Set an array of unique values as the property of an object. Supports setting deeply nested properties using using object-paths/dot notation.
Install with npm:
$ npm install --save union-value
var union = require('union-value');
var obj = {};
union(obj, 'a.b.c', ['one', 'two']);
union(obj, 'a.b.c', ['three']);
console.log(obj);
//=> {a: {b: {c: [ 'one', 'two', 'three' ] }}}
a.b.c
) to get a nested value from an object. | homepage'a.b.c'
) paths. | homepagePull requests and stars are always welcome. For bugs and feature requests, please create an issue.
(This project's readme.md is generated by verb, please don't edit the readme directly. Any changes to the readme must be made in the .verb.md readme template.)
To generate the readme, run the following command:
$ npm install -g verbose/verb#dev verb-generate-readme && verb
Running and reviewing unit tests is a great way to get familiarized with a library and its API. You can install dependencies and run tests with the following command:
$ npm install && npm test
Jon Schlinkert
Copyright © 2017, Jon Schlinkert. Released under the MIT License.
This file was generated by verb-generate-readme, v0.4.2, on February 25, 2017.


MBMLRuntimeFunctions Class Reference
| Inherits from | NSObject |
|---|---|
| Declared in | MBMLRuntimeFunctions.h |
Overview
This class implements a set of MBML functions and supporting methods that provide access to Objective-C runtime information.
These functions are exposed to the Mockingbird environment via
<Function ... />
declarations in the MBDataEnvironmentModule.xml
file.
For more information on MBML functions, see the MBMLFunction
class.
Helper method for looking up classes
+ resolveClass:error:
Given a Class or a string containing the name of an Objective-C class, this method returns a Class object.
+ (Class)resolveClass:(id)resolveCls error:(inout MBMLFunctionError **)errPtr
Parameters
resolveCls |
A |
|---|---|
errPtr |
If non- |
Return Value
A Class
object representing the specified class, or nil
if
the specified class was not recognized by the Objective-C runtime.
Discussion
Note: This method is not exposed to the Mockingbird environment as a an MBML function.
Declared In
MBMLRuntimeFunctions.h
Looking up Classes
+ classExists:
Determines whether a given class exists in the Objective-C runtime.
+ (id)classExists:(NSString *)className
Parameters
className |
The function’s input parameter. |
|---|
Return Value
If there is a class with the given name, @YES
is returned.
Otherwise, @NO
.
Discussion
This Mockingbird function accepts a single string expression yielding the name of the class.
Expression usage
^classExists(NSString)
The expression above would return YES
, because the class
NSString
is part of the iOS SDK.
Declared In
MBMLRuntimeFunctions.h
+ getClass:
Returns the Class
object for the given class name.
+ (id)getClass:(NSString *)className
Parameters
className |
The function’s input parameter. |
|---|
Return Value
If there is a class with the given name, the corresponding
Class
object is returned. Otherwise, nil
is returned.
Discussion
This Mockingbird function accepts a single string expression yielding the name of the class.
Expression usage
Note: This function is exposed to the Mockingbird environment with a name that differs from that of its implementing method:
^class(NSString)
The expression above would return the Class
object representing the
NSString
class.
Declared In
MBMLRuntimeFunctions.h
Accessing singleton instances
+ singleton:
Returns the singleton instance of a given class.
+ (id)singleton:(id)forCls
Parameters
forCls |
The function’s input parameter. |
|---|
Return Value
If there is a class with the given name, the corresponding
Class
object is returned. Otherwise, nil
is returned.
Discussion
This Mockingbird function accepts a single object expression yielding either
a Class
object or an NSString
containing the name of an Objective-C class.
If the specified class responds to the selector instance
, this function
returns that instance.
Expression usage
^singleton(MBServiceManager)
The expression above would return the singleton instance of the
MBServiceManager
class.
Declared In
MBMLRuntimeFunctions.h
Getting a class's hierarchy
+ inheritanceHierarchyForClass:
Returns an array containing the inheritance hierarchy of a given class.
+ (id)inheritanceHierarchyForClass:(id)forCls
Parameters
forCls |
The function’s input parameter. |
|---|
Return Value
An array containing the Objective-C class hierarchy for the
specified class. If no such class was found, nil
is returned.
Discussion
This Mockingbird function accepts a single parameter, the class expression,
an object expression yielding either a Class
object or an NSString
containing the name of an Objective-C class.
The first item in the returned array will contain the Class
object
representing the class identified by class.
Each subsequent item in the array will contain a Class
representing the
superclass of the item that preceded it.
The last item in the returned array will contain the root class of the
hierarchy (which is usually NSObject
).
Expression usage
^inheritanceHierarchyForClass(NSMutableString)
The expression above would return an array containing three objects: the
Class
object for the NSMutableString
class, the Class
object for
NSString
, and finally the Class
for NSObject
.
Declared In
MBMLRuntimeFunctions.h
Checking for method implementations
+ objectRespondsToSelector:
Determines whether a given object instance responds to a specific Objective-C message selector.
+ (id)objectRespondsToSelector:(NSArray *)params
Parameters
params |
The function’s input parameters. |
|---|
Return Value
If object instance referenced in the object expression
responds to the selector specified in the selector expression,
the return value will be @YES
. Otherwise, @NO
will be returned.
Discussion
This Mockingbird function accepts two pipe-separated parameters:
The object, an object expression yielding an
NSObject
instance, andThe selector, a string expression yielding the selector name.
Expression usage
Note: This function is exposed to the Mockingbird environment with a name that differs from that of its implementing method:
^respondsToSelector($myName|isEqualToString:)
The expression above would evaluate to true if the expression $myName
yields an NSString
instance (or an instance of any other class that
responds to the isEqualToString:
message).
Declared In
MBMLRuntimeFunctions.h
+ classRespondsToSelector:
Determines whether a given class responds to a specific Objective-C message selector.
+ (id)classRespondsToSelector:(NSArray *)params
Parameters
params |
The function’s input parameters. |
|---|
Return Value
If the Objective-C class referenced in the class expression
responds to the selector specified in the selector expression,
the return value will be @YES
. Otherwise, @NO
will be returned.
Discussion
This Mockingbird function accepts two pipe-separated parameters:
The class, an object expression yielding either a
Class
object or anNSString
containing the name of an Objective-C classThe selector, a string expression yielding the selector name.
Expression usage
Note: This function is exposed to the Mockingbird environment with a name that differs from that of its implementing method:
^instancesRespondToSelector(NSString|availableStringEncodings)
The expression above evaluates to @YES
because the NSString
class responds
to the availableStringEncodings
message.
Declared In
MBMLRuntimeFunctions.h


| Long-Term Capital Gains | Default | Edited |
|---|---|---|
| Rates | - | {{ locals.inputs.long_term_cap }} |
| Dividends | Default | Edited |
| Dividends | 1 | {{ locals.inputs.dividends }} |
| Additional Medicare Tax on Unearned Income | Default | Edited |
| Rate | 0.038 | {{ locals.inputs.medicare_unearned_inc_rate }} |
| Threshold | - | {{ locals.inputs.medicare_unearned_inc_threshold }} |
| Income Tax | Default | Edited |
| Income Tax | - | {{ locals.inputs.income_tax_rate }} |


Refreshed on {{$ctrl.refreshed | date: "dd-MMM-yyyy hh:mm a"}}
Updated On {{::item.postdate | date: 'dd-MMM-yyyy hh:mm a'}}
Year Published: {{::item.yearPublished}}
Released at Gencon? {{::item.released ? 'Yes' : 'No'}}


Yes,Life do Matters
And We Care
Promise Care, where Life is celebrated .
As it's okay to be warry of deadly COVID-19 virus but worry not as we are beside you in your fight against the Corona virus.Be informative and fit ahead of time so you can strongly overcome this situation. Explore


--- title: kgl_humanprotein keywords: fastai sidebar: home_sidebar summary: "A top 12% solution for Human Protein Atlas Single-cell Classification Kaggle" description: "A top 12% solution for Human Protein Atlas Single-cell Classification Kaggle" nb_path: "nbs/index.ipynb" ---
Competition page: https://www.kaggle.com/c/hpa-single-cell-image-classification/


Premier League Set To Make Simple VAR Change For Match Going Fans
by Gary ConnaughtonEven when VAR seemingly gets the decision right, it still seems to piss off a lot of people. Liverpool's game against Crystal Palace yesterday was the perfect example.
James Tomkins scored in the opening half to give the home side the lead, but the goal would be reviewed by the VAR official. It soon became clear that the Palace man had bundled over Dejan Lovren in his attempts to get to the ball, with the goal rightly ruled out.
Even though this was a fairly clear foul it didn't stop some people complaining about the technology's influence.
It is fair to say that VAR has not been a success in the Premier League, especially when you consider the impact it has had on those in the stadium. Match going supporters have been treated as an afterthought.
While television viewers can watch the process in making these decisions, those in the stadiums have been left in the dark.
That is set to change somewhat next month, with chief of referees Mike Reilly revealing that more information will be given to the supporters during a VAR check.
The Independent report that whereas big screens in the stadium would previously just read 'Checking Penalty', they will now give the reason for the delay. For example, it would now say 'Checking Penalty – Possible Handball’.
This would be a small, but welcome, change. Reilly said that improving the experience of those in the stadium is a major priority.
We are trying constantly to balance the accurate decision-making and the benefits we get from that, with not impacting adversely on the fans’ and player experience in the stadium.
We are acutely conscious we don’t want stoppages of three minutes if we can avoid it, but at the same time when you end up with three or four offside events leading up to a goal, we have to get that right...
We are all in the same place which is ‘let’s try and improve the experience of fans in the stadium and improve the information we provide’, because the more we do that, the more we get everybody’s engagement.


Use VIE² to query backend services to analyze the following text for entities.
This is a small test, where Steve Jobs and Barack Obama sing a song.
Use VIE² to parse RDFa
Thomas Unknown
Add manual annotations
Alter the annotations
Use VIE² to serialize knowledge in RDFa
This is a sample text with Barack Obama in RDFa format.


