Method List
Search:
- #== GameMachine::Vector
- #actor GameMachine::Actor::Ref
- #actor_system GameMachine::Actor::System
- #actor_system GameMachine::Akka
- #actual GameMachine::Clients::TestClientProxy
- #actual= GameMachine::Clients::TestClientProxy
- #add_bucket GameMachine::Hashring
- add_cluster_member GameMachine::ClusterMonitor
- add_hashring GameMachine::Actor::Base
- add_remote_member GameMachine::ClusterMonitor
- #address GameMachine::Akka
- #address GameMachine::Actor::System
- address_for GameMachine::Akka
- akka GameMachine::Application
- #akka_config GameMachine::Akka
- app_root GameMachine
- #ask GameMachine::Actor::Ref
- aspect GameMachine::Actor::Base
- aspects GameMachine::Actor::Base
- #attack Demo::NpcController
- authenticated? GameMachine::Handlers::Authentication
- #bucket_for GameMachine::Hashring
- #buckets GameMachine::Hashring
- #cache GameMachine::WriteBehindCache
- call_dbproc GameMachine::ObjectDb
- #chat_channel GameMachine::Helpers::GameMessage
- #chat_channels GameMachine::Helpers::GameMessage
- #chat_message GameMachine::Helpers::GameMessage
- #clear GameMachine::Clients::TestClientProxy
- clear_client_connections GameMachine::PlayerRegistry
- clear_observers GameMachine::PlayerRegistry
- clear_player_ids GameMachine::PlayerRegistry
- #client GameMachine::Clients::TestClientProxy
- #client_connection GameMachine::Helpers::GameMessage
- client_connection_for GameMachine::PlayerRegistry
- #client_disconnect GameMachine::Helpers::GameMessage
- client_disconnect GameMachine::PlayerRegistry
- client_id_for GameMachine::PlayerRegistry
- #client_message GameMachine::Helpers::GameMessage
- #cluster GameMachine::Akka
- #cluster? GameMachine::Akka
- cluster_members GameMachine::ClusterMonitor
- #config GameMachine::Actor::System
- #config GameMachine::Clients::TestClientProxy
- config GameMachine::Application
- #config_name GameMachine::Akka
- #configure_logging GameMachine::Logger
- #connect GameMachine::DataStores::Couchbase
- #connect GameMachine::Clients::UdtClient
- #connect GameMachine::DataStores::Mapdb
- connect_udt GameMachine::Clients::Client
- #create GameMachine::Actor::Factory
- create GameMachine::Actor::Factory
- #create! GameMachine::Actor::System
- create_actor_ring GameMachine::Hashring
- #create_child_controller GameMachine::GameSystems::SingletonRouter
- #create_npc Demo::Game
- #create_player_controller GameMachine::GameSystems::PlayerManager
- #create_singleton_routers GameMachine::GameSystems::SingletonManager
- #ctx GameMachine::Clients::TestClientProxy
- #ctx= GameMachine::Clients::TestClientProxy
- #current_entity GameMachine::Helpers::GameMessage
- data GameMachine::GameSystems::GameData
- #dbproc GameMachine::ObjectDbProc::ClassMethods
- dbprocs GameMachine::ObjectDb
- #destroy_child_controller GameMachine::GameSystems::SingletonRouter
- #destroy_player_controller GameMachine::GameSystems::PlayerManager
- #destroy_state GameMachine::Helpers::StateMachine
- #disconnect GameMachine::Clients::UdtClient
- #distance GameMachine::Vector
- #distance_squared GameMachine::Vector
- #distributed GameMachine::Actor::Builder
- #do_expect GameMachine::Clients::TestClientProxy
- #echo GameMachine::Bot::Chat
- echo GameMachine::GameSystems::LocalEcho
- #echo_test GameMachine::Helpers::GameMessage
- #entities GameMachine::Helpers::GameMessage
- #entity GameMachine::Helpers::GameMessage
- #entity GameMachine::GameSystems::SingletonController
- #entity_with_component GameMachine::Clients::TestClientProxy
- env GameMachine
- #error_message GameMachine::Helpers::GameMessage
- find GameMachine::Actor::Base
- find_distributed GameMachine::Actor::Base
- find_distributed_local GameMachine::Actor::Base
- find_remote GameMachine::Actor::Base
- #get GameMachine::DataStores::Memory
- get GameMachine::ObjectDb
- #getEndpointUri GameMachine::Endpoints::Http::Auth
- #get_entity GameMachine::ObjectDb
- #get_neighbors GameMachine::Helpers::GameMessage
- #grid GameMachine::GameSystems::EntityTracking
- #has_entity? GameMachine::Helpers::GameMessage
- hashring GameMachine::Actor::Base
- #hashring GameMachine::Akka
- hashrings GameMachine::Actor::Base
- http_post GameMachine::Clients::Client
- image GameMachine::MonoLib
- included GameMachine::ObjectDbProc
- #init_cluster! GameMachine::Akka
- init_mono GameMachine::MonoLib
- #init_player Demo::PlayerController
- #initialize GameMachine::DataStore
- #initialize GameMachine::Clients::UdtClient
- #initialize GameMachine::Clients::TestClientProxy
- #initialize GameMachine::Daemon
- #initialize GameMachine::Actor::System
- #initialize GameMachine::Actor::Builder
- #initialize GameMachine::Actor::Ref
- #initialize GameMachine::Helpers::GameMessage
- #initialize GameMachine::Actor::Factory
- #initialize GameMachine::Vector
- #initialize GameMachine::Clients::Client
- #initialize GameMachine::Hashring
- #initialize GameMachine::DataStores::Memory
- initialize! GameMachine::Application
- #initialize! GameMachine::Akka
- #initialize_states GameMachine::Helpers::StateMachine
- #inspect GameMachine::Vector
- #interpolate GameMachine::Vector
- #join_chat GameMachine::Helpers::GameMessage
- #join_chat_groups GameMachine::Bot::Chat
- #kill_all GameMachine::Daemon
- #leave_chat GameMachine::Helpers::GameMessage
- load_from GameMachine::GameSystems::GameData
- #load_game_data Demo::Game
- load_games GameMachine::Application
- #load_state GameMachine::Helpers::StateMachine
- local_path GameMachine::Actor::Base
- #logfile GameMachine::Logger
- #logger GameMachine::Logger
- #login GameMachine::Bot::Chat
- #max_writes_per_second GameMachine::WriteBehindCache
- #move Demo::NpcController
- #name GameMachine::Clients::TestClientProxy
- #name GameMachine::Actor::Builder
- #name GameMachine::Actor::System
- #name GameMachine::Akka
- #neighbors GameMachine::GameSystems::SingletonController
- #neighbors GameMachine::Helpers::GameMessage
- #nodes GameMachine::Hashring
- notify_observers GameMachine::PlayerRegistry
- #notify_observers GameMachine::ClusterMonitor
- #notify_player_controller GameMachine::Handlers::Authentication
- obj GameMachine::MonoLib
- observers_for GameMachine::PlayerRegistry
- #onReceive GameMachine::Endpoints::Udt
- #onReceive GameMachine::Actor::Base
- #onReceive GameMachine::Endpoints::Http::Auth
- #on_receive GameMachine::WriteBehindCache
- #on_receive Demo::PlayerController
- #on_receive GameMachine::MessageQueue
- #on_receive GameMachine::Clients::TestClient
- #on_receive Demo::CombatController
- #on_receive GameMachine::Actor::Base
- #on_receive GameMachine::Handlers::Game
- #on_receive GameMachine::ObjectDb
- #on_receive GameMachine::GameSystems::PlayerManager
- #on_receive GameMachine::ClusterMonitor
- #on_receive GameMachine::GameSystems::SingletonRouter
- #on_receive GameMachine::GameSystems::Chat
- #on_receive GameMachine::Handlers::Request
- #on_receive GameMachine::Endpoints::Udp
- #on_receive GameMachine::GameSystems::SingletonManager
- #on_receive GameMachine::GameSystems::EntityTracking
- #on_receive GameMachine::MonoTest
- #on_receive GameMachine::Handlers::Authentication
- #on_receive Demo::NpcController
- #on_receive GameMachine::Scheduler
- #on_receive GameMachine::PlayerGateway
- #on_receive GameMachine::GameSystems::SingletonController
- #on_receive GameMachine::GridReplicator
- #on_receive GameMachine::GameSystems::ChatTopic
- #on_receive GameMachine::GameSystems::LocalEcho
- #on_receive GameMachine::GameSystems::RemoteEcho
- #on_receive GameMachine::GameSystems::EntityLoader
- #on_receive GameMachine::Bot::Client
- #on_receive GameMachine::GameSystems::PrivateChat
- #on_receive GameMachine::Bot::Chat
- #on_receive GameMachine::PlayerRegistry
- #on_receive GameMachine::GameSystems::ChatManager
- #on_receive GameMachine::SystemMonitor
- #path GameMachine::Actor::Ref
- #pidfile GameMachine::Daemon
- #pidfiles GameMachine::Daemon
- #player GameMachine::PlayerGateway
- player_controller GameMachine::Actor::Base
- #player_id GameMachine::Helpers::GameMessage
- player_id_for GameMachine::PlayerRegistry
- #player_logout GameMachine::Helpers::GameMessage
- player_logout GameMachine::PlayerRegistry
- #player_move GameMachine::Helpers::GameMessage
- #points GameMachine::Hashring
- #position GameMachine::GameSystems::SingletonController
- #post_init GameMachine::GridReplicator
- #post_init GameMachine::Clients::TestClient
- #post_init GameMachine::GameSystems::SingletonRouter
- #post_init GameMachine::Bot::Chat
- #post_init GameMachine::PlayerGateway
- #post_init GameMachine::GameSystems::SingletonController
- #post_init GameMachine::GameSystems::SingletonManager
- #post_init Demo::CombatController
- #post_init Demo::PlayerController
- #post_init GameMachine::GameSystems::EntityTracking
- #post_init GameMachine::SystemMonitor
- #post_init GameMachine::ObjectDb
- #post_init GameMachine::MonoTest
- #post_init GameMachine::Endpoints::Udp
- #post_init GameMachine::GameSystems::PlayerManager
- #post_init GameMachine::WriteBehindCache
- #post_init GameMachine::GameSystems::ChatTopic
- #post_init GameMachine::Bot::Client
- #post_init GameMachine::GameSystems::ChatManager
- #post_init GameMachine::GameSystems::Chat
- #preStart GameMachine::ClusterMonitor
- #preStart GameMachine::SystemMonitor
- #preStart GameMachine::MessageQueue
- #preStart GameMachine::Endpoints::Udp
- #preStart GameMachine::GameSystems::PrivateChat
- #preStart GameMachine::Endpoints::Udt
- #preStart GameMachine::Scheduler
- put GameMachine::ObjectDb
- #queue GameMachine::WriteBehindCache
- #receive GameMachine::Clients::UdtClient
- #receive_message GameMachine::Clients::Client
- #ref GameMachine::Clients::TestClientProxy
- register GameMachine::Application
- register_observer GameMachine::PlayerRegistry
- register_player GameMachine::PlayerRegistry
- #register_player GameMachine::Handlers::Authentication
- registered GameMachine::Application
- remote_members GameMachine::ClusterMonitor
- #remove_bucket GameMachine::Hashring
- remove_client_connection GameMachine::PlayerRegistry
- remove_cluster_member GameMachine::ClusterMonitor
- remove_observers GameMachine::PlayerRegistry
- remove_player_id GameMachine::PlayerRegistry
- remove_remote_member GameMachine::ClusterMonitor
- #replicate GameMachine::PlayerGateway
- #replicate_players GameMachine::PlayerGateway
- reset_hashrings GameMachine::Actor::Base
- #save_state GameMachine::Helpers::StateMachine
- #schedule_activities GameMachine::Bot::Chat
- #schedule_update GameMachine::GridReplicator
- #schedule_update GameMachine::GameSystems::SingletonManager
- #send_at_once GameMachine::GameSystems::SingletonManager
- #send_chat_message GameMachine::Bot::Chat
- #send_in_slices GameMachine::GameSystems::SingletonManager
- #send_message GameMachine::Actor::Ref
- #send_message GameMachine::Clients::UdtClient
- #send_message GameMachine::Clients::Client
- #send_to_game_handler GameMachine::Handlers::Authentication
- send_to_player Demo::CombatController
- #send_to_player GameMachine::Helpers::GameMessage
- #send_to_player GameMachine::ProtobufExtensions::ClientMessageSender
- #send_to_player_controller GameMachine::GameSystems::PlayerManager
- #send_to_server GameMachine::Clients::TestClientProxy
- #send_to_server GameMachine::Bot::Chat
- send_udt GameMachine::Clients::Client
- #sender GameMachine::Actor::Base
- #servers GameMachine::DataStores::Couchbase
- #set GameMachine::Vector
- #set GameMachine::DataStores::Mapdb
- #set GameMachine::DataStores::Memory
- #set_entity GameMachine::ObjectDb
- set_player_controller GameMachine::Actor::Base
- #set_player_health Demo::PlayerController
- #set_store GameMachine::DataStore
- #setup_signal_handlers GameMachine::Daemon
- #should_receive_component GameMachine::Clients::TestClientProxy
- #shutdown GameMachine::DataStores::Memory
- #shutdown GameMachine::DataStores::Mapdb
- #shutdown GameMachine::Daemon
- #shutdown! GameMachine::Actor::System
- #start Demo::Game
- #start GameMachine::Actor::Builder
- #start Demo::NpcController
- start GameMachine::Bot::Client
- start GameMachine::Clients::TestClient
- #start GameMachine::GameSystems::SingletonController
- #start GameMachine::Akka
- start GameMachine::Application
- start_actor_system GameMachine::Application
- start_core_systems GameMachine::Application
- start_endpoints GameMachine::Application
- start_game_systems GameMachine::Application
- start_handlers GameMachine::Application
- #start_player GameMachine::Bot::Client
- start_udt_client GameMachine::Bot::Client
- #stdout GameMachine::Logger
- stop GameMachine::Application
- #stop GameMachine::Clients::TestClientProxy
- #stop GameMachine::Akka
- stop_actor_system GameMachine::Application
- #tell GameMachine::Actor::Ref
- test_mono GameMachine::MonoLib
- thread_test GameMachine::MonoTest
- #to_byte_array GameMachine::Helpers::GameMessage
- #to_entity GameMachine::Helpers::GameMessage
- #track GameMachine::GameSystems::SingletonController
- #track_entity GameMachine::Helpers::GameMessage
- unregister_player GameMachine::PlayerRegistry
- unregister_player GameMachine::Handlers::Authentication
- #update Demo::NpcController
- #valid_authtoken? GameMachine::Handlers::Authentication
- #wait_for_ctx GameMachine::Clients::TestClientProxy
- #with_name GameMachine::Actor::Builder
- #with_parent GameMachine::Actor::Builder
- #with_router GameMachine::Actor::Builder
- #write_interval GameMachine::WriteBehindCache
- #write_pidfile GameMachine::Daemon
- #x GameMachine::Vector
- #y GameMachine::Vector
- #z GameMachine::Vector
- #zero GameMachine::Vector
- #zero? GameMachine::Vector


