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menu for all occasions
Special events
With sufficient space to host an event of up to 70 people


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Vishnu Mahayagyan
14-06-2018 to 24-06-2018
Donate here and get blessings by "Maryada Purushottam(The Perfect Man) SRIRAM"
Click Here To Donate
Lord Rama, in the words of Swami Vivekananda, is "the embodiment of truth, of morality, the ideal son, the ideal husband, and above all, the ideal king". Rama is widely accepted to be an actual historical figure - a "tribal hero of ancient India" - whose deeds form the great Hindu epic of Ramayana or The Romance of Rama, written by the ancient Sanskrit poet Valmiki. According to the Hindus belief, Rama lived in the Treta Yug. Although historians are of the conviction that Rama was not particularly deified until the 11th century AD. Tulsidas' wonderful version of the Sanskrit epic "Ramayana" into "Ramcharitmanas" greatly enhanced the popularity of Rama as a Hindu god and gave rise to various devotional groups.
We are a group of youth of a small village called as “PYAREPUR” (The Place Where Love Resides) and we are organizing a Mahayagyan to worship Lord Rama and Mata Janki(His Wife) also known as Sita. This Mahayagyan is organized not only to worship Sri Ram but to unite the people of our village. In previous years the people of this village are so busy in their life that they almost forgot their responsibilities towards their village and due to this-the place of love started losing its beauty and strength. We are confident to regain our love and strength through this Mahayagyan and for this noble cause we seek your help.
President: Prince Sharma
Contact no:-<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Address:
Organiser: Pragati Nehru Yuva Club
Vill + P.O:- Pyarepur, P.S.- Giriyak
Near Pawapuri (5km from jal mandir and
Vardhman Medical College and Hospital)
District - Nalanda, State: Bihar, PIN- 803115
Contact:<PHONE_NUMBER> (Committee Member).


Page not found
It looks like you are trying to access a page that has either been deleted or never existed in the first place.
Maybe you would like to read a post or two, or go back home.
It looks like you are trying to access a page that has either been deleted or never existed in the first place.
Maybe you would like to read a post or two, or go back home.


FINITE
Section: Linux Programmer's Manual (3)
Updated: 2013-07-22
Index
Return to Main Contents
NAME
finite, finitef, finitel, isinf, isinff, isinfl, isnan, isnanf, isnanl -
BSD floating-point classification functions
SYNOPSIS
#include <math.h>
int finite(double x);
int finitef(float x);
int finitel(long double x);
int isinf(double x);
int isinff(float x);
int isinfl(long double x);
int isnan(double x);
int isnanf(float x);
int isnanl(long double x);
Feature Test Macro Requirements for glibc (see
feature_test_macros(7)):
finite(),
finitef(),
finitel():
-
_BSD_SOURCE || _SVID_SOURCE
isinf():
-
_BSD_SOURCE || _SVID_SOURCE || _XOPEN_SOURCE >= 600 || _ISOC99_SOURCE;
or
cc -std=c99
isinff(),
isinfl():
-
_BSD_SOURCE || _SVID_SOURCE
isnan():
-
_BSD_SOURCE || _SVID_SOURCE || _XOPEN_SOURCE || _ISOC99_SOURCE;
or
cc -std=c99
isnanf(),
isnanl():
-
_BSD_SOURCE || _SVID_SOURCE || _XOPEN_SOURCE >= 600
DESCRIPTION
The
finite(),
finitef(),
and
finitel()
functions return a nonzero value if
x
is neither infinite
nor a "not-a-number" (NaN) value, and 0 otherwise.
The
isnan(),
isnanf(),
and
isnanl()
functions return a nonzero value if
x
is a NaN value,
and 0 otherwise.
The
isinf(),
isinff(),
and
isinfl()
functions return 1 if
x
is positive infinity, -1 if
x
is negative infinity, and 0 otherwise.
ATTRIBUTES
Multithreading (see pthreads(7))
The
finite(),
finitef(),
finitel(),
isinf(),
isinff(),
isinfl(),
isnan(),
isnanf(),
and
isnanl()
functions are thread-safe.
NOTES
Note that these functions are obsolete.
C99 defines macros
isfinite(),
isinf(),
and
isnan()
(for all types) replacing them.
Further note that the C99
isinf()
has weaker guarantees on the return value.
See
fpclassify(3).
SEE ALSO
fpclassify(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- ATTRIBUTES
-
- Multithreading (see pthreads(7))
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:55:12 GMT, September 18, 2014


Honest is a social network where you can earn Bitcoin Cash if you create value. Our mission to improve the quality of content on the internet and to get people paid for doing work they love. Read more...
Be the first to create it!


Absolute path or path relative to the workspace of a file that contains the Ontrack DSL script to execute.
Two objects are injected into the script:
ontrack
- base access to Ontrack.jenkins
- allows interaction with Jenkins. See the
Wiki for the list
of supported methods.
See the Wiki for the complete documentation.


Katie Price dons full hazmat suit with elbow dusters to clean mucky mansion
Katie Price’s mucky mansion has been the focus of a headline (or 20) but the former glamour model is putting some elbow grease into tidying up her home.
Photos of Katie getting stuck in with the cleaning have emerged, and it looks like she needed to invest in some protective gear before taking on the mammoth task.
Katie suited up in full white overalls, complete with a hood to keep her hair clean and dusters fixed to her elbows, knees and feet.
We’re not quite sure shuffling around on all fours is going to be the most effective way of making her cluttered home liveable again, but we’ll give her an A+ for effort.
The look may be a far cry from her glamorous modelling days, but it’s definitely going to make some pretty excellent content for her upcoming show, My Crazy Life.
Advertisement Advertisement
Katie’s spent the past few months renovating her home after it was given the less than favourable moniker when fans spotted the junk and mess cluttering up her home in the back of her videos.
The mother of five, 41, has kept tight-lipped about her plans for the £2million Sussex home, with rumours she was turning it into a wedding venue or doing it up in order to sell the property on.
Fans were treated to a guided tour of the house on Katie’s YouTube channel where she showed off her home in the middle of the renovation.
She explained: ‘My house… Everyone is calling it “mucky mansion”. Yes, because I’m not living in my house. It’s empty.
‘I’m doing stuff to it and something has gone wrong. I’ll explain that in due course. I have builders inside my house, my post gets moved here, there and everywhere.’
Katie had revealed she moved out of the main home and into her annexe while the works were being done.
Here’s hoping the elbow dusters hold up – it looks like there was plenty of use for them.
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


{% extends 'base.html' %} {% import 'bootstrap/wtf.html' as wtf %} {% import 'macros.html' as macro %} {% block content %}
{{post.content}}
What are your thoughts on the post


JOT internal code to handle the process of "Views" This usually means parsing JOT templates and outputting HTML.


yay this is a quick website that i just built because its night and i wanna go sleep knowing that i made a website for my extension anyway heres the link to it if you wanna download it here it is
also if you want to commit to the extension then here ya go


Through great efforts i pursued my commands over FRONT END DEVELOPMENT (STATIC WEBSITE DEVELOPMENT)
.
I had sharpen my skills in HTML ,CSS ,JAVA-SCRIPT,BOOTSTRAP,and became capable of framing a good WEBSITE.
I'm quietly confident, naturally curious, and perpetually working on improving my chops one design problem at a time.
Web development is the work involved in developing a website for the Internet or an intranet.
I value simple content structure, clean design patterns, and thoughtful interactions.
I am always there to help you out ,if any querry occurs related website development ,do contact me.
Here are a few design projects I've worked on recently. Want to see more? Email me
Static photography website with some amazing color effects and design.
Visit WebsiteInterested in working together?
We should queue up a chat.
Living, learning, & leveling up one day at a time.


