (one may specify sounds before, during, and after other sounds). The CSS properties also allow authors to vary the quality of synthesized speech (voice type, frequency, inflection, etc.).
h1, h2, h3, h4, h5, h6 { voice-family: paul; stress: 20; richness: 90; cue-before: url("ping.au") } p.heidi { azimuth: center-left } p.peter { azimuth: right } p.goat { volume: x-soft }
This will direct the speech synthesizer to speak headers in a voice (a kind of "audio font") called "paul", on a flat tone, but in a very rich voice. Before speaking the headers, a sound sample will be played from the given URL. Paragraphs with class "heidi" will appear to come from front left (if the sound system is capable of spatial audio), and paragraphs of class "peter" from the right. Paragraphs with class "goat" will be very soft.
Angle values are denoted by <angle> in the text. Their format is a <number> immediately followed by an angle unit identifier.
Angle unit identifiers are:
Angle values may be negative. They should be normalized to the range 0-360deg by the user agent. For example, -10deg and 350deg are equivalent.
For example, a right angle is '90deg' or '100grad' or '1.570796326794897rad'.
Like for <length>, the unit may be omitted, if the value is zero: '0deg' may be written as '0'.
Time values are denoted by <time> in the text. Their format is a <number> immediately followed by a time unit identifier.
Time unit identifiers are:
Time values may not be negative.
Like for <length>, the unit may be omitted, if the value is zero: '0s' may be written as '0'.
Frequency values are denoted by <frequency> in the text. Their format is a <number> immediately followed by a frequency unit identifier.
Frequency unit identifiers are:
Frequency values may not be negative.
For example, 200Hz (or 200hz) is a bass sound, and 6kHz is a treble sound.
Like for <length>, the unit may be omitted, if the value is zero: '0Hz' may be written as '0'.
| Value: | <number> | <percentage> | silent | x-soft | soft | medium | loud | x-loud | inherit |
| Initial: | medium |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | refer to inherited value |
| Media: | aural |
| Computed value: | number |
Volume refers to the median volume of the waveform. In other words, a highly inflected voice at a volume of 50 might peak well above that. The overall values are likely to be human adjustable for comfort, for example with a physical volume control (which would increase both the 0 and 100 values proportionately); what this property does is adjust the dynamic range.
Values have the following meanings:
User agents should allow the values corresponding to '0' and '100' to be set by the listener. No one setting is universally applicable; suitable values depend on the equipment in use (speakers, headphones), the environment (in car, home theater, library) and personal preferences. Some examples:
The same author style sheet could be used in all cases, simply by mapping the '0' and '100' points suitably at the client side.
This property specifies whether text will be rendered aurally and if so, in what manner. The possible values are:
Note the difference between an element whose 'volume' property has a value of 'silent' and an element whose 'speak' property has the value 'none'. The former takes up the same time as if it had been spoken, including any pause before and after the element, but no sound is generated. The latter requires no time and is not rendered (though its descendants may be).
| Value: | <time> | <percentage> | inherit |
| Initial: | 0 |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | see prose |
| Media: | aural |
| Computed value: | time |
| Value: | <time> | <percentage> | inherit |
| Initial: | 0 |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | see prose |
| Media: | aural |
| Computed value: | time;; |
These properties specify a pause to be observed before (or after) speaking an element's content. Values have the following meanings:
Note. In CSS3 pauses are inserted around the cues and content rather than between them. See [CSS3SPEECH] for details.
The pause is inserted between the element's content and any 'cue-before' or 'cue-after' content.
Authors should use relative units to create more robust style sheets in the face of large changes in speech-rate.
| Value: | [ [<time> | <percentage>]{1,2} ] | inherit |
| Initial: | see individual properties |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | see descriptions of 'pause-before' and 'pause-after' |
| Media: | aural |
| Computed value: | see individual properties |
The 'pause' property is a shorthand for setting 'pause-before' and 'pause-after'. If two values are given, the first value is 'pause-before' and the second is 'pause-after'. If only one value is given, it applies to both properties.
h1 { pause: 20ms } /* pause-before: 20ms; pause-after: 20ms */ h2 { pause: 30ms 40ms } /* pause-before: 30ms; pause-after: 40ms */ h3 { pause-after: 10ms } /* pause-before unspecified; pause-after: 10ms */
| Value: | <uri> | none | inherit |
| Initial: | none |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | absolute URI or 'none' |
| Value: | <uri> | none | inherit |
| Initial: | none |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | absolute URI or 'none' |
Auditory icons are another way to distinguish semantic elements. Sounds may be played before and/or after the element to delimit it. Values have the following meanings:
a {cue-before: url("bell.aiff"); cue-after: url("dong.wav") } h1 {cue-before: url("pop.au"); cue-after: url("pop.au") }
| Value: | [ <'cue-before'> || <'cue-after'> ] | inherit |
| Initial: | see individual properties |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | see individual properties |
The 'cue' property is a shorthand for setting 'cue-before' and 'cue-after'. If two values are given, the first value is 'cue-before' and the second is 'cue-after'. If only one value is given, it applies to both properties.
The following two rules are equivalent:
h1 {cue-before: url("pop.au"); cue-after: url("pop.au") } h1 {cue: url("pop.au") }
If a user agent cannot render an auditory icon (e.g., the user's environment does not permit it), we recommend that it produce an alternative cue.
Please see the sections on the :before and :after pseudo-elements for information on other content generation techniques. 'Cue-before' sounds and 'pause-before' gaps are inserted before content from the ':before' pseudo-element. Similarly, 'pause-after' gaps and 'cue-after' sounds are inserted after content from the ':after' pseudo-element.
| Value: | <uri> [ mix || repeat ]? | auto | none | inherit |
| Initial: | auto |
| Applies to: | all elements |
| Inherited: | no |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | absolute URI, rest as specified |
Similar to the 'cue-before' and 'cue-after' properties, this property specifies a sound to be played as a background while an element's content is spoken. Values have the following meanings:
blockquote.sad { play-during: url("violins.aiff") } blockquote Q { play-during: url("harp.wav") mix } span.quiet { play-during: none }