I have a very wide range of interest and enjoy the constant challenge of learning new things. It is important to me to expand my knowledge of popular languages and technologies in hopes of finding new and creative ways to solve problems.
ResumeMentored and guided students transitioning into Computer Science from unrelated fields through assisting in demonstrations, discussions, class presentations, and individual tutoring.
Assisted in presenting and promoting the Northeastern Align programing to prospective students at events and through other means of communication. Field questions regarding the program and my personal experience. Be a liaison for students needing aid with faculty and staff.
Designed and fabricated custom parts to meet customers project expectations including finishing and installation. Build company website including analytics and a Google Adwords Campain. Manage relationships with customers and business partners to establish trust and confidence.
Collaboratively produced, edited, and drafted legal documents and packages, formal legal form submissions, business sale agreements, trademarks, and more. Duties also included client correspondence and relations, filing, editing, and managing all of the office IT needs - maintenance and installation of computers, servers, and networking equipment.
GPA: 3.52
GPA: 3.49
Besides developing software, I like to spend much of my free time outside building cars, mountain biking, or just spending time in the sun with my dog Shadow. He is a big part of my day and we both really enjoys our walks. Being in California has the advantage of year round outdoor activities so we like to take advantage as much as possible.
When it's time to come inside, I am an avid basketball player and really enjoy playing music. My primary instruments are Drums and Piano. Some of my favorite composers are Bach, Chopin, Duke Ellington, Herbie Handcock, and more! I also really love building robots so I frequently design and 3D print whatever I need.
Please enter a valid email address or it will fail the check!


Basic Media
Video
This browser doesn't support native video
Audio
This browser doesn't support native audio


The XMLHttpRequest Object
Select a customer:
Person 1
Person 2
Person 3
Person 4
Person 5
Person 6
Person 7
Customer info will be listed here...


BeanFactory
public class BeanFactory
| Field Summary | |
|---|---|
| private mixed | Map of the objects that have already been initialized. |
| private mixed | Path of the file containing the xml-definition of the generators that can be used. |
| private mixed | The DOMDocument of the 'context.xml'. |
| private mixed | The DOMXPath of the 'context.xml'. |
| private static mixed | Singleton instance of the ObjectFactory. |
| Constructor Summary | |
|---|---|
BeanFactory(string contextXMLPath) Constructor. |
| Method Summary | |
|---|---|
| private stdclass | _initializeBean(string beanId) Constructs an instance of the Object with the given id. |
| private array | _initializeConstructorArgs(string beanId) Initializes the constructor arguments for the bean with the referenced id. |
| private void | _initializeProperties(string beanId, stdclass instance) Initializes the properties for the bean with the referenced id. |
| private void | _initializePropertyArray(string beanId, stdclass instance) Initializes an array out of a properties list. |
| private void | Loads the 'context.xml'. |
| static void | addContext(string contextXMLPath, boolean overwrite) Adds a custom context to the BeanFactory. |
| static void | clean() Removes the singleton / |
| static stdclass | getBeanById(string beanId) Returns the Object with the given id. |
| static void | initialize(string contextXMLPath) Initializes the singleton instance of this Class. |
private mixed $beans = array()
Map of the objects that have already been initialized. */
private mixed $contextXMLPath = null
Path of the file containing the xml-definition of the generators that can be used. */
private mixed $contextXml
The DOMDocument of the 'context.xml'. May contain user elements later */
private mixed $domXPath = null
The DOMXPath of the 'context.xml'. */
private static mixed $instance = null
Singleton instance of the ObjectFactory. */
public BeanFactory(string contextXMLPath)
Constructor. Private constructor since only used by 'initialize'.
private stdclass _initializeBean(string beanId)
Constructs an instance of the Object with the given id. Arguments from the context xml file will be passed to the Object.
private array _initializeConstructorArgs(string beanId)
Initializes the constructor arguments for the bean with the referenced id. Constructor arguments are noted as
private void _initializeProperties(string beanId, stdclass instance)
Initializes the properties for the bean with the referenced id. Properties are noted as
private void _initializePropertyArray(string beanId, stdclass instance)
Initializes an array out of a properties list. Properties are noted as
This will result an array of that kind:
$myarray['key1'] = 'value1';
$myarray['key2'] = 'value2';
A setter method will be called on the target bean: setMyarray($myarray)
private void _loadContextXML()
Loads the 'context.xml'. /
public static void addContext(string contextXMLPath, boolean overwrite)
Adds a custom context to the BeanFactory.
public static void clean()
Removes the singleton /
public static stdclass getBeanById(string beanId)
Returns the Object with the given id.
public static void initialize(string contextXMLPath)
Initializes the singleton instance of this Class.
Used to initiate and retrieve Objects for Dependency Injection.
Several scopes can be set:
Spring Framework supports the singleton scope, too. This means a bean is available for the the whole container for the whole runtime. This is not (easily) possible for PHP applications and should only be used under special circumstances. The bean must be serialized to harddisk for an interchange between sessions and request. However, this could be done with a BeanSerializer, which is in the scope for a latter Piwi release. /


A tag <code> é usada quando queremos citar um trecho de código de programa.
Exemplo: O comando console.log()
pertence à linguagem Javascript
Para formatar várias linhas, usamos a tag <pre>
COMPUTE media = (mensal+bimestral)/2.
DISPLAY "A media do aluno e: ", media
STOP RUN.
Como dizia minha vó: Antes mal acompanhado do que só.
For 50 years, WWF has been protecting the future of nature. The world's leading conservation organization, WWF works in 100 countries and is supported by 1.2 million members in the United States and close to 5 million globally. WWF's unique way of working combines global reach with a foundation in science, involves action at every level from local to global, and ensures the delivery of innovative solutions that meet the needs of both people and nature.
Estou estudando HTML.
Estou estudando HTML


US calls on Haftar to end Tripoli push, warns against Russia role
Washington accuses Moscow of exploiting the conflict and urges Khalifa Haftar to stop offensive on the Libyan capital.
The United States has called on Libyan renegade General Khalifa Haftar to stop a months-long offensive on Tripoli and said it would back the country's internationally recognised government against "Russia's attempts to exploit the conflict".
The statement was the most explicit call by Washington against the self-styled Libyan National Army of Haftar, who was earlier praised by President Donald Trump.
More:
"The United States calls on the 'Libyan National Army' to end its offensive on Tripoli," the US State Department said in a joint statement released on Thursday after talks with a delegation from Libya's Tripoli-based Government of National Accord in Washington.
Libya has been split between rival camps based in Tripoli and the east since 2014, a result of the divisions that surfaced following the NATO-backed overthrow of long-time ruler Muammar Gaddafi three years earlier.
Haftar's LNA launched an offensive in April to seize the capital and has been battling forces aligned with the GNA.
He is backed by Egypt, the United Arab Emirates and most recently Russian mercenaries, according to diplomats and Tripoli officials. The LNA denies it has foreign backing.
The New York Times reported earlier this month that the Russian mercenaries were believed to be controlled by an ally of President Vladimir Putin, an allegation denied by Moscow.
"The US delegation, representing a number of US government agencies, underscored support for Libya's sovereignty and territorial integrity in the face of Russia's attempts to exploit the conflict against the will of the Libyan people," the statement said.
In a move seen by some as a sign Washington might be backing Haftar, Trump called the eastern-based commander in the first weeks of the offensive, which has left more than 1,000 people dead and failed to breach Tripoli's defences.
Trump "recognised Field Marshal Haftar's significant role in fighting terrorism and securing Libya's oil resources, and the two discussed a shared vision for Libya's transition to a stable, democratic political system," the White House said at the time.
But the US later distanced itself from Haftar and joined calls for a ceasefire.