Note: Rudderstack's JS SDK CDN: https://cdn.rudderlabs.com/v1.1/rudder-analytics.min.js
Rudderstack's destination SDKs url : https://cdn.rudderlabs.com/v1.1/js-integration
Refer this link for more info: Setup-for-V1.1-JS-SDK-test-cases


cTexture2D describes a 2D bitmap texture used for OpenGL texture-mapping More...
#include <CTexture2D.h>
Public Member Functions | |
| cTexture2D () | |
| Constructor of cTexture2D. | |
| ~cTexture2D () | |
| Destructor of cTexture2D. | |
| bool | loadFromFile (const char *a_fileName) |
| Load an image file (CHAI currently supports 24-bit .bmp and 32-bit .tga files). | |
| void | render () |
| Enable texturing and set this texture as the current texture. | |
| void | markForUpdate () |
| Call this to force texture re-initialization. | |
| void | setEnvironmentMode (const GLint &a_environmentMode) |
| Set the environment mode (GL_MODULATE, GL_DECAL, GL_BLEND, GL_REPLACE, or -1 for "don't set"). | |
| GLint | getEnvironmentMode () |
| Get the environment mode status. | |
| void | setWrapMode (const GLint &a_wrapSmode, const GLint &a_wrapTmode) |
| Set the texture wrap mode. | |
| GLint | getWrapSmode () |
| Get the texture wrap mode of S. | |
| GLint | getWrapTmode () |
| Get the texture wrap mode of T. | |
| void | setMagnificationFunction (GLint a_magnificationFunction) |
| Set the magnification function. | |
| GLint | getMagnificationFunction () |
| Get current magnification function. | |
| void | setMinifyingFunction (GLint a_minifyingFunction) |
| Set the minification function. | |
| GLint | getMinifyingFunction () |
| Get current magnification function. | |
| void | setSphericalMappingEnabled (bool a_enabled) |
| Set spherical mapping mode ON or OFF. | |
| bool | getSphericalMappingEnabled () |
| Get the status of the spherical mapping mode. | |
Public Attributes | |
| cImageLoader | m_image |
| Image loader (use this to get data about the texture itself). | |
| cColorf | m_color |
| Environmental color. |
cTexture2D describes a 2D bitmap texture used for OpenGL texture-mapping
| cTexture2D::cTexture2D | ( | ) |
Constructor of cTexture2D.
A texture contains a 2D bitmap which can be projected onto the polygons of a 3D solid.
| bool cTexture2D::loadFromFile | ( | const char * | a_fileName | ) | [virtual] |
Load an image file (CHAI currently supports 24-bit .bmp and 32-bit .tga files).
Load an image file (CHAI currently supports 24-bit .bmp and 32-bit .tga files on all platforms and should support all formats on Windows)
Reimplemented from cGenericTexture.
| void cTexture2D::render | ( | ) | [virtual] |
Enable texturing and set this texture as the current texture.
Enable texturing and set this texture as the current texture
Reimplemented from cGenericTexture.
| void cTexture2D::setMagnificationFunction | ( | GLint | a_magnificationFunction | ) |
Set the magnification function.
The texture magnification function is used when the pixel being textured maps to an area less than or equal to one texture element. It sets the texture magnification function to either GL_NEAREST or GL_LINEAR.
| a_magnificationFunction | value shall be either GL_NEAREST or GL_LINEAR. |
| void cTexture2D::setMinifyingFunction | ( | GLint | a_minifyingFunction | ) |
Set the minification function.
The texture minifying function is used whenever the pixel being textured maps to an area greater than one texture element. There are six defined minifying functions. Two of them use the nearest one or nearest four texture elements to compute the texture value. The other four use mipmaps. A mipmap is an ordered set of arrays representing the same image at progressively lower resolutions. If the texture has dimensions 2nx2m there are max(n, m) + 1 mipmaps. The first mipmap is the original texture, with dimensions 2nx2m. Each subsequent mipmap has dimensions 2k1x2l1 where 2 kx2l are the dimensions of the previous mipmap, until either k = 0 or l = 0. At that point, subsequent mipmaps have dimension 1x2l1 or 2k1x1 until the final mipmap, which has dimension 1x1. Mipmaps are defined using glTexImage1D or glTexImage2D with the level-of-detail argument indicating the order of the mipmaps. Level 0 is the original texture; level bold max(n, m) is the final 1x1 mipmap.
| a_minifyingFunction | value shall be either GL_NEAREST or GL_LINEAR. |
| void cTexture2D::setWrapMode | ( | const GLint & | a_wrapSmode, | |
| const GLint & | a_wrapTmode | |||
| ) |
Set the texture wrap mode.
Sets the wrap parameter for texture coordinate s to either GL_CLAMP or GL_REPEAT. GL_CLAMP causes s coordinates to be clamped to the range [0,1] and is useful for preventing wrapping artifacts when mapping a single image onto an object. GL_REPEAT causes the integer part of the s coordinate to be ignored; OpenGL uses only the fractional part, thereby creating a repeating pattern. Border texture elements are accessed only if wrapping is set to GL_CLAMP. Initially, GL_TEXTURE_WRAP_S is set to GL_REPEAT.
| a_wrapSmode | value shall be either GL_REPEAT or GL_CLAMP | |
| a_wrapTmode | value shall be either GL_REPEAT or GL_CLAMP |


What Textpedia is can be be understood by considering a sample use case.
Imagine for a moment that you're in a meeting with an important client and several other participants are present. The presenter or the client mentions a word, or phrase you have only the vaguest idea about.
Because of the importance of this meeting you can't use your phone to research the keyword, but you know that if you don't make a note about it or something like that, the pressures of the day will probably cause you to forget.
You'd really like to know what the keyword/phrase means so what do you do?
You could...


Three area football teams head to Camp Randall
Level 4 Playoffs - State Semifinals
by Kevin LewisMadison - Waunakee trailed Waukesha West 15-0, but the Warriors ripped off 34 unanswered points to win 34-15 and punch a ticket to the Division 2 state title game. They'll face Brookfield East Friday at 1 P.M. at Camp Randall.
DeForest won a 14-9 slugfest over New Berlin Eisenhower to earn a spot in the Division 3 championship. The Norskies will play Menasha in the Friday 10 A.M. game.
Black Hawk/Warren, IL will match up with Edgar for the Division 7 crown. The reigning champion Warriors topped Lourdes Academy 34-7. The Warriors will play Edgar Thursday at 10 A.M.


The UPF settings tab allows you to open the Manage Patchers Modal or reload patchers from disk.
You can reload UPF patchers by pressing Alt + F5 when a modal view is not active.
Note: reloading patchers does not update patcher settings changes, including GUI changes to your patcher's settings tab in the Manager Patchers Modal.


[§448H-2] License required. No person shall operate or practice as an elevator mechanic unless the person is licensed under this chapter. [L 1971, c 190, pt of §1; gen ch 1985]


The goal of this project is to recreate a functioning version of Jack Sparrow's Compass as seen in the films Pirates of the Caribbean. The interesting thing about this compass is that it doesn't point north. It points to what you want most.
The plan is to use 3D printing for the top and bottom of the compass housing. For the electronics, I plan to use an Arduino Pro Mini Arduino Nano 33 BLE, a micro continuous rotation servo, a GPS reciever, a magnatometer, a Hall Effect Sensor, a 9V battery, and a buck converter. On power up, the Arduino will get it's current position, calculate the heading to the pre-programmed destination, determine north with the magnatometer, and rotate the servo the correct number of degrees to point towards the destination.
{{ post.date | date: "%B %-d, %Y" }}


A Sample Page for iFrame testing
This is a sample page for testing iFrame, where the PRS form is embedded within the landing page.
To add the PRS form in your iFrame, follow these steps
- Download "Seamless" javascript package from https://github.com/rsmoorthy/seamless/releases/download/v1.0/seamless_v1.0.min.js and install in your server
- Include Seamless in your page header, along with jQuery, BootStrap and Bootbox
<link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" type="text/css"> <link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap-theme.min.css" type="text/css"> <script src="//cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script> <script src="//cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js"></script> <script type="text/javascript" src="//cdnjs.cloudflare.com/ajax/libs/bootbox.js/4.4.0/bootbox.min.js"></script> <script src="/js/seamless_v1.0.min.js" type="text/javascript"></script> (OR) <script src="https://s3.ap-south-1.amazonaws.com/rsm-seamless/seamless.min.js" type="text/javascript"></script>
- Include the following script in your html page, at the bottom
<script type="text/javascript"> $(function() { var seamless = new Seamless({acceptFrom:'http://prsdemo.isha.in'}); }); </script>
- Include the iframe pointing to prs form, as below.
<iframe id="prs" src="http://prsdemo.isha.in/html/forms.html/registration_test1_yaml" scrolling="no" style="overflow: hidden; border: none; height: 80%; width: 100%"/>
Sample iFrame Below
- Check how it works on mobile as well as desktop
- If you press Register, without pressing "I Agree to Terms and Conditions", you will notice a popup appearing on the main window itself.
- This is a first version, we can improve this a lot as we go along.
Some more Text below
- This is a sample page and sample test form.
- This is a first version, we can improve this a lot as we go along.


Brian Sheridan
About
|
Projects
|
Blog
|
Contact
Hi, I'm Brian.
I'm an aspiring web developer, former video game fanatic,
philosophy enthusiast, and hopeful voluntaryist.


${title.encodeAsHtml()} ${subtitle?.encodeAsHtml()} Authors: ${authors.encodeAsHtml()} Grails Version: ${grailsVersion} ${content}


ongoing notes comparing computing resources
Comparing compute resources:
AWS EC2, DigitalOcean, and Google Compute Engine look significantly cheaper than Rackspace, Azure, etc. (The Economist has estimated that AWS EC2 prices are roughly 3% below cost).
DigitalOcean tends to have the lowest price point for on-demand use of a given set of resources; usually comparable to the Amazon or EC2 persistent (e.g. monthly) use. All-inclusive pricing (storage, networking etc) and limited options make choices simple and total costs easy to anticipate, though lack the flexibility to fine-tune a configuration to pay for exactly what you need.
Google offers the best rate for higher-cpu instances, (e.g. 16 cores); primarily by provisioning them with less memory (0.9 GB/core) than DO or EC2.
Google configures disk space separately from instance type. Cost of $0.04 per GB for spinning disk storage, is quite a bit cheaper than the ~ $0.10/GB for AWS EBS, and in fact nearly S3 price ~ $0.03. This makes it a potentially compelling platform for high storage, low compute instances.
Presumably this is non-local storage like EBS, since they also offer a (very expensive) local SSD option (limited to 375 GB increments, each inc at $81.75 / mo). Though if the non-local nature creates a significant latency, it isn’t clear why one would buy non-local SSD option for $0.17/GB.
Using Google Compute Engine
Web interface is cleaner but much less flexible than Amazon’s, most tasks must be done with the commandline SDK tool instead, gcloud
(such as modifying firewall settings other than port 80 and port 443). For instance, this tutorial on running an Ubuntu Snappy Core image instead of the usual defaults.
Docker-machine makes this a bit easier, as usual. The user needs to create a project name and project id on the Google Cloud Engine web interface. Only the project-name is required as an argument to docker-machine (e.g. no authentication token), auth must be done interactively through a web terminal. It doesn’t seem that there is a way to turn on http
port or otherwise configure the firewall. By default, all inbound ports are shut. By mapping to port 80
and checking ‘allow http’ in the web interface one can still access an app like RStudio-server (my mapping docker ports appropriately), but there doesn’t seem to be any way to automate this. (In contrast, with Amazon EC2, docker-machine automatically attaches a ‘security-group’ called docker-machine
, so a user can customize the ports permitted on that security group to have, e.g. 8787 open by default.)
Performance comparisons
CPU performance varies within a single provider.
- DO CPUs appear 2.3 - 2.4 Ghz with ~ 15MB cache,
- Google CPUs around 2.6 Ghz with ~ 20MB cache,
- Amazon ~ 2.8 Ghz with ~ 25 MB cache
network i/o speeds seem reasonable on all.
Using resources with docker-machine
docker-machine with docker save: after committing the container, this saves the container locally rather than on the host. With even reasonable download speeds ~ 0.5MB/s this is quite slow. DigitalOcean download speeds are ~ 680 Mb/s, and upload about half that. Better yet, pushing the container back to the Hub only pushes the changed layers anyway, so is nearly instantaneous. The Hub also provides a private repo for free.
This provides a pretty compelling workflow of
docker-machine create ...
docker run <...> user/private
and do stuffdocker commit container user/private
,docker push user/private
,docker-machine rm -f ...
in order to deploy one’s work on temporary machine with appropriate power, and then save the work and close the machine. Using docker instead of the standard image ‘snapshots’ is probably faster and nicely independent of the machine and provider; and in this case the snapshot storage is free of cost as well.


I am currently working full-time as a manager at
Music Go Round Roseville and Burnsville, MN.
I just graduated in May with a degree in Computer Information Technology.
I have learned a great deal about computer networking and security,
software development, web development, operating systems, and multiple
programming/scripting languages and services. I have had a passion for web
development since age 12 when I created a website for my band with
writing HTML from scratch with Microsoft Notepad. I have created
multiple websites for friends and family since then. Going to
school helped me gain a greater understanding of development
best practices along with using the right methodologies.
Specialties: Java, Java EE, HTML5, CSS3, JavaScript, JQuery, JSP, MySQL,
Bootstrap, PHP, Perl, Linux, Eclipse, and RegEx.
Please feel free to reach out to me at<EMAIL_ADDRESS>You can also view my personal homepage at www.patrickviker.com or
view my git repository at github.com/pviker.
Social


Snap is an open source, optionally typed, object oriented scripting language for the Java platform. The learning curve is small for anyone with experience of Java, JavaScript, TypeScript, or Scala, amongst others. It has excellent integration with the host platform, and can do whatever can be done with Java and much more.
The language is ideal for embedding in to an existing application, and is a fraction of the size of similar languages for the Java platform. In addition to embedding it can be run as a standalone interpreter and has an development environment which allows scripts to be debugged and profiled.
Snap comes with a development environment that is available over HTTP on any web browser that supports Web Sockets. To develop and debug scripts the devlopment environment can be started with the command shown below. All that is required is a web browser and Java 1.5 or greater. The development environment is a single JAR file that can be downloaded from here. In addition an embeddable version is available which can be integrated in to any Java application.
java -jar snapd.jar --mode=develop --directory=work --port=4457
The development environment can use hot stand-by agents to improve responsiveness, the agent pool can be configured on the command line. In addition an agent can connect through the HTTP port using the HTTP CONNECT request and begin a debug session. An example configuration is shown below.
java -jar snapd.jar --mode=develop --directory=work --port=4457 --agent-pool=4 --server-only=true
To begin it is recommended that you download the interpreter. The interpreter requires Java to be installed on the host machine. Once you have downloaded the interpreter you can begin running scripts right away. All you need to do is specify the script file relative to the current directory.
java -jar snap.jar --script=/run.snap
If your scripts are in a different directory to the current working directory, then it can be specified on the command line. The script will be loaded relative to that directory.
java -jar snap.jar --script=/run.snap --directory=/path/to/scripts
If you would like to specify arguments that will be available within the script to be executed then each argument provided on the command line will be available as a named variable throughout the script. Any argument beginning with the '--' prefix will be available as a named variable within the script. For example the variables 'blah', 'foo', and 'bar' could be provided as follows.
java -jar snap.jar --script=/run.snap --directory=/path/to/scripts --blah=10 --foo='some text' --bar=55
If all you want to do is to evaluate some expression then you can specify that expression on the command line. The expression will be executed and if there is any output then that will be displayed to the console.
java -jar snap.jar --evaluate="println(1000 / 2.5)"
If you would like to embed the interpreter in to an existing application then all you need to do is create a compiler to compile a script. A script is compiled to an executable object, which is essentially an execution tree representing the script. Finally, once you have the executable object, you can provide a set of bindings that will be accessible to the script. These bindings can be provided by creating a model object.
File file = new File('/path/to/scripts'); // path used to load scripts Store store = new FileStore(file); Context context = new StoreContext(store); Compiler compiler = new ResourceCompiler(context); Map map = new HashMap(); Model model = new MapModel(map); // bindings for the script map.put("blah", 10); map.put("foo", "some text"); map.put("bar", 50); Executable executable = compiler.compile('/run.snap'); executable.execute(model); // execute the script
There are a number of configuration options for creating a compiler. For more information you can browse through the code which is hosted here.


Tejashwi Yadav acts as guardian angel after student’s desperate plea for education loan falls on deaf ears
by JKR StaffRJD leader Tejashwi Yadav is earning plaudits for his extraordinary act of kindness after his timely help helped a student from Bihar avoid cancellation of his college admission.
Student Mohd Faiz Uddin had approached Bihar Chief Minister Nitish Kumar and Deputy Chief Minister Sushil Modi for immediate help since his application for an education loan was pending for more than six months. The delay, according to the student, had posed questions on his future since his admission in Delhi’s Jamia Hamdard was likely to be cancelled if he did not clear his fees by 13 November.
The student did not receive any response from either the chief minister or his deputy. Although, Journalist Prashant Kumar tagged RJD leader Tejashwi asking if he could help save the future of the student.
Not only did Tejashwi intervene but also ensured that Rs 1 lakh was immediately credited to the student’s account. the RJD leader later tweeted, “Dear @MdFaiz105, Kindly update if the same has been credited in account or not. I’m told it was done yesterday.”
The student later thanked Tejashwi for his timely help and saving his future.
Tejashwi’s small act of kindness has become a huge talking point on Twitter with many hailing him as a guardian angel of a desperate student. Social media users have also slammed the state government for ignoring the desperate plea of a student, who wanted to take advantage of a key scheme launched by the Chief Minister Nitish Kumar-led government.


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|
Vortex Lite ISO C++ API Reference Guide
|
The examples source code can be found in the examples folder of your Lite installation. The descriptions below summarize what each example demonstrates. Towards the bottom of the page you will find instructions on how to build and run the examples.
The basic hello example is used to illustrate the necessary steps to setup DCPS entities.
It consists of 2 executables:
The publisher sends a single Hello World sample. The sample contains 2 fields :
When the subscriber receives the sample, it displays the userID field and the message field.
To build the example simply do .. code-block:: bash cd share/Lite/examples/<API>/helloworld make Where <API> = ddsc for c99. ddscxx for the latest C++ PSM mkdir build cd build cmake .. make
Most of our examples are provided in publisher and subscriber form. They reside in their respective folder within each example directory. It is recommended that you start these from different terminals and the working directory must be writeable.
This example does not require any options to run.


| Class and Description |
|---|
| Addresses |
| Flavor
A flavor is an available hardware configuration for a server.
|
| FloatingIP
Check Floating IP Wiki
page.
|
| Image
An image is a collection of files used to create or rebuild a server.
|
| RebootType |
| SecurityGroup
Defines a security group
|
| Server
A server is a virtual machine instance in the OpenStack Nova system.
|
| Class and Description |
|---|
| ImageStatus
In-flight images will have the status attribute set to SAVING and the conditional progress
element (0- 100% completion) will also be returned.
|
| ServerStatus
Servers contain a status attribute that can be used as an indication of the current server state.
|
| Class and Description |
|---|
| Flavor
A flavor is an available hardware configuration for a server.
|
| Image
An image is a collection of files used to create or rebuild a server.
|
| Server
A server is a virtual machine instance in the OpenStack Nova system.
|
| ServerStatus
Servers contain a status attribute that can be used as an indication of the current server state.
|
| Class and Description |
|---|
| Flavor
A flavor is an available hardware configuration for a server.
|
| Image
An image is a collection of files used to create or rebuild a server.
|
| Server
A server is a virtual machine instance in the OpenStack Nova system.
|
| Class and Description |
|---|
| AbsoluteLimit |
| AbsoluteLimit.Builder |
| Action |
| Address |
| Address.Builder |
| Addresses |
| Addresses.Builder |
| Flavor
A flavor is an available hardware configuration for a server.
|
| Flavor.Builder |
| FloatingIP
Check Floating IP Wiki
page.
|
| FloatingIP.Builder |
| Image
An image is a collection of files used to create or rebuild a server.
|
| Image.Builder |
| ImageStatus
In-flight images will have the status attribute set to SAVING and the conditional progress
element (0- 100% completion) will also be returned.
|
| Limits |
| Limits.Builder |
| RateLimit
RateLimit.
|
| RateLimit.Builder |
| RateLimitUnit |
| RebootType |
| Resource |
| Resource.Builder |
| Resource.LinkType |
| SecurityGroup
Defines a security group
|
| SecurityGroup.Builder |
| Server
A server is a virtual machine instance in the OpenStack Nova system.
|
| Server.Builder |
| ServerStatus
Servers contain a status attribute that can be used as an indication of the current server state.
|
| Class and Description |
|---|
| Server
A server is a virtual machine instance in the OpenStack Nova system.
|
Copyright © 2009-2013 jclouds. All Rights Reserved.


Panipat Institute of Engineering and Technology (PIET) was established by the Vidhya Peeth Education trust in the year 2006. It was the time when state and NCR was striving of producing World Class technocrats and Managers. PIET was Self Financed Initiative by a Private Management which always had an eye on quality of technical education. The great philosophy of PIET management has created an edifice that is strengthening its foundation, which can only rise higher and higher. Quality and Integrity is the essence for achieving Excellence at PIET. Members of Executive Academic Advisory Board being eminent personalities and philanthropists which have been successful in their endeavors set out with a common desire to design to serve the community through value based education. When we look at some option of best engineering Institute in National Capital Region, PIET stands out of a clear leader. A Multidisciplinary institute affiliated to kurukshetra university, Kurukshetra approved by AICTE, New Delhi provides Undergraduate programs in Engineering and Management. PIET has consistently pushed the war of excellence to garner riche for itself among the Ten Engineering institute in Delhi/NCR in the field of Engineering Education. It has focus on its goal of producing Entrepreneurs, world class Manager and technocrats employable throughout the globe. The institute has one of the largest student populations among engineering colleges of NCR. PIET is the most preferred destination for the students from across the country which could be attributed to the quality of education, infrastructure, healthy teaching Learning practices as well as producing industry ready students. PIET is a family of aspiring students supportive parents, committed faculty and visionary Management who all are working together to make a difference in education field.
The Institution is fully committed in translating the Vision of the Management into a reality with its focused approached to develop and offer young, committed and ethical graduates to diverse professional environments, specialised in different disciplines of Engineering and Management. The Institution apart from strictly adhering to course curriculum of the affiliating University also in the forefront in offering several value based activities / programmes, appropriately tailor made to transform and mould the raw and budding members of the Learners Community as fully trained professionals who are directly acceptable by professional environments. Extended Training Activites on advanced software’s relevant to different discipline. Hands-on training on advanced facilities / instruments , periodic visits to professional environments, Lectures / Talks by eminent personalities in the chosen disciplines of Engineering and Management, extensive and value additional type of interactions with professional environments, mentorship, group study schemes, to cit a few are begin implemented and seriously followed so as to profile the much needed value addition to the profiles of the Learners Community.


KT
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Published : {{ page.date | date_to_string }} | Last Updated : {{ page.updated | date_to_string }}
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comments powered by Disqus.
Credits:
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This tutorial shows how to build a Deep Learning model in H2O for classification and regression problems.
H2O Deep Learning is based on a multi-layer feed-forward artifical neural network that is trained with stochastic gradient descent using back-propragation. The network can contain a large number of hidden layers consisting of neurons with tanh, rectifier and maxout activation functions. Advanced features such as adaptive learning rate, rate annealing, momentum training, dropout, L1/L2 regularization, checkpointing and grid search enable high predictive accuracy. Each compute node trains a copy of the global model parameters on its local data with multi-threading (asynchronously), and contributes periodically to the global model via model averaging across the network.
Vocabulary:


gangway
menu
Heptio Gangway Kubernetes Authentication
This utility will help you authenticate with your Kubernetes cluster with an OpenID Connect (OIDC) flow. Sign in to get started.
Sign In


Tests for Grunt Responsive Images
https://github.com/andismith/grunt-responsive-images
srcset Test
PictureFill Test


sprint two - core: learning reflections
22 April 2020
1. What is your long term goal and or career pathway?
From what I have seen with future growth and opportunities I think I would like to see how I go with DevOps. I believe to start with I should build a good strong foundation in the production life-cycle of software, and build experience into a Senior Dev role. I have been looking at Companies that I could see myself working for and definitely Atlassian is one of those companies that I could work for. They have Front-end and Java jobs that require a good grasp of React/JS and then Java as well. But I'm all for developing my language toolset.
2. What skills (non-technical - human skills) would you like to see developed in yourself while at EDA?
I come from a Operations background as senior supervior and as Production/Operations Manager. So I'd like to see how I can bring these sills to the table within the dev environment. Not necessarily managing people, but seeing how I can lead from a Developer perspective. How I can mentor, and support the growth of the the others in my cohort and project teams. Once I can gain the practical experience I think a role as a Senior Developer would suit me well as I enjoy mentoring and developing others.
3. A description of your own strengths and limitations
I'm bold, I like to make a decision and run with it. I'm passionate about people and how they can make or brake your organisation. A limitation is that sometimes I can struggle with Impostor syndrome, and that can rattle me a bit. Sometimes I'm not the greatet with focus, but I am a professional and stick to my guns about honouring commitments and doing what I say I will do.
4. Based on the above description, a commitment to how you will manage your workload in this programme. This should include - an explanation of how you will manage yourself to work productively and safely with other learners, facilitators and industry/community representatives.
I have begun to use a 30min pomodoro timer to keep track and to work on focused 30min sprints, and then to have a break to keep me fresh, and then to focus on another 30min sprint. I will be exercising in the mornings to keep me fresh and energised. I will be professional in my actions, with respecting and honoring those around me. I have a keen interest in networking and will be respectful of other peoples time and their commitments to their study.
5. A commitment as to how and where you will seek help in a timely way.
If I come across a roadblock, I will set my pomodoro timer for 1 hour, and do my best to figure it out. If not I will turn to Stack overflow, and google to figure it out, if I haven't after 30 minutes I will reach out to my cohort and those on slack.
6. A description of what you expect from the facilitation team
I want someone to help point me in the right direction to work out the solutions needed, rather than being 'shown to the well' so to speak. Also, sometimes I can delve to deep in an area of interest but which is a tanget to what I should be focusing on. I guess to some extent I enjoy keeping myself accountable and checking in on what I am doing. So perhaps 2 video zoom session a week would be great during Foundations.
7. Any scheduling information such as block-out times when you are committed to other things. For example, you might have whanau commitments such as school drop-offs. It’s important to plan these in.
Right now I am transitioning out of doing part-time night-shifts.. but should be finished by the first week of May.
This blog is to showcase the various projects and tasks that I will be completing throughout my time with Enspiral Dev Academy.
- sprint one - technical: The Story of Te Houtaewa
- sprint one - core: identity, values and strengths
- sprint two - technical: classes vs. ids
- sprint two - core: learning reflections
- sprint three - core: emotional intelligence
- sprint three - technical: JavaScript, the DOM, and DevTools
- sprint four - core: neuroplasticity and growth mindset
- sprint four - technical: problem solving


{% extends "email.html" %} {% block main %}
Hello {{email_config.title}} administrators,
The following user has requested access:
{{req.email}}


This is the web portal for the Trolligarchian stock market. From here you can find information about all stocks, place orders and download historical price data for analysis.
This Exchange came into use shortly after the passing of the Fiscal and Treasury Act 2020
The act's author, Aligallaton, who can be found on the discord server in various guises of Tory Chancellors, is also the first person who created this exchange system. The backend is an utter mess, run from it at
all times.
The Exchange has always been digital, as it was seen as the simplest solution during it's inception. The Exchange is managed by the Chancellor of the Exchequer/Controller-General of Finance, who is in charge of all the colony's finances
and monetary decisions, and apparently this falls into their remit.


North Carolina county removes Confederate statue
by The Associated PressPITTSBORO, N.C. (AP) — A Confederate statue has been removed overnight from a historic North Carolina courthouse.
Chatham County officials issued a news release late Tuesday night saying work had begun to take down the soldier that stood in front of the courthouse in Pittsboro since 1907.
News outlets report that a subdued crowd of several dozen watched the work. Even the base of the statue was gone before dawn.
It has been rare for public officials to take down Confederate statues in North Carolina since the enactment of a 2015 state historic monuments law restricting the removal of public monuments.
But county officials argued in court that the monument was private property, owned by the United Daughters of the Confederacy, and a judge hearing the group’s challenge declined to block the removal.
The Associated Press


ADD A NEW FOOTBALL TEAM
Team Name
Team Logo Url
Squad Picture Url
Founded On
Home Ground
Team Cost
Team Website
Team Owner
Team Coach
Team Sponsor
Country
About


Search Job
- Previous
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Volunteer job posted by Sterre Jansen
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target = $scope.adapter.gridAdapter.columnFromPoint(evt.clientX, evt.clientY)Another method is moveBefore() which provides an ability to move one column in front of another:
column.moveBefore(target)
| {{item.name}} | ||
|---|---|---|
| {{item.col1}} | {{item.col2}} | {{item.col3}} |


This function handles known issues in the MSD. For example, a mechanism starting mid-year and reporting on TX_CURR will duplicate TX_CURR targets. This function resolves that by removing those known cases and stores cases relevant to your data frame.
The best workflow is to filter your dataset down to a country and/or technical area of interest before running `resolve_knownissues()`. When you run the function, it will print out any known issues to the console (and can) even store them to your Global Environment, so it makes sense to limit the data first to what you are using/care about.
The list of known issues is maintained by USAID/SIEI division. To access the table requires having a USAID email account and can be accessed via `browse_knownissues()`.
resolve_knownissues(df, store_excl = FALSE)
| df | standard MSD data frame, typically after its been filtered |
|---|---|
| store_excl | should the known exclusions be store in the Global Envir? |
df excluding known targets/results issues
[browse_knownissues()] to view table in Google Sheets; [set_email()] to store USAID email; [load_secrets()] to load credentials into session.
if (FALSE) { library(tidyverse) library(glamr) load_secrets() # or googlesheets4::gs4_auth() df_msd <- si_path() %>% return_latest("OU_IM") %>% read_rds() df_mwi <- df_msd %>% filter(operatingunit == "Malawi", indicator == "TX_CURR") df_mwi_resolved <- df_mwi %>% resolve_knownissues() }


Adult polar bears have no natural predators except other polar bears. Cubs less than one year old sometimes are prey to wolves and other carnivores. Newborn cubs may be cannibalized by malnourished mothers or adult male polar bears.


Marks & Spencer is now selling Christmas jumpers for dogs!
They're available in store or online
by Charlotte RodriguesMarks & Spencer is now selling Christmas jumpers for dogs.
The Chevron Stripe bone print jumper comes in a cream mix and grey mix colours.
It's available in three sizes and costs £19.50.
On the website, the description says: "Your pooch will look delightful and stay cosy in this novelty dog jumper.
"This knitted dog jumper features bright chevron stripes topped with a bone print, measure your dog from the base of the neck to the base of the tail to find the perfect fit."
Read More: Amazon is selling fluffy white Christmas tree skirts that look like snow
The jumpers are available both in store and online.
Marks & Spencer is also selling a Ham and Turkey dog toy, Bells dog toy and Champagne bottle dog toy which are the perfect Christmas presents for your pooch.
Read More: B&M is selling a doormat sensor that plays Christmas songs for £4.99
While the ham and turkey toy is £8, you can pick up the bells and champagne bottle for £6 each.
If you want to go all out for your pet for Christmas this year, you can pick up a pet advent calendar from Wilko.
Wilko also sells its own Christmas Rawhide Dog stocking for £5.
Or if you're looking for something slightly cheaper, you can pick up a 20 pack of Wilko's own Christmas Dog Treats which are Chewy Turkey and Sage strips just 80p.
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think of this story.


- Select a name for annotation in your Smith entry
- Open a new Perseus window and search for the figure you want to annotate with a characterization
Example: A search for Glaucus in Perseus(in a case where you mean to search for the son of Sisyphus) returns entries on Glaucus of Carystus and Glaucus' exchange with Diomedes in the Iliad. Also returns a passage of Strabo, 9.2.24 describing how Glaucus of Potniae (the son of Sisyphus) was torn up by his own mares. Since this passage describes the figure you are looking for, select the Greek word that corresponds to "torn up" (διασπασθέντα) as a characterization
- In Perseus, find the Greek or Latin word (or phrase) that describes your figure and its English equivalent
- Copy the Citation URN from the Perseus page on which you found the text. E.g. urn:cts:greekLit:tlg0099.tlg001.perseus-grc1:9.2.23>
- Append the actual text you want to reference, separating with an @
- Example: urn:cts:greekLit:tlg0099.tlg001.perseus-grc1:9.2.23@διασπασθέντα
- Find the English equivalent on Perseus and insert its URN with the corresponding English text
- Example: urn:cts:greekLit:tlg0099.tlg001.perseus-eng1:9.2.23@torn to pieces
- Return to Plokamos
- Select the subtitle of your entry "A Dictionary of Greek and Roman biography and mythology. William Smith, LLD, ed. 1890" or click on it if it is already underlined.
- On the popover, click the button for characterizations
- When you click the plus-Button to create a new entry, the name of the figure will be prepopulated
- In the second text field, enter the Citation URN for the English term
AND/OR
- In the third box, enter the URN for the Greek/Latin term
- Create one entry at a time and 'Apply'
- Remember to Commit your work before leaving the dictionary page.
This process allows us to display the words that the ancients associated with the figure you are annotating


If you are not married, you can hide this section.
If you are married, you can expand this section.
In addition to your own primary benefit, married couples may earn spousal Social Security benefits based on each other's records.
To make it easier to read this section of the report, please enter a name for yourself and for your spouse below. The names need not be real, just something you can use to keep track of who is who in the remainder of the report.
Your name: Spouse's name:
For {{recipient.name}}, we will use the values based on your primary insurance amount of (${{recipient.primaryInsuranceAmountFloored() | number:0}} / month) and your birthdate ({{recipient.birthdate().HumanBirthdate()}}). For {{spouse.name}}, you must enter these two values.
You can compute {{spouse.name}}'s primary insurance amount by using this tool once for {{spouse.name}}. Note the value, and then reload using this tool for {{recipient.name}}, entering {{spouse.name}}'s primary insurance amount below.
{{recipient.name}}'s
Primary Insurance Amount:
${{recipient.primaryInsuranceAmountFloored() | number:0}} / month
{{spouse.name}}'s
Primary Insurance Amount:
$
/ month
{{spouse.name}} was born on {{spouse.birthdate().HumanBirthdate()}}.
{{higherEarner().name}} has the higher Primary Insurance Amount, which means that {{higherEarner().name}} is not eligible for any spousal benefits. This is because the benefit one receives is always the larger of the personal or spousal benefit. Because {{higherEarner().name}}'s own benefit is larger than {{higherEarner().name}}'s spousal benefit, {{higherEarner().name}} does not get any spousal benefit.
{{lowerEarner().name}} has the lower Primary Insurance Amount, therefore {{lowerEarner().name}} may be eligible to receive a spousal benefit based on {{higherEarner().name}}'s personal benefit. However, in this case, {{lowerEarner().name}} will not receive a spousal benefit as {{lowerEarner().name}}'s own personal benefit is more than the spousal benefit earned on {{higherEarner().name}}'s Social Security record.
{{lowerEarner().name}} has the lower Primary Insurance Amount, therefore {{lowerEarner().name}} may be eligible to receive a spousal benefit based on {{higherEarner().name}}'s personal benefit.
Let's start by calculating {{lowerEarner().name}}'s spousal benefit at {{lowerEarner().name}}'s normal retirement age: 50% of {{higherEarner().name}}'s personal benefit minus {{lowerEarner().name}}'s own personal benefit.
{{lowerEarner().name}}'s Spousal Benefit:
( ${{higherEarner().primaryInsuranceAmountFloored() | number:0}}
x
50%
) - ${{lowerEarner().primaryInsuranceAmountFloored() | number:0}}
= ${{spousalBenefit() | number:0}}
This spousal benefit (${{spousalBenefit() | number:0}}) is the amount {{lowerEarner().name}} will receive in addition to {{lowerEarner().name}}'s own personal benefit of ${{lowerEarner().primaryInsuranceAmountFloored() | number:0}}.
Since neither {{recipient.name}} nor {{spouse.name}} are eligible for a spousal benefit, the date each person selects to begin receiving benefits has no effect on the total benefit of the other person. Use the calculations above for Early and Delayed Retirement to make a decision about the best time for each person to begin retirement benefits.
Note that {{lowerEarner().name}}'s total benefit (combined personal and spousal benefit) is equal to 50% of {{higherEarner().name}}'s personal benefit, regardless of {{lowerEarner().name}}'s personal benefit. This is the case, as long as {{lowerEarner().name}}'s personal benefit is less than 50% of {{higherEarner().name}}'s personal benefit.
The personal benefit section at the top of this document describes how personal benefits are adjusted when beginning benefits earlier than the Normal Retirement Age (NRA) or alternately, delaying past NRA.
Spousal benefits do not increase if claimed after Normal Retirement Age, but are reduced before Normal Retirement Age ({{lowerEarner().normalRetirementDate().monthFullName()}} {{lowerEarner().normalRetirementDate().year()}}) at the same rate as personal benefits.
The month {{spouse.name}} reaches specific ages may seem "off by one" from what you entered. Social Security follows English common law that finds that a person "attains" an age on the day before their birthdate. Because {{spouse.name}} was born on the first of the month, this means that {{spouse.name}} attains age {{spouse.exampleAge().age}} on {{spouse.exampleAge().month}} {{spouse.exampleAge().day}}, {{spouse.exampleAge().year}}.
If {{lowerEarner().name}} begins spousal benefits before Normal Retirement Age ({{lowerEarner().normalRetirementDate().monthFullName()}} {{lowerEarner().normalRetirementDate().year()}}), the spousal benefit will be permanently reduced per year you start early by:
The date that {{lowerEarner().name}}'s spousal benefits start is the later of either {{lowerEarner().name}}'s benefits start date or {{higherEarner().name}}'s benefits start date. This can lead to different reduction multipliers for the personal and spousal benefits.
There are two rules that apply to the date that {{lowerEarner().name}} can begin taking benefits:
You can see how the selected starting benefit dates for both spouses will affect the final monthly benefits, using the sliders in the chart below:
Your device's small screen will not display the combined retirement benefit interface correctly, so it has been hidden to avoid breaking the rest of this page. To view, you can:
{{higherEarner().name}} starts benefits at age:
{{lowerEarner().name}} starts benefits at age:
| In {{spousalSelection.dateText}}, | ||
| {{higherEarner().name}}'s Benefit: | ${{spousalSelection.higherEarnerBenefit | number:0}} | |
| {{lowerEarner().name}}'s Benefit: | ${{spousalSelection.lowerEarnerBenefit | number:0}} | |
| Total Benefit: | ${{(spousalSelection.higherEarnerBenefit + spousalSelection.lowerEarnerBenefit) | number:0}} |
You may find it oddly specific that the earliest {{spouse.name}} can actually begin benefits is at age 62 and 1 month. Benefit eligibility is calculated based on the first month that you are a particular age throughout the entire month. For most, this is the month after their birthdate.
For example, the first month that {{spouse.name}} is {{spouse.exampleAge().age}} throughout the entire month is {{followingMonth(spouse.exampleAge()).month}} {{followingMonth(spouse.exampleAge()).year}}.
Upon the death of {{higherEarner().name}}, {{lowerEarner().name}} will receive Survivor Benefits. Survivor benefits for {{lowerEarner().name}} will replace {{lowerEarner().name}}'s own benefit, if the Survivor Benefit is larger.
The amount of the Survivor Benefit is typically the amount of {{higherEarner().name}}'s personal benefit at the date of death. This will be reduced if {{lowerEarner().name}} begins collecting the Survivor Benefit before {{lowerEarner().name}}'s Normal Retirement Age ({{lowerEarner().normalRetirementDate().monthFullName()}} {{lowerEarner().normalRetirementDate().year()}}).
More detail on Survivor Benefits can be found at ssa.gov/planners/survivors.
The value of the Survivor Benefit may be something to consider when choosing initial benefit dates for both spouses, but especially {{higherEarner().name}}. Delaying current benefits can have an effect similar to life insurance by providing a higher Survivor Benefit which will persist throughout the surviving spouse's life.


JOHN MARK QUIANIO
amagi amagi amagi amagi
academy academy academy academy
A
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Lyrics of the Hearthside
by Paul Laurence Dunbar
The End of the Chapter
Additional Information
- Year Published: 1913
- Language: English
- Country of Origin: United States of America
- Source: Dunbar, P.L. (1913). The Complete Poems of Paul Laurence Dunbar. New York: Dodd, Mead, and Company.
-
Readability:
- Flesch–Kincaid Level: 6.5
- Word Count: 198
- Genre: Poetry
- Keywords: destiny, fate, life
- ✎ Cite This
Downloads
Ah, yes, the chapter ends to–day;
We even lay the book away;
But oh, how sweet the moments sped
Before the final page was read!
We tried to read between the lines
The Author’s deep–concealed designs;
But scant reward such search secures;
You saw my heart and I saw yours.
The Master,—He who penned the page
And bade us read it,—He is sage:
And what he orders, you and I
Can but obey, nor question why.
We read together and forgot
The world about us. Time was not.
Unheeded and unfelt, it fled.
We read and hardly knew we read.
Until beneath a sadder sun,
We came to know the book was done.
Then, as our minds were but new lit,
It dawned upon us what was writ;
And we were startled. In our eyes,
Looked forth the light of great surprise.
Then as a deep–toned tocsin tolls,
A voice spoke forth: “Behold your souls!”
I do, I do. I cannot look
Into your eyes: so close the book.
But brought it grief or brought it bliss,
No other page shall read like this!


BIB2BIB(1) BIB2BIB(1) NAME BibX2bib - Filter BibTeX data bases SYNOPSIS bib2bib [options] <filename> ... DESCRIPTION bib2bib is a filter tool that reads one or several bibliography files, filters the entries with respect to a given criterion, and outputs the list of selected keys together with a new bibliography file containing only the selected entries. If no input file is given then input is taken from the standard input. Additionally, bib2bib may output a file containing all the keys of entries that satisfy the condition. This second file is suitable for input as option -citefile to bibtex2html OPTIONS -ob filename specify the filename where the selected entries are output. If not given, it defaults to standard output. -oc filename specify the filename where the list of selected keys is output. If not given, this file is not created. -c condition specify a condition for selecting the entries. The output will retain only the entries that satisfy this condition. If several such condition are given, then only the entries that satisfy all the conditions are selected. The syntax of conditions is described in the complete documentation, notice that it is better to escape shell expansions in that conditions, in other words, you should write conditions between quotes. -expand expand all abbreviations in the output file. Notice that there is no way to ask for expansion of cross-references, since the meaning of such an expansion is unclear: it's better to let bibtex (via bibtex2html) handle the cross-references himself, depending on the style considered. -s field sorts the entries of the bibliography with respect to the given field, which may be $key or $type to refer to the key or to the entry type, as for filter conditions. This options may be used several times to specifiy a lexicographic order, such as by year, then by keys. When the field is $date then entries are sorted chronologically. When sorting, the resulting bibliography will always contain the comments first, then the preambles, then the abbreviations, and finally the regular entries. Be warned that such a sort may put cross-references before entries that refer to them, so be cau‐ tious. -r reverses the sort -q , --quiet be quiet. -w , --warn-error stop at the first warning. USAGE WITH BIBTEX2HTML Notice that the two output files are suitable for use with bibtex2html. A typical use would be bib2bib -oc citefile -ob bibfile.bib -c condition file1.bib file2.bib ... bibtex2html -citefile citefile bibfile.bib which will produce exactly the HTML file for the selected references. Notice finally that bibtex2html will expand the strings (by default, unless you specify the -noexpand option) but not the cross-references. AUTHOR Bib2bib has been written by Jean-Christophe FilliÃ¢tre and Claude MarchÃ©. This manpage has been compiled by Ralf Treinen from the origi‐ nal bib2bib documentation. SEE ALSO bibtex2html(1),bibtex(1),hevea(1) http://www.lri.fr/~filliatr/bibtex2html/ For complete documentation, please check the bibtex2html manual. On a Debian system the manual can be found at /usr/share/doc/bibtex2html both in the formats postscript and HTML, or via the Debian help system. BIB2BIB(1)


Loose horses cause danger for drivers on South Derbyshire road
They've had an absolute mare
by Beth PriddingPolice have shared a picture of loose horses who were found wandering around a country lane in Barrow-on-Trent.
Officers from Derbyshire Police had just started a night shift last night, November 24, when they came across the three horses.
A spokesman for Swadlincote Response Unit wrote on Twitter: "Group two's night shift started off with a mare of a job!"
All the horses were returned to where they had come from and are safe and well, police confirmed.
Police have been asked in which road, and what time, the horses were found, but said they could not find that information.
We've got apps for both Android and Apple devices which can be tailored to deliver the news and sport that you're interested in.
And if you download our app, you can sign up for push notifications for Burton by selecting 'discover' and choosing 'Burton' from the 'more topics' list.


class: center, middle Elements of Data Science - F2019 # Model Evaluation and Selection 10/14/2019 --- #In this lecture .smallest[ - Metrics: Regression Review - RMSE - R^2 - Overfitting and Underfitting - Train/Test split - Bias/Variance Tradeoff - Tuning Hyperparameters - Cross Validation - Grid Search - Plotting Model Fit - Validation Curve - Learning Curve - Metrics: Classification - Confusion Matrix - Accuracy/Error - Precision - Recall - ROC - AUC] --- # Steps to Choosing a Model -- count:false 1. Create Held-Aside Set (Train/Test Split) -- count:false 2. Determine Metric (or combination of metrics) -- count:false 2. Get a Baseline for comparison -- count:false 3. Use Cross-Validation to fit Hyperparameters and Choose Model -- count:false 4. Evaluate Chosen Model on Held-Aside Set --- # Goal of Prediction -- count:false - Given an item $x$, predict a value $\hat{y}$ -- count:false - often less interested in why than in how well -- count:false - How well will model generalize? -- count:false - **Generalization**: - how well will model predict on data that it hasn't seen yet --- # Overfitting/Underfitting -- count:false - **Overfitting**: poor generalization due to complexity - learning noise in training data -- count:false - **Underfitting**: poor generalization due to simplicity - not flexibile enough to learn concept -- count:false - Can we find a balance between simplicity and complexity? - we want a balance between **bias** and **variance** --- # Bias-Variance Tradeoff .center[ ![](images/05.03-bias-variance.png)] --- # Bias Variance Tradeoff .center[ ![](images/05.03-bias-variance-2.png)] --- # Bias-Variance Tradeoff Q : What happens when we retrain model on different training sets? -- count:false .center[ ![:scale 60%](images/bias_variance_targets.jpeg)] --- # Bias-Variance Tradeoff
-- .center[ ![:scale 60%](images/bias-variance-tradeoff.png)] --- # Overfitting/Underfitting -- count:false - **Overfitting**: poor generalization due to complexity - learning noise in training data - model has high **variance** and low **bias** -- count:false - **Underfitting**: poor generalization due to simplicity - not flexibile enough to learn concept - model has high **bias** and low **variance** --- # Avoiding Overfitting/Underfitting -- count:false - **Overfitting**: poor generalization due to complexity - learning noise in training leading to poor generalization -- count:false - Never train and evaluate on the same set of data! - train test split - cross-validation -- count:false - Keep the model as simple as possible - Occom's Razor - Increase Bias -- count:false - **Underfitting**: poor generalization due to simplicity - Increase Variance --- # Train/Test Split -- count:false .center[![](images/train_test_split.png)]
-- count:false - Training set: portion of dataset used for training -- count:false - Test or Held-aside set: portion of the dateset used for evaluation -- count:false - Want your test set to reflect the same distribution as training -- count:false .smallest[From https://www.researchgate.net/figure/Train-Test-Data-Split_fig6_325870973] --- # Train/Test Split -- count:false .smaller[ ```python from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=.1, stratify=y, random_state=123) ```] -- count:false - stratify: maintain the same class distribution -- count:false - random_state: only use for testing code (get same split every time) -- count:false - How big should test be? Large enough to capture variance of dataset. - depends on the dataset and the models being trained --- # Sample Dataset: Regression ```python import pandas as pd import seaborn as sns ``` ```python tips = sns.load_dataset('tips') tips = sns.load_dataset('tips') X_tips = tips[['total_bill','size']] y_tips = tips.tip ``` --- # Regression: Train/Test Split -- count:false .smaller[ ```python X_tips.shape, y_tips.shape # original dataset ```] .smaller[ ``` ((244, 2), (244,)) ```] -- count:false .smaller[ ```python # split into a train and test set X_train,X_test,y_train,y_test = train_test_split(X_tips, y_tips, random_state=123) ```] -- count:false .smaller[ ```python X_train.shape, X_test.shape ```] .smaller[ ``` ((183, 2), (61, 2)) ```] -- count:false .smaller[ ```python y_train.shape, y_test.shape ```] .smaller[ ``` ((183,), (61,)) ```] -- count:false .smaller[ ```python X_test.shape[0]/X_tips.shape[0] # default size of test ```] .smaller[ ``` 0.25 ```] --- # Regression Metrics Review How well is our model doing?
-- count:false - $R^2$: describes explained variance, want **higher** (sklearn default) .center[$R^2 = 1 - \frac{\sum \left(\hat{y}_i - y_i\right)^2}{\sum \left(\bar{y} - y_i\right)^2}$]
-- count:false - Adjusted $R^2$: takes into account number of parameters, want **higher** .center[$R\_{adj}^2 = 1 - (1 - R^2)\frac{n}{n-m-1}$] --- # Regression Metrics Review How well is our model doing?
-- count:false - Mean Squared Error (MSE) : describes error, want **lower** .center[$MSE = \frac{1}{n}\sum \left(\hat{y}_i - y_i\right)^2$]
-- count:false - Root Mean Squared Error (RMSE) : in scale of y, want **lower** .center[$RMSE = \sqrt{MSE}$] --- # Baseline Before we begin, how well can we do with a simple guess?
-- count:false > Simple guess for regression: return the mean of y
-- count:false > Simple guess for classification: majority class --- # Regression: Baseline -- count:false ```python from sklearn.dummy import DummyRegressor dummy_reg = DummyRegressor().fit(X_train,y_train) ``` -- count:false ```python dummy_re.predict([[0,0], [1,1]]) # get predictions for 2 items ``` ``` array([2.99, 2.99]) ``` -- count:false ```python y_train.mean() ``` ``` 2.99 ``` -- count:false ```python dummy_reg.score(X_test,y_test) ``` ``` -0.00 ``` --- # Regression: Baseline Adj-$R^2$ -- count:false ```python from sklearn.metrics import r2_score ``` -- count:false ```python yhat = dummy_reg.predict(X_test) # get predictions ``` -- count:false ```python r2_score(y_hat,y_test) ``` ``` -0.00 ``` -- count:false ```python def adj_r2_score(r2,num_items,num_parameters): return 1 - (1-r2)*num_items/(num_items-num_parameters-1) ``` -- count:false ```python adj_r2_score(r2_score(y_test,y_hat),*X_test.shape) ``` ``` -0.05 ``` --- # Regression: Baseline RMSE -- count:false ```python from sklearn.metrics import mean_squared_error ``` -- count:false ```python mean_squared_error(y_test,y_hat) ``` ``` 1.47 ``` -- count:false ```python def rmse(mse) return np.sqrt(mse) ``` -- count:false ```python rmse(mean_squared_error(y_test,y_hat)) ``` ``` 1.21 ``` --- # Regression: Linear Regression -- count:false ```python from sklearn.linear_model import LinearRegression ``` -- count:false ```python linr = LinearRegression().fit(X_train,y_train) ``` -- count:false ```python linr.score(X_test,y_test) ``` ``` 0.47 ``` -- count:false ```python adj_r2_score(linr.score(X_test,y_test),*X_test.shape) ``` ``` 0.44 ``` -- count:false ```python rmse(mean_squared_error(y_test,linr.predict(X_test))) ``` ``` 0.88 ``` --- # Cross Validation But how can we be sure our test/validation set is representative? Is there some way we could get an average across multiple test sets? -- count:false ### $k$-Fold Cross Validation -- count:false 1. split dataset into $k$ subsets -- count:false 2. for each subset -- count:false - train on the other $k-1$ subsets -- count:false - test on this subset to get a score -- count:false 3. average across all scores --- # 3-Fold Cross Validation .center[ ![](images/cv_1.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_2.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_3.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_4.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_5.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_6.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_7.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_8.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_9.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_10.png)] --- # 3-Fold Cross Validation .center[ ![](images/cv_11.png)] --- # 10-Fold Cross Validation .center[ ![:scale 100%](images/cv10fold.png)] --- # Cross Validation -- count:false - Can be used for: - tuning hyperparameters - model selection - any time we need estimate of model performance -- count:false - Issue: each fold requires training the model -- count:false - What values can $k$ take? -- count:false - min: 2 -- count:false - max: $n$ (Leave-One-Out CV) --- # Cross Validation in Sklearn ```python from sklearn.model_selection import cross_val_score ``` -- count:false ```python # using r2_score scores = cross_val_score(linr, X_train, y_train, cv=5, scores ``` ``` array([0.53, 0.61, 0.51, 0.25, 0.42]) ``` -- count:false ```python print(f'{np.mean(scores)} +- {2*np.std(scores)}') ``` ``` 0.47 +- 0.24 ``` --- # Cross Validation in Sklearn ```python # using mean squared error (note: neg due to maximization) scores = cross_val_score(linr, X_train, y_train, cv=5, scoring='neg_mean_squared_error') -scores ``` ``` array([0.78, 0.63, 0.72, 1.78, 1.31]) ``` -- count:false ```python print(f'{np.mean(-scores)} +- {2*np.std(scores)}') ``` ``` 1.04 +- 0.88 ``` --- # Compare Models with CV ```python from sklearn.linear_model import Lasso lasso = Lasso(alpha=1.0) ``` -- count:false ```python scores = cross_val_score(lasso, X_train, y_train, cv=5, scoring='neg_mean_squared_error') -scores ``` ``` array([2.11, 0.99, 1. , 0.73, 1.18]) ``` -- count:false ```python print(f'{np.mean(-scores)} +- {2*np.std(scores)}') ``` ``` 1.20 +- 0.95 ``` --- # Tuning Hyperparameters with CV ```python mean_scores = [] for alpha in [.01, .1, .5, .9, .99, 1]: lasso = Lasso(alpha=alpha) scores = cross_val_score(lasso,X_train,y_train,cv=5, scoring='neg_mean_squared_error') mean_scores.append( (alpha,-np.mean(scores)) ) ``` -- count:false ```python # find the setting with the lowest mse sorted(mean_scores,key=lambda x:x[1])[0] ``` ``` (0.01, 1.17) ``` --- # Tuning Hyperparameters with CV ```python from sklearn.linear_model import LassoCV ``` -- count:false ```python alphas = [.01, .1, .5, .9, .99, 1] lassocv = LassoCV(alphas=alphas,cv=5).fit(X_train,y_train) ``` -- count:false ```python lassocv.alpha_ ``` ``` 0.01 ``` --- # Visualize Hyperparameter Tuning ### Validation Curve .center[ ![](images/05.03-validation-curve.png)] --- # Visualize Hyperparameter Tuning ```python from sklearn.model_selection import validation_curve depths = np.arange(1,15) train_scores,test_scores = validation_curve(DecisionTreeRegressor(), X_train, y_train, 'max_depth', depths, cv=5, scoring='r2' ) mean_train_scores = np.mean(train_scores,1) mean_test_scores = np.mean(test_scores,1) ``` --- # Visualize Hyperparameter Tuning .smallest[ ```python plt.plot(depth, mean_train_scores, 'o-', color='b',label='training score'); plt.plot(depth, mean_test_scores, 'o-', color='r', label='validation score'); plt.xlabel('max_depth'), plt.ylabel('r2_score'); plt.title('Validation Curve for DecisionTreeRegressor'); plt.hlines(0,0,14,ls=':'); plt.legend(loc='best'); ```] .center[![:scale 60%](images/validation_curve.png)] --- # More Than One Parameter? **GridSearch:** Search over a 'grid' of hyperparameter settings - Example: Random Forest - number of trees - max depth ```python from sklearn.ensemble import RandomForestRegressor ``` --- # GridSearch with CV -- count:false .smaller[ ```python from sklearn.model_selection import GridSearchCV ```] -- count:false .smaller[ ```python params = {'n_estimators':[5,10,20], 'max_depth': [3,5,10]} ```] -- count:false .smaller[ ```python gscv = GridSearchCV(RandomForestRegressor(),params,cv=3, scoring='neg_mean_squared_error') gscv.fit(X_train,y_train) ```] -- count:false .smaller[ ```python print(gscv.best_params_) ```] -- count:false .smaller[ ``` {'max_depth': 3, 'n_estimators': 20} ```] -- count:false .smaller[ ```python scores = cross_val_score(gscv.best_estimator_,X_train,y_train,cv=5, scoring='neg_mean_squared_error') print(f'{np.mean(-scores)} +- {2*np.std(scores)}') ```] .smaller[ ``` 1.38 +- 1.31 ```] --- # What's the effect of dataset size? ### Learning Curve .center[ ![](images/05.03-learning-curve.png)] --- # What's the effect of dataset size? .smaller[ ```python from sklearn.model_selection import learning_curve train_sizes,train_scores,test_scores = learning_curve(gscv.best_estimator_, X_train, y_train, cv=5, scoring='r2' ) mean_train_scores = np.mean(train_scores,1) mean_test_scores = np.mean(test_scores,1) ```] --- # What's the effect of dataset size? .smallest[ ```python plt.plot(train_sizes, mean_train_scores, 'o-', color="b", label="training score"); plt.plot(train_sizes, mean_test_scores, 'o-', color="r", label="validation score") plt.xlabel('training set size'), plt.ylabel('r2_score'); plt.title('Learning Curve for DecisionTreeRegressor'); plt.legend(loc="best"); ```] .center[ ![:scale 60%](images/learning_curve.png)] --- # Review So Far .smaller[ - Overfitting and Underfitting - Bias/Variance Tradeoff - Train/Test split - Model Selection - Baseline - Tuning Hyperparameters - Cross Validation - Grid Search - Plotting Model Fit - Validation Curve - Learning Curve] ### .center[Questions?] --- # Next Up: Classification .smaller[ - Metrics: Classification - Confusion Matrix - Accuracy/Error - Precision - Recall - ROC - AUC] --- # Sample Dataset: Classification .smallest[ ```python from sklearn import datasets ```] .smallest[ ```python wine = datasets.load_wine() X_wine = pd.DataFrame(wine.data,columns=wine.feature_names) y_wine = wine.target features_wine = wine.feature_names[3:5] X_wine = X_wine.iloc[y_wine < 2,3:5].apply(lambda x: (x-x.mean())/x.std()).values y_wine = y_wine[y_wine < 2] X_wine.shape,y_wine.shape ```] .smallest[ ``` ((130, 2), (130,)) ```] .smallest[ ```python # split into a train and test set X_train,X_test,y_train,y_test = train_test_split(X_wine,y_wine, stratify=y_wine, test_size=.1, random_state=123) ```] -- count:false .smallest[ ```python X_train.shape, X_test.shape ```] .smallest[ ``` ((117, 2), (13, 2)) ```] -- count:false .smaller[ ```python y_train.shape, y_test.shape ```] .smaller[ ``` ((117,), (13,)) ```] --- # Classification: Confusion Matrix What are the different ways we can get things wrong?
-- count:false In Binary Classification: .center[ ![:scale 45%](images/confusion_matrix.png) ]
.smallest[ From https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62] --- # Classification: Accuracy
The number correct out of the total -- count:false $$ \text{accuracy}\left(\hat{y},y\right) = \frac{1}{n} \sum\_{i=1:n} \delta \left[\hat{y}\_i = y\_i\right] $$ where $$ \delta(a) = \begin{cases} 1 &\text{if } a \text{ is true}, \\\\ 0 &\text{o.w.} \end{cases} $$ --- # Classification: Baseline Accuracy What's our simple guess? -- count:false .smaller[ ```python sum(y_train == 1) / len(y_train) ```] .smaller[ ``` 0.55 ```] -- count:false .smaller[ ```python from sklearn.dummy import DummyClassifier dummy_cl = DummyClassifier(strategy='most_frequent') dummy_cl.fit(X_train,y_train) dummy_cl.predict([[0,0]]) ```] .smaller[ ``` array([1]) ```] -- count:false .smaller[ ```python scores = cross_val_score(dummy_cl,X_train,y_train,cv=5) print(f'{np.mean(scores)} +- {2*np.std(scores)}') ```] .smaller[ ``` 0.55 +- 0.02 ```] --- # Classification: LogReg Accuracy ```python from sklearn.linear_model import LogisticRegression logr = LogisticRegression() scores = cross_val_score(logr,X_train,y_train,cv=5) print(f'{np.mean(scores)} +- {2*np.std(scores)}') ``` ``` 0.82 +- 0.13 ``` --- # Classification: Error -- count:false error = 1-accuracy -- count:false Different kinds of error .center[ ![:scale 45%](images/confusion_matrix.png) ] --- # Precision vs. Recall ### Precision Out of the ones predicted positive, how many are truly positive? $$ \text{precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} $$ -- count:false ### Recall Out of the truly positive, how many did I call positive? $$ \text{recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} $$ --- # Precision-Recall Curve But how do we decide if something is positive or negative? -- count:false Often, set a threshold : $$ \hat{y}_i = \begin{cases} 1 &\text{if } P(y_i=1|x_i) > \text{threshold}, \\\\ 0 &\text{o.w.} \end{cases} $$ -- count:false Usually, threshold = .5, but it doesn't have to be. -- count:false What happens if we change it? --- # Precision-Recall Curve -- count:false 1. Get class prediction probabilities -- count:false 2. Order by $P(y=1|x)$ -- count:false 3. Move threshold for calling $x_i$ positive -- count:false 4. Record precision and recall --- # Precision-Recall Curve -- count:false .smallest[ ```python # return predicted class probabilities y_pred_logr = logr.predict_proba(X_train) y_pred_logr[:3,:] ```] .smallest[ ``` array([[0.78, 0.22], [0.15, 0.85], [0.61, 0.39]]) ```] -- count:false .smallest[ ```python # get a matrix of p(y_i=1) and y_i pairs tmp = np.transpose(np.vstack([y_pred_logr[:,1],y_train])) # sort by p(y_i=1) descending tmp = np.array(sorted(tmp,key=lambda x:x[0])[::-1]) tmp[:3] ```] .smallest[ ``` array([[0.98, 1. ], [0.97, 1. ], [0.97, 1. ]]) ```] -- count:false .smallest[ ```python # or just let sklearn do it from sklearn.metrics import precision_recall_curve precision, recall, thresholds = precision_recall_curve(y_train, y_pred_logr[:,1]) ```] --- # Precision-Recall Curve .smallest[ ```python fig,ax = plt.subplots(1,2,figsize=(12,4)) ax[0].step(recall, precision, color='b', alpha=0.2, where='post'); ax[0].fill_between(recall, precision,color='b', step='post', alpha=0.2) ax[0].set_xlabel('Recall');ax[0].set_ylabel('Precision'); ax[1].plot(thresholds,precision[:-1], label='Precision') ax[1].plot(thresholds,recall[:-1],label='Recall') ax[1].legend() ax[1].set_xlabel('threshold');ax[1].set_ylabel('measure'); ```] .center[![:scale 100%](images/precision_recall_curves.png)] --- # f1-score -- count:false - Usually, we just want one number to optimize -- count:false - **f1-score**: harmonic mean of precision and recall -- count:false - eg. weighted average of the precision and recall $$ F_1 = 2 \cdot \frac{\text{precision} \cdot \text{recall}}{\text{precision} + \text{recall}} $$ -- count:false .smaller[ ```python scores = cross_val_score(logr,X_train,y_train,cv=5,scoring='f1') print(f'{np.mean(scores)} +- {2*np.std(scores)}') ``` ``` 0.84 +- 0.11 ```] -- count:false .smaller[ ```python scores = cross_val_score(dummy_cl,X_train,y_train,cv=5,scoring='f1') print(f'{np.mean(scores)} +- {2*np.std(scores)}') ``` ``` 0.71 +- 0.02 ```] --- # ROC Curve - **R**eceiver **O**perating **C**haracteristic -- count:false - displays FPR vs TPR -- count:false - or (1-Specificity) vs. Sensitivity -- count:false - True Positive Rate (TPR) = Sensitivity = Recall = $\frac{TP}{(TP + FN)}$ -- count:false - False Positive Rate (FPR) = (1 - Specificity) = $\frac{FP}{(FP + TN)}$ -- count:false - True Negative Rate (TNR) = Specificity = $\frac{TN}{(TN + FP)}$ --- # ROC Curve ```python # again sklearn to the rescue from sklearn.metrics import roc_curve fpr_logr, tpr_logr, _ = roc_curve(y_train, y_pred_logr[:,1]) ``` --- # ROC Curve ```python def plot_roc(curves): fig,ax = plt.subplots(1,1,figsize=(6,6)) lw = 2 for fpr,tpr,model_name in curves: l1, = ax.plot(fpr, tpr, lw=lw, label=model_name) ax.plot([0, 1], [0, 1], color='k', lw=lw, linestyle='--') ax.set_xlim([0.0, 1.0]) ax.set_ylim([0.0, 1.05]) ax.set_xlabel('False Positive Rate') ax.set_ylabel('True Positive Rate') ax.set_aspect('equal', 'box') ax.set_title('Receiver operating characteristic example') ax.legend() ``` --- # ROC Curve .smallest[ ```python curves = [(fpr_logr,tpr_logr,'logr')] plot_roc(curves); ```] .center[![:scale 45%](images/roc_curve_01.png)] --- # ROC Curve .smallest[ ```python fpr\_dummy, tpr\_dummy, \_ = roc_curve(y_train, dc.predict\_proba(X\_train)[:,1]) # Compare dummy curves.append((fpr_dummy,tpr_dummy,'dummy')); plot_roc(curves); ```] .center[![:scale 45%](images/roc_curve_02.png)] --- # ROC Curve ```python #Compare against knn from sklearn.neighbors import KNeighborsClassifier params = {'n_neighbors':[3,5,15,20]} gs_knn = GridSearchCV(KNeighborsClassifier(), params, cv=5, scoring='f1') gs_knn.fit(X_train,y_train) y_pred_knn = gs_knn.predict_proba(X_train) print(gs_knn.best_params_) ``` ``` {'n_neighbors': 15} ``` --- # ROC Curve .smallest[ ```python fpr\_knn, tpr\_knn, \_ = roc_curve(y\_train, y\_pred_knn[:,1]) curves.append((fpr\_knn,tpr\_knn,'knn')); plot_roc(curves); ```] .center[![:scale 45%](images/roc_curve_03.png)] --- # ROC and AUC -- count:false - But again, we'd like one number to optimize -- count:false - **A**rea **U**nder the **C**urve -- count:false ```python from sklearn.metrics import roc_auc_score ``` -- count:false ```python for name,model in [('dummy',dc),('logr',logr),('knn',knn)]: y_pred = model.predict_proba(X_train) auc = roc_auc_score(y_train,y_pred[:,1]) print('{:5s} auc = {:0.3f}'.format(name,auc)) ``` ``` dummy auc = 0.50 logr auc = 0.87 knn auc = 0.94 ``` --- # Evaluate for Generalization Once we're done fitting model, check against test set ```python knn.score(X_test,y_test) ``` ``` 0.69 ``` --- # Review: Steps to Choosing a Model - Create Held-Aside Set (Train/Test Split) - Determine Metric (or combination of metrics) - Get a Baseline for comparison - Use Cross-Validation to fit Hyperparameters and Choose Model - Evaluate Chosen Model on Held-Aside Set --- # Review Classification Metrics - Confusion Matrix - Accuracy/Error - Precision - Recall - $F_1$ Score - ROC - AUC --- # Aside: Single Features, Single Sample .smaller[ ```python linr.fit(X_train.iloc[:,0],y_train) ```] .smaller[ ``` ValueError: Expected 2D array, got 1D array instead: array=[25.56 21.5 34.3 26.86 27.05 10.7...] ... Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample. ```] -- count:false .smaller[ ```python X_train.iloc[:,0].shape ```] .smaller[ ``` (183,) ```] -- count:false .smaller[ ```python X_train.iloc[:,0].values.reshape(-1,1).shape ```] .smaller[ ``` (183, 1) ```] --- class:middle # Questions?


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|
| file | pmlib_boardconfig.h |
| | This file contains structure declarations for aggregating board specific power management related information such as - root clocks, ganging of voltage rails.
|
| |
Board Configuration is used along with the PMLIB Clock rate APIs. (Clock Rate Configuration) Board configuration takes into account the ganging of voltage rails which can happen outside the device boundaries. Board configuration is also used to find all the dependent CPUs in the voltage rails which are ganged and if the new voltage is found to satisfy the OPP requirement of any ganged CPU the voltage is changed along with the DPLL configuration.
It also captures the clock frequencies of clock sources input to device (like an external oscillator frequency).
| uint32_t* PMLIBBoardConfigGetRootClks |
( |
void |
| ) |
|
Get Root Clock array which has all root clock frequencies for root clocks.
- Returns
- Return pointer to the root-clock array list.
Get array which provides the relationship between Voltage Rails shared across multiple voltage domains.
- Returns
- Return pointer to the array which provides the relationship between Voltage Rails shared across multiple voltage domains pmlibBoardVdRailShare_t


The spatial interpolation of genetic distances between samples is based on a modified kriging method that accepts a genetic distance matrix and generates a map of probability of lineage presence. This package also offers tools to generate a map of potential contact zones between groups with user-defined thresholds in the tree to account for old and recent divergence. Additionally, it has functions for IDW interpolation using genetic data and midpoints.
| Version: | 1.1.1 |
| Suggests: | geometry |
| Published: | 2015-11-04 |
| Author: | Pedro Tarroso, Guillermo Velo-Anton, Silvia Carvalho |
| Maintainer: | Pedro Tarroso <ptarroso at cibio.up.pt> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.r-project.org, http://webpages.icav.up.pt/pessoas/ptarroso/phylin/phylin.html, https://github.com/ptarroso/phylin |
| NeedsCompilation: | no |
| In views: | MissingData |
| CRAN checks: | phylin results |
| Reference manual: | phylin.pdf |
| Vignettes: |
Phylin: a step-by-step tutorial |
| Package source: | phylin_1.1.1.tar.gz |
| Windows binaries: | r-devel: phylin_1.1.1.zip, r-release: phylin_1.1.1.zip, r-oldrel: phylin_1.1.1.zip |
| OS X binaries: | r-release: phylin_1.1.1.tgz, r-oldrel: phylin_1.1.1.tgz |
| Old sources: | phylin archive |
Please use the canonical form https://CRAN.R-project.org/package=phylin to link to this page.


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Notes on feature engineering in Machine Learning.
Feature Scaling
Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data pre-processing step.
The simplest method is rescaling the range of features to scale the range in [0, 1] or [−1, 1]. The general formula is given as:
X’ = (X – Xmin)/(Xmax – Xmin)
where, X’ is the value we want to rescale, X is the given value, Xmax is the largest value of X and Xmin the smallest.
Let us consider, old weights = [115,140,175] and we are going to scale for the value 140.
X’ = (140 – 115)/(175 – 115) = 0.41666
Therefore, the range is, [0,0.41666,1]
Feature Selection
Why do we have to perform feature selection?
- Knowledge discovery, Interpretability and to gain some insights.
- Curse of dimensionality.
There are two methods of Feature Selection :
Filtering – Filter type methods select variables regardless of the model. They are based only on general features like the correlation with the variable to predict. Filter methods suppress the least interesting variables. They are mainly used as a pre-process method.
Set of all features –> Selecting the best subset –> Learning Algorithm –> Performance
Wrapping – Wrapper methods evaluate subsets of variables which allows, unlike filter approaches, to detect the possible interactions between variables.
Feature Transformation
Feature transformation is a group of methods that create new features (predictor variables). Feature selection is a subset of feature transformation.
Consider an ‘X’ space having ‘n’ features, using feature transformation we are going to transform X to have ‘m’ features, where m < n.
This is done by defining some matrix Px which is a subspace to which we are going to project ‘X’ space. The new features are combination of the old features.
There are many types explained below –
Principal Component Analysis (PCA)
A movie camera takes a 3-D information and flatten it to 2-D without too much loss of information.
What does all of this have to do with PCA?
PCA takes a dataset with a lot of dimensions and flatten it to two or three dimensions so we can look at it.
It tries to find a meaningful way to flatten the data by focusing on the things that are different between cells.
Here, the weights are termed Loadings. And array of loadings is called “Eigen Vector”.
PCA review :
- Systematized way to transform input features into principal components (PC)
- Use new PCs as new features.
- PCs are directions in data that maximize variance when you project/compress down onto them.
- The more variance of data along a PC, the higher that PC is ranked.
- Most variance, most information would be the first PC.
- Second-most variance would be the second PC.
- Max number of PCs = number of input features.
Typical example of PCA is in eigenfaces.
PCA is a linear algebraic approach.
Independent Components Analysis (ICA)
It is a computational method for separating a multivariate signal into additive sub-components. This is done by assuming that the sub-components are non-Gaussian signals and that they are statistically independent from each other.
A common example application is the “cocktail party problem” of listening in on one person’s speech in a noisy room.
ICA is a probabilistic approach.
Random Component Analysis (RCA)
Uses random way to transform input features into principal components (PC)
Linear Discriminant Analysis (LDA)
Finds a projection that descriminates based on the label.
Fundamental assumption is different, although they do the same thing, which is to capture the original data in some new transform space that is somehow better.


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