Spatial audio is an important stylistic property for aural presentation. It provides a natural way to tell several voices apart, as in real life (people rarely all stand in the same spot in a room). Stereo speakers produce a lateral sound stage. Binaural headphones or the increasingly popular 5-speaker home theater setups can generate full surround sound, and multi-speaker setups can create a true three-dimensional sound stage. VRML 2.0 also includes spatial audio, which implies that in time consumer-priced spatial audio hardware will become more widely available.
Values have the following meanings:
This property is most likely to be implemented by mixing the same signal into different channels at differing volumes. It might also use phase shifting, digital delay, and other such techniques to provide the illusion of a sound stage. The precise means used to achieve this effect and the number of speakers used to do so are user agent-dependent; this property merely identifies the desired end result.
h1 { azimuth: 30deg } td.a { azimuth: far-right } /* 60deg */ #12 { azimuth: behind far-right } /* 120deg */ p.comment { azimuth: behind } /* 180deg */
If spatial-azimuth is specified and the output device cannot produce sounds behind the listening position, user agents should convert values in the rearwards hemisphere to forwards hemisphere values. One method is as follows:
| Value: | <angle> | below | level | above | higher | lower | inherit |
| Initial: | level |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | normalized angle |
Values of this property have the following meanings:
The precise means used to achieve this effect and the number of speakers used to do so are undefined. This property merely identifies the desired end result.
h1 { elevation: above } tr.a { elevation: 60deg } tr.b { elevation: 30deg } tr.c { elevation: level }
| Value: | <number> | x-slow | slow | medium | fast | x-fast | faster | slower | inherit |
| Initial: | medium |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | number |
This property specifies the speaking rate. Note that both absolute and relative keyword values are allowed (compare with 'font-size'). Values have the following meanings:
| Value: | [[<specific-voice> | <generic-voice> ],]* [<specific-voice> | <generic-voice> ] | inherit |
| Initial: | depends on user agent |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
The value is a comma-separated, prioritized list of voice family names (compare with 'font-family'). Values have the following meanings:
h1 { voice-family: announcer, male } p.part.romeo { voice-family: romeo, male } p.part.juliet { voice-family: juliet, female }
Names of specific voices may be quoted, and indeed must be quoted if any of the words that make up the name does not conform to the syntax rules for identifiers. It is also recommended to quote specific voices with a name consisting of more than one word. If quoting is omitted, any white space characters before and after the voice family name are ignored and any sequence of white space characters inside the voice family name is converted to a single space.
| Value: | <frequency> | x-low | low | medium | high | x-high | inherit |
| Initial: | medium |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | frequency |
Specifies the average pitch (a frequency) of the speaking voice. The average pitch of a voice depends on the voice family. For example, the average pitch for a standard male voice is around 120Hz, but for a female voice, it's around 210Hz.
Values have the following meanings:
| Value: | <number> | inherit |
| Initial: | 50 |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
Specifies variation in average pitch. The perceived pitch of a human voice is determined by the fundamental frequency and typically has a value of 120Hz for a male voice and 210Hz for a female voice. Human languages are spoken with varying inflection and pitch; these variations convey additional meaning and emphasis. Thus, a highly animated voice, i.e., one that is heavily inflected, displays a high pitch range. This property specifies the range over which these variations occur, i.e., how much the fundamental frequency may deviate from the average pitch.
Values have the following meanings:
Specifies the height of "local peaks" in the intonation contour of a voice. For example, English is a stressed language, and different parts of a sentence are assigned primary, secondary, or tertiary stress. The value of 'stress' controls the amount of inflection that results from these stress markers. This property is a companion to the 'pitch-range' property and is provided to allow developers to exploit higher-end auditory displays.
Values have the following meanings:
| Value: | <number> | inherit |
| Initial: | 50 |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
Specifies the richness, or brightness, of the speaking voice. A rich voice will "carry" in a large room, a smooth voice will not. (The term "smooth" refers to how the wave form looks when drawn.)
Values have the following meanings:
An additional speech property, 'speak-header', is described below.
| Value: | code | none | inherit |
| Initial: | none |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
This property specifies how punctuation is spoken. Values have the following meanings:
| Value: | digits | continuous | inherit |
| Initial: | continuous |
| Applies to: | all elements |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
This property controls how numerals are spoken. Values have the following meanings:
When a table is spoken by a speech generator, the relation between the data cells and the header cells must be expressed in a different way than by horizontal and vertical alignment. Some speech browsers may allow a user to move around in the 2-dimensional space, thus giving them the opportunity to map out the spatially represented relations. When that is not possible, the style sheet must specify at which points the headers are spoken.
| Value: | once | always | inherit |
| Initial: | once |
| Applies to: | elements that have table header information |
| Inherited: | yes |
| Percentages: | N/A |
| Media: | aural |
| Computed value: | as specified |
This property specifies whether table headers are spoken before every cell, or only before a cell when that cell is associated with a different header than the previous cell. Values have the following meanings:
Each document language may have different mechanisms that allow authors to specify headers. For example, in HTML 4 ([HTML4]), it is possible to specify header information with three different attributes ("headers", "scope", and "axis"), and the specification gives an algorithm for determining header information when these attributes have not been specified.
This HTML example presents the money spent on meals, hotels and transport in two locations (San Jose and Seattle) for successive days. Conceptually, you can think of the table in terms of an n-dimensional space. The headers of this space are: location, day, category and subtotal. Some cells define marks along an axis while others give money spent at points within this space. The markup for this table is:

Travel Expense Report

 	 Meals	 Hotels	 Transport	 subtotal

 San Jose

 25-Aug-97	 37.74	 112.00	 45.00

 26-Aug-97	 27.28	 112.00	 45.00

 subtotal	 65.02	 224.00	 90.00	 379.02

 Seattle

 27-Aug-97	 96.25	 109.00	 36.00

 28-Aug-97	 35.00	 109.00	 36.00

 subtotal	 131.25	 218.00	 72.00	 421.25

 Totals	 196.27	 442.00	 162.00	 800.27

By providing the data model in this way, authors make it possible for speech enabled-browsers to explore the table in rich ways, e.g., each cell could be spoken as a list, repeating the applicable headers before each data cell:
San Jose, 25-Aug-97, Meals: 37.74 San Jose, 25-Aug-97, Hotels: 112.00 San Jose, 25-Aug-97, Transport: 45.00 ...
The browser could also speak the headers only when they change:
San Jose, 25-Aug-97, Meals: 37.74 Hotels: 112.00 Transport: 45.00 26-Aug-97, Meals: 27.28 Hotels: 112.00 ...
This style sheet describes a possible rendering of HTML 4:
@media aural { h1, h2, h3, h4, h5, h6 { voice-family: paul, male; stress: 20; richness: 90 } h1 { pitch: x-low; pitch-range: 90 } h2 { pitch: x-low; pitch-range: 80 } h3 { pitch: low; pitch-range: 70 } h4 { pitch: medium; pitch-range: 60 } h5 { pitch: medium; pitch-range: 50 } h6 { pitch: medium; pitch-range: 40 } li, dt, dd { pitch: medium; richness: 60 } dt { stress: 80 } pre, code, tt { pitch: medium; pitch-range: 0; stress: 0; richness: 80 } em { pitch: medium; pitch-range: 60; stress: 60; richness: 50 } strong { pitch: medium; pitch-range: 60; stress: 90; richness: 90 } dfn { pitch: high; pitch-range: 60; stress: 60 } s, strike { richness: 0 } i { pitch: medium; pitch-range: 60; stress: 60; richness: 50 } b { pitch: medium; pitch-range: 60; stress: 90; richness: 90 } u { richness: 0 } a:link { voice-family: harry, male } a:visited { voice-family: betty, female } a:active { voice-family: betty, female; pitch-range: 80; pitch: x-high } }
For information, here is the list of properties implemented by Emacspeak, a speech subsystem for the Emacs editor.
(We thank T. V. Raman for the information about implementation status of aural properties.)


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