Suite Blah #3
2016.01.04
Happy New Year !
Objects are everywhere and many remain singularly existent: my watch is an object, my brother's car is an object, I myself am an object. I consider all 3 'things' to be objects fairly independent of each other or any other object - my watch has been on my wrist since my birthday in 2013, my brother's car has been his since its purchase last summer, and finally I have been my own object since the day of my birth! I am me, no one else is me; and to my knowledge my extraordinary good looks, sharp wit and remarkable presence in all social scenarios are largely unmatched by anyone on planet earth.
However this is not to say that objects cannot be considered in the contexts of their groupings with other objects - and this is how, theoretically, arrays and hashes work in the coding world to store objects and information on the web. Despite my undeniable, blinding value as an individual I have countlessly, and currently am, a part of many groups. I work at a law firm in New York called Desmarais LLP, i could easily be grouped into a figurative/theoretical container with the rest of my coworkers - we all work at Desmarais LLP. In much the same way an array is a container of objects - just like a directory of Desmarais LLP would list a set of names of the individuals who work here.
An array works like this, as a container, or 'set' of objects. Within this set of objects, people (objects) could be newly hired to work here (added), fired (subtracted) perhaps bought out by another, larger lawfirm - (added to another array (that will never happen, Desmarais LLP will forever be its own!)). An array is a group of objects just like my office is a group of people:
[Emmet, John, Paul, Jonas, Alan, Sean, Matt]
But what if someone wanted to call myself or one of my co-workers directly? Surely a container with merely a list of our names would hardly suffice to help to this end. A telephone directly would be much more helpful - a listing of employees with a single, direct phone line listed next to each name. VIOLA! Accessing each empoyee via phone is remarkably easy, each has a KEY (phone code) by which they can be accessed. In the same way a hash is very much like an array, but for each object a KEY value is assessed to access/retrieve the object quickly:
phone= { "5448" => "Emmet", "4892" => "Paul", "2394 => "Jonas", "6893" => "Alan" "8904" => "Sean" "6090" => "Matt" }
Both serve as containers of objects at any given time. The objects can be shifted, retrieved, added and taken out in arrays, and beyond that, can be accessed by their unique key value in hashes.
Happy New Year, may your 2016 be like no other!


Seeds are 2, 3, 5 and 7 with equivalent probability (in regards to the random results, I'm not talking about the JS random generator quality here).
You can join number if they one of them can be divided by the other, in this case their values will be summed and the merge tile will contains the result.
25th march 2014: New token for score to avoid collision with FIB scores, sorry for resetting you high scores :/
How to play: Use your arrow keys to move the tiles. When two tiles which can devide each other touch, they merge into one, adding their values!
Simple modification of 2048 created by Gabriele Cirulli. Based on 1024 by Veewo Studio and conceptually similar to Threes by Asher Vollmer.


We can get 'em running running, running...
Just like fire, burning out the way
If I can light the world up for just one day
Watch this madness, colorful charade
No one can be just like me any way
Just like magic, I'll be flying free
I'mma disappear when they come for me
I kick that ceiling, what you gonna take?
No one can be just like me any way
Just like fire, uh...
And people like to laugh at you cause they are all the same
(mmm, mmm)
See I would rather we just go our different way than play the game
And no matter the weather, we can do it better
You and me together forever and ever
We don't have to worry about a thing about a thing
We can get 'em running, running, running
We can get 'em running, running, running
Just like fire, burning out the way
If I can light the world up for just one day
Watch this madness, colorful charade
No one can be just like me any way
Just like magic, I'll be flying free
I'mma disappear when they come for me
I kick that ceiling, what you gonna take?
No one can be just like me any way
Just like fire, fire, fire
Running, running, running
We can get 'em running, running, running
So I can get it running
Just cause nobody's done it
Y'all don't think I can run it
But look, I've been here, I've done it
Impossible?
(What, what!?)
Oh, what you gonna do?
(What, what!?)
Oh, what you gonna do?
(What, what!?)
Oh, what you gonna do?

No one can be just like me any way
Just like fire, fire!
Running, running, running
Just like fire!
(Running, running, running...)


DSC FULafia | WEB 101
Menu
Enroll
Resources
Enroll now..!!
Loading…
STEPS TO ENROLL FOR THIS WORKSHOP
Fill the form provided on this page
You will instantly receive whatsapp group invitation link in your email
You will be also receive link to the resources
Read through the resources and complete all tasks and project
Successful participants will get gifts :-)
Send a mail to
<EMAIL_ADDRESS>If you wish to contribute or any other thing else
Kindly note that this workshop is to run for three (3) weeks starting November 13th 2019.
Resources
TASKS/PROJECT
Copyright © DSC FULafia 2020


|
|
| Map (IJSRuntime jsRuntime) |
| |
| void | RaiseOnInitialized () |
| | This method MUST be called only once by the Blazor component upon rendering, and never by the user. More...
|
| |
| void | AddLayer (Layer layer) |
| | Add a layer to the map. More...
|
| |
| void | RemoveLayer (Layer layer) |
| | Remove a layer from the map. More...
|
| |
| IReadOnlyCollection< Layer > | GetLayers () |
| | Get a read only collection of the current layers. More...
|
| |
|
void | FitBounds (PointF corner1, PointF corner2, PointF? padding=null, float? maxZoom=null) |
| |
|
void | PanTo (PointF position, bool animate=false, float duration=0.25f, float easeLinearity=0.25f, bool noMoveStart=false) |
| |
|
async Task< LatLng > | GetCenter () |
| |
|
async Task< float > | GetZoom () |
| |
|
delegate void | MapEventHandler (object sender, Event e) |
| |
|
delegate void | MapResizeEventHandler (object sender, ResizeEvent e) |
| |
|
void | NotifyZoomLevelsChange (Event e) |
| |
|
void | NotifyResize (ResizeEvent e) |
| |
|
void | NotifyUnload (Event e) |
| |
|
void | NotifyViewReset (Event e) |
| |
|
void | NotifyLoad (Event e) |
| |
|
void | NotifyZoomStart (Event e) |
| |
|
void | NotifyMoveStart (Event e) |
| |
|
void | NotifyZoom (Event e) |
| |
|
void | NotifyMove (Event e) |
| |
|
void | NotifyZoomEnd (Event e) |
| |
|
void | NotifyMoveEnd (Event e) |
| |
|
void | NotifyMouseMove (MouseEvent eventArgs) |
| |
|
void | NotifyKeyPress (Event eventArgs) |
| |
|
void | NotifyKeyDown (Event eventArgs) |
| |
|
void | NotifyKeyUp (Event eventArgs) |
| |
|
void | NotifyPreClick (MouseEvent eventArgs) |
| |
|
delegate void | MouseEventHandler (Map sender, MouseEvent e) |
| |
|
void | NotifyClick (MouseEvent eventArgs) |
| |
|
void | NotifyDblClick (MouseEvent eventArgs) |
| |
|
void | NotifyMouseDown (MouseEvent eventArgs) |
| |
|
void | NotifyMouseUp (MouseEvent eventArgs) |
| |
|
void | NotifyMouseOver (MouseEvent eventArgs) |
| |
|
void | NotifyMouseOut (MouseEvent eventArgs) |
| |
|
void | NotifyContextMenu (MouseEvent eventArgs) |
| |
|
| LatLng | Center = new LatLng() [get, set] |
| | Initial geographic center of the map More...
|
| |
| float | Zoom [get, set] |
| | Initial map zoom level More...
|
| |
| float? | MinZoom [get, set] |
| | Minimum zoom level of the map. If not specified and at least one GridLayer or TileLayer is in the map, the lowest of their minZoom options will be used instead. More...
|
| |
| float? | MaxZoom [get, set] |
| | Maximum zoom level of the map. If not specified and at least one GridLayer or TileLayer is in the map, the highest of their maxZoom options will be used instead. More...
|
| |
| Tuple< LatLng, LatLng > | MaxBounds [get, set] |
| | When this option is set, the map restricts the view to the given geographical bounds, bouncing the user back if the user tries to pan outside the view. More...
|
| |
|
string | Id [get] |
| |


--- title: On Business Agility excerpt: Team-level scaled agility is a local optimization that will get you only that far. In the complex Explore domain, you'll need the whole organisation to respond to rapid change and new opportunities. In other words, you'll also need Business Agility. date: '2022-08-29 13:21:47 +0100' categories: - Agile - Books - Motivation - Teams image: path: /assets/images/social/the_6_enablers_of_business_agility.png ---
What is Business Agility and why is it important? Team-level scaled agility without the required structural and cultural changes is a local optimisation that will get you only that far. In the complex Explore domain, you'll need the whole organisation to respond to rapid change and new opportunities. In other words, you'll also need business agility with 21st-century leadership.
The 6 Enablers of Business Agility by Karim Harbott is one of the best books on business agility I've read so far (see the list of some other books I've liked later in the article).
The goal is not to increase agility; the goal is to create better outcomes by increasing agility in the whole organisation, involving technology, product, people and business leadership—affecting all the behaviours that shape the underlying culture. Most importantly, we want to create value for the customer.
What's interesting to note here is that Alistair Cockburn, one of the co-signers of the Agile Manifesto, says himself that:
Agile has nothing to do with software. I consider it an accident of history that a group of programmers decoded this particular thing.
Many books about business agility focus only on certain areas, much like their team agility counterparts focus only on team-level agility. These other areas include decentralised leadership, mindset and organisational culture. These books are motivational and well-written, and only a few of them have business agility in their title. What is common in these books is that team agility (Ways of Working) is just one part of the big picture. The second common denominator is their focus on modern leadership.
What I like about Karim's book is that it presents a practical holistic picture of what you need to do to succeed, with just the right amount of history and context to get the actionable message through. The book is very well-researched, well-written, and well-organised, putting the team-level agility (and the related frameworks) into a larger context and prerequisite: the supporting organisational ecosystem.
You'd better pay close attention to all these six enablers described in the book in your organisation—not only Ways of Working—to avoid the sometimes-heard comment agility doesn't work here
:)
The book closes with Karim's Business Agility Canvas, a tool to make transformation strategy coherent, coordinated, and transparent
, in his words. All in all, a balanced book that connects the dots nicely.
I wrote about Distributed Agile Teams earlier, and Karim's book certainly gave me plenty more food for thought, filling some gaps in the big picture.


lorum ipsum play play play
lorum ipsum blah blah blah blah blah
I'm a journalist with a background in biochemistry and a penchant for data and documents. I gravitate towards stories about public health and the environment. In my spare time, I'm probably doodling.


Abstraction
The ability to recognise common patterns in the desired behaviour of the program and organise your code around them. The ability to design systems and to structure code so that whenever X happens, so does Y. The ability to structure loops so that they’re always executed the right number of times and so each statement is executed always and only when it should be. The ability to structure data so that the requisite parts are always present when they should be, never when they shouldn’t be, and it’s always possible to find the information that may be needed. The ability to document your code in an approachable way, so that the most important parts can be understood without having to understand all the details first. The ability to recognise and apply the appropriate architectural or design patterns for the current situation. All of these things are abstraction.
Edsger W. Dijkstra observed this in 1972:
effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer.He also said that
We all know that the only mental tool by means of which a very finite piece of reasoning can cover a myriad cases is called abstraction; as a result the effective exploitation of his powers of abstraction must be regarded as one of the most vital activities of a competent programmer.True in the 1970s, and even more true now.


Your Virtual Machine is up and running. Now we are waiting for a computing project to start. This should not take more than a minute.
Depending on how many CPUs you have allocated to your Virtual Machine one or more projects will be started in the same time in your computer. For each project, you are actively helping the scientists with their work!


This project uses an Integer Linear Program (ILP) to optimally match GTAs to classes. By adding GTA and Instructor preferences as factors in the objective function and using required skills and GTA experience as constraints, the system finds an optimal solution.
Using data obtained from wearable devices and Electronic Health Records to keep track of users habits and health.
A step ahead of the idea of quantified self that includes more parameters than just activity and fitness related tracking.


Find government data
Use government data to learn how government works, hold it to account, carry out research or build APIs and services
Business and economy
Small businesses, industry, imports, exports and trade
Environment
Weather, flooding, rivers, air quality, geology and agriculture
Mapping
Addresses, boundaries, land ownership, aerial photographs, seabed and land terrain
Crime and justice
Courts, police, prisons, offenders, borders and immigration
Government
Staff numbers and pay, local councillors and department business plans
Society
Employment, benefits, household finances, poverty and population
Defence
Armed forces, health and safety, search and rescue
Government spending
Includes all payments by government departments over £25,000
Towns and cities
Includes housing, urban planning, leisure, waste and energy consumption


Marc Steen
Deserves detailed reading along with other Steen papers on co-design.
"A useful definition is also provided by Kleinsmann and Valkenburg, who described co-design as ‘the process in which actors from different disciplines share their knowledge about both the design process and the design content ... in order to create shared understanding on both aspects ... and to achieve the larger common objective: the new product to be designed’"
"What happens in (co-)design can be understood more adequately as a process of abduction—a term coined by pragmatist philosopher C.S. Peirce, which refers to a type of reasoning that is different from deduction or induction: ‘deduction proves that something must be; induction shows that something actually is operative; abduction merely suggests that something may be.’"
"In design thinking, problems and possible solutions are explored and developed and evaluated simultaneously in an iterative process:"
"Dewey distinguished between primary experiences of ‘gross, macroscopic, crude subject-matters’, that is, experiences ‘as the result of a minimum of incidental
reflection’, and secondary experiences of ‘refined, derived objects of reflection’, that is, experiences ‘in consequence of continued and regulated reflective inquiry’. Furthermore, Dewey promoted an ‘empirical method’ of moving back and forth between practices (primary experiences) and reflections (secondary experiences) in order to develop practical knowledge: knowledge that is based on practices and that is practically applicable."


Create
Create a project.
Open
Open a project to package it.
Project name:
Project location:
Create
Open A Project
Open
Packaging


History 3816G / Digital Humanities 3902G:
Introduction to Digital History
Tuesdays, 6pm
Room UC-222
Contact me
Devon Elliott
<EMAIL_ADDRESS>Office Hours: Tuesdays, 4:00 - 5:00pm, Lawson Hall Room 1208 or by appointment
Upcoming CAS Talk
- Jesse Walker
Wednesday, January 28, 2015, 4:00pm, SEB 2202
The United States of Paranoia: Conspiracy Theories in U.S. History
Annotating and Referring: Bibliographic Databases
Readings:
Annotating and Referring: Bibliographic Databases
Technologies:
Screenshots
- On Mac -- CMD + Shift + 3 or CMD + Shift + 4
- On PC:
PrtScn (PrintScreen) -- saves it to Clipboard
Windows + PrtScn -- saves to file
Windows + Alt + PrtScn -- saves the active window
** on laptops, you might need to use a fn(function) key as well
Major Essay Assignment
Choose a topic for which primary sources are available. Apply one or more of the tools in the class to those sources. Write a history of your topic that uses secondary literature to introduce and set the context for your paper, and then explain and describe your digital work with the sources. Does your work complement and reinforce arguments made in the secondary literature? Does it contradict or indicate alternative ideas about the topic? Sample topics and resources will be provided, but you're also welcome to find and choose your own. Check with me -- I can help.
Possible Topics
- Media/Entertainment -- Vaudeville, radio, television, film. Popular photography, from film to digital. Publishing books, newspapers. Music.
- Popular religion -- Spiritualism, evangelists, New Thought, etc.
- Sports
- Advertising and consumer culture
- Fads -- toys, ideas, movements (sci. fi., the future, homesteading, car culture, etc.)
Possible Sources
- Internet Archive
- Google Books
- Project Gutenberg
- Western Library resources -- ie., Chronicling America
- Digital Public Library of America
- Other databases and repositories -- comic books, sheet music, lyrics, tablature, images, etc.
Getting Started
- What's your topic?
- What are you going to ask about it?
- What secondary sources are availble?
- What digital primary source(s) can you introduce to your analysis?
- What tools will you apply or try with the source(s)?
- Contact me for assistance!
Examples:
- Vaudeville
- UFOs
- National monuments
- Roadside Attractions
- Historical / Commemorative plaques
- Sports events
- Map events or make a timeline
- Text analysis -- gendered pronouns, Google Ngrams, Voyant
- Make an online exhibit
- Virtually reconstruct something from the past
- Create a network graph or other visualization
INTERMISSION
See you next Tuesday!
Have a great week!
Contact me at<EMAIL_ADDRESS>or stop by Lawson Hall Room 1208 on Tuesdays, 4:00-5:00. I'm also available before and after class on Tuesdays, or by appointment.


Treebank Statistics: UD_Icelandic-PUD: Features: Person
This feature is universal.
It occurs with 3 different values: 1
, 2
, 3
.
2136 tokens (11%) have a non-empty value of Person
.
638 types (10%) occur at least once with a non-empty value of Person
.
318 lemmas (7%) occur at least once with a non-empty value of Person
.
The feature is used with 7 part-of-speech tags: VERB (1136; 6% instances), AUX (876; 5% instances), PRON (116; 1% instances), NOUN (3; 0% instances), ADJ (2; 0% instances), PROPN (2; 0% instances), ADP (1; 0% instances).
VERB
1136 VERB tokens (57% of all VERB
tokens) have a non-empty value of Person
.
The most frequent other feature values with which VERB
and Person
co-occurred: Case=EMPTY (1136; 100%), Gender=EMPTY (1136; 100%), VerbForm=Fin (1136; 100%), Mood=Ind (1071; 94%), Voice=Act (990; 87%), Number=Sing (823; 72%), Tense=Past (703; 62%).
VERB
tokens may have the following values of Person
:
1
(57; 5% of non-emptyPerson
): þurfum, held, verðum, fer, horfði, höldum, veit, vil, vildi, býst2
(5; 0% of non-emptyPerson
): Deilir, Látið, heyrið, tókuð, vilt3
(1074; 95% of non-emptyPerson
): sagði, fór, varð, segir, hafði, kom, átti, þarf, gerði, tókEMPTY
(862): nota, gera, gert, hafa, fá, koma, komið, sjá, taka, notað
| Paradigm vilja | 1 | 2 | 3 |
|---|---|---|---|
| Mood=Ind|Number=Sing|Tense=Past | vildi | ||
| Mood=Ind|Number=Sing|Tense=Pres | vil | vilt | vill |
| Mood=Ind|Number=Plur|Tense=Past | vildu | ||
| Mood=Ind|Number=Plur|Tense=Pres | vilja | ||
| Mood=Sub|Number=Sing|Tense=Past | vildi | ||
| Mood=Sub|Number=Plur|Tense=Past | vildu |
Person
seems to be lexical feature of VERB
. 91% lemmas (278) occur only with one value of Person
.
AUX
876 AUX tokens (90% of all AUX
tokens) have a non-empty value of Person
.
The most frequent other feature values with which AUX
and Person
co-occurred: VerbForm=Fin (876; 100%), Voice=Act (876; 100%), Mood=Ind (756; 86%), Number=Sing (637; 73%), Tense=Pres (492; 56%).
AUX
tokens may have the following values of Person
:
1
(23; 3% of non-emptyPerson
): hef, erum, getum, var, er, höfum, get, hafði, munum, skulum2
(1; 0% of non-emptyPerson
): getur3
(852; 97% of non-emptyPerson
): er, var, voru, eru, hefur, hafa, hafði, höfðu, hafi, væriEMPTY
(98): verið, vera, hafa, geta, verði
| Paradigm geta | 1 | 2 | 3 |
|---|---|---|---|
| Mood=Ind|Number=Sing|Tense=Past | gat | ||
| Mood=Ind|Number=Sing|Tense=Pres | get | getur | getur |
| Mood=Ind|Number=Plur|Tense=Past | gátu | ||
| Mood=Ind|Number=Plur|Tense=Pres | getum | geta | |
| Mood=Sub|Number=Sing|Tense=Past | gæti | ||
| Mood=Sub|Number=Sing|Tense=Pres | geti | ||
| Mood=Sub|Number=Plur|Tense=Past | gætu | ||
| Mood=Sub|Number=Plur|Tense=Pres | getum | geti |
PRON
116 PRON tokens (8% of all PRON
tokens) have a non-empty value of Person
.
The most frequent other feature values with which PRON
and Person
co-occurred: Gender=EMPTY (116; 100%), PronType=Prs (116; 100%), Case=Nom (85; 73%), Number=Sing (63; 54%).
PRON
tokens may have the following values of Person
:
1
(107; 92% of non-emptyPerson
): ég, við, okkar, okkur, mig, mér, mín2
(9; 8% of non-emptyPerson
): þið, þú, ykkar, ykkur, þérEMPTY
(1254): það, hann, því, þetta, þess, hún, hans, þeir, sér, þeirra
NOUN
3 NOUN tokens (0% of all NOUN
tokens) have a non-empty value of Person
.
The most frequent other feature values with which NOUN
and Person
co-occurred: Case=EMPTY (3; 100%), Definite=EMPTY (3; 100%), Gender=EMPTY (3; 100%), Number=Plur (2; 67%).
NOUN
tokens may have the following values of Person
:
1
(1; 33% of non-emptyPerson
): hefðum3
(2; 67% of non-emptyPerson
): hafiEMPTY
(4098): árið, ár, hluta, árum, ára, október, stað, öld, fólk, áhrif
ADJ
2 ADJ tokens (0% of all ADJ
tokens) have a non-empty value of Person
.
The most frequent other feature values with which ADJ
and Person
co-occurred: Case=EMPTY (2; 100%), Definite=EMPTY (2; 100%), Degree=EMPTY (2; 100%), Gender=EMPTY (2; 100%).
ADJ
tokens may have the following values of Person
:
1
(1; 50% of non-emptyPerson
): stoltust3
(1; 50% of non-emptyPerson
): fæstEMPTY
(1165): fyrsta, hægt, síðustu, nýja, eigin, fyrstu, margir, meira, miklu, fleiri
PROPN
2 PROPN tokens (0% of all PROPN
tokens) have a non-empty value of Person
.
The most frequent other feature values with which PROPN
and Person
co-occurred: Case=EMPTY (2; 100%), Gender=EMPTY (2; 100%).
PROPN
tokens may have the following values of Person
:
1
(1; 50% of non-emptyPerson
): Trump3
(1; 50% of non-emptyPerson
): BeustEMPTY
(1462): Krist, the, Trump, of, Evrópu, Hong, Kong, de, Breta, Bretlandi
ADP
1 ADP tokens (0% of all ADP
tokens) have a non-empty value of Person
.
ADP
tokens may have the following values of Person
:
3
(1; 100% of non-emptyPerson
): undirEMPTY
(2341): í, á, til, við, um, með, fyrir, af, frá, eftir
Relations with Agreement in Person
The 10 most frequent relations where parent and child node agree in Person
:
VERB –[conj]–> VERB (104; 62%),
VERB –[parataxis]–> VERB (23; 51%),
VERB –[acl:relcl]–> VERB (3; 60%),
PRON –[conj]–> VERB (1; 100%),
VERB –[parataxis]–> AUX (1; 100%).


Exercise #1
1:-Find Largest Number
Enter The Button And Calculate The Largest Value between Two Numbers
Try It
2:-Find Largest Number
Enter The Button & Calculate The Largest Value In Three Number
3:-Find Vowel
Enter Any One Character and Find This Character is Vowel or Not
Try It
4:-Find Each Word Alert
Enter the Sentenc and Find Each Word Alert
Try it
5:-Calculate The Sum & Multiplication
Claculate the Sum Of the Numbers
Sum
Claculate the Multiply Of the Numbers
Multiply
6:-Computes The Reversal
Press The button and Enter The String and compute The Revesal of the String
Enter String
7:-Find Longest Word
Press The Button And Enter The String and Find Longest Word In The String
Enter String
8:-Compare Words And Integer Number
Press The Button & Enter words and integer Number. Find The words That are Longer Then Number
Try It


|
mesh - mesh handle
x# - x position of mesh y# - y position of mesh z# - z position of mesh |
|
Moves all vertices of a mesh.
See also: PositionEntity, MoveEntity, TranslateEntity. |
|
; PositionMesh Example
; -------------------- ; In this example we will demonstrate the use of the PositionMesh command. ; Unlike PositionEntity, PositionMesh actually modifies the actual mesh structure. ; So whereas using PositionEntity 0,0,1 would only move an entity by one unit the first time it was ; used, PositionMesh 0,0,1 will move the mesh by one unit every time it is used. ; This is because PositionEntity positions an entity based on a fixed mesh structure, whereas ; PositionMesh actually modifies the mesh structure itself. Graphics3D 640,480 SetBuffer BackBuffer() camera=CreateCamera() light=CreateLight() ; Create cube mesh cube=CreateCube() ; Position cube in front of camera so we can see it PositionEntity cube,0,0,5 While Not KeyDown(1) ; If space bar pressed then position mesh 1 unit along the z axis. Also set syntax$ text. If KeyHit(57)=True Then PositionMesh cube,0,0,1 : syntax$="PositionMesh 0,0,1" RenderWorld Text 0,0,"Press space to position the mesh 1 unit along the z axis" Text 0,20,syntax$ Flip Wend End |


Autoharmonizer
Play
Calibrate
Graph
Match pitch
Harmonize pitch
Raw frequency:
0
Hz
Frequency:
0
Hz
Harmony frequency:
0
Hz
Calibration countdown:
300
Calibrated frequency:
--
Hz
Your note:
50


I just matched the color of plastic spoons with the bowls dishing out ice cream for the kids. I don't even know who I am anymore. #parenting
7/7/2015, 8:11:33 PM
Favs: 2
Retweets: 0
link--- layout: tweet ---
I just matched the color of plastic spoons with the bowls dishing out ice cream for the kids. I don't even know who I am anymore. #parenting
7/7/2015, 8:11:33 PM
Favs: 2
Retweets: 0
link


We would like to acknowledge and thank our amazing team and partners. Richard Carder and Jake Snyder were the DataKind Data Ambassadors. Lukas Martinelli, Tom MacWright, Lew Ting, Mohammed Kemal, and many others contributed significantly, as well as volunteers who participated in our Data Jam and Data Dives.
DKDC Chapter Leaders Max Richman and Judy Yang assisted with project supervision.
Thank you Zach Cahalan (CCUSA partner liaison) for providing guidance to the team, we hope the Disaster Operations Map makes a true and positive impact in your work!
Catholic Charities USA is a national organization that offers support to member agencies,
provides disaster relief and promotes poverty-reduction through research and legislative reform.
DataKind brings high-impact organizations together with leading data scientists to use data science in the service of humanity.
Our work helps organizations develop evidence-based decision making, increase efficiency, and enhance their data literacy.


Artificial hair integrations, more commonly known as hair extensions or hair weaves, add length and/or fullness to human hair. Hair extensions are usually clipped, glued, or sewed on to other hair by incorporating additional human or synthetic hair.
Weaves are an alternative protective style to wigs and braids.
Artificial hair integrations, more commonly known as hair extensions or hair weaves, add length and/or fullness to human hair. Hair extensions are usually clipped, glued, or sewed on to other hair by incorporating additional human or synthetic hair.


Users List
Edit Profile
Complete your profile
Username
Email address
Name
Last Name
Select image
Change
Remove
Update Profile


{% extends 'base.html'%} {% import 'macros.html' as macro %} {% block content%}
|🔮 Did you know that the word NEWS is actually an acronymn for North, East, West and South? |🔮 Did you know that the first pages to be printed on a newspaper are the paid advertisements and the remaining spaces is what they use for their content?


{% extends "base.html" %} {% load crispy_forms_tags %} {% block content %}
Are you sure you want to delete the lesson?


The rsparkling extension package provides bindings to H2O’s distributed machine learning algorithms via sparklyr. In particular, rsparkling allows you to access the machine learning routines provided by the Sparkling Water Spark package.
Together with sparklyr’s dplyr interface, you can easily create and tune H2O machine learning workflows on Spark, orchestrated entirely within R.
rsparkling provides a few simple conversion functions that allow the user to transfer data between Spark DataFrames and H2O Frames. Once the Spark DataFrames are available as H2O Frames, the h2o R interface can be used to train H2O machine learning algorithms on the data.
A typical machine learning pipeline with rsparkling might be composed of the following stages. To fit a model, you might need to:
sdf_*
and ft_*
family of functions to generate new columns, or partition your data set,as_h2o_frame
function,You can install the rsparkling package from CRAN as follows:
install.packages("rsparkling")
Then set the Sparkling Water version for rsparkling.:
options(rsparkling.sparklingwater.version = "2.1.0")
For Spark 2.0.x
set rsparkling.sparklingwater.version
to 2.0.3
instead, for Spark 1.6.2
use 1.6.8
.
Now let’s walk through a simple example to demonstrate the use of H2O’s machine learning algorithms within R. We’ll use h2o.glm to fit a linear regression model. Using the built-in mtcars
dataset, we’ll try to predict a car’s fuel consumption (mpg
) based on its weight (wt
), and the number of cylinders the engine contains (cyl
).
First, we will initialize a local Spark connection, and copy the mtcars
dataset into Spark.
library(rsparkling)
library(sparklyr)
library(h2o)
library(dplyr)
sc <- spark_connect("local", version = "2.1.0")
mtcars_tbl <- copy_to(sc, mtcars, "mtcars")
Now, let’s perform some simple transformations – we’ll
# transform our data set, and then partition into 'training', 'test'
partitions <- mtcars_tbl %>%
filter(hp >= 100) %>%
mutate(cyl8 = cyl == 8) %>%
sdf_partition(training = 0.5, test = 0.5, seed = 1099)
Now, we convert our training and test sets into H2O Frames using rsparkling conversion functions. We have already split the data into training and test frames using dplyr.
training <- as_h2o_frame(sc, partitions$training, strict_version_check = FALSE)
## Warning in h2o.clusterInfo():
## Your H2O cluster version is too old (6 months and 25 days)!
## Please download and install the latest version from http://h2o.ai/download/
test <- as_h2o_frame(sc, partitions$test, strict_version_check = FALSE)
## Warning in h2o.clusterInfo():
## Your H2O cluster version is too old (6 months and 25 days)!
## Please download and install the latest version from http://h2o.ai/download/
Alternatively, we can use the h2o.splitFrame()
function instead of sdf_partition()
to partition the data within H2O instead of Spark (e.g. partitions <- h2o.splitFrame(as_h2o_frame(mtcars_tbl), 0.5)
)
# fit a linear model to the training dataset
glm_model <- h2o.glm(x = c("wt", "cyl"),
y = "mpg",
training_frame = training,
lambda_search = TRUE)
For linear regression models produced by H2O, we can use either print()
or summary()
to learn a bit more about the quality of our fit. The summary()
method returns some extra information about scoring history and variable importance.
glm_model
## Model Details:
## ==============
##
## H2ORegressionModel: glm
## Model ID: GLM_model_R_1503684109678_1
## GLM Model: summary
## family link regularization
## 1 gaussian identity Elastic Net (alpha = 0.5, lambda = 0.05468 )
## lambda_search
## 1 nlambda = 100, lambda.max = 5.4682, lambda.min = 0.05468, lambda.1se = -1.0
## number_of_predictors_total number_of_active_predictors
## 1 2 2
## number_of_iterations training_frame
## 1 0 frame_rdd_32_8d800eecc2806f51fa504de54664e58
##
## Coefficients: glm coefficients
## names coefficients standardized_coefficients
## 1 Intercept 32.997281 16.625000
## 2 cyl -0.906688 -1.349195
## 3 wt -2.712562 -2.282649
##
## H2ORegressionMetrics: glm
## ** Reported on training data. **
##
## MSE: 2.03293
## RMSE: 1.425808
## MAE: 1.306314
## RMSLE: 0.08238032
## Mean Residual Deviance : 2.03293
## R^2 : 0.8265696
## Null Deviance :93.775
## Null D.o.F. :7
## Residual Deviance :16.26344
## Residual D.o.F. :5
## AIC :36.37884
The output suggests that our model is a fairly good fit, and that both a cars weight, as well as the number of cylinders in its engine, will be powerful predictors of its average fuel consumption. (The model suggests that, on average, heavier cars consume more fuel.)
Let’s use our H2O model fit to predict the average fuel consumption on our test data set, and compare the predicted response with the true measured fuel consumption. We’ll build a simple ggplot2 plot that will allow us to inspect the quality of our predictions.
library(ggplot2)
# compute predicted values on our test dataset
pred <- h2o.predict(glm_model, newdata = test)
# convert from H2O Frame to Spark DataFrame
predicted <- as_spark_dataframe(sc, pred, strict_version_check = FALSE)
# extract the true 'mpg' values from our test dataset
actual <- partitions$test %>%
select(mpg) %>%
collect() %>%
`[[`("mpg")
# produce a data.frame housing our predicted + actual 'mpg' values
data <- data.frame(
predicted = predicted,
actual = actual
)
# a bug in data.frame does not set colnames properly; reset here
names(data) <- c("predicted", "actual")
# plot predicted vs. actual values
ggplot(data, aes(x = actual, y = predicted)) +
geom_abline(lty = "dashed", col = "red") +
geom_point() +
theme(plot.title = element_text(hjust = 0.5)) +
coord_fixed(ratio = 1) +
labs(
x = "Actual Fuel Consumption",
y = "Predicted Fuel Consumption",
title = "Predicted vs. Actual Fuel Consumption"
)
Although simple, our model appears to do a fairly good job of predicting a car’s average fuel consumption.
As you can see, we can easily and effectively combine dplyr data transformation pipelines with the machine learning algorithms provided by H2O’s Sparkling Water.
Once the H2OContext
is made available to Spark (as demonstrated below), all of the functions in the standard h2o R interface can be used with H2O Frames (converted from Spark DataFrames). Here is a table of the available algorithms:
| Function | Description |
|---|---|
h2o.glm |
Generalized Linear Model |
h2o.deeplearning |
Multilayer Perceptron |
h2o.randomForest |
Random Forest |
h2o.gbm |
Gradient Boosting Machine |
h2o.naiveBayes |
Naive-Bayes |
h2o.prcomp |
Principal Components Analysis |
h2o.svd |
Singular Value Decomposition |
h2o.glrm |
Generalized Low Rank Model |
h2o.kmeans |
K-Means Clustering |
h2o.anomaly |
Anomaly Detection via Deep Learning Autoencoder |
Additionally, the h2oEnsemble R package can be used to generate Super Learner ensembles of H2O algorithms:
| Function | Description |
|---|---|
h2o.ensemble |
Super Learner / Stacking |
h2o.stack |
Super Learner / Stacking |
A model is often fit not on a dataset as-is, but instead on some transformation of that dataset. Spark provides feature transformers, facilitating many common transformations of data within a Spark DataFrame, and sparklyr exposes these within the ft_*
family of functions. Transformers can be used on Spark DataFrames, and the final training set can be sent to the H2O cluster for machine learning.
| Function | Description |
|---|---|
ft_binarizer
|
Threshold numerical features to binary (0/1) feature |
ft_bucketizer
|
Bucketizer transforms a column of continuous features to a column of feature buckets |
ft_discrete_cosine_transform
|
Transforms a length NN real-valued sequence in the time domain into another length NN real-valued sequence in the frequency domain |
ft_elementwise_product
|
Multiplies each input vector by a provided weight vector, using element-wise multiplication. |
ft_index_to_string
|
Maps a column of label indices back to a column containing the original labels as strings |
ft_quantile_discretizer
|
Takes a column with continuous features and outputs a column with binned categorical features |
ft_sql_transformer
|
Implements the transformations which are defined by a SQL statement |
ft_string_indexer
|
Encodes a string column of labels to a column of label indices |
ft_vector_assembler
|
Combines a given list of columns into a single vector column |
We will use the iris
data set to examine a handful of learning algorithms and transformers. The iris data set measures attributes for 150 flowers in 3 different species of iris.
iris_tbl <- copy_to(sc, iris, "iris", overwrite = TRUE)
iris_tbl
## # Source: table<iris> [?? x 5]
## # Database: spark_connection
## Sepal_Length Sepal_Width Petal_Length Petal_Width Species
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ... with more rows
Convert to an H2O Frame:
iris_hf <- as_h2o_frame(sc, iris_tbl, strict_version_check = FALSE)
Use H2O’s K-means clustering to partition a dataset into groups. K-means clustering partitions points into k
groups, such that the sum of squares from points to the assigned cluster centers is minimized.
kmeans_model <- h2o.kmeans(training_frame = iris_hf,
x = 3:4,
k = 3,
seed = 1)
To look at particular metrics of the K-means model, we can use h2o.centroid_stats()
and h2o.centers()
or simply print out all the model metrics using print(kmeans_model)
.
# print the cluster centers
h2o.centers(kmeans_model)
## petal_length petal_width
## 1 1.462000 0.24600
## 2 5.566667 2.05625
## 3 4.296154 1.32500
# print the centroid statistics
h2o.centroid_stats(kmeans_model)
## Centroid Statistics:
## centroid size within_cluster_sum_of_squares
## 1 1 50.00000 1.41087
## 2 2 48.00000 9.29317
## 3 3 52.00000 7.20274
Use H2O’s Principal Components Analysis (PCA) to perform dimensionality reduction. PCA is a statistical method to find a rotation such that the first coordinate has the largest variance possible, and each succeeding coordinate in turn has the largest variance possible.
pca_model <- h2o.prcomp(training_frame = iris_hf,
x = 1:4,
k = 4,
seed = 1)
pca_model
## Model Details:
## ==============
##
## H2ODimReductionModel: pca
## Model ID: PCA_model_R_1503684109678_3
## Importance of components:
## pc1 pc2 pc3 pc4
## Standard deviation 7.861342 1.455041 0.283531 0.154411
## Proportion of Variance 0.965303 0.033069 0.001256 0.000372
## Cumulative Proportion 0.965303 0.998372 0.999628 1.000000
##
##
## H2ODimReductionMetrics: pca
##
## No model metrics available for PCA
Use H2O’s Random Forest to perform regression or classification on a dataset. We will continue to use the iris dataset as an example for this problem.
As usual, we define the response and predictor variables using the x
and y
arguments. Since we’d like to do a classification, we need to ensure that the response column is encoded as a factor (enum) column.
We can split the iris_hf
H2O Frame into a train and test set (the split defaults to 75/25 train/test).
splits <- h2o.splitFrame(iris_hf, seed = 1)
Then we can train a Random Forest model:
rf_model <- h2o.randomForest(x = x,
y = y,
training_frame = splits[[1]],
validation_frame = splits[[2]],
nbins = 32,
max_depth = 5,
ntrees = 20,
seed = 1)
Since we passed a validation frame, the validation metrics will be calculated. We can retrieve individual metrics using functions such as h2o.mse(rf_model, valid = TRUE)
. The confusion matrix can be printed using the following:
h2o.confusionMatrix(rf_model, valid = TRUE)
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 7 0 0 0.0000 = 0 / 7
## versicolor 0 13 0 0.0000 = 0 / 13
## virginica 0 1 10 0.0909 = 1 / 11
## Totals 7 14 10 0.0323 = 1 / 31
To view the variable importance computed from an H2O model, you can use either the h2o.varimp()
or h2o.varimp_plot()
functions:
h2o.varimp_plot(rf_model)
The Gradient Boosting Machine (GBM) is one of H2O’s most popular algorithms, as it works well on many types of data. We will continue to use the iris dataset as an example for this problem.
Using the same dataset and x
and y
from above, we can train a GBM:
gbm_model <- h2o.gbm(x = x,
y = y,
training_frame = splits[[1]],
validation_frame = splits[[2]],
ntrees = 20,
max_depth = 3,
learn_rate = 0.01,
col_sample_rate = 0.7,
seed = 1)
Since this is a multi-class problem, we may be interested in inspecting the confusion matrix on a hold-out set. Since we passed along a validatin_frame
at train time, the validation metrics are already computed and we just need to retreive them from the model object.
h2o.confusionMatrix(gbm_model, valid = TRUE)
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 7 0 0 0.0000 = 0 / 7
## versicolor 0 13 0 0.0000 = 0 / 13
## virginica 0 1 10 0.0909 = 1 / 11
## Totals 7 14 10 0.0323 = 1 / 31
Use H2O’s Deep Learning to perform regression or classification on a dataset, extact non-linear features generated by the deep neural network, and/or detect anomalies using a deep learning model with auto-encoding.
In this example, we will use the prostate
dataset available within the h2o package:
path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate_df <- spark_read_csv(sc, "prostate", path)
head(prostate_df)
## # Source: lazy query [?? x 9]
## # Database: spark_connection
## ID CAPSULE AGE RACE DPROS DCAPS PSA VOL GLEASON
## <int> <int> <int> <int> <int> <int> <dbl> <dbl> <int>
## 1 1 0 65 1 2 1 1.4 0.0 6
## 2 2 0 72 1 3 2 6.7 0.0 7
## 3 3 0 70 1 1 2 4.9 0.0 6
## 4 4 0 76 2 2 1 51.2 20.0 7
## 5 5 0 69 1 1 1 12.3 55.9 6
## 6 6 1 71 1 3 2 3.3 0.0 8
Once we’ve done whatever data manipulation is required to run our model we’ll get a reference to it as an h2o frame then split it into training and test sets using the h2o.splitFrame
function:
prostate_hf <- as_h2o_frame(sc, prostate_df, strict_version_check = FALSE)
splits <- h2o.splitFrame(prostate_hf, seed = 1)
Next we define the response and predictor columns.
y <- "VOL"
#remove response and ID cols
x <- setdiff(names(prostate_hf), c("ID", y))
Now we can train a deep neural net.
dl_fit <- h2o.deeplearning(x = x, y = y,
training_frame = splits[[1]],
epochs = 15,
activation = "Rectifier",
hidden = c(10, 5, 10),
input_dropout_ratio = 0.7)
Evaluate performance on a test set:
h2o.performance(dl_fit, newdata = splits[[2]])
## H2ORegressionMetrics: deeplearning
##
## MSE: 279.1715
## RMSE: 16.70843
## MAE: 13.18417
## RMSLE: 1.879956
## Mean Residual Deviance : 279.1715
Note that the above metrics are not reproducible when H2O’s Deep Learning is run on multiple cores, however, the metrics should be fairly stable across repeat runs.
H2O’s grid search capabilities currently supports traditional (Cartesian) grid search and random grid search. Grid search in R provides the following capabilities:
H2OGrid
class: Represents the results of the grid searchh2o.getGrid(<grid_id>, sort_by, decreasing)
: Display the specified gridh2o.grid
: Start a new grid search parameterized by
algorithm = "gbm"
)ntrees = 100
)hyper_parameters
: attribute for passing a list of hyper parameters (e.g., list(ntrees=c(1,100), learn_rate=c(0.1,0.001))
)search_criteria
: optional attribute for specifying more a advanced search strategyBy default, h2o.grid()
will train a Cartesian grid search – meaning, all possible models in the specified grid. In this example, we will re-use the prostate data as an example dataset for a regression problem.
splits <- h2o.splitFrame(prostate_hf, seed = 1)
y <- "VOL"
#remove response and ID cols
x <- setdiff(names(prostate_hf), c("ID", y))
After prepping the data, we define a grid and execute the grid search.
# GBM hyperparamters
gbm_params1 <- list(learn_rate = c(0.01, 0.1),
max_depth = c(3, 5, 9),
sample_rate = c(0.8, 1.0),
col_sample_rate = c(0.2, 0.5, 1.0))
# Train and validate a grid of GBMs
gbm_grid1 <- h2o.grid("gbm", x = x, y = y,
grid_id = "gbm_grid1",
training_frame = splits[[1]],
validation_frame = splits[[1]],
ntrees = 100,
seed = 1,
hyper_params = gbm_params1)
# Get the grid results, sorted by validation MSE
gbm_gridperf1 <- h2o.getGrid(grid_id = "gbm_grid1",
sort_by = "mse",
decreasing = FALSE)
gbm_gridperf1
## H2O Grid Details
## ================
##
## Grid ID: gbm_grid1
## Used hyper parameters:
## - col_sample_rate
## - learn_rate
## - max_depth
## - sample_rate
## Number of models: 36
## Number of failed models: 0
##
## Hyper-Parameter Search Summary: ordered by increasing mse
## col_sample_rate learn_rate max_depth sample_rate model_ids
## 1 1.0 0.1 9 1.0 gbm_grid1_model_35
## 2 0.5 0.1 9 1.0 gbm_grid1_model_34
## 3 1.0 0.1 9 0.8 gbm_grid1_model_17
## 4 0.5 0.1 9 0.8 gbm_grid1_model_16
## 5 1.0 0.1 5 0.8 gbm_grid1_model_11
## mse
## 1 88.10947523138782
## 2<PHONE_NUMBER>994892
## 3<PHONE_NUMBER>1923726
## 4<PHONE_NUMBER>351778
## 5<PHONE_NUMBER>109763
##
## ---
## col_sample_rate learn_rate max_depth sample_rate model_ids
## 31 0.5 0.01 3 0.8 gbm_grid1_model_1
## 32 0.2 0.01 5 1.0 gbm_grid1_model_24
## 33 0.5 0.01 3 1.0 gbm_grid1_model_19
## 34 0.2 0.01 5 0.8 gbm_grid1_model_6
## 35 0.2 0.01 3 1.0 gbm_grid1_model_18
## 36 0.2 0.01 3 0.8 gbm_grid1_model_0
## mse
## 31<PHONE_NUMBER>723162
## 32<PHONE_NUMBER>5687294
## 33<PHONE_NUMBER>3785045
## 34<PHONE_NUMBER>5305735
## 35<PHONE_NUMBER>6919456
## 36<PHONE_NUMBER>617712
H2O’s Random Grid Search samples from the given parameter space until a set of constraints is met. The user can specify the total number of desired models using (e.g. max_models = 40
), the amount of time (e.g. max_runtime_secs = 1000
), or tell the grid to stop after performance stops improving by a specified amount. Random Grid Search is a practical way to arrive at a good model without too much effort.
The example below is set to run fairly quickly – increase max_runtime_secs
or max_models
to cover more of the hyperparameter space in your grid search. Also, you can expand the hyperparameter space of each of the algorithms by modifying the definition of hyper_param
below.
# GBM hyperparamters
gbm_params2 <- list(learn_rate = seq(0.01, 0.1, 0.01),
max_depth = seq(2, 10, 1),
sample_rate = seq(0.5, 1.0, 0.1),
col_sample_rate = seq(0.1, 1.0, 0.1))
search_criteria2 <- list(strategy = "RandomDiscrete",
max_models = 50)
# Train and validate a grid of GBMs
gbm_grid2 <- h2o.grid("gbm", x = x, y = y,
grid_id = "gbm_grid2",
training_frame = splits[[1]],
validation_frame = splits[[2]],
ntrees = 100,
seed = 1,
hyper_params = gbm_params2,
search_criteria = search_criteria2)
# Get the grid results, sorted by validation MSE
gbm_gridperf2 <- h2o.getGrid(grid_id = "gbm_grid2",
sort_by = "mse",
decreasing = FALSE)
To get the best model, as measured by validation MSE, we simply grab the first row of the gbm_gridperf2@summary_table
object, since this table is already sorted such that the lowest MSE model is on top.
gbm_gridperf2@summary_table[1,]
## Hyper-Parameter Search Summary: ordered by increasing mse
## col_sample_rate learn_rate max_depth sample_rate model_ids
## 1 0.4 0.01 5 0.5 gbm_grid2_model_31
## mse
## 1<PHONE_NUMBER>1149807
In the examples above, we generated two different grids, specified by grid_id
. The first grid was called grid_id = "gbm_grid1"
and the second was called grid_id = "gbm_grid2"
. However, if we are using the same dataset & algorithm in two grid searches, it probably makes more sense just to add the results of the second grid search to the first. If you want to add models to an existing grid, rather than create a new one, you simply re-use the same grid_id
.
There are two ways of exporting models from H2O – saving models as a binary file, or saving models as pure Java code.
The more traditional method is to save a binary model file to disk using the h2o.saveModel()
function. To load the models using h2o.loadModel()
, the same version of H2O that generated the models is required. This method is commonly used when H2O is being used in a non-production setting.
A binary model can be saved as follows:
h2o.saveModel(my_model, path = "/Users/me/h2omodels")
One of the most valuable features of H2O is it’s ability to export models as pure Java code, or rather, a “Plain Old Java Object” (POJO). You can learn more about H2O POJO models in this POJO quickstart guide. The POJO method is used most commonly when a model is deployed in a production setting. POJO models are ideal for when you need very fast prediction response times, and minimal requirements – the POJO is a standalone Java class with no dependencies on the full H2O stack.
To generate the POJO for your model, use the following command:
h2o.download_pojo(my_model, path = "/Users/me/h2omodels")
Finally, disconnect with:
## [1] 1
You can learn more about how to take H2O models to production in the productionizing H2O models section of the H2O docs.
If you are new to H2O for machine learning, we recommend you start with the Intro to H2O Tutorial, followed by the H2O Grid Search & Model Selection Tutorial. There are a number of other H2O R tutorials and demos available, as well as the H2O World 2015 Training Gitbook, and the Machine Learning with R and H2O Booklet (pdf).


Felitrix@meow:~$ whoami
Howdy, my name is Felipe Koji called Felitrix, and and I'm an open source programming and projects
enthusiast.
Currently, I am self-taught, because I find it much easier to learn, making an effort to debug a
code, to code what you want and not step by step with a simple code that has nothing to do with my
main objective, searching the internet how to solve such a problem since we have it, why not use it?
I have some goals in mind which is to be GNU/Linux user of my primary machine, finish some courses
even though I'm self-taught I see the need to take some courses to enter the professional
environment.
One of my precepts is as long as I am active, I will make the internet free for everyone; an open
source promotes the development of the technology in its entirety; a healthy Internet, that's why I
support Mozilla.
cya█


'Can we lose it? Can we lose the title this month?' - Lambert looks ahead to Town's top-of-the-table clash with Wycombe
by Stuart WatsonIpswich Town host Wycombe in a top-of-the-table League One clash tomorrow night. STUART WATSON spoke to Blues boss Paul Lambert about the match following Saturday's 2-2 home draw with Blackpool.
Q: Wycombe scored in stoppage-time today. That puts them five points clear...
A: It's a disaster isn't it!? An absolute unbelievable disaster! Honestly!
I think we've played, what, 45 games and have one game left… It's a disaster!
Q: It feels like a big game...
A: Honestly, it's a disaster! We've played 17 games, yeah? Maybe we can lose the title this month. Can we lose it? Can we lose the title this month?
Maybe not. Let's see what happens in May. We've got two games in hand and we're a good side. Make no mistake about it, we're a good side.
Q: It's first versus second...
A: Yeah, and it's November. You get absolutely nothing in November. We are a right good side.
We've lost a couple of games all season... Let's see what happens.
Q: Are you surprised Wycombe are up there?
A: Do you know what, I don't know. I'm only concentrating on us. They've had a great start and I'm pretty sure if you asked Gareth (Ainsworth) he'd be saying the same.
Let's see what happens. You get nothing in November.
Q: Wycombe is where you started your managerial career in England of course...
A: It was a really good little club at that time. We got into the (League Two) play-offs and (League Cup) semi-final, which was great.
I had some good guys there, some really good players. It's a good little club.
Q: Do you still speak to anyone there?
A: Alan Parry I still speak to. AP is still there. The only thing wrong with him is he's a Rangers fan! Matty Bloomfield played under me.
Q: And Matty's coaching a couple of nights at Ipswich isn't he (with the Under-18s)?
A: I think so, yeah. I've only seen him once or twice.

A: If I see him I'll tell him 'well done you've won the league in November because the Ipswich newspaper says so'.
Q: So you're saying this game shouldn't be hyped up at all?
A: No, it's just another game.  That won't change, long after I'm away.
Nearly 20,000 here again is unbelievable. The atmosphere inside the stadium is unbelievable. The club's going the right way.


Whoopsie! You need a browser that can support iframes! I suggest Firefox, its pretty darn handy.
This experiment is broken...
More Experiments


About Polymer
Polymer was founded by two engineers’ love of dessert.
When they took time off to learn how the best dessert was made at scale, they saw firsthand that factory floors were beautiful, messy, and filled with opportunities to make work easier, faster, and more enjoyable.
After countless hours in factories working directly with factory teams, we built the first version of Polymer. Today, we’re building a product to detangle all factory floor operations, so small manufacturers all over the world can build better, faster, and smarter.
Join
Our small team is growing. We’re looking for software engineers who lorem ipsum sit dolor amet.
APPLY AT POLYMERTraits
A list of qualities, traits, and personal strengths habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
Experience
A brief description of trade and industry experiencetristique senectus et netus et malesuada fames ac turpis egestas.
Skills
Some preferred skills and tools we expect people to have habitant morbi tristique senectus et netus et malesuada.


- When you save your profile, the app saves a file in the following path of your directory:
/_system/apps/bcpie-bcpie/public/profile.json
- We recommend placing the following snippet at the top of your page templates:
{module_json, json="/_system/apps/bcpie-bcpie/public/profile.json" collection="profile" template=""}
- Now you can add your profile data to your pages with a simple Liquid syntax. Your profile data is inside a collection called "profile". You can access your data like this:
- {{profile.name}} (renders the company name)
- {{profile.brand}}
- {{profile.contact.firstname}}
- Here is the entire json output of the profile collection (if you have saved any data, it will show up here):
{{profile | json}}
- Please note: You will need to place the snippet (#2) on every overall module layout where you would like to use it. You don't need to add it to layouts that are rendered with tag syntax ({tag_blahblah}) if you have added it to its parent module layout ({module_blahblah}).
-
Developer Setup
-
Delete
- Replace the Liquid markup instances for this app with static text on any page where you added it.


#{extends 'jqueryui/layout.html'/} #{set title:'Autocomplete'/} #{set 'moreScripts'} #{/set}
Use a label-only autocomplete widget to suggest text values for an input field.
Type to populate the list, and select a label from the list.
The widget is based on the the following input
element, using the data-url
attribute value
for the server-side data source.
<input name="location" class="autocomplete" data-url="@{jqueryui.Autocomplete.label()}">
The autocomplete.js
JavaScript applies the jQuery UI autocomplete plug-in to the input
element.
#{includeVerbatim '/public/javascripts/autocomplete.js'/}
The jqueryui.Autocomplete.label(String term)
action renders a JSON-encoded list of strings that match
the user-entered term
parameter.
${label}
Note that there is no view template, because renderJSON
renders a response directly.
Use a label/value autocomplete to suggest input options that have a separate label and value.
The HTML input is almost the same as the example above, except that the form control name is different, because the submitted value will be an ID value instead of a string label.
<input name="location.id" class="autocomplete-value"
data-url="@{jqueryui.Autocomplete.value()}">
This client implementation uses more JavaScript in autocomplete-value.js
to handle label/value pairs,
with an additional hidden field in the form.
#{includeVerbatim '/public/javascripts/autocomplete-value.js'/}
The jqueryui.Autocomplete.value(String term)
action renders a JSON-encoded list of label/value pairs
that match the user-entered term
parameter.
${value}
#{includeVerbatim '/app/models/jqueryui/AutocompleteValue.java'/}


Barebone
.sass
Typography
Grid
Colors
Comming soon
Check Typography to see preview of what's going to be like


Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
Flexatron05-Dec-18 01:29 PM
good idea
1
I had a table somewhere, hope its still up to date
A Step-by-step Guide How to mine Pirate (ARRR) easily without owning ASIC’s or GPU’s or any other RIG.
1
included a table with data from 5-12-2018
ComputerGenie 👴👴👴05-Dec-18 02:20 PM
@FishyGuts
this contest is getting rediculous
exile13Yesterday at 1:26 PM
join us on the new discord server! https://discord.gg/yk6Rzz7
KOSIUSYesterday at 2:08 PM
https://discordapp.com/invite/DaDqTkc
MixicYesterday at 2:50 PM
https://medium.com/@KomodoRussia/bounty-pirate-60d26677cd8
Medium
Bounty PIRATE – Komodo Platform RU – Medium
Ok….. Time to set sail Pirates. Bounty!!!
FlexatronToday at 12:48 AM
Attention all Pirates: Official Pirate Discord Server invite link: https://discord.gg/mBZhZgz
(edited)
in feken #deleted-channel
Cryptofist05-Dec-18 02:22 PM
I was concerned about this when it was first mentioned ^ lol
it should go away soon, since only so many people can come from this one location.
Flexatron05-Dec-18 02:24 PM
Ill cool down
Acura05-Dec-18 03:06 PM
I was asked this question:
what would be the advantage of piratechain if zcash implemented 100% sksnarks and dpow (or another 51% attack protection)?
@yevgenydevine @chainalysis As soon as the shielded addresses are good enough that we can make them the default and migrate users to them. (This will take years.)
jl777c05-Dec-18 03:14 PM
(This will take years.)
and dpow
if the zcash roadmap is to become like PIRATE without dpow over the next years, that seems very good for PIRATE
already PIRATE will be the first sapling only chain
pretty sure zcash will allow sprout to exist since they dont have any zaddr exchanges bogging down with sprout processing
Flexatron05-Dec-18 03:22 PM
Pirate is more agile as its small
Disadvantage is that we need to wait for. Blossom
jl777c05-Dec-18 03:24 PM
why?
what does blossom provide that PIRATE needs?
Flexatron05-Dec-18 03:27 PM
Don't know much about blossom yet, but there may come a time when we want to scale further tps wise?
@zooko @yevgenydevine @chainalysis Or they can use $ARRR now from @KomodoPlatform and @PirateChain
#JustSayin
jl777c05-Dec-18 03:31 PM
@Flexatron so why to wait for blossom?
if we cant even articulate what it provides
FishyGuts05-Dec-18 05:03 PM
Please no more server invite spam here in Komodo.
Please.
If it continues, we will have to do something about it.
Thank you,
ComputerGenie 👴👴👴05-Dec-18 07:58 PM
fek me, now I have @Deleted User spamming me with invite (edited)
armands06-Dec-18 09:08 AM
Questions after reading techloverhd.com arrr sapling article. Is it planned to make it possible to migrate from zc to zs adress if coins on agama for macos? Or need specifically win version? Or may u advise to just send coins to DP to make things easier? I'm so bad at CLI. Thx!
jl777c06-Dec-18 09:11 AM
pretty sure there will be zmigrate GUI
3
that will issue the single cli command
blubbi06-Dec-18 11:35 PM
First impression of the new Pirate Beer!!
It's a German handcrafted Beer. Brewed after German purity law passed in 1516.
It is a bottom-fermented beer with an alcohol content of approx. 5.4 % and 12.8% original gravity.
It's a full-bodied beer with a strong taste, perfect for drinking alone as a Captain or together with Mateys.
Distribution starts in 1-2 weeks after the first 100 beer get botteled.
Only ARRR as Payment accepted.
https://ibb.co/rbp9nKN
i want to set a 3 gpu rig to mine pirate, what port should i connect to? the difficulty in port 2096 seems a little too high for 3 gtx 1060. Please help
GPU Port (diff 0.1): stratum+tcp://stratum.piratepool.io:2095
Multiple GPU Port (diff 10): stratum+tcp://stratum.piratepool.io:2096
Algorismus07-Dec-18 11:22 PM
Thanks @webworker01
i also want to ask, how can i set the intensity to 50 on DSTM miner?
i'm assuming the intensity value needs conversion when using dstm
Algorismus07-Dec-18 11:35 PM
there's an example for ewbf and bminer only on the getting started page. on the help menu of dstm i get the following values as example 0.9 and 0.85 seem waay to far from 50
#intensity=0:0.9,1:0.85 # Reduce the load which is put on the gpu valid
# range ]1.0-0.0[. Format: comma separated list
# of dev_id:intensity values e.g '0:0.893,2:0.8'
sorry if i was unclear previously
@webworker01
makes sens. so 50% should be .5
right?
thank you
yes, dstm do not apply changes to all gpus by one command
i have to set the intensity for each gpu
yes, i agree. the dev team of dstm was doing an amazing job. i don't know why they abandoned the project
there was no releases after v 6.0.2(edited)
Captain_Jack08-Dec-18 04:38 AM
anyone have good instructions for setting up an equihash mining pool?
MPOS or is that still the thing to use?
FishyGuts08-Dec-18 06:12 AM
@ComputerGenie 👴👴👴 linked you one in mining. Also, be sure to join the new official pirate discord.
@webworker01 (webworker01#3884)
You tipped @Decker612.749598 ARRR
A network fee of 0.0001 ARRR was deducted from the amount.
This operation is pending. You will be notified when when it has finished.
FishyGuts08-Dec-18 09:40 AM
You gotta be close to 2K arrr by now @Decker
Thanks for all you do!
house08-Dec-18 02:52 PM
‘... be sure to join the new official pirate discord.’
Yer genie ya slack arse
Zanzarismo08-Dec-18 06:38 PM
!arrrbalance
Zanzarismo08-Dec-18 09:13 PM
!arrrtip balance
ARRR TipBotBOT08-Dec-18 09:13 PM
TipBot
Please use #tip-bot to talk to TipBot.
NutellaLicka09-Dec-18 10:11 AM
I'm trying to sync the RKT chain, any idea why it'd be stuck at block 331 (I understand this was the block sapling was implemented)
I have updated to the 0.3.0 komodo daemon
jl777c09-Dec-18 10:33 AM
RKT needs a different repo
using the normal release will make it stuck at 331, sapling activation
NutellaLicka09-Dec-18 10:44 AM
thanks might create another test chain on the komodo repo to test sapling pools
If I create an -ac-private=1 chain with the updated komodo daemon, will it be automatically start with sapling integration from block 1(edited)
jl777c09-Dec-18 10:48 AM
no
sapling activation is around 1am Dec 15th UTC
NutellaLicka09-Dec-18 10:49 AM
copy, I see why i'd need the other repo now
jl777c09-Dec-18 10:52 AM
the only difference is the sapling activation timestamp in ~/komodo/src/komodo_defs.h
you can just change it to any timestamp to change when sapling activates
NutellaLicka09-Dec-18 10:59 AM
So I'd have to ensure both nodes that start the chain had the same timestamp (say for in an hours time). Then I'd be able to create a chain with all sapling addresses
jl777c09-Dec-18 11:03 AM
yes
SHossain09-Dec-18 05:23 PM
@dukeleto@jeezy please update your PIRATE pool to latest komodo daemon and start using sapling ready pool software. please ping me once your pool is sapling ready.
SHossain09-Dec-18 05:33 PM
@That_Guy8 ^^
NutellaLicka10-Dec-18 12:12 AM
Did anyone who tested the RKT chain run into this problem? I am stuck on block 331. I have compiled from the FSM branch and my timestamp in komodo_defs.h is showing 2 days ago (indicating to me sapling should be activated).
Yeah I see that, I don't know why it keeps getting stuck
Should it automatically enable sapling? There isn't anything else I need to edit is there?
SHossain10-Dec-18 01:00 AM
sapling already activated on this chain
NutellaLicka10-Dec-18 01:00 AM
Thought so. Hmm
NutellaLicka10-Dec-18 01:50 AM
I had to rm the RKT file in .komodo and restart the chain. Seemed to fix it
ComputerGenie 👴👴👴10-Dec-18 02:12 AM
@SHossain Given that the "ACTIVATION" date is correct and both of the "DEADLINE" dates have past, it shouldn't matter as long as the "DEADLINE" is in the past
if you set "DEADLINE" to yesterday it would still yield the same results as the rest of the network has already accepted it as having past and now a requirement
The only issue that could arise is if you set it in the future and tried to do sprout - in which case your node would believe it valid and the rest of the network would refuse it(edited)


If you"re seeing this message, that means
JavaScript has been disabled on your browser
, please
enable JS
to make this app work.


The activity feed
feature is analagous to a newsfeed. Similar to
applications such as Facebook, Twitter, and news apps, its goal is to present fresh and
timely content that is personalized and should encourage students to check for
new information regularly.
Content in the feed is ephemeral in nature. Items are pushed down the feed as newer items appear. It should contain “want-to-know” or “should-know” information for students.
Message priority: Low
Medium
Message relevance: Medium
High


The Big Night (1996) A film review by John Schuurman Copyright 1996 John Schuurman
Co-Directors: CAMPBELL SCOTT and STANLEY TUCCI Screenplay by: JOSEPH TROPIANO, STANLEY TUCCI
At the end of this fine food film, after all the Italian passion -- the shouting, the kissing, the fighting, the yearning -- is over, and defeat is certain for all has been spent, there is still the cooking of an omelet.
Secundo, the brother whom we think has lost the most, (he possesses none of the genius of the food artist Primo), cooks an omelet as an act of service for his helper and his brother. This simple three egg omelet is perhaps the last bit of food that will be given life in this restaurant called "Paradise."
We watch as this omelet is made. We watch the whole thing, without aid of time lapse photography, words, or music -- only the clicks of spatula and fork and our melancholia to help us. We watch the omelet from the idea of it, to the gathering of material for it, through the cooking, the serving, and at last the eating of it. And when at the final bite, a hesitant arm is extended over the great divide -- Secundo to Primo, business man to artist, pragmatist to idealist -- we know we have seen something god-like. It is an act full of love and forgiveness and promise.
It is quite a divide that must be bridged because this is a culture clash film. It is Old World vs. New World; principle vs. utility; fine cuisine vs. beer and pizza; the holiness of genius vs. base and filthy lucre.
Secundo, who cooks the omelet and extends the arm, is not the cook. He is the business man. He and his brother Primo are immigrants from Italy who have come to make their fortune in America. Secundo would like to compromise in matters of taste for the sake of the business. Primo cannot -- will not -- do it. "If you sacrifice, then the work dies."
If you know an artist (in the radical sense of that word) for whom to compromise artistic standards in any way means pandering to the philistine taste of base people or else prostituting the artistic soul for mere money or worldly success, you will be reminded of him or her in this film.
In art the question is always, "Who is art for?" Is art for the sake of ART or perhaps for the "gods?" Is the work the thing? Is the artist's authenticity and integrity ultimate? Must the artist be true only to himself and the rest be damned? Or is business the god who must be served? You have heard the arguments before. "You have to give people what they want or you won't be able to give them any thing at all." In this movie the line is, "I am a business man. I am what I need to be at the time."
So which wins in this movie? Neither. After a final "Big Night," and an omelet the morning after, Paradise must close. Business has lost -- art too.
But as we leave we are hopeful if somewhat melancholy. Hope arises as Secundo cooks and then places his hesitant hand on Primo's shoulder.  They suggest a third and better way: not art for ART, nor art for money; not the artist as rare self-indulgent eccentric, nor as pitiable prostitute who has sold his genius for a mess of the world's tripe.. Rather art as service, art as love, art as, "let me make something beautiful for you."
=============
This movie has an "R" rating because of rough language and a suggested adulterous sexual situation.
For more reviews by John Schuurman see: http://www.mcs.com/~wcrc/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Quick-start
Models
Options & Analyse
SegmentPy BETA 0.1 (Pronunciation: Segment Pie) is a open-source graphical interface which facilitates training machine learning models for CT-image segmentation. This package includes also analytic tools.
Installation
Quick Tutorial
Plug-ins
For Developpers
Credits & Contributors
Index Module Index Search Page


---
title: Meetings
order: 1
---
Lunch Meetup
We meetup for lunch every Wednesday, check back for the next meeting place.
- First Wednesday of the Month:
- Second Wednesday of the Month:
- Third Wednesday of the Month:
- Fourth Wednesday of the Month:
- Fifth Wednesday of the Month:
Regular Meeting Time
- Day:
- First Thursday of every month
- Time:
- 7:00pm
- Location:
- Domo Technologies, Inc. 772 East Utah Valley Drive,
American Fork, UT 84003
View Larger Map


devices
Efficient Website Development
Hello, I'm Thomas! I'm a JavaScript developer with a hands-on perspective for combining business operations and dynamic applications.
group
User Experience Focused
I have a strong passion for serving and helping others that has led me to many pursuits in hopes of finding where I can make a difference. My various work backgrounds help translate the best way to analytically implement ideas into applications.
settings
Skills:
Languages,
Libraries, and Frameworks.
HTML, CSS, Bootstrap, Materialize, JavaScript, jQuery, AJAX, FireBase, Axios, Express.js, Node.js, MySQL, Git, Wix, and Wordpress


jQuery is a fast, small, and feature-rich JavaScript library. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to-use API that works across a multitude of browsers. With a combination of versatility and extensibility, jQuery has changed the way that millions of people write JavaScript.
To work on mobile devices requires a light and fast version of the frameworks for JavaScript, so MobileUI is equipped with Zepto.js.
Zepto is a minimalist JavaScript library for modern browsers with a largely jQuery-compatible API. If you use jQuery, you already know how to use Zepto.
To use this component you need install with the command:
mobileui install jquery
See the Zepto documentation and its resources at zeptojs.com.


If you want to run tests that are not part of Concordion, you can
write your own runner by implementing the org.concordion.api.Runner
interface.
You tell Concordion to use your alternative runner by specifying the full name of your runner class:
link text
Using a full class name in the instrumentation can be less future-proof, so,
instead, you can use an alias specified as a System parameter
(concordion.runner.alias = full-class-name
).
Include the following command-line parameter for the JVM:
-Dconcordion.runner.myRunner = com.example.MyRunner
Then use the alias "myRunner":
link text
Given we have a custom runner (com.example.ExampleRunner
)
that returns the following results for different hrefs:
| Href Matches | Result to Return |
|---|---|
| Pass.* | SUCCESS |
| Fail.* | FAILURE |
| Except.* | EXCEPTION |
| Ignore.* | IGNORED |
| .* | (No result. Throw an exception instead.) |
And we've included an alias to it, on the JVM command-line:
-Dconcordion.runner.exampleRunner = com.example.ExampleRunner
When we run the following instrumentation for different values of href:

 My spec

We get the following outcomes:
| Href | Result Reported |
|---|---|
| PassingSpec.html | SUCCESS |
| FailingSpec.html | FAILURE |
| ExceptionSpec.html | FAILURE |
| IgnoredSpec.html | IGNORED |
| XXX.html | EXCEPTION |
Note that if the test runner returns a RunnerResult containing an exception this is reported as a failure, not as an exception. An exception is only reported if the runner itself throws an exception.