Speech
Ofqual's Chief Regulator gave a speech on A level reform to Westminster Education Forum.
Good morning.
It is good to be here at what should be an interesting morning. You will be hearing from some of those directly involved in the reform of A levels – Isabel Nisbet from ALCAB, Professor Mark Smith, Paul Dodd of OCR, Gareth Pierce of WJEC – and from others, well placed to comment. I do hope you find the morning worthwhile.
As I am first to speak, I have the chance to set the scene and also to debunk a few myths. Let me do that.
A levels are still the most popular qualification at key stage 5, still the most common entry qualification for higher education. Our national and international research shows that they are still highly respected, both nationally and internationally. What is more, our 2012 research shows that when compared to equivalent qualifications in other countries in Europe, North America, East Asia and Australasia, A levels compare well, albeit we did find differences.
So for example, students at this level generally study fewer subjects here than in the other countries we evaluated, and they study them here in greater depth. We cut depth versus breadth differently, with the focus on depth within a subject rather than breadth across subjects, and leading to a three year degree course rather than four. And our three year degree courses are popular with international students as well as with our students here.
So our A levels are off to a good start when compared to other individual qualifications internationally, because we do not operate a baccalaureate style system, but expect instead the deeper study of individual subjects in A levels. Elsewhere, students are required to study a number of core subjects – English and Maths being common – and then choose from a range of other subjects as well.
We are unusual then, in the freedom of choice that A level students enjoy as they choose subjects freely here, and in the limited of number of subjects they generally study.
We have also been unusual in the amount of optionality we have in our current suite of A levels. From our comparative research we can see that in the other countries, subjects usually have a common core of subject knowledge (content), and noticeably fewer if any choices – optional routes through the qualification.
We also found differences in assessment, for example, the more prevalent use of sophisticated multiple choice items in assessment elsewhere. Here we find multiple choice less attractive, and associate it with lower demand – and yet we can see that designed well, it can be sufficiently challenging and enable much more of the subject to be assessed. Of course it suits some subjects much more than others.
So far as comparing specific subjects is concerned, I was struck in our research by the different approaches to maths – maths on its own and maths in science. We are unusual in covering both pure and applied maths in one qualification, for example, and in the comparatively limited range of maths on offer, with other countries offering other, more challenging maths qualifications alongside their A level equivalents. In maths then we have traditionally cut breadth versus depth differently in this country, with our focus more on breadth. And then finally in the sciences, we have traditionally put more emphasis than others on the mathematical content for each science, and we continue to do so in the reformed science A levels – and of course there will be a relationship between the science qualifications and the maths qualifications sitting alongside them, in each country.
Our international research findings have been fed into the work on the reform of A and AS levels, but they did not drive it – they were not the reason for reform. A levels are being reformed for two reasons: to update the content and to move from modular to linear assessments. Most subjects have not been reviewed or updated for about eight or ten years, and they are ripe for review – they are at the end of the usual shelf life of key qualifications and it is not unusual to be reviewing and updating them at this stage of the game.
The move to linear assessment is not uncontroversial – there are differing views. When we proposed the move, most of those responding to our consultation favoured the removal of January assessments. But as you know the biggest concern has been the decision of Government to decouple AS and A level. We at Ofqual take a neutral stance: decoupling is a matter for Government. Our job has been to be sure that it is delivered well, with good AS and A level qualifications on offer to schools.
Should any future Government wish to recouple, it can be done, albeit the timing of the election is awkward, as new decoupled A and AS levels will be in schools ready for first teaching in September 2015. The choice would be to do one of two things. Either let new A and AS levels run for two years while exam boards design coupled qualifications using the new content, or else leave current A and AS levels to run for two years while exam boards update them with new content. We appreciate this uncertainty is uncomfortable, but hopefully it is helpful to narrow the uncertainty for you, by explaining the two options and the likely timescales should Government decide to recouple A and AS levels.
Let me touch now on co-teachability. It is clear Government policy that new AS and A levels can be co-teachable. All of the AS and A levels that we at Ofqual have accredited so far are designed by exam boards to be co-teachable. As you are considering your curriculum offers you will no doubt look to the accredited specifications currently available, but even if you did not you can assume co-teachability. Exam boards are not obliged to produce co-teachable A and AS level specifications, but they are doing so universally – and that is no surprise, given that that is likely to be what you want should you be considering AS levels.
A level reform is now well underway, and being delivered in demanding timescales by exam boards. Subjects have been prioritised, with high volume and traditional subjects in the first tranche. Subject content has been reviewed in several ways, depending on the extent of change thought to be needed, and you will have the chance to hear from Isabel Nesbit shortly followed by Professor Mark Smith, both seasoned and experienced in the subject review arrangements.
From Ofqual’s perspective the arrangements have worked well. We have been able to provide technical advice, able to influence and shape and clarify and substantially improve the assessment objectives and able to consider, once new subject content is proposed, whether it: represents a sufficiently coherent programme of study; whether it is sufficiently clear and well expressed so that competing exam boards will produce sufficiently comparable qualifications; and whether it appears to be of suitable demand – so far as one can tell at that stage.
In considering subject content proposals we have in mind of course the aims of A and AS level reform. We are updating content and improving assessment arrangements. We are not setting out to make A and AS levels more demanding per se, or to set a different performance standard.
Let me talk now just briefly about three aspects of our work on assessment. Firstly we have required exam boards to set out in detail their assessment strategies in each subject, covering for example their approaches to domain sampling (making sure that enough of the curriculum is assessed) and to item design (e.g. the balance between long and short questions) and to mark schemes. Secondly, our accreditation process has been comprehensive, and delivered with a new tranche of experts – and some of our 650 experts may be in this room. Thirdly, the balance of exam and non-exam assessment, where we are seeking a balance that reflects the balance and nature of the subject content, and also to strike the right balance taking into account three competing considerations, competing tensions that you will be familiar with.
To explain, we expect A levels to produce a sufficiently reliable indicator of student performance, a reliable rank order enabling higher education and employers to rely on them for selection purposes. We also recognise their use and stated purpose in measuring and reporting on school and college achievements – that is, accountability, which, were it the only consideration, would lead us towards bullet proof assessment design. Then, last and by no means least, A level qualifications must embed the curriculum and be likely to deliver curriculum aims and a good and worthwhile experience for students.
The most difficult decisions, the most finely balanced decisions we have made here are in the individual sciences, where as many of you know, practical science skills will be assessed and reported on separately. We believe this to be the best way to reconcile the three competing tensions I have just spoken of, and I was heartened to see the results of an AQA teacher survey recently showing 89% of science teachers saying that the changes will lead them to undertake more practical work with students. We will be tracking how science A and AS levels are delivered in schools – not because we regulate schools – of course, we do not – but because we want to know that these qualifications are actually delivering curriculum aims sufficiently well, as well as meeting the other requirements just mentioned.
Let me make some reflections now in the time I have left.
People generally agree that it is right for HE to be more involved in determining subject content than has been the case in recent years, and we have seen that to work well. The consultations run by DfE have been helpful, indeed critical and we have seen the DfE adjust or rebalance content proposals in the light of consultation responses, and new subject content has been generally well received by the sector.
In the review we have taken the opportunity to limit, although not eradicate, the number of alternative routes through the qualifications – building on our international research findings. In some subjects the new content is exclusively core content; in others the core content is a noticeably higher proportion than it is at the moment.
The biggest subject content changes are in history, where we move from the study of one century to the study of two. I am reassured by the fact that in our international study we found no consensus as to what is history or agreement on how many eras one should study, or what is an era, for that matter. Instead, historians agreed on the value of studying history, and the skills to be developed and assessed in the subject, for example, critical thinking skills.
The changes in modern foreign languages content are significant as well, and reflect more closely the way the subjects are approached in higher education, with the study of language more clearly in a cultural setting.
The subject content in the individual sciences still includes relevant maths and indeed that maths is now more clearly specified. Of the three sciences, it is Chemistry that is presenting the biggest challenge for us and for exam boards. No specification is accredited yet, but rest assured the chemists amongst you. This is not because of any neglect or lack of application, but because we are maintaining a standard at Ofqual and we will not accredit Chemistry specs until they meet that standard. You would not expect anything less.
As expected, the most difficult subjects are maths and further maths, where there is more work to be done to be sure that the new content requirements, particularly those relating to problem solving are sufficiently well specified and then commonly understood by each exam board. And we want to be sure of course that the new A and AS levels sit sensibly alongside the new GCSE.
Finally, looking to the future, we are keeping to the timetable for reform, but not slavishly. It is the quality of the qualification and the educational outcomes that matter above all, and that is behind our advice to Government in relation to A level Maths.
Thank you. Thank you for listening.
Published: 26 November 2014


Follow the steps below to get started with your Site Template:
- Open the
Package/HTML
Folder to find all the Templates Files - You will need to Upload these files to your Web Server using FTP in order to use it on your Website.
- Make sure you upload the required files/folders listed below:
HTML/css
- Extra Stylesheets FolderHTML/images
- Images FolderHTML/js
- Javacripts FolderHTML/css/main.css
- Main Stylesheet File you can also rename it to style.css (for wordpress)HTML/index.html
- Index File/Homepage
- You're now good to go..! Start adding your Content and show off your Brand New Beautiful Website in style.
 Please upgrade your browser to improve your experience.
 <![endif]-->

How to use CSS / JS files
CSS and JS both are important part of the website. we've included all the plugins css/js inside main.css, and main.js files with proper commenting to avoid more http request to server and to make website farster.
main.css Inside HTML > CSS you can find main.css which contains all the css.
<link rel="stylesheet" href="css/main.css">
Bootstrap.css Inside HTML > CSS you can find bootstrap.css which is the main part of the website as Service Provider based on bootstrap 3 it is recommended to include bootstrap.css file right after main.css file.
<link rel="stylesheet" href="css/bootstrap.css">
Color.css Inside HTML > CSS you can find color-default.css which is compulsory for website's color scheming it is recommended to include color.css file right after bootstrap.css file. To understand more please refer color schemes section.
<link rel="stylesheet" href="css/color.css">
main.js Inside HTML > JS you can find main.js which contains all the custom and plugins code used in website.

Logo Settings
The Logo Container can be found in the Header Container - .navbar-header

Note The Logo Image's maximum width can be 200px and maximum height can be 61px
Color Schemes
You can change your Website's Color Scheme in an instant. You simply need to change the Color Code in the css/colors.css
file & you are good to go. Follow these quick steps to get going:
-
Make sure you add the
css/colors.css
stylesheet in your head after the bootstrap.css stylesheets. ... <link rel="stylesheet" href="css/bootstrap.css" type="text/css" />  <link rel="stylesheet" href="css/colors.css" type="text/css" /> ...
- Now simply change the Codes according to your requirements.
Changing Fonts
Change your Fonts on the Fly as we have included fonts in main.css file. Service Provider uses 2 Fonts namely: Worksans
&Opensans
from the Google Fonts Library. You can find the Linking to the Font Files in the main.css
file.
@import url(https://fonts.googleapis.com/css?family=Open+Sans:300,400,600,700,800|Work+Sans:300,400,500,600,700);
In order to change the Fonts, you will need to Edit the Above Links with your Custom Font if you plan to use a Google Font or Remove it complete if you plan to use a Self Hosted font. Here is an Example for using Self Hosted Fonts.
Website Optimization Tips
A Fast & Optimized Website has several factors which needs to be implemented in order to achieve the desired results. There are several Optimization Techniques available which will definitely affect your Website's Performance in a Positive Way & we want to share a few of them with you:
-
gZip Compression & Browser Caching
This is probably one of the Most Important Techniques you should definitely implement in order to bump up your Website's Loading Speed. gZip Compression is used to compress the Files that are delivered when loading a Website. It covers HTML, CSS, Javascript & Font Files along with other miscellaneous text files. Where as Browser Caching also covers Images & Videos apart from including the above files. This is used to saves the Static Data in your Browser itself so that when you open the Next Pages on the Same Website, the content does not gets Downloaded again, loading the Website fast.
gZip Compression & Browser Caching can be enabled using the
.htaccess
File on an Apache Web Server. You can use the Codes from here: https://github.com/h5bp/html5-boilerplate/blob/master/dist/.htaccess to enable these modules on your server. -
Image Compression & Optimization
We tend to use Lots of Images on our Websites but we often do not make efforts to Compress & Optimize them. Remember, the Larger the Image, the more time it takes to download and therefore this slows your website loading times affecting User Experience. Your customer will leave your website if it does not load within 3-5 Seconds which adversely affects your Sales. Therefore, it is important to Resize, Optimize & Compress your Images before using it on your Website. Here are some Tips which might come handy in optimizing images:
- Resize your Images: Resize your Images before using it on your Website. Do not just Download an Image & place it as it is in your Website's

Tag without resizing it. The size/resolution of the Image matters since it is not recommended to use an Image size of1200px
x800px
in a Content Size of300px
x200px
as this unnecessary. Resize it to300px
x200px
- Image Formats: There are three common file types that are used for web images which are JPEG, GIF, & PNG. For images with a Flat Background use JPEG images, for images with a Transparent background use PNG images and for images with Animations use GIF images.
- Compressing Images: Images Compression is important as it considerably reduces the size without losing the quality. There are several FREE Image Optimization Tools available to Download.
For MAC use ImageOptim
For Windows use Riot for compressing JPEG Images & PNG Gauntlet for PNG Images.
- Resize your Images: Resize your Images before using it on your Website. Do not just Download an Image & place it as it is in your Website's
-
CSS & jQuery Minifications
It is also recommended that you Combine & Minify all your CSS Files to a single CSS File & all Javascript Files to a single JS File since Minification reduces the size of the File and Combining the files helps in reducing the number of HTTP requests made to the server. This is also an Important Factor in increasing the speed of your website. There are several tools available online to Minify your CSS & JS Files. Our recommendations are:
For CSS use CSS Minifier and For Javascript use Javascript Minifier. -
Content Delivery Network
You can use a CDN to further speed up your website. You can use the CDn to deliver static files of your website like CSS, JS, Images & Font Files. There are several CDN Hosting Providers available on the Internet but we would recommend MaxCDN or CloudFlare. Note: CDN setup requires Extra monthly Fees to setup, so it is completely optional & according to your needs.
-
Fast Web Hosting Servers
A lot depends on your Web Hosting Servers, so it is recommended that you choose a Hosting Company/Server that provides a Reliable & a Fast Hosting Service. You can check out some recommended Hosting Services here: http://themeforest.net/get_hosting.
Columns & Grid
Bootstrap Grid
Slider Types & their Options
Service Provider includes owl Sliders for you to be used on any Page with 100s of Options.
- Owl Slider 2
jQuery('#tg-featuredprofileslider').owlCarousel({ items:1, nav:true, loop:true, autoplay:false, smartSpeed:450, navClass: ['tg-btnprev', 'tg-btnnext'], animateOut: 'fadeOut', animateIn: 'fadeIn', navContainerClass: 'tg-featuredprofilesbtns', navText: [ 'prev', 'next', ], });
Introduction
Services Provider boasts of a huge number of handy Pages which are quite powerful, flexible, functional & easy to use. Setting up pages is very easy & Self Explanatory. Here is the List of Pages included with Service Provider:
index
index -1
about us
404error
appointment
bloggrid
bloglist
blogdetail
categories3col
categories4col
commingsoon
dashboard
dashboardappointment
dashboardappointmentsetting
dashboardappointmentsetting2
dashboardbusinesshours
dashboardfavoritlisting
dashboardinvoices
dashboardmanagejobs
dashboardmanageservice
dashboardmanageteams
dashboardprivacysetting
dashboardprivatemessages
dashboardprofilesetting
dashboardsecuritysetting
dashboardupgradepackage
joblisting
jobdetail
listinggrid-v1
listinggrid-v2
listing-list-detail
listinglist-v1
listinglist-v2
loginregister
Each of the above mentioned Pages are easily extendable, flexible & easy to use. You can find the sample codes in their respective files. We are explaining a few of them for your Reference.
Sections
Setup content you want to stand out. You can use Parallax Images or HTML5 Videos as Sections.
Setting up Sections:
Setup your Sections outside the .container
element. sections padding-top and padding-boottom is 80px by default but you can change it by adding handy classes like no-padding, padding-top-20 etc.
Light Section:

Google Maps
You can add Embedded Google Maps to any Page using the following setup:
-
Step 1:
Add the Google Maps Specific Scripts in the

Tag below thejs/main.js
script linking:
-
Step 2:
Add a
div
which will contain your Google Map:

Icons
We've used Font Awesome icons and Linericons
Version 1.0
Released on 06 March 2017
- Released Service Provider Template


This is a paragraph with some bold text and some italics text.
This is a link.
This is some small text.
Subscript
Superscript
Inline code
Keyboard Input
This is some preformatted text.
This is some quoted text from another website or person.
| Name | Surname | Alias |
|---|---|---|
| Chad | Wilberts | MrOne |
| Adam | Smith | TheSmith |
| Sophia | Canderson | Candee |
| Name | Surname | Alias |
|---|---|---|
| Chad | Wilberts | MrOne |
| Adam | Smith | TheSmith |
| Sophia | Canderson | Candee |
| Lawful | Neutral | Chaotic | |
|---|---|---|---|
| Good | Yoda | Luke Skywalker | Chewbacca |
| Neutral | C-3PO | Boba Fett | Han Solo |
| Bad | Darth Vader | Emperor Palpatine | Jabba the Hutt |
Card content...
Card content...
Content
Content
Content
Content
Content
Content
Content
Content
This is the first tab's content.
This is the second tab's content.
This is the third tab's content.
This is the first accordion section's content.
This is the second accordion section's content.
This is the first collapse section's content.
This is the second collapse section's content.


@desc()
Want to show off the benefits of type-safety?
- Download Typesafe Activator
- Create a new app using the Reactive Stocks template
- Open Reactive Stocks in your browser: localhost:9000
-
See what happens when you make a typo in one of the following files:
conf/routes
app/controllers/Application.java
app/controllers/StockSentiment.scala
app/views/index.scala.html
(Hint: Reload your browser to see a pretty compile error)
What to show off how easy it is to deploy containerless Play applications?
- From a command line in the root of the Reactive Stocks app you just created, run:
activator dist
- Copy the generated zip file to a different machine / server, extract the zip, and run the start script in the
bin
directory
Of course deployment can be even more agile when you combine the Typesafe Reactive Platform with tools and services like Heroku, Docker, Mesos, etc.


{% extends "survey/app_base.html" %} {% block head_title %}Home{% endblock %} {% if user.is_athenticated %} {% block head_extra %} {% endblock %} {% endif %} {% block content %}
Your Facebook profile can be thought of as a reflection of your online identity with your likes representing yourself to others. Our application aims to categorize “likes” by the frequency they appear together on you and your friend’s pages.
You will be first prompted to allow the application to download your “likes” in addition to your friends' “likes.” Once finished downloading, the application will display categories from the information, prompting the you to name the categories shown. After naming the categories, the application will show you your profile and how it fits into your named categories.
Your download has been started. It takes a while, so you should come back in a bit to check if it's done. Until then, you can feel free to close this page and go on about your day. Just come back to this url to check if it's done.
downloading user data: 0/0
calculating pmis: 0/0 (this one will take a bit longer)
finding categories


PIDOF
Section: Linux System Administrator's Manual (8)Updated: 01 Sep 1998
Index Return to Main Contents
NAME
pidof -- find the process ID of a running program.SYNOPSIS
pidof [-s] [-c] [-x] [-o omitpid] [-o omitpid..] program [program..]DESCRIPTION
Pidof finds the process id's (pids) of the named programs. It prints those id's on the standard output. This program is on some systems used in run-level change scripts, especially when the system has a System-V like rc structure. In that case these scripts are located in /etc/rc?.d, where ? is the runlevel. If the system has a start-stop-daemon (8) program that should be used instead.OPTIONS
- -s
- Single shot - this instructs the program to only return one pid.
- -c
- Only return process ids that are running with the same root directory. This option is ignored for non-root users, as they will be unable to check the current root directory of processes they do not own.
- -x
- Scripts too - this causes the program to also return process id's of shells running the named scripts.
- -o omitpid
- Tells pidof to omit processes with that process id. The special pid %PPID can be used to name the parent process of the pidof program, in other words the calling shell or shell script.
EXIT STATUS
- 0
- At least one program was found with the requested name.
- 1
- No program was found with the requested name.
NOTES
pidof is actually the same program as killall5; the program behaves according to the name under which it is called.When pidof is invoked with a full pathname to the program it should find the pid of, it is reasonably safe. Otherwise it is possible that it returns pids of running programs that happen to have the same name as the program you're after but are actually other programs. Note that that the executable name of running processes is calculated with readlink(2), so symbolic links to executables will also match.
SEE ALSO
shutdown(8), init(8), halt(8), reboot(8), killall5(8)AUTHOR
Miquel van Smoorenburg<EMAIL_ADDRESS>Index
This document was created by man2html, using the manual pages.
Time: 05:34:28 GMT, December 24, 2015


§481L-4 Civil remedies. A retail lessor who fails to comply with the requirements of this chapter shall be deemed to have engaged in an unfair or deceptive act or practice in the conduct of trade or commerce within the meaning of section 480-2. [L 1997, c 82, pt of §1; am L 2008, c 19, §28]


import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..utils import check_X_y
from ..utils.validation import _num_samples
from ..utils.multiclass import check_classification_targets
class LinearSVC(BaseEstimator, LinearClassifierMixin,
SparseCoefMixin):
"""Linear Support Vector Classification.
Similar to SVC with parameter kernel='linear', but implemented in terms of
liblinear rather than libsvm, so it has more flexibility in the choice of
penalties and loss functions and should scale better to large numbers of
samples.
This class supports both dense and sparse input and the multiclass support
is handled according to a one-vs-the-rest scheme.
Read more in the :ref:`User Guide <svm_classification>`.
Parameters
----------
penalty : string, 'l1' or 'l2' (default='l2')
Specifies the norm used in the penalization. The 'l2'
penalty is the standard used in SVC. The 'l1' leads to ``coef_``
vectors that are sparse.
loss : string, 'hinge' or 'squared_hinge' (default='squared_hinge')
Specifies the loss function. 'hinge' is the standard SVM loss
(used e.g. by the SVC class) while 'squared_hinge' is the
square of the hinge loss.
dual : bool, (default=True)
Select the algorithm to either solve the dual or primal
optimization problem. Prefer dual=False when n_samples > n_features.
tol : float, optional (default=1e-4)
Tolerance for stopping criteria.
C : float, optional (default=1.0)
Penalty parameter C of the error term.
multi_class : string, 'ovr' or 'crammer_singer' (default='ovr')
Determines the multi-class strategy if `y` contains more than
two classes.
``"ovr"`` trains n_classes one-vs-rest classifiers, while
``"crammer_singer"`` optimizes a joint objective over all classes.
While `crammer_singer` is interesting from a theoretical perspective
as it is consistent, it is seldom used in practice as it rarely leads
to better accuracy and is more expensive to compute.
If ``"crammer_singer"`` is chosen, the options loss, penalty and dual
will be ignored.
fit_intercept : boolean, optional (default=True)
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be already centered).
intercept_scaling : float, optional (default=1)
When self.fit_intercept is True, instance vector x becomes
``[x, self.intercept_scaling]``,
i.e. a "synthetic" feature with constant value equals to
intercept_scaling is appended to the instance vector.
The intercept becomes intercept_scaling * synthetic feature weight
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features.
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased.
class_weight : {dict, 'balanced'}, optional
Set the parameter C of class i to ``class_weight[i]*C`` for
SVC. If not given, all classes are supposed to have
weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
verbose : int, (default=0)
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in liblinear that, if enabled, may not work
properly in a multithreaded context.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
max_iter : int, (default=1000)
The maximum number of iterations to be run.
Attributes
----------
coef_ : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
``coef_`` is a readonly property derived from ``raw_coef_`` that
follows the internal memory layout of liblinear.
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
Examples
--------
>>> from sklearn.svm import LinearSVC
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = LinearSVC(random_state=0)
>>> clf.fit(X, y)
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=0)
>>> print(clf.coef_)
[[ 0.08551385 0.39414796 0.49847831 0.37513797]]
>>> print(clf.intercept_)
[ 0.28418066]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]
Notes
-----
The underlying C implementation uses a random number generator to
select features when fitting the model. It is thus not uncommon
to have slightly different results for the same input data. If
that happens, try with a smaller ``tol`` parameter.
The underlying implementation, liblinear, uses a sparse internal
representation for the data that will incur a memory copy.
Predict output may not match that of standalone liblinear in certain
cases. See :ref:`differences from liblinear <liblinear_differences>`
in the narrative documentation.
References
----------
`LIBLINEAR: A Library for Large Linear Classification
<http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__
See also
--------
SVC
Implementation of Support Vector Machine classifier using libsvm:
the kernel can be non-linear but its SMO algorithm does not
scale to large number of samples as LinearSVC does.
Furthermore SVC multi-class mode is implemented using one
vs one scheme while LinearSVC uses one vs the rest. It is
possible to implement one vs the rest with SVC by using the
:class:`sklearn.multiclass.OneVsRestClassifier` wrapper.
Finally SVC can fit dense data without memory copy if the input
is C-contiguous. Sparse data will still incur memory copy though.
sklearn.linear_model.SGDClassifier
SGDClassifier can optimize the same cost function as LinearSVC
by adjusting the penalty and loss parameters. In addition it requires
less memory, allows incremental (online) learning, and implements
various loss functions and regularization regimes.
"""
def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=1e-4,
C=1.0, multi_class='ovr', fit_intercept=True,
intercept_scaling=1, class_weight=None, verbose=0,
random_state=None, max_iter=1000):
self.dual = dual
self.tol = tol
self.C = C
self.multi_class = multi_class
self.fit_intercept = fit_intercept
self.intercept_scaling = intercept_scaling
self.class_weight = class_weight
self.verbose = verbose
self.random_state = random_state
self.max_iter = max_iter
self.penalty = penalty
self.loss = loss
[docs] def fit(self, X, y, sample_weight=None):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
sample_weight : array-like, shape = [n_samples], optional
Array of weights that are assigned to individual
samples. If not provided,
then each sample is given unit weight.
Returns
-------
self : object
Returns self.
"""
# FIXME Remove l1/l2 support in 1.0 -----------------------------------
msg = ("loss='%s' has been deprecated in favor of "
"loss='%s' as of 0.16. Backward compatibility"
" for the loss='%s' will be removed in %s")
if self.loss in ('l1', 'l2'):
old_loss = self.loss
self.loss = {'l1': 'hinge', 'l2': 'squared_hinge'}.get(self.loss)
warnings.warn(msg % (old_loss, self.loss, old_loss, '1.0'),
DeprecationWarning)
# ---------------------------------------------------------------------
if self.C < 0:
raise ValueError("Penalty term must be positive; got (C=%r)"
% self.C)
X, y = check_X_y(X, y, accept_sparse='csr',
dtype=np.float64, order="C")
check_classification_targets(y)
self.classes_ = np.unique(y)
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
X, y, self.C, self.fit_intercept, self.intercept_scaling,
self.class_weight, self.penalty, self.dual, self.verbose,
self.max_iter, self.tol, self.random_state, self.multi_class,
self.loss, sample_weight=sample_weight)
if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
if self.fit_intercept:
intercept = self.intercept_[1] - self.intercept_[0]
self.intercept_ = np.array([intercept])
return self
class LinearSVR(LinearModel, RegressorMixin):
"""Linear Support Vector Regression.
Similar to SVR with parameter kernel='linear', but implemented in terms of
liblinear rather than libsvm, so it has more flexibility in the choice of
penalties and loss functions and should scale better to large numbers of
samples.
This class supports both dense and sparse input.
Read more in the :ref:`User Guide <svm_regression>`.
Parameters
----------
C : float, optional (default=1.0)
Penalty parameter C of the error term. The penalty is a squared
l2 penalty. The bigger this parameter, the less regularization is used.
loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' (default='epsilon_insensitive')
Specifies the loss function. 'l1' is the epsilon-insensitive loss
(standard SVR) while 'l2' is the squared epsilon-insensitive loss.
epsilon : float, optional (default=0.1)
Epsilon parameter in the epsilon-insensitive loss function. Note
that the value of this parameter depends on the scale of the target
variable y. If unsure, set ``epsilon=0``.
dual : bool, (default=True)
Select the algorithm to either solve the dual or primal
optimization problem. Prefer dual=False when n_samples > n_features.
tol : float, optional (default=1e-4)
Tolerance for stopping criteria.
fit_intercept : boolean, optional (default=True)
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be already centered).
intercept_scaling : float, optional (default=1)
When self.fit_intercept is True, instance vector x becomes
[x, self.intercept_scaling],
i.e. a "synthetic" feature with constant value equals to
intercept_scaling is appended to the instance vector.
The intercept becomes intercept_scaling * synthetic feature weight
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features.
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased.
verbose : int, (default=0)
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in liblinear that, if enabled, may not work
properly in a multithreaded context.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
max_iter : int, (default=1000)
The maximum number of iterations to be run.
Attributes
----------
coef_ : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is a readonly property derived from `raw_coef_` that
follows the internal memory layout of liblinear.
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
Examples
--------
>>> from sklearn.svm import LinearSVR
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = LinearSVR(random_state=0)
>>> regr.fit(X, y)
LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,
intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,
random_state=0, tol=0.0001, verbose=0)
>>> print(regr.coef_)
[ 16.35750999 26.91499923 42.30652207 60.47843124]
>>> print(regr.intercept_)
[-4.29756543]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-4.29756543]
See also
--------
LinearSVC
Implementation of Support Vector Machine classifier using the
same library as this class (liblinear).
SVR
Implementation of Support Vector Machine regression using libsvm:
the kernel can be non-linear but its SMO algorithm does not
scale to large number of samples as LinearSVC does.
sklearn.linear_model.SGDRegressor
SGDRegressor can optimize the same cost function as LinearSVR
by adjusting the penalty and loss parameters. In addition it requires
less memory, allows incremental (online) learning, and implements
various loss functions and regularization regimes.
"""
def __init__(self, epsilon=0.0, tol=1e-4, C=1.0,
loss='epsilon_insensitive', fit_intercept=True,
intercept_scaling=1., dual=True, verbose=0,
random_state=None, max_iter=1000):
self.tol = tol
self.C = C
self.epsilon = epsilon
self.fit_intercept = fit_intercept
self.intercept_scaling = intercept_scaling
self.verbose = verbose
self.random_state = random_state
self.max_iter = max_iter
self.dual = dual
self.loss = loss
[docs] def fit(self, X, y, sample_weight=None):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
sample_weight : array-like, shape = [n_samples], optional
Array of weights that are assigned to individual
samples. If not provided,
then each sample is given unit weight.
Returns
-------
self : object
Returns self.
"""
# FIXME Remove l1/l2 support in 1.0 -----------------------------------
msg = ("loss='%s' has been deprecated in favor of "
"loss='%s' as of 0.16. Backward compatibility"
" for the loss='%s' will be removed in %s")
if self.loss in ('l1', 'l2'):
old_loss = self.loss
self.loss = {'l1': 'epsilon_insensitive',
'l2': 'squared_epsilon_insensitive'
}.get(self.loss)
warnings.warn(msg % (old_loss, self.loss, old_loss, '1.0'),
DeprecationWarning)
# ---------------------------------------------------------------------
if self.C < 0:
raise ValueError("Penalty term must be positive; got (C=%r)"
% self.C)
X, y = check_X_y(X, y, accept_sparse='csr',
dtype=np.float64, order="C")
penalty = 'l2' # SVR only accepts l2 penalty
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
X, y, self.C, self.fit_intercept, self.intercept_scaling,
None, penalty, self.dual, self.verbose,
self.max_iter, self.tol, self.random_state, loss=self.loss,
epsilon=self.epsilon, sample_weight=sample_weight)
self.coef_ = self.coef_.ravel()
return self
class SVC(BaseSVC):
"""C-Support Vector Classification.
The implementation is based on libsvm. The fit time complexity
is more than quadratic with the number of samples which makes it hard
to scale to dataset with more than a couple of 10000 samples.
The multiclass support is handled according to a one-vs-one scheme.
For details on the precise mathematical formulation of the provided
kernel functions and how `gamma`, `coef0` and `degree` affect each
other, see the corresponding section in the narrative documentation:
:ref:`svm_kernels`.
Read more in the :ref:`User Guide <svm_classification>`.
Parameters
----------
C : float, optional (default=1.0)
Penalty parameter C of the error term.
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to pre-compute the kernel matrix from data matrices; that matrix
should be an array of shape ``(n_samples, n_samples)``.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function.
It is only significant in 'poly' and 'sigmoid'.
probability : boolean, optional (default=False)
Whether to enable probability estimates. This must be enabled prior
to calling `fit`, and will slow down that method.
shrinking : boolean, optional (default=True)
Whether to use the shrinking heuristic.
tol : float, optional (default=1e-3)
Tolerance for stopping criterion.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
class_weight : {dict, 'balanced'}, optional
Set the parameter C of class i to class_weight[i]*C for
SVC. If not given, all classes are supposed to have
weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
decision_function_shape : 'ovo', 'ovr', default='ovr'
Whether to return a one-vs-rest ('ovr') decision function of shape
(n_samples, n_classes) as all other classifiers, or the original
one-vs-one ('ovo') decision function of libsvm which has shape
(n_samples, n_classes * (n_classes - 1) / 2).
.. versionchanged:: 0.19
decision_function_shape is 'ovr' by default.
.. versionadded:: 0.17
*decision_function_shape='ovr'* is recommended.
.. versionchanged:: 0.17
Deprecated *decision_function_shape='ovo' and None*.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
Attributes
----------
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [n_SV, n_features]
Support vectors.
n_support_ : array-like, dtype=int32, shape = [n_class]
Number of support vectors for each class.
dual_coef_ : array, shape = [n_class-1, n_SV]
Coefficients of the support vector in the decision function.
For multiclass, coefficient for all 1-vs-1 classifiers.
The layout of the coefficients in the multiclass case is somewhat
non-trivial. See the section about multi-class classification in the
SVM section of the User Guide for details.
coef_ : array, shape = [n_class-1, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is a readonly property derived from `dual_coef_` and
`support_vectors_`.
intercept_ : array, shape = [n_class * (n_class-1) / 2]
Constants in decision function.
Examples
--------
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import SVC
>>> clf = SVC()
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
SVR
Support Vector Machine for Regression implemented using libsvm.
LinearSVC
Scalable Linear Support Vector Machine for classification
implemented using liblinear. Check the See also section of
LinearSVC for more comparison element.
"""
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma='auto',
coef0=0.0, shrinking=True, probability=False,
tol=1e-3, cache_size=200, class_weight=None,
verbose=False, max_iter=-1, decision_function_shape='ovr',
random_state=None):
super(SVC, self).__init__(
impl='c_svc', kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, tol=tol, C=C, nu=0., shrinking=shrinking,
probability=probability, cache_size=cache_size,
class_weight=class_weight, verbose=verbose, max_iter=max_iter,
decision_function_shape=decision_function_shape,
random_state=random_state)
class NuSVC(BaseSVC):
"""Nu-Support Vector Classification.
Similar to SVC but uses a parameter to control the number of support
vectors.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <svm_classification>`.
Parameters
----------
nu : float, optional (default=0.5)
An upper bound on the fraction of training errors and a lower
bound of the fraction of support vectors. Should be in the
interval (0, 1].
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function.
It is only significant in 'poly' and 'sigmoid'.
probability : boolean, optional (default=False)
Whether to enable probability estimates. This must be enabled prior
to calling `fit`, and will slow down that method.
shrinking : boolean, optional (default=True)
Whether to use the shrinking heuristic.
tol : float, optional (default=1e-3)
Tolerance for stopping criterion.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
class_weight : {dict, 'balanced'}, optional
Set the parameter C of class i to class_weight[i]*C for
SVC. If not given, all classes are supposed to have
weight one. The "balanced" mode uses the values of y to automatically
adjust weights inversely proportional to class frequencies as
``n_samples / (n_classes * np.bincount(y))``
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
decision_function_shape : 'ovo', 'ovr', default='ovr'
Whether to return a one-vs-rest ('ovr') decision function of shape
(n_samples, n_classes) as all other classifiers, or the original
one-vs-one ('ovo') decision function of libsvm which has shape
(n_samples, n_classes * (n_classes - 1) / 2).
.. versionchanged:: 0.19
decision_function_shape is 'ovr' by default.
.. versionadded:: 0.17
*decision_function_shape='ovr'* is recommended.
.. versionchanged:: 0.17
Deprecated *decision_function_shape='ovo' and None*.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
Attributes
----------
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [n_SV, n_features]
Support vectors.
n_support_ : array-like, dtype=int32, shape = [n_class]
Number of support vectors for each class.
dual_coef_ : array, shape = [n_class-1, n_SV]
Coefficients of the support vector in the decision function.
For multiclass, coefficient for all 1-vs-1 classifiers.
The layout of the coefficients in the multiclass case is somewhat
non-trivial. See the section about multi-class classification in
the SVM section of the User Guide for details.
coef_ : array, shape = [n_class-1, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is readonly property derived from `dual_coef_` and
`support_vectors_`.
intercept_ : array, shape = [n_class * (n_class-1) / 2]
Constants in decision function.
Examples
--------
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
NuSVC(cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, nu=0.5, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
SVC
Support Vector Machine for classification using libsvm.
LinearSVC
Scalable linear Support Vector Machine for classification using
liblinear.
"""
def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0,
shrinking=True, probability=False, tol=1e-3, cache_size=200,
class_weight=None, verbose=False, max_iter=-1,
decision_function_shape='ovr', random_state=None):
super(NuSVC, self).__init__(
impl='nu_svc', kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, tol=tol, C=0., nu=nu, shrinking=shrinking,
probability=probability, cache_size=cache_size,
class_weight=class_weight, verbose=verbose, max_iter=max_iter,
decision_function_shape=decision_function_shape,
random_state=random_state)
class SVR(BaseLibSVM, RegressorMixin):
"""Epsilon-Support Vector Regression.
The free parameters in the model are C and epsilon.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <svm_regression>`.
Parameters
----------
C : float, optional (default=1.0)
Penalty parameter C of the error term.
epsilon : float, optional (default=0.1)
Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
within which no penalty is associated in the training loss function
with points predicted within a distance epsilon from the actual
value.
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function.
It is only significant in 'poly' and 'sigmoid'.
shrinking : boolean, optional (default=True)
Whether to use the shrinking heuristic.
tol : float, optional (default=1e-3)
Tolerance for stopping criterion.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
Attributes
----------
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]
Support vectors.
dual_coef_ : array, shape = [1, n_SV]
Coefficients of the support vector in the decision function.
coef_ : array, shape = [1, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is readonly property derived from `dual_coef_` and
`support_vectors_`.
intercept_ : array, shape = [1]
Constants in decision function.
sample_weight : array-like, shape = [n_samples]
Individual weights for each sample
Examples
--------
>>> from sklearn.svm import SVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = SVR(C=1.0, epsilon=0.2)
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
See also
--------
NuSVR
Support Vector Machine for regression implemented using libsvm
using a parameter to control the number of support vectors.
LinearSVR
Scalable Linear Support Vector Machine for regression
implemented using liblinear.
"""
def __init__(self, kernel='rbf', degree=3, gamma='auto', coef0=0.0,
tol=1e-3, C=1.0, epsilon=0.1, shrinking=True,
cache_size=200, verbose=False, max_iter=-1):
super(SVR, self).__init__(
'epsilon_svr', kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, tol=tol, C=C, nu=0., epsilon=epsilon, verbose=verbose,
shrinking=shrinking, probability=False, cache_size=cache_size,
class_weight=None, max_iter=max_iter, random_state=None)
class NuSVR(BaseLibSVM, RegressorMixin):
"""Nu Support Vector Regression.
Similar to NuSVC, for regression, uses a parameter nu to control
the number of support vectors. However, unlike NuSVC, where nu
replaces C, here nu replaces the parameter epsilon of epsilon-SVR.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <svm_regression>`.
Parameters
----------
C : float, optional (default=1.0)
Penalty parameter C of the error term.
nu : float, optional
An upper bound on the fraction of training errors and a lower bound of
the fraction of support vectors. Should be in the interval (0, 1]. By
default 0.5 will be taken.
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function.
It is only significant in 'poly' and 'sigmoid'.
shrinking : boolean, optional (default=True)
Whether to use the shrinking heuristic.
tol : float, optional (default=1e-3)
Tolerance for stopping criterion.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
Attributes
----------
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]
Support vectors.
dual_coef_ : array, shape = [1, n_SV]
Coefficients of the support vector in the decision function.
coef_ : array, shape = [1, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is readonly property derived from `dual_coef_` and
`support_vectors_`.
intercept_ : array, shape = [1]
Constants in decision function.
Examples
--------
>>> from sklearn.svm import NuSVR
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = NuSVR(C=1.0, nu=0.1)
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma='auto',
kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
verbose=False)
See also
--------
NuSVC
Support Vector Machine for classification implemented with libsvm
with a parameter to control the number of support vectors.
SVR
epsilon Support Vector Machine for regression implemented with libsvm.
"""
def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3,
gamma='auto', coef0=0.0, shrinking=True, tol=1e-3,
cache_size=200, verbose=False, max_iter=-1):
super(NuSVR, self).__init__(
'nu_svr', kernel=kernel, degree=degree, gamma=gamma, coef0=coef0,
tol=tol, C=C, nu=nu, epsilon=0., shrinking=shrinking,
probability=False, cache_size=cache_size, class_weight=None,
verbose=verbose, max_iter=max_iter, random_state=None)
class OneClassSVM(BaseLibSVM):
"""Unsupervised Outlier Detection.
Estimate the support of a high-dimensional distribution.
The implementation is based on libsvm.
Read more in the :ref:`User Guide <svm_outlier_detection>`.
Parameters
----------
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.
nu : float, optional
An upper bound on the fraction of training
errors and a lower bound of the fraction of support
vectors. Should be in the interval (0, 1]. By default 0.5
will be taken.
degree : int, optional (default=3)
Degree of the polynomial kernel function ('poly').
Ignored by all other kernels.
gamma : float, optional (default='auto')
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
If gamma is 'auto' then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function.
It is only significant in 'poly' and 'sigmoid'.
tol : float, optional
Tolerance for stopping criterion.
shrinking : boolean, optional
Whether to use the shrinking heuristic.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
Attributes
----------
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [nSV, n_features]
Support vectors.
dual_coef_ : array, shape = [1, n_SV]
Coefficients of the support vectors in the decision function.
coef_ : array, shape = [1, n_features]
Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.
`coef_` is readonly property derived from `dual_coef_` and
`support_vectors_`
intercept_ : array, shape = [1,]
Constant in the decision function.
"""
def __init__(self, kernel='rbf', degree=3, gamma='auto', coef0=0.0,
tol=1e-3, nu=0.5, shrinking=True, cache_size=200,
verbose=False, max_iter=-1, random_state=None):
super(OneClassSVM, self).__init__(
'one_class', kernel, degree, gamma, coef0, tol, 0., nu, 0.,
shrinking, False, cache_size, None, verbose, max_iter,
random_state)
[docs] def fit(self, X, y=None, sample_weight=None, **params):
"""
Detects the soft boundary of the set of samples X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Set of samples, where n_samples is the number of samples and
n_features is the number of features.
sample_weight : array-like, shape (n_samples,)
Per-sample weights. Rescale C per sample. Higher weights
force the classifier to put more emphasis on these points.
Returns
-------
self : object
Returns self.
Notes
-----
If X is not a C-ordered contiguous array it is copied.
"""
super(OneClassSVM, self).fit(X, np.ones(_num_samples(X)),
sample_weight=sample_weight, **params)
return self
[docs] def decision_function(self, X):
"""Signed distance to the separating hyperplane.
Signed distance is positive for an inlier and negative for an outlier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Returns
-------
X : array-like, shape (n_samples,)
Returns the decision function of the samples.
"""
dec = self._decision_function(X)
return dec
[docs] def predict(self, X):
"""
Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
[n_samples_test, n_samples_train]
Returns
-------
y_pred : array, shape (n_samples,)
Class labels for samples in X.
"""
y = super(OneClassSVM, self).predict(X)
return np.asarray(y, dtype=np.intp)


module Insn:sig
..end
Assembly instruction.
On a high level, the instruction is a pair of the opcode and operands. A BIL code, that describes semantics of the instruction may be attached to it. Also, semantic tags (or flags) may add further information about the instruction.
The instruction are usually created by a low level machinery, and analyzed on the later stages. So, usually, there is no need to create one manually.
For example, each block is a sequence of instructions (see
Std.Block.insns
), also with each non-synthetic term there is an
an Std.Disasm.insn
field, that stores an instruction from which
the term was born.
typet =
Std.insn
val of_basic : ?bil:Std.bil -> Std.Disasm_expert.Basic.full_insn -> t
The following functions will create insn
instances from a lower
level representation.
val name : t -> string
returns backend specific name of instruction
val asm : t -> string
target-specific assembler string representing the instruction
val bil : t -> Std.bil
returns BIL program specifying instruction semantics
val ops : t -> Std.op array
instruction operands
A property or a semantic tag is some kind of attribute
associated with an instruction. Usually a property is a
boolean, it either holds or not. In our case we employ modular
logic, and a property can have an intermediate state between
true and false. That means, that we have two kinds of
relations, strong "must" and weaker "may". The must
property is known to be a property associated with the
instruction. It is a strong knowledge. For example, if an
instruction has jump
property, then it is guaranteed to be a
jump instruction. On the other hand, the may
property
represent some uncertain knowledge. For example, the load
property is may
as it designates that an instruction may
access the main memory, or may not access, as it depends on some
information, that cannot be deduced statically.
type
must =
| |
Must |
type
may =
| |
May |
type 'a
property
val new_property : 'a -> string -> 'a property
new_property must_or_may name
creates a new instruction
property with the specified name.
val jump : must property
the instruction performs a non-regular control flow
val conditional : must property
under some dynamic condition the instruction may perform a non-regular control flow
val indirect : must property
the instruction is jump with a target that is not a constant
val call : must property
the instruction is a call to subroutine.
val return : must property
instruction is a return from a call
val affect_control_flow : may property
the instruction may perform a non-regular control flow
val load : may property
the instruction may load from memory
val store : may property
the instruction may store to memory
val is : must property -> t -> bool
is property insn
is true
if insn
has property
val may : may property -> t -> bool
may propery insn
is true
if insn
has property
val must : must property -> t -> t
must property insn
postulate that insn
must have the property
val mustn't : must property -> t -> t
must property insn
postulate that insn
must not have the property
val should : may property -> t -> t
must property insn
postulate that insn
may have the property
val shouldn't : may property -> t -> t
must property insn
postulate that insn
shouldn't have the property
val pp_adt : t Std.printer
pp_adt
prints instruction in ADT format, suitable for reading
by evaluating in many languages, e.g. Python, Js, etc
module Trie:sig
..end
Prefix Tree This module provides a trie data structure where a sequence of instructions is used as a key (and an individual instruction as a token).
include Regular.S


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Don't you wish you were here?
Check out the annual climatology for Oahu
Please see the javascript component documentation for more information on using the LAS components for creating simple user interfaces.


About Me
My Projects
Contact Details
Hey I'm Amit Frechter
a product manager learning web development
My GitHub


This was an experiment.
At approximately 9:00 p.m., I prepared the liquids.
Two tablespoons each of commercial kava kava extract,
homemade marijuana tincture, and absinthe. This mixture
was put in the freezer to chill. At approximately 9:30,
I removed it from the freezer and added as much cold
sparkling water as the total mixture. It turned an opaque,
swirling greenish color - the fabled louche of absinthe.
I downed it in one gulp. The aftertaste was not as
unpleasant as expected - the bitterness of absinthe with
sweet anise overtones.
I was at home, relaxed, with no pressing concerns. By
10:00 p.m., I was the only one still up. My lips were
beginning to feel numb, the sign that the kava was taking
effect. I was feeling slightly numb in my extremities.
By 10:15 p.m., I was feeling quite good - even more relaxed,
optimistic and peaceful. I set up my Dreamachine (a homemade
device that produces light flickers in the low alpha range).
I then smoked a small bowl of high-grade marijuana. As
it began to take effect, I sat down at the Dreamachine.
The visual effects came on quite rapidly. The initial
entoptic imagery gave way to a ceaseless kaleidoscopic
array of color, shape and light, reminiscent of some
accounts I've read of mescaline's effects. I began to
see clear images within the swirling vortex, forming,
rising up to full prominence, then being sucked down and
replaced with new ones. My spirit soared as I began to
see _through_ the vortex, into a calm and glowing level
marked by a stable geometrical pattern. It appeared to be
hexagonal, in glowing green and yellow - although colors
such that I've never seen with mortal eyes.
After some ten or twelve minutes of this rapt contemplation
of the fiery fractal mandala that lies on the other side of
darkness, I got up, went to the couch and laid down. Immediately
upon closing my eyes, I began to see closed eye visuals (CEVs).
These morphed and transformed as inexorably as the ones
before. After settling in and becoming quite comfortable,
I took up a whipped cream dispenser, exhaled, and took in
a moderate lungful of nitrous oxide.
As I exhaled, I could tell the sensation of air passing
through my trachea had already changed. Inhale, exhale. . .
and my body began to experience Pure Pleasure. I felt myself
ascending the World Tree and emerging into the brilliance
of the Living World. Every joy and pleasure of mortal life
was a mere shadow or gossamer simulacrum of this experience.
Even as I soared above the canopy into the clear blue beyond,
I knew that gravity was pulling me back. A long long descent
back to my body, where simply being stoned out of my gourd
felt like being stone cold sober by contrast.
I put everything away, brushed my teeth, took two ibuprofen,
drank a glass of water, and went to bed. Got up seven hours
later feeling fine.
Do NOT attempt this if you are going to be in public.
I intend to repeat this experiment, but not soon and not
very often. It was an extreme experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The charming adventures of an analyst and his solver.
Python obfuscation using ROT13 encoding.
No series of JAPHs would be complete without ROT13. This is the example through which aspiring Perl programmers learn to use tr and its synonym y. In Perl the basic ROT13 JAPH starts as:
$foo = 'whfg nabgure crey unpxre';
$foo =~ y/a-z/n-za-m/;
print $foo;
Python has nothing quite so elegant in its default namespace. However, this does give us the opportunity to explore a little used aspect of strings: the translate method. If we construct a dictionary of ordinals we can accomplish the same thing with a touch more effort.
import string
table = {
ord(x): ord(y) for x, y in zip(
string.ascii_lowercase,
string.ascii_lowercase[13:] + string.ascii_lowercase
)
}
print('whfg nabgure clguba unpxre'.translate(table))
We obfuscate the construction of this translation dictionary and, for added measure, use getattr to find the print function off of __builtins__. This will likely only work in Python 3.2, since the order of attributes on __builtins__ matters.
getattr(vars()[list(filter(lambda _:'\x5f\x62'in _,dir
()))[0]], dir(vars()[list(filter(lambda _:'\x5f\x62'in
_, dir()))[0]])[list(filter(lambda _:_ [1].startswith(
'\x70\x72'),enumerate(dir(vars()[list(filter(lambda _:
'\x5f\x62'in _,dir()))[0]]))))[0][0]])(getattr('whfg '
+'''nabgure clguba unpxre''', dir('0o52')[0o107])({ _:
(_-0o124) %0o32 +0o141 for _ in range(0o141, 0o173)}))


Support for the Drizzle database.
The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.
Drizzle is a variant of MySQL. Unlike MySQL, Drizzle’s default storage engine is InnoDB (transactions, foreign-keys) rather than MyISAM. For more Notable Differences, visit the Drizzle Documentation.
The SQLAlchemy Drizzle dialect leans heavily on the MySQL dialect, so much of the SQLAlchemy MySQL documentation is also relevant.
As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with Drizzle are importable from the top level dialect:
from sqlalchemy.dialects.drizzle import \
BIGINT, BINARY, BLOB, BOOLEAN, CHAR, DATE, DATETIME,
DECIMAL, DOUBLE, ENUM, FLOAT, INT, INTEGER,
NUMERIC, TEXT, TIME, TIMESTAMP, VARBINARY, VARCHAR
Types which are specific to Drizzle, or have Drizzle-specific construction arguments, are as follows:
Bases: sqlalchemy.types.BIGINT
Drizzle BIGINTEGER type.
Construct a BIGINTEGER.
Bases: sqlalchemy.dialects.drizzle.base._StringType, sqlalchemy.types.CHAR
Drizzle CHAR type, for fixed-length character data.
Construct a CHAR.
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.drizzle.base._NumericType, sqlalchemy.types.DECIMAL
Drizzle DECIMAL type.
Construct a DECIMAL.
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.drizzle.base._FloatType
Drizzle DOUBLE type.
Construct a DOUBLE.
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.mysql.base.ENUM
Drizzle ENUM type.
Construct an ENUM.
Example:
Column(‘myenum’, ENUM(“foo”, “bar”, “baz”))
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.drizzle.base._FloatType, sqlalchemy.types.FLOAT
Drizzle FLOAT type.
Construct a FLOAT.
| Parameters: |
|
|---|
Bases: sqlalchemy.types.INTEGER
Drizzle INTEGER type.
Construct an INTEGER.
Bases: sqlalchemy.dialects.drizzle.base._NumericType, sqlalchemy.types.NUMERIC
Drizzle NUMERIC type.
Construct a NUMERIC.
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.drizzle.base._FloatType, sqlalchemy.types.REAL
Drizzle REAL type.
Construct a REAL.
| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.drizzle.base._StringType, sqlalchemy.types.TEXT
Drizzle TEXT type, for text up to 2^16 characters.
Construct a TEXT.
| Parameters: |
|
|---|
Bases: sqlalchemy.types.TIMESTAMP
Drizzle TIMESTAMP type.
Bases: sqlalchemy.dialects.drizzle.base._StringType, sqlalchemy.types.VARCHAR
Drizzle VARCHAR type, for variable-length character data.
Construct a VARCHAR.
| Parameters: |
|
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Support for the Drizzle database via the MySQL-Python driver.
Documentation and download information (if applicable) for MySQL-Python is available at: http://sourceforge.net/projects/mysql-python


Kiansjet's QuickLinks
A bodged QuickLinks service similar to Bit.ly and the now discontinued Goo.gl.
You can find the source code here, but please do not use this as a secure QuickLinks service.
- This service is as clientside as possible (It is hosted as a static site on GitHub Pages and delivered by Cloudflare).
- Every time you navigate to a QuickLink, the entire QuickLinks directory is loaded by your client and parsed for the relevant QuickLink.
- Due to this static model with little server involvement, all QuickLinks are public, so don't use forks of this service to secretly create QuickLinks.


I'm a Manchester based Tattoo Artist specialising in black work. Based in a private studio just a short walk from Deansgate Metrolink and Oxford Road train stations.
My artwork and designs are not your typical pocket watch and roses. I take inspiration from artists such as Ralph Steadman, Adam Riches and Anthony Rondinone.
For enquiries or bookings please message me on:
Click to open Google map


DbSettingsThis class contains various database-level settings. To override the documented default value for a database, append the setting in the database URL: "jdbc:h2:test;ALIAS_COLUMN_NAME=TRUE" when opening the first connection to the database. The settings can not be changed once the database is open.Some settings are a last resort and temporary solution to work around a problem in the application or database engine. Also, there are system properties to enable features that are not yet fully tested or that are not backward compatible. aliasColumnNameDatabase setting
ALIAS_COLUMN_NAME (default: false).When enabled, aliased columns (as in SELECT ID AS I FROM TEST) return the alias (I in this case) in ResultSetMetaData.getColumnName() and 'null' in getTableName(). If disabled, the real column name (ID in this case) and table name is returned. This setting only affects the default and the MySQL mode. When using any other mode, this feature is enabled for compatibility, even if this database setting is not enabled explicitly. analyzeAutoDatabase setting
ANALYZE_AUTO (default: 2000).After changing this many rows, ANALYZE is automatically run for a table. Automatically running ANALYZE is disabled if set to 0. If set to 1000, then ANALYZE will run against each user table after about 1000 changes to that table. The time between running ANALYZE doubles each time since starting the database. It is not run on local temporary tables, and tables that have a trigger on SELECT. analyzeSampleDatabase setting
ANALYZE_SAMPLE (default: 10000).The default sample size when analyzing a table. databaseToUpperDatabase setting
DATABASE_TO_UPPER (default: true).Database short names are converted to uppercase for the DATABASE() function, and in the CATALOG column of all database meta data methods. Setting this to "false" is experimental. When set to false, all identifier names (table names, column names) are case sensitive (except aggregate, built-in functions, data types, and keywords). dbCloseOnExitDatabase setting
DB_CLOSE_ON_EXIT (default: true).Close the database when the virtual machine exits normally, using a shutdown hook. defaultConnectionDatabase setting
DEFAULT_CONNECTION (default: false).Whether Java functions can use DriverManager.getConnection("jdbc:default:connection") to
get a database connection. This feature is disabled by default for
performance reasons. Please note the Oracle JDBC driver will try to
resolve this database URL if it is loaded before the H2 driver.defaultEscapeDatabase setting
DEFAULT_ESCAPE (default: \).The default escape character for LIKE comparisons. To select no escape character, use an empty string. defragAlwaysDatabase setting
DEFRAG_ALWAYS (default: false).Each time the database is closed, it is fully defragmented (SHUTDOWN DEFRAG). dropRestrictDatabase setting
DROP_RESTRICT (default: true).Whether the default action for DROP TABLE and DROP VIEW is RESTRICT. earlyFilterDatabase setting
EARLY_FILTER (default: false).This setting allows table implementations to apply filter conditions early on. estimatedFunctionTableRowsDatabase setting
ESTIMATED_FUNCTION_TABLE_ROWS (default:
1000).The estimated number of rows in a function table (for example, CSVREAD or FTL_SEARCH). This value is used by the optimizer. functionsInSchemaDatabase setting
FUNCTIONS_IN_SCHEMA (default:
true).If set, all functions are stored in a schema. Specially, the SCRIPT statement will always include the schema name in the CREATE ALIAS statement. This is not backward compatible with H2 versions 1.2.134 and older. largeResultBufferSizeDatabase setting
LARGE_RESULT_BUFFER_SIZE (default: 4096).Buffer size for large result sets. Set this value to 0 to disable the buffer. largeTransactionsDatabase setting
LARGE_TRANSACTIONS (default: true).Support very large transactions maxCompactCountDatabase setting
MAX_COMPACT_COUNT
(default: Integer.MAX_VALUE).The maximum number of pages to move when closing a database. maxCompactTimeDatabase setting
MAX_COMPACT_TIME (default: 200).The maximum time in milliseconds used to compact a database when closing. maxMemoryRowsDistinctDatabase setting
MAX_MEMORY_ROWS_DISTINCT (default:
10000).The maximum number of rows kept in-memory for SELECT DISTINCT queries. If more than this number of rows are in a result set, a temporary table is used. maxQueryTimeoutDatabase setting
MAX_QUERY_TIMEOUT (default: 0).The maximum timeout of a query in milliseconds. The default is 0, meaning no limit. Please note the actual query timeout may be set to a lower value. nestedJoinsDatabase setting
NESTED_JOINS (default: true).Whether nested joins should be supported. optimizeDistinctDatabase setting
OPTIMIZE_DISTINCT (default: true).Improve the performance of simple DISTINCT queries if an index is available for the given column. The optimization is used if:
optimizeEvaluatableSubqueriesDatabase setting
OPTIMIZE_EVALUATABLE_SUBQUERIES (default:
true).Optimize subqueries that are not dependent on the outer query. optimizeInListDatabase setting
OPTIMIