Start via Cloud Partners
Using PyTorch with AWS
To gain the full experience of what PyTorch has to offer, a machine with at least one dedicated NVIDIA GPU is necessary. While it is not always practical to have your own machine with these specifications, there are our cloud based solutions to allow you to test and use PyTorch’s full features.
AWS provides both:
- Deep Learning AMIs: dedicated, pre-built machine learning instances, complete with PyTorch
- Deep Learning Base AMI: bare Linux and Windows instances for you to do a custom install of PyTorch.
Quick Start on Deep Learning AMI
If you want to get started with a Linux AWS instance that has PyTorch already installed and that you can login into from the command-line, this step-by-step guide will help you do that.
- Sign into your AWS console. If you do not have an AWS account, see the primer below.
- Click on
Launch a virtual machine
. - Select
Deep Learning AMI (Ubuntu)
.This gives you an instance with a pre-defined version of PyTorch already installed. If you wanted a bare AWS instance that required PyTorch to be installed, you could choose the
Deep Learning Base AMI (Ubuntu)
, which will have the hardware, but none of the software already available. - Choose a GPU compute
p3.2xlarge
instance type.You can choose any of the available instances to try PyTorch, even the free-tier, but it is recommended for best performance that you get a GPU compute or Compute optimized instance. Other instance options include the Compute Optimized c5-series (e.g.,
c5.2xlarge
) or the General Compute t2-series or t3-series (e.g.,t2.2xlarge
). It is important to note that if you choose an instance without a GPU, PyTorch will only be running in CPU compute mode, and operations may take much, much longer. - Click on
Review and Launch
. - Review the instance information and click
Launch
. - You will want to
Create a new key pair
if you do not have one already to use. Pick a name and download it locally via theDownload Key Pair
button. - Now click on
Launch Instances
. You now have a live instance to use for PyTorch. If you click onView Instances
, you will see your running instance. - Take note of the
Public DNS
as this will be used tossh
into your instance from the command-line. - Open a command-line prompt
- Ensure that your key-pair has the proper permissions, or you will not be able to log in. Type
chmod 400 path/to/downloaded/key-pair.pem
. - Type
ssh -i path/to/downloaded/key-pair.pem ubuntu@<Public DNS that you noted above>
. e.g.,ssh -i ~/Downloads/aws-quick-start.pem<EMAIL_ADDRESS>. If asked to continue connection, typeyes
. - You should now see a prompt similar to
ubuntu@ip-100-30-20-95
. If so, you are now connected to your instance. - Verify that PyTorch is installed by running the verification steps below.
If you chose the
Deep Learning Base AMI (Ubuntu)
instead of theDeep Learning AMI (Ubuntu)
, then you will need to install PyTorch. Follow the Linux getting started instructions in order to install it.
Quick Start Verification
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)
The output should be something similar to:
tensor([[0.3380, 0.3845, 0.3217],
[0.8337, 0.9050, 0.2650],
[0.2979, 0.7141, 0.9069],
[0.1449, 0.1132, 0.1375],
[0.4675, 0.3947, 0.1426]])
Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:
import torch
torch.cuda.is_available()
AWS Primer
In order to use AWS, you need to set up an AWS account, if you do not have one already. You will create a username (your email address), password and an AWS account name (since you can create multiple AWS accounts for different purposes). You will also provide contact and billing information. The billing information is important because while AWS does provide what they call “free-tier” instances, to use PyTorch you will want more powerful, paid instances.
Once you are logged in, you will be brought to your AWS console. You can even learn more about AWS through a set of simple tutorials.
GPU-based instances
Generally, you will be using Amazon Elastic Compute Cloud (or EC2) to spin up your instances. Amazon has various instance types, each of which are configured for specific use cases. For PyTorch, it is highly recommended that you use the accelerated computing, or p3, instances. They are tailored for the high compute needs of machine learning.
The expense of your instance is directly correlated to the number of GPUs that it contains. The p3.2xlarge instance is the smallest, cheapest instance and can actually be suitable for many use cases.
Creating and Launching an Instance
Once you decided upon your instance type, you will need to create, optionally configure and launch your instance. You can connect to your instance from the web browser or a command-line interface. Here are guides for instance launch for various platforms:
AWS SageMaker
With SageMaker service AWS provides a fully-managed service that allows developers and data scientists to build, train, and deploy machine learning models.
See AWS documentation to learn how to configure Amazon SageMaker with PyTorch.
Pre-Built AMIs
AWS provides instances (called AWS Deep Learning AMIs) pre-built with a modern version of PyTorch. The available AMIs are:
- Ubuntu
- Amazon Linux
- Windows 2016
Amazon has written a good blog post on getting started with pre-built AMI.
Installing PyTorch From Scratch
You may prefer to start with a bare instance to install PyTorch. Once you have connected to your instance, setting up PyTorch is the same as setting up locally for your operating system of choice.
Using PyTorch with Google Cloud
To gain the full experience of what PyTorch has to offer, a machine with at least one dedicated NVIDIA GPU is necessary. While it is not always practical to have your own machine with these specifications, there are our cloud based solutions to allow you to test and use PyTorch’s full features.
Google Cloud provides both:
- dedicated, pre-built machine learning platforms, complete with PyTorch
- bare Linux and Windows virtual machines for you to do a custom install of PyTorch.
Google Cloud Primer
In order to use Google Cloud, you need to set up an Google account, if you do not have one already. You will create a username (typically an @gmail.com
email address) and password. After words, you will be able to try Google Cloud. You will also provide contact and billing information. The billing information is initially used to prove you are a real person. And then, after your trial, you can choose to upgrade to a paid account.
Once you are logged in, you will be brought to your Google Cloud console. You can even learn more about Google Cloud through a set of simple tutorials.
Cloud Deep Learning VM Image
Google Cloud provides no setup required, pre-configured virtual machines to help you build your deep learning projects. Cloud Deep Learning VM Image is a set of Debian-based virual machines that allow you to build and run machine PyTorch learning based applications.
GPU-based Virtual Machnes
For custom virtual machines, generally you will want to use Compute Enginee Virtual Machine instances), with GPU enabled, to build with PyTorch. Google has various virtual machine types and pricing options, with both Linux and Windows, all of which can be configured for specific use cases. For PyTorch, it is highly recommended that you use a GPU-enabled virtual machines. They are tailored for the high compute needs of machine learning.
The expense of your virutal machine is directly correlated to the number of GPUs that it contains. One NVIDIA Tesla P100 virtual machine, for example, can actually be suitable for many use cases.
Installing PyTorch From Scratch
You may prefer to start with a bare instance to install PyTorch. Once you have connected to your instance, setting up PyTorch is the same as setting up locally for your operating system of choice.
Using PyTorch with Azure
To gain the full experience of what PyTorch has to offer, a machine with at least one dedicated NVIDIA GPU is necessary. While it is not always practical to have your own machine with these specifications, there are our cloud based solutions to allow you to test and use PyTorch’s full features.
Azure provides:
- dedicated, pre-built machine learning virtual machines, complete with PyTorch.
- bare Linux and Windows virtual machines for you to do a custom install of PyTorch.
- notebooks to help you learn PyTorch and machine learning.
- a machine learning service with a robust Python SDK to help you train and deploy PyTorch models at cloud scale.
Azure Primer
In order to use Azure, you need to set up an Azure account, if you do not have one already. You will use a Microsoft-recognized email address and password. You will also verify your identity by providing contact and billing information. The billing information is necessary because while Azure does provide free usage credits and free services, you may need or want higher-end services as well.
Once you are logged in, you will be brought to your Azure portal. You can even learn more about Azure through a set of simple video tutorials.
GPU-based Virtual Machines
Generally, you will be using Azure Virtual Machines) to begin with PyTorch. Microsoft has various virtual machine types and pricing options, with both Linux and Windows, all of which are configured for specific use cases. For PyTorch, it is highly recommended that you use the GPU optimized, virtual machines. They are tailored for the high compute needs of machine learning.
The expense of your virtual machine is directly correlated to the number of GPUs that it contains. The NC6 virtual machine is, for example, one of the smallest, cheapest virtual machines and can actually be suitable for many use cases.
Azure Machine Learning Service
The Azure Machine Learning Service provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. See the documentation to learn how to use PyTorch with Azure Machine Learning.
Pre-Configured Data Science Virtual Machines
Azure provides pre-configured data learning and machine learning virtual machines. PyTorch are available on many of these - for example here is the documentation for how to setup an Azure virtual machine on Ubuntu Linux.
Installing PyTorch From Scratch
You may prefer to start with a bare virtual machine to install PyTorch. Once you have connected to your virtual machine, setting up PyTorch is the same as setting up locally for your operating system of choice.


| Class and Description |
|---|
| XSDeclarationPool
This class is pool that enables caching of XML Schema declaration objects.
|
| Class and Description |
|---|
| ElementPSVImpl
Element PSV infoset augmentations implementation.
|
| SchemaGrammar
This class is to hold all schema component declaration that are declared
within one namespace.
|
| SchemaGrammar.BuiltinSchemaGrammar |
| SchemaGrammar.Schema4Annotations
A partial schema for schemas for validating annotations.
|
| SchemaNamespaceSupport
This class customizes the behaviour of the util.NamespaceSupport
class in order to easily implement some features that we need for
efficient schema handling.
|
| SubstitutionGroupHandler
To store and validate information about substitutionGroup
|
| XMLSchemaException
This exception might be thrown by any constraint checking method.
|
| XMLSchemaValidator.KeyValueStore
Key value store.
|
| XMLSchemaValidator.ShortVector
A simple vector for
short s. |
| XMLSchemaValidator.ValueStoreBase
Value store implementation base class.
|
| XMLSchemaValidator.ValueStoreCache
Value store cache.
|
| XMLSchemaValidator.XPathMatcherStack
Stack of XPath matchers for identity constraints.
|
| XMLSchemaValidator.XSIErrorReporter
A wrapper of the standard error reporter.
|
| XSAnnotationImpl
This is an implementation of the XSAnnotation schema component.
|
| XSAttributeDecl
The XML representation for an attribute declaration
schema component is an <attribute> element information item
|
| XSAttributeGroupDecl
The XML representation for an attribute group declaration
schema component is a global <attributeGroup> element information item
|
| XSAttributeUseImpl
The XML representation for an attribute use
schema component is a local <attribute> element information item
|
| XSComplexTypeDecl
The XML representation for a complexType
schema component is a <complexType> element information item
|
| XSDDescription
All information specific to XML Schema grammars.
|
| XSElementDecl
The XML representation for an element declaration
schema component is an <element> element information item
|
| XSElementDeclHelper |
| XSGrammarBucket
A class used to hold the internal schema grammar set for the current instance
|
| XSGroupDecl
The XML representation for a group declaration
schema component is a global <group> element information item
|
| XSModelGroupImpl
Store schema model group declaration.
|
| XSNotationDecl
The XML representation for a NOTATION declaration
schema component is a global <notation> element information item
|
| XSParticleDecl
Store schema particle declaration.
|
| XSWildcardDecl
The XML representation for a wildcard declaration
schema component is an <any> or <anyAttribute> element information item
|
| Class and Description |
|---|
| XSAnnotationImpl
This is an implementation of the XSAnnotation schema component.
|
| Class and Description |
|---|
| SubstitutionGroupHandler
To store and validate information about substitutionGroup
|
| XMLSchemaException
This exception might be thrown by any constraint checking method.
|
| XSComplexTypeDecl
The XML representation for a complexType
schema component is a <complexType> element information item
|
| XSDeclarationPool
This class is pool that enables caching of XML Schema declaration objects.
|
| XSElementDecl
The XML representation for an element declaration
schema component is an <element> element information item
|
| Class and Description |
|---|
| SchemaGrammar
This class is to hold all schema component declaration that are declared
within one namespace.
|
| SchemaNamespaceSupport
This class customizes the behaviour of the util.NamespaceSupport
class in order to easily implement some features that we need for
efficient schema handling.
|
| XSDDescription
All information specific to XML Schema grammars.
|
| XSDeclarationPool
This class is pool that enables caching of XML Schema declaration objects.
|
| XSElementDecl
The XML representation for an element declaration
schema component is an <element> element information item
|
| XSGrammarBucket
A class used to hold the internal schema grammar set for the current instance
|
| Class and Description |
|---|
| SchemaGrammar
This class is to hold all schema component declaration that are declared
within one namespace.
|
| Class and Description |
|---|
| XMLSchemaLoader
This class implements xni.grammars.XMLGrammarLoader.
|
| XMLSchemaValidator
The XML Schema validator.
|
Copyright © 2023. All rights reserved.


Type something:
title: Title date: 2017-11-26 tags: - tag - tag --- ## Contents 1. MathJax inline: $\Gamma \sin\alpha_1 <> x_2$ 2. MathJax block: $$\begin{bmatrix} \cos\alpha&-\sin\alpha \\ \sin\alpha&\cos\alpha \end{bmatrix}$$ * let's try some code with << or >> or `>>`: ```c++ #include
int main() { std::cout << "Hello, $world$" << std::endl; } ``` > **Quote** >> *Today is a fine day*
Use
$\LaTeX$
to type formulæ and
Markdown
to format text.
Powered by
kerzol/markdown-mathjax
, modified by
Jin
.


Restel.fi: built by humans, for humans
Bart Feenstra & Jonna Tiainen
Jonna Tiainen
Finland
What do I do:
- Holistic Agile Coach
- Scrum Master
- Code Newbie: Java & JavaScript
Houstonian
Houston Inc.
- System deliveries
- System integration
- Agile Training
Drupal & community
- Drupal user for a year and a half
- First time DrupalCon speaker
Bart Feenstra
Engineer
- Knows some PHP, Python, Java, & Javascript, with a pinch of SQL
- I ♥ automation
Drupal & community
- Drupal user for 11, contributor for 9, and core developer for 8 years
- Founded the Dutch Drupal foundation
- Conference and meetup organizer
Druid
- 27 Druids
- 2 offices: Helsinki (2012) & Amsterdam (2015)
- 3 full-time remote employees
- Finland’s largest hotel and restaurant operator
- Operates 43 hotels and over 240 restaurants
- Serves several websites and web applications
The Project
A long time ago
in a galaxy far, far away..
Strating point:
War of the worlds
- Hotelworld, Eventworld, Restaurantworld, etc.
- The worlds did not communicate
Strating point:
War of the worlds
- Content chaos
- Confusing to customers
- Expensive to maintain all the worlds
End goal:
End of the worlds
- Fusion of the world in to new Restel.fi
- Controlled content
End goal:
End of the worlds
- Easy to get information
- Feeling of safety
The situation looked & felt like:
Going in to a battle
- Define your strategy
- Don't mind the rubbles
- Take on the challenge!
The monster must be caged!
The GoJIRA
- Fear can be a monster
- You have to cage that fear!
Kick-ass sidekick
The Druids
- Knowledge on Drupal 8
- Best vibes
- Scrum & Agile
Going thru through project is like
Rafting a river
- Don't drown in the stream
- Trust your partners
- Avoid the waterfalls
How do we get thru this?
The solution:
Scrum fit!
- Don't quit during the first weeks
- Repeat untill it feels natural
- Reward yourself
The last surprise:
Down with the illness
dis·trib·ute
To scatter or spread.
Why remote teams?
- Hometown
- Different offices (3 for this project)
- Working from home
- Sickness
- Appointments outside 'office' hours
- ...
- ...
- Laundry
To distribute is to modernize
Everything must be electronic.
Grease monkeys hard at work
Contribute
Team night out
Team night out
A special kind of sunrise
Has anyone seen our Product Owner?
And where's our infra expert?


--- layout: page ---
PicoTorrent is designed to be a tiny, easy-to-use BitTorrent client with low memory usage.
We made PicoTorrent to go back to the basics of what is needed from a BitTorrent client. No ads, carefully selected features, and a native user interface integrating seamlessly with your desktop.
PicoTorrent is built directly on the Windows APIs which means that it is native looking on all Windows versions. We designed it to be as simple as possible to use, and all torrent controls are hidden in context menus.


The PDredesign readiness assessment that you’re about to partake in helps districts investigate the current state of their professional development (PD) systems and take action to optimize structures and impact. The assessment combines perspectives from various stakeholders and serves as a launchpad for deeper conversation about ongoing PD strategy.


Looking for food..
My favorite foods
Here is a link to my owners webpage First Webpage.
Check out Epicodus. It's a great school for learning web programming!


|
OpenXR Provider
|
#include "XRCore.h"
Public Member Functions | |
| XRCore (XRAppInfo xrAppInfo, XRAppGraphicsInfo xrAppGraphicsInfo, bool bEnableDepth) | |
| ~XRCore () | |
| Class Destructor. More... | |
| void | PollXREvents () |
| Poll for OpenXR events so app can act on it (e.g. Session state changes, visibility mask changes, etc) More... | |
| std::vector< const char * > | GetEnabledExtensionNames () const |
| XRExtHandTracking * | GetExtHandTracking () const |
| XRGraphicsAPI * | GetGraphicsAPI () const |
| bool | GetIsDepthSupported () const |
| std::shared_ptr< spdlog::logger > | GetLogger () const |
| XREventHandler * | GetXREventHandler () const |
| std::vector< void * > | GetXREnabledExtensions () const |
| XrInstance | GetXRInstance () const |
| XrSession | GetXRSession () const |
| XrSpace | GetXRSpace () const |
| XrSystemId | GetXRSystemId () const |
| XrSystemProperties | GetXRSystemProperties () const |
| void | SetXRSession (XrSession xrSession) |
| XrResult | XRBeginSession () |
| XrResult | XREndSession () |
Private Member Functions | |
| void | OpenXRInit (bool bEnableDepthTextureSupport=false) |
| void | EnableInstanceExtensions (std::vector< const char * > &pXRExtensions, bool bEnableDepthTextureSupport) |
| void | ExecuteCallbacks (XrEventDataBuffer xrEvent) |
| XrResult | LoadXRSystem () |
| void | WorldInit (XRAppGraphicsInfo *pXRAppGraphicsInfo) |
Private Attributes | |
| bool | m_bIsDepthSupported = false |
| If depth textures are supported. More... | |
| uint32_t | m_nAppVersion |
| Version of the application using this library. More... | |
| uint32_t | m_nEngineVersion |
| Version of the engine using this library. More... | |
| std::string | m_sAppName |
| Name of the application using this library. More... | |
| std::string | m_sEngineName |
| Name of the engine using this library. More... | |
| std::vector< const char * > | m_vAppEnabledExtensions |
| List of OpenXR extension names that are activated and supported by the currently active OpenXR runtime. More... | |
| std::vector< void * > | m_vAppRequestedExtensions |
| Array of OpenXR extension object that the app requested to be activated - may or may not be activated depending on active OpenXR support. More... | |
| std::vector< void * > | m_vXRAppEnabledExtensions |
| Array of OpenXR enabled extension objects that are activated and supported by the currently active OpenXR runtime. More... | |
| std::shared_ptr< spdlog::logger > | m_pLogger |
| Pointer to the logger. More... | |
| XREventHandler * | m_pXREventHandler = nullptr |
| Pointer to the active Event Handler that broadcasts events to listeners via their registered callback functions. More... | |
| XRGraphicsAPI * | m_pXRGraphicsAPI = nullptr |
| Pointer to the graphics api object that handles graphics api (e.g. OpenGL, Vulkan, etc) specific rendering state and functions. More... | |
| XrInstance | m_xrInstance = XR_NULL_HANDLE |
| The current OpenXR instance. More... | |
| XrResult | m_xrLastCallResult = XR_SUCCESS |
| Results of the last call to the OpenXR api. More... | |
| XrReferenceSpaceType | m_xrReferenceSpaceType = { XR_REFERENCE_SPACE_TYPE_STAGE } |
| Type of reference space currently is use (roomscale, seated) More... | |
| XrSession | m_xrSession = XR_NULL_HANDLE |
| The active OpenXR Session. More... | |
| XrSessionState | m_xrSessionState = { XR_SESSION_STATE_UNKNOWN } |
| Current session state. More... | |
| XrSpace | m_xrSpace = XR_NULL_HANDLE |
| The application space which represents the user's play space. More... | |
| XrSystemId | m_xrSystemId = XR_NULL_SYSTEM_ID |
| The active OpenXR runtime's system id. More... | |
| XrSystemProperties | m_xrSystemProperties = { XR_TYPE_SYSTEM_PROPERTIES } |
| The active OpenXR runtime's system properties. More... | |
| XrSpace | m_xrViewSpace = XR_NULL_HANDLE |
| The headset (HMD) view space. More... | |
| XrTime | m_xrPredictedDisplayTime = { 0 } |
| The current predicted display time. More... | |
| XRExtHandTracking * | m_pXRHandTracking = nullptr |
| OpenXRProvider::XRCore::XRCore | ( | XRAppInfo | xrAppInfo, |
| XRAppGraphicsInfo | xrAppGraphicsInfo, | ||
| bool | bEnableDepth | ||
| ) |
Class Constructor
| [in] | xrAppInfo | App metadata used to create an instance and session to the active OpenXR runtime |
| [in] | xrAppGraphicsInfo | App graphics metadata based on current active api |
| [in] | bEnableDepth | Whether depth texture support should be enabled if the runtime supports it |
| OpenXRProvider::XRCore::~XRCore | ( | ) |
Class Destructor.
|
private |
Enable provided extensions if supported by the currently active OpenXR runtime
| [in] | pXRExtensions | List of extensions names to enable if the active OpenXR runtime supports it |
|
private |
Execute all registered callback functions. Used after an OpenXR poll and an event state has been triggered by the runtime
| [in] | xrEventType | The type of OpenXR event that triggered the callback |
| [in] | eXREventData |
|
inline |
Getter for the array of extensions names that are active in the current OpenXR session
|
inline |
Getter for hand tracking extension object
|
inline |
Getter for the graphics api object that handles graphics api (e.g. OpenGL, Vulkan, etc) specific rendering state and functions
|
inline |
Getter if depth textures are supported and enabled
|
inline |
Getter for the logger object
|
inline |
Getter for the enabled OpenXR extension objects
|
inline |
Getter for the Event Handler object that broadcasts events to listeners via their registered callback functions
|
inline |
Getter for the active OpenXR instance
|
inline |
Getter for the active OpenXR session
|
inline |
Getter for the current app reference space
|
inline |
Getter for the current OpenXR System Id
|
inline |
Getter for the current OpenXR system properties
|
private |
Load the current OpenXR runtime and retrieve its system properties
|
private |
Initialize OpenXR - establish a connection to an active runtime. (1) Set application general info (e.g. App name, version, engine name, etc) (2) Check runtime's supported extensions and tag ones selected by the app to enable (e.g. Graphics type, Visibility Mask, Handtracking, etc), (3) Create an instance (object that allows communication to the runtime) and (4) Get xr system (representing a collection of physical xr devices) from active OpenXR runtime
| void OpenXRProvider::XRCore::PollXREvents | ( | ) |
Poll for OpenXR events so app can act on it (e.g. Session state changes, visibility mask changes, etc)
|
inline |
Setter for the currently active OpenXR session
| [in] | xrSession | Set the active OpenXR session |
|
private |
Initialize the world - setup the space and time the app will live in (1) Create a session (app communication instance to render frames or send/receive input to/from the runtime) and (2) Create a reference space (room-scale vs seated)
| [in] | pXRAppGraphicsInfo | Pointer to the application's graphics api (e.g. OpenGL, DirectX, Vulkan) dependent graphics info |
| XrResult OpenXRProvider::XRCore::XRBeginSession | ( | ) |
Call OpenXR xrBeginSession to start app frame synchronization
| XrResult OpenXRProvider::XRCore::XREndSession | ( | ) |
Call OpenXR xrEndSession to allow runtime to safely transition back to idle
|
private |
If depth textures are supported.
|
private |
Version of the application using this library.
|
private |
Version of the engine using this library.
|
private |
Pointer to the logger.
|
private |
Pointer to the active Event Handler that broadcasts events to listeners via their registered callback functions.
|
private |
Pointer to the graphics api object that handles graphics api (e.g. OpenGL, Vulkan, etc) specific rendering state and functions.
|
private |
|
private |
Name of the application using this library.
|
private |
Name of the engine using this library.
|
private |
List of OpenXR extension names that are activated and supported by the currently active OpenXR runtime.
|
private |
Array of OpenXR extension object that the app requested to be activated - may or may not be activated depending on active OpenXR support.
|
private |
Array of OpenXR enabled extension objects that are activated and supported by the currently active OpenXR runtime.
|
private |
The current OpenXR instance.
|
private |
Results of the last call to the OpenXR api.
|
private |
The current predicted display time.
|
private |
Type of reference space currently is use (roomscale, seated)
|
private |
The active OpenXR Session.
|
private |
Current session state.
|
private |
The application space which represents the user's play space.
|
private |
The active OpenXR runtime's system id.
|
private |
The active OpenXR runtime's system properties.
|
private |
The headset (HMD) view space.


TactJS :: Events in Behaviors
Add and Remove Delegates (click the square)
Events in behaviors (click the square)


Default Title
Welcome to the
Donation Station
Login below, or click
here
to create a new account.
Username
Password


I am a Computer Science major at Georgia Institute of Technology, and my concentrations are People and Systems and Architecture.
This summer, I'll be a teaching assistant in a summer camp which educates kids about engineering and programming. Additionally, I am currently working on this project and I'm excited to work on all the aspects.
In computer science, I am interested in human-computer interaction, software engineering, and web development.
The hobbies I enjoy doing are Bollywood dance, nail art, watching old 90s/2000s shows, and travelling.


ASA Dialup VPN Configuration
07 June 2014
This Cisco dialup VPN configuration worked for ASA 5505 running on version 8.4(7). All upper case letters are labels and should be changed according to your standards and taste. IP addresses are representational.
Network Object Mapping
object network OBJ_VPN_ENDPOINT1
subnet <IP_ADDRESS> <IP_ADDRESS>
description OBJ_VPN_ENDPOINT1 is a label
DHCP Pool
ip local pool VPN_DHCP_POOL1 <IP_ADDRESS>-<IP_ADDRESS>
Access Control List
access-list ACL_SPLIT_TUNNEL standard permit <IP_ADDRESS> <IP_ADDRESS>
access-list OUTSIDE-INSIDE extended permit ip object VPN_DHCP_POOL1 object OBJ_VPN_ENDPOINT1
Network Address Translation
nat (outside,outside) source static VPN_DHCP_POOL1 VPN_DHCP_POOL1 destination
static OBJ_VPN_ENDPOINT1 OBJ_VPN_ENDPOINT1
description NAT EXEMPTION FOR VPN CLIENT IPs TO VPN ENDPOINTS
Crypto Map
crypto dynamic-map DYN_CMAP_OUTSIDE 1 set ikev1 transform-set 3DES_SHA
crypto dynamic-map DYN_CMAP_OUTSIDE 1 set reverse-route
crypto map CMAP_OUTSIDE 99 ipsec-isakmp dynamic DYN_CMAP_OUTSIDE
Group Policy
group-policy GPOLICY_POLICY1 internal
group-policy GPOLICY_POLICY1 attributes
dns-server value <IP_ADDRESS>
vpn-tunnel-protocol ikev1 ikev2
split-tunnel-policy tunnelspecified
split-tunnel-network-list value ACL_SPLIT_TUNNEL
Tunnel Group
tunnel-group DIALUP_TUNNEL_GROUP type remote-access
tunnel-group DIALUP_TUNNEL_GROUP general-attributes
address-pool VPN_DHCP_POOL1
default-group-policy GPOLICY_POLICY1
tunnel-group DIALUP_TUNNEL_GROUP ipsec-attributes
ikev1 pre-shared-key ********
Dialup Credentials
username USERNAME password INSECURE encrypted
username USERNAME attributes
service-type remote-access


Japanese word test
Insert that If you want range of Test (Number of Excel)
-select-
(Range :
~
)
min
max
order
random


code package¶
Submodules¶
code.deep_forest module¶
-
class
code.deep_forest.
DeepForest
(num_trees, depth, num_features, split_ratio, hidden)¶ Bases:
torch.nn.modules.module.Module
Deep Forest class. This represents a deep forest, composed of multiple trees.
-
compute_importance
(feats)¶ Function to tabulate and compute the final importance scores :param feats: the background needed for the shapley scores
-
forward
(x, device=device(type='cpu'))¶ Forward pass function. Calls the forward function of every tree and finds the best prediction for every input given all tree predictions :param x: the input features :return predictions:
-
gen_tree_features
(num_trees, depth, num_features, split_ratio)¶ Function to generate the features subsets for all of the trees :param num_trees: the number of trees in the forest :param depth: The depth of the each tree :param num_features: the number of features in the dataset :param split_ratio: the ratio of features to be split on
-
loss
(x, y, device=device(type='cpu'))¶ Calculate the loss. :param x: the input features :param y: associated labels
-
populate_best
(x, y)¶ Precomputation step to find the mode of the left and right split. :param x: the input features :param y: associated labels
-
training
: bool¶
-
code.deep_tree module¶
-
class
code.deep_tree.
Leaf
¶ Bases:
torch.nn.modules.module.Module
Main leaf node class. This is the leaf of a decision tree
-
forward
(x, device=device(type='cpu'))¶ Forward function, returns the last computed best predictor for the leaf :param x: inputs to the tree, [num_inputs, num_features] :return: the last computed best predictor for the leaf
-
loss
(x, y, loss, device)¶
-
populate_best
(x, y)¶ Determines the best predictor for the node as a constant, returns the mode of the inputted labels
-
training
: bool¶
-
-
class
code.deep_tree.
Node
(features, hidden, depth, id, importance={})¶ Bases:
torch.nn.modules.module.Module
Main tree class. This represents a deep decision tree with learnable decision boundaries.
-
compute_importance
(feats)¶ Function to tabulate and compute the final importance scores :param feats: the background needed for the shapley scores
-
forward
(x, device=device(type='cpu'))¶ Forward function, applies the splitter to an input tensor recursively (cascades data through tree) :param x: inputs to the tree, [num_inputs, num_features] :return: the predictions for the inputs [num_inputs]
-
get_splitter_scores
(feats)¶ Function to derive the shapley scores for the splitter w.r.t to the features used at the node :param feats: the features that represent the background with which to compute these scores
-
loss
(x, y, loss, device=device(type='cpu'))¶ Loss function, applies the backpropagation of the loss recursively through the tree :param x: inputs to the tree, [num_inputs, num_features] :param y: associated labels :param loss: loss value, inputted at initial value of 0 :return: total loss
-
populate_best
(x, y)¶ Pre-computation step to find the mode of the left and right split. - x: the input features - y: associated labels
-
training
: bool¶
-
code.train_forest module¶
-
code.train_forest.
train_model
(model, x_train, x_test, y_train, y_test, epochs, log=True, device=device(type='cpu'))¶ Main function to train a deep forest model :param model: A deep_tree or forest model :param x_train: training feats :param x_test: testing feats :param y_train: training labels :param y_test: testing labels :param epochs: number of epochs :param log: print training :param device: a pytorch device for training


Commands Guide
Overview
All hadoop commands are invoked by the bin/hadoop script. Running the hadoop script without any arguments prints the description for all commands.
Usage: hadoop [--config confdir] [COMMAND] [GENERIC_OPTIONS] [COMMAND_OPTIONS]
Hadoop has an option parsing framework that employs parsing generic options as well as running classes.
| COMMAND_OPTION | Description |
|---|---|
| --config confdir | Overwrites the default Configuration directory. Default is ${HADOOP_HOME}/conf. |
| GENERIC_OPTIONS | The common set of options supported by multiple commands. |
| COMMAND
COMMAND_OPTIONS |
Various commands with their options are described in the following sections. The commands have been grouped into User Commands and Administration Commands. |
Generic Options
The following options are supported by dfsadmin, fs, fsck, job and fetchdt. Applications should implement Tool to support GenericOptions.
| GENERIC_OPTION | Description |
|---|---|
| -conf <configuration file> | Specify an application configuration file. |
| -D <property=value> | Use value for given property. |
| -fs <local|namenode:port> | Specify a namenode. |
| -jt <local|jobtracker:port> | Specify a job tracker. Applies only to job. |
| -files <comma separated list of files> | Specify comma separated files to be copied to the map reduce cluster. Applies only to job. |
| -libjars <comma seperated list of jars> | Specify comma separated jar files to include in the classpath. Applies only to job. |
| -archives <comma separated list of archives> | Specify comma separated archives to be unarchived on the compute machines. Applies only to job. |
User Commands
Commands useful for users of a hadoop cluster.
archive
Creates a hadoop archive. More information can be found at Hadoop Archives.
Usage: hadoop archive -archiveName NAME <src>* <dest>
| COMMAND_OPTION | Description |
|---|---|
| -archiveName NAME | Name of the archive to be created. |
| src | Filesystem pathnames which work as usual with regular expressions. |
| dest | Destination directory which would contain the archive. |
distcp
Copy file or directories recursively. More information can be found at Hadoop DistCp Guide.
Usage: hadoop distcp <srcurl> <desturl>
| COMMAND_OPTION | Description |
|---|---|
| srcurl | Source Url |
| desturl | Destination Url |
fs
Usage: hadoop fs [GENERIC_OPTIONS] [COMMAND_OPTIONS]
Runs a generic filesystem user client.
The various COMMAND_OPTIONS can be found at File System Shell Guide.
fsck
Runs a HDFS filesystem checking utility. See Fsck for more info.
Usage: hadoop fsck [GENERIC_OPTIONS] <path> [-move | -delete | -openforwrite] [-files [-blocks [-locations | -racks]]]
| COMMAND_OPTION | Description |
|---|---|
| <path> | Start checking from this path. |
| -move | Move corrupted files to /lost+found |
| -delete | Delete corrupted files. |
| -openforwrite | Print out files opened for write. |
| -files | Print out files being checked. |
| -blocks | Print out block report. |
| -locations | Print out locations for every block. |
| -racks | Print out network topology for data-node locations. |
fetchdt
Gets Delegation Token from a NameNode. See fetchdt for more info.
Usage: hadoop fetchdt [GENERIC_OPTIONS] [--webservice <namenode_http_addr>] <path>
| COMMAND_OPTION | Description |
|---|---|
| <fileName> | File name to store the token into. |
| --webservice <https_address> | use http protocol instead of RPC |
jar
Runs a jar file. Users can bundle their Map Reduce code in a jar file and execute it using this command.
Usage: hadoop jar <jar> [mainClass] args...
The streaming jobs are run via this command. Examples can be referred from Streaming examples
Word count example is also run using jar command. It can be referred from Wordcount example
job
Command to interact with Map Reduce Jobs.
Usage: hadoop job [GENERIC_OPTIONS] [-submit <job-file>] | [-status <job-id>] | [-counter <job-id> <group-name> <counter-name>] | [-kill <job-id>] | [-events <job-id> <from-event-#> <#-of-events>] | [-history [all] <jobOutputDir>] | [-list [all]] | [-kill-task <task-id>] | [-fail-task <task-id>] | [-set-priority <job-id> <priority>]
| COMMAND_OPTION | Description |
|---|---|
| -submit <job-file> | Submits the job. |
| -status <job-id> | Prints the map and reduce completion percentage and all job counters. |
| -counter <job-id> <group-name> <counter-name> | Prints the counter value. |
| -kill <job-id> | Kills the job. |
| -events <job-id> <from-event-#> <#-of-events> | Prints the events' details received by jobtracker for the given range. |
| -history [all] <jobOutputDir> | -history <jobOutputDir> prints job details, failed and killed tip details. More details about the job such as successful tasks and task attempts made for each task can be viewed by specifying the [all] option. |
| -list [all] | -list all displays all jobs. -list displays only jobs which are yet to complete. |
| -kill-task <task-id> | Kills the task. Killed tasks are NOT counted against failed attempts. |
| -fail-task <task-id> | Fails the task. Failed tasks are counted against failed attempts. |
| -set-priority <job-id> <priority> | Changes the priority of the job. Allowed priority values are VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW |
pipes
Runs a pipes job.
Usage: hadoop pipes [-conf <path>] [-jobconf <key=value>, <key=value>, ...] [-input <path>] [-output <path>] [-jar <jar file>] [-inputformat <class>] [-map <class>] [-partitioner <class>] [-reduce <class>] [-writer <class>] [-program <executable>] [-reduces <num>]
| COMMAND_OPTION | Description |
|---|---|
| -conf <path> | Configuration for job |
| -jobconf <key=value>, <key=value>, ... | Add/override configuration for job |
| -input <path> | Input directory |
| -output <path> | Output directory |
| -jar <jar file> | Jar filename |
| -inputformat <class> | InputFormat class |
| -map <class> | Java Map class |
| -partitioner <class> | Java Partitioner |
| -reduce <class> | Java Reduce class |
| -writer <class> | Java RecordWriter |
| -program <executable> | Executable URI |
| -reduces <num> | Number of reduces |
queue
command to interact and view Job Queue information
Usage : hadoop queue [-list] | [-info <job-queue-name> [-showJobs]] | [-showacls]
| COMMAND_OPTION | Description |
|---|---|
| -list | Gets list of Job Queues configured in the system. Along with scheduling information associated with the job queues. |
| -info <job-queue-name> [-showJobs] | Displays the job queue information and associated scheduling information of particular job queue. If -showJobs options is present a list of jobs submitted to the particular job queue is displayed. |
| -showacls | Displays the queue name and associated queue operations allowed for the current user. The list consists of only those queues to which the user has access. |
version
Prints the version.
Usage: hadoop version
CLASSNAME
hadoop script can be used to invoke any class.
Usage: hadoop CLASSNAME
Runs the class named CLASSNAME.
classpath
Prints the class path needed to get the Hadoop jar and the required libraries.
Usage: hadoop classpath
Administration Commands
Commands useful for administrators of a hadoop cluster.
balancer
Runs a cluster balancing utility. An administrator can simply press Ctrl-C to stop the rebalancing process. See Rebalancer for more details.
Usage: hadoop balancer [-threshold <threshold>]
| COMMAND_OPTION | Description |
|---|---|
| -threshold <threshold> | Percentage of disk capacity. This overwrites the default threshold. |
daemonlog
Get/Set the log level for each daemon.
Usage: hadoop daemonlog -getlevel <host:port> <name>
Usage: hadoop daemonlog -setlevel <host:port> <name> <level>
| COMMAND_OPTION | Description |
|---|---|
| -getlevel <host:port> <name> | Prints the log level of the daemon running at <host:port>. This command internally connects to http://<host:port>/logLevel?log=<name> |
| -setlevel <host:port> <name> <level> | Sets the log level of the daemon running at <host:port>. This command internally connects to http://<host:port>/logLevel?log=<name> |
datanode
Runs a HDFS datanode.
Usage: hadoop datanode [-rollback]
| COMMAND_OPTION | Description |
|---|---|
| -rollback | Rollsback the datanode to the previous version. This should be used after stopping the datanode and distributing the old hadoop version. |
dfsadmin
Runs a HDFS dfsadmin client.
Usage: hadoop dfsadmin [GENERIC_OPTIONS] [-report] [-safemode enter | leave | get | wait] [-refreshNodes] [-finalizeUpgrade] [-upgradeProgress status | details | force] [-metasave filename] [-setQuota <quota> <dirname>...<dirname>] [-clrQuota <dirname>...<dirname>] [-help [cmd]]
| COMMAND_OPTION | Description |
|---|---|
| -report | Reports basic filesystem information and statistics. |
| -safemode enter | leave | get | wait | Safe mode maintenance command.
Safe mode is a Namenode state in which it 1. does not accept changes to the name space (read-only) 2. does not replicate or delete blocks. Safe mode is entered automatically at Namenode startup, and leaves safe mode automatically when the configured minimum percentage of blocks satisfies the minimum replication condition. Safe mode can also be entered manually, but then it can only be turned off manually as well. |
| -refreshNodes | Re-read the hosts and exclude files to update the set of Datanodes that are allowed to connect to the Namenode and those that should be decommissioned or recommissioned. |
| -finalizeUpgrade | Finalize upgrade of HDFS. Datanodes delete their previous version working directories, followed by Namenode doing the same. This completes the upgrade process. |
| -upgradeProgress status | details | force | Request current distributed upgrade status, a detailed status or force the upgrade to proceed. |
| -metasave filename | Save Namenode's primary data structures
to <filename> in the directory specified by hadoop.log.dir property.
<filename> will contain one line for each of the following 1. Datanodes heart beating with Namenode 2. Blocks waiting to be replicated 3. Blocks currrently being replicated 4. Blocks waiting to be deleted |
| -setQuota <quota> <dirname>...<dirname> | Set the quota <quota> for each directory <dirname>.
The directory quota is a long integer that puts a hard limit on the number of names in the directory tree. Best effort for the directory, with faults reported if 1. N is not a positive integer, or 2. user is not an administrator, or 3. the directory does not exist or is a file, or 4. the directory would immediately exceed the new quota. |
| -clrQuota <dirname>...<dirname> | Clear the quota for each directory <dirname>. Best effort for the directory. with fault reported if 1. the directory does not exist or is a file, or 2. user is not an administrator. It does not fault if the directory has no quota. |
| -help [cmd] | Displays help for the given command or all commands if none is specified. |
mradmin
Runs MR admin client
Usage: hadoop mradmin [ GENERIC_OPTIONS ] [-refreshQueueAcls]
| COMMAND_OPTION | Description |
|---|---|
| -refreshQueueAcls | Refresh the queue acls used by hadoop, to check access during submissions and administration of the job by the user. The properties present in mapred-queue-acls.xml is reloaded by the queue manager. |
jobtracker
Runs the MapReduce job Tracker node.
Usage: hadoop jobtracker [-dumpConfiguration]
| COMMAND_OPTION | Description |
|---|---|
| -dumpConfiguration | Dumps the configuration used by the JobTracker alongwith queue configuration in JSON format into Standard output used by the jobtracker and exits. |
namenode
Runs the namenode. More info about the upgrade, rollback and finalize is at Upgrade Rollback
Usage: hadoop namenode [-format [-force] [-nonInteractive]] | [-upgrade] | [-rollback] | [-finalize] | [-importCheckpoint]
| COMMAND_OPTION | Description |
|---|---|
| -format [-force] [-nonInteractive] | Formats the namenode. It starts the namenode, formats it and then shuts it down. User will be prompted for input if the name directories exist on the local filesystem. -nonInteractive: User will not be prompted for input if the name directories exist in the local filesystem and the format will fail. -force: formats the namenode and the user will NOT be prompted to confirm formatting of name directories in the local filesystem. If -nonInteractive option is specified it will be ignored. |
| -upgrade | Namenode should be started with upgrade option after the distribution of new hadoop version. |
| -rollback | Rollsback the namenode to the previous version. This should be used after stopping the cluster and distributing the old hadoop version. |
| -finalize | Finalize will remove the previous state of the files system. Recent upgrade will become permanent. Rollback option will not be available anymore. After finalization it shuts the namenode down. |
| -importCheckpoint | Loads image from a checkpoint directory and save it into the current one. Checkpoint dir is read from property fs.checkpoint.dir |
secondarynamenode
Runs the HDFS secondary namenode. See Secondary Namenode for more info.
Usage: hadoop secondarynamenode [-checkpoint [force]] | [-geteditsize]
| COMMAND_OPTION | Description |
|---|---|
| -checkpoint [force] | Checkpoints the Secondary namenode if EditLog size >= fs.checkpoint.size. If -force is used, checkpoint irrespective of EditLog size. |
| -geteditsize | Prints the EditLog size. |
tasktracker
Runs a MapReduce task Tracker node.
Usage: hadoop tasktracker


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Basic example with SELECT element and hovering caption
Basic SELECT (
before
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after
)
xxx
Not that bad
Average
Good
Perfect


Attack Cycle
People easily got facilitate with some attractive things like beauty , profit etc shown over digital world which in actual is a trap. In the digital world it is necessary for every professional, businessman and student to see both positive and negative aspects of anything related to technology. So lets understand the attack cycle of social engineering.
- INVESTIGATION The very first phase of attack where attacker prepares the ground for attack. In this they will choose victim very smartly. Then there comes the role of information gathering process where they collect the maximum information about victim. In this process your social media platforms(Instagram, Facebook, Snapchat, LinkedIn etc) helps them telling a lot about you, your likes /dislikes, your professional background, even your nature.These information are enough for a good social engineer to hack you. After gaining the most of information, they choose the method of attack that could have more probability to be successful.
- HOOK The second phase is making connection with victim. They will approach victim with very friendly nature and by spinning a story they will try their best to target your emotions and winning your trust. The connection might be very small like helping someone at office door and letting them in, however connection might be a little long as hacker may become your online friend over instagram. It all depends what kind of attack hacker is doing and also the method which is adopted.
- PLAY After making the successful connection, hacker will execute the attack. Again depending upon the type of attack, execution will be in different form. Like after winning trust of victim they convinced him to click on some link that seems out be malicious later. Thus taking out your sensitive data, your money and more is done under this process. Or considering above example if you let someone into your company, he will collect all data of company with him.
- EXIT The hack is incomplete for any hacker without deleting all traces of his hack. They will delete all interaction done with victim. Covering each and every attack, they will remove all traces of malware and bringing the charade to normal. After this everything would seem like nothing had happened.


First
This is the first blog that I published in this space.
Actually, I have another blog on Netease, which has run since 2010. In that blog, I share my experience on research, life, and also reprint some article that I think good.
I write this blog mainly for learning the new script, that is markdown.
First, let me try how to insert a picture on blog.
A MATLAB plot of the Basin of Attraction for a function. (Image by Yossi Farjoun.)
Second, I want to try the function of highlighting source codes. For example, the followings are some MATLAB codes
This failed!!
Who can tell me the Program language that markdown can recognise? Itis would be very nice!
OK, this is the end!
I will publish all blogs on Linux platform.
comments powered by Disqus


SUBROUTINE CGBMV(TRANS,M,N,KL,KU,ALPHA,A,LDA,X,INCX,BETA,Y,INCY) * .. Scalar Arguments .. COMPLEX ALPHA,BETA INTEGER INCX,INCY,KL,KU,LDA,M,N CHARACTER TRANS * .. * .. Array Arguments .. COMPLEX A(LDA,*),X(*),Y(*) * .. * * Purpose * ======= * * CGBMV performs one of the matrix-vector operations * * y := alpha*A*x + beta*y, or y := alpha*A'*x + beta*y, or * * y := alpha*conjg( A' )*x + beta*y, * * where alpha and beta are scalars, x and y are vectors and A is an * m by n band matrix, with kl sub-diagonals and ku super-diagonals. * * Arguments * ========== * * TRANS - CHARACTER*1. * On entry, TRANS specifies the operation to be performed as * follows: * * TRANS = 'N' or 'n' y := alpha*A*x + beta*y. * * TRANS = 'T' or 't' y := alpha*A'*x + beta*y. * * TRANS = 'C' or 'c' y := alpha*conjg( A' )*x + beta*y. * * Unchanged on exit. * * M - INTEGER. * On entry, M specifies the number of rows of the matrix A. * M must be at least zero. * Unchanged on exit. * * N - INTEGER. * On entry, N specifies the number of columns of the matrix A. * N must be at least zero. * Unchanged on exit. * * KL - INTEGER. * On entry, KL specifies the number of sub-diagonals of the * matrix A. KL must satisfy 0 .le. KL. * Unchanged on exit. * * KU - INTEGER. * On entry, KU specifies the number of super-diagonals of the * matrix A. KU must satisfy 0 .le. KU. * Unchanged on exit. * * ALPHA - COMPLEX . * On entry, ALPHA specifies the scalar alpha. * Unchanged on exit. * * A - COMPLEX array of DIMENSION ( LDA, n ). * Before entry, the leading ( kl + ku + 1 ) by n part of the * array A must contain the matrix of coefficients, supplied * column by column, with the leading diagonal of the matrix in * row ( ku + 1 ) of the array, the first super-diagonal * starting at position 2 in row ku, the first sub-diagonal * starting at position 1 in row ( ku + 2 ), and so on. * Elements in the array A that do not correspond to elements * in the band matrix (such as the top left ku by ku triangle) * are not referenced. * The following program segment will transfer a band matrix * from conventional full matrix storage to band storage: * * DO 20, J = 1, N * K = KU + 1 - J * DO 10, I = MAX( 1, J - KU ), MIN( M, J + KL ) * A( K + I, J ) = matrix( I, J ) * 10 CONTINUE * 20 CONTINUE * * Unchanged on exit. * * LDA - INTEGER. * On entry, LDA specifies the first dimension of A as declared * in the calling (sub) program. LDA must be at least * ( kl + ku + 1 ). * Unchanged on exit. * * X - COMPLEX array of DIMENSION at least * ( 1 + ( n - 1 )*abs( INCX ) ) when TRANS = 'N' or 'n' * and at least * ( 1 + ( m - 1 )*abs( INCX ) ) otherwise. * Before entry, the incremented array X must contain the * vector x. * Unchanged on exit. * * INCX - INTEGER. * On entry, INCX specifies the increment for the elements of * X. INCX must not be zero. * Unchanged on exit. * * BETA - COMPLEX . * On entry, BETA specifies the scalar beta. When BETA is * supplied as zero then Y need not be set on input. * Unchanged on exit. * * Y - COMPLEX array of DIMENSION at least * ( 1 + ( m - 1 )*abs( INCY ) ) when TRANS = 'N' or 'n' * and at least * ( 1 + ( n - 1 )*abs( INCY ) ) otherwise. * Before entry, the incremented array Y must contain the * vector y. On exit, Y is overwritten by the updated vector y. * * * INCY - INTEGER. * On entry, INCY specifies the increment for the elements of * Y. INCY must not be zero. * Unchanged on exit. * * * Level 2 Blas routine. * * -- Written on 22-October-1986. * Jack Dongarra, Argonne National Lab. * Jeremy Du Croz, Nag Central Office. * Sven Hammarling, Nag Central Office. * Richard Hanson, Sandia National Labs. * * * .. Parameters .. COMPLEX ONE PARAMETER (ONE= (1.0E+0,0.0E+0)) COMPLEX ZERO PARAMETER (ZERO= (0.0E+0,0.0E+0)) * .. * .. Local Scalars .. COMPLEX TEMP INTEGER I,INFO,IX,IY,J,JX,JY,K,KUP1,KX,KY,LENX,LENY LOGICAL NOCONJ * .. * .. External Functions .. LOGICAL LSAME EXTERNAL LSAME * .. * .. External Subroutines .. EXTERNAL XERBLA * .. * .. Intrinsic Functions .. INTRINSIC CONJG,MAX,MIN * .. * * Test the input parameters. * INFO = 0 IF (.NOT.LSAME(TRANS,'N') .AND. .NOT.LSAME(TRANS,'T') .AND. + .NOT.LSAME(TRANS,'C')) THEN INFO = 1 ELSE IF (M.LT.0) THEN INFO = 2 ELSE IF (N.LT.0) THEN INFO = 3 ELSE IF (KL.LT.0) THEN INFO = 4 ELSE IF (KU.LT.0) THEN INFO = 5 ELSE IF (LDA.LT. (KL+KU+1)) THEN INFO = 8 ELSE IF (INCX.EQ.0) THEN INFO = 10 ELSE IF (INCY.EQ.0) THEN INFO = 13 END IF IF (INFO.NE.0) THEN CALL XERBLA('CGBMV ',INFO) RETURN END IF * * Quick return if possible. * IF ((M.EQ.0) .OR. (N.EQ.0) .OR. + ((ALPHA.EQ.ZERO).AND. (BETA.EQ.ONE))) RETURN * NOCONJ = LSAME(TRANS,'T') * * Set LENX and LENY, the lengths of the vectors x and y, and set * up the start points in X and Y. * IF (LSAME(TRANS,'N')) THEN LENX = N LENY = M ELSE LENX = M LENY = N END IF IF (INCX.GT.0) THEN KX = 1 ELSE KX = 1 - (LENX-1)*INCX END IF IF (INCY.GT.0) THEN KY = 1 ELSE KY = 1 - (LENY-1)*INCY END IF * * Start the operations. In this version the elements of A are * accessed sequentially with one pass through the band part of A. * * First form y := beta*y. * IF (BETA.NE.ONE) THEN IF (INCY.EQ.1) THEN IF (BETA.EQ.ZERO) THEN DO 10 I = 1,LENY Y(I) = ZERO 10 CONTINUE ELSE DO 20 I = 1,LENY Y(I) = BETA*Y(I) 20 CONTINUE END IF ELSE IY = KY IF (BETA.EQ.ZERO) THEN DO 30 I = 1,LENY Y(IY) = ZERO IY = IY + INCY 30 CONTINUE ELSE DO 40 I = 1,LENY Y(IY) = BETA*Y(IY) IY = IY + INCY 40 CONTINUE END IF END IF END IF IF (ALPHA.EQ.ZERO) RETURN KUP1 = KU + 1 IF (LSAME(TRANS,'N')) THEN * * Form y := alpha*A*x + y. * JX = KX IF (INCY.EQ.1) THEN DO 60 J = 1,N IF (X(JX).NE.ZERO) THEN TEMP = ALPHA*X(JX) K = KUP1 - J DO 50 I = MAX(1,J-KU),MIN(M,J+KL) Y(I) = Y(I) + TEMP*A(K+I,J) 50 CONTINUE END IF JX = JX + INCX 60 CONTINUE ELSE DO 80 J = 1,N IF (X(JX).NE.ZERO) THEN TEMP = ALPHA*X(JX) IY = KY K = KUP1 - J DO 70 I = MAX(1,J-KU),MIN(M,J+KL) Y(IY) = Y(IY) + TEMP*A(K+I,J) IY = IY + INCY 70 CONTINUE END IF JX = JX + INCX IF (J.GT.KU) KY = KY + INCY 80 CONTINUE END IF ELSE * * Form y := alpha*A'*x + y or y := alpha*conjg( A' )*x + y. * JY = KY IF (INCX.EQ.1) THEN DO 110 J = 1,N TEMP = ZERO K = KUP1 - J IF (NOCONJ) THEN DO 90 I = MAX(1,J-KU),MIN(M,J+KL) TEMP = TEMP + A(K+I,J)*X(I) 90 CONTINUE ELSE DO 100 I = MAX(1,J-KU),MIN(M,J+KL) TEMP = TEMP + CONJG(A(K+I,J))*X(I) 100 CONTINUE END IF Y(JY) = Y(JY) + ALPHA*TEMP JY = JY + INCY 110 CONTINUE ELSE DO 140 J = 1,N TEMP = ZERO IX = KX K = KUP1 - J IF (NOCONJ) THEN DO 120 I = MAX(1,J-KU),MIN(M,J+KL) TEMP = TEMP + A(K+I,J)*X(IX) IX = IX + INCX 120 CONTINUE ELSE DO 130 I = MAX(1,J-KU),MIN(M,J+KL) TEMP = TEMP + CONJG(A(K+I,J))*X(IX) IX = IX + INCX 130 CONTINUE END IF Y(JY) = Y(JY) + ALPHA*TEMP JY = JY + INCY IF (J.GT.KU) KX = KX + INCX 140 CONTINUE END IF END IF * RETURN * * End of CGBMV . * END


DSTEd stores your projects in your installed game.Please select your installed Don't Starve Together directory.


"MANEH "portion (Ezekiel 45:12), rendered “pound” (1 Kings 10:17; Ezra 2:69; Nehemiah 7:71, 72), a weight variously estimated, probably about 2 1/2 or 3 lbs. A maneh of gold consisted of a hundred common shekels(q.v.). (Comp. 1 Kings 10:17, and 2 Chronicles 9:16).
"MANEH "portion (Ezekiel 45:12), rendered “pound” (1 Kings 10:17; Ezra 2:69; Nehemiah 7:71, 72), a weight variously estimated, probably about 2 1/2 or 3 lbs. A maneh of gold consisted of a hundred common shekels(q.v.). (Comp. 1 Kings 10:17, and 2 Chronicles 9:16).


ransampl - random number sampling
#include <ransampl.h>
ransampl_ws* ransampl_alloc( int n );
void ransampl_set( ransampl_ws *ws, double *p );
int ransampl_draw( ransampl_ws *ws, double ran1, double ran2 );
void ransampl_free( ransampl_ws *ws );
These routines allow to draw a random index according to given probabilities p[0], .., p[n-1].
The implementation uses the alias method of Walker and Vose in a form given by Schwarz. Using precomputed tables, the cost for a single drawing is only O(1). Therefore this is the recommended method for M drawings if M>>n>>1.
To prepare, a workspace must be allocated by ransampl_alloc, and some tables must be precomputed by ransampl_set. The supplied probabilities p are not requested to be normalized.
For each random drawing, the user must supply two uniform random numbers ran1, ran2 to ransampl_draw.
Finally, ransampl_free deallocates the workspace.
Draw representative inhabitants according to the given population numbers of nine federal states of Austria. This example is also contained in the source distribution, demo/sampling1.c.
#include <stdio.h>
#include <gsl/gsl_rng.h>
#include "ransampl.h"
int main()
{
const int M=1000000;
int i, m;
// Discrete probability distribution example:
const int n = 9;
// states of Austria
const char* names[] = {
"Wien", "Niederoesterreich", "Oberoesterreich",
"Tirol", "Kaernten", "Salzburg",
"Vorarlberg", "Burgenland", "Steiermark" };
// inhabitants in millions as of 2011 [www.statistik.at]
double p[] = { 1.721573, 1.614661, 1.415020,
.711161, .558056, .532713,
.370833, .285377, .1211506 };
// Initialize random number generator:
gsl_rng_env_setup();
gsl_rng* rng = gsl_rng_alloc( gsl_rng_default );
// Allocate workspace, and precompute tables:
printf( "Precomputing tables ...\n" );
ransampl_ws* ws = ransampl_alloc( n );
ransampl_set( ws, p );
// Draw M random samples; accumulate histogram 'cumul':
printf( "Drawing %i samples ...\n", M );
double cumul[n];
for ( i=0; i<n; ++i )
cumul[i] = 0;
for ( m=0; m<M; ++m ) {
i = ransampl_draw( ws,
gsl_rng_uniform(rng),
gsl_rng_uniform(rng) );
cumul[i] += 1;
}
// Print given probability and obtained frequency:
printf( "Result (input->output):\n");
double sum = 0;
for ( int i=0; i<n; ++i )
sum += p[i];
printf( " %-18s %-9s %-9s %-9s\n",
"state", "N (Mio.)", "rel", "sim" );
for ( int i=0; i<n; ++i )
printf( " %-18s %9.7f %9.7f %9.7f\n",
names[i], p[i], p[i]/sum, ((double)cumul[i])/M );
// Free workspace and terminate:
ransampl_free( ws );
return 0;
}
Copyright (C): Joachim Wuttke 2013, Forschungszentrum Juelich GmbH
Software: FreeBSD License
Documentation: Creative Commons Attribution Share Alike
Homepage: http://apps.jcns.fz-juelich.de/ransampl
Please report bugs to the author <j.wuttke@fz-juelich.de>


Provides information about the element.
Provides access to its containing set object.
Allows controlling the lifetime of the element.
Set elements may be added to and removed from the hierarchy at runtime.
They bear user-defined names. They may exist independently, outside any
container.
New set element instances generally are created through members of
::com::sun::star::lang::XSingleServiceFactory or,
if supported, ::com::sun::star::lang::XMultiServiceFactory
on an implementation of SetUpdate. Initially, they are not
contained in a set object and have no meaningful name.
While an instance is not contained in a set object, it is owned by
the client and can be disposed by calling
::com::sun::star::lang::XComponent::dispose(). The name
of the object can freely be changed in that situation though without
persistent effect.
When the instance is inserted into a set (this includes replacing an
existing element), ownership is transferred to the container.
While it is contained in the container, clients must not dispose the
object. When inserted, the name of the object is fixed and is used to
identify it within the container. An implementation may support
::com::sun::star::container::XNamed::setName() even in
this case. If it does, changing the name has the same effect of removing
the object (under the old name) and then reinserting it into the same
container (using the new name).
When an instance is removed from a set (this includes being replaced by
a new element), ownership is transferred to the client again. It can then be
disposed or reinserted into a container. An instance can only be inserted
into a container, if it was obtained from the same hierarchy.
When a set element is removed from its set from outside the hierarchy, the
container disposes of the object. This occurrence can be detected by registering
a ::com::sun::star::lang::XEventListener with the object.
If an implementation is part of a read-only view of the hierarchy,
changing the name or parent is not supported (the object can't be removed from
its container anyway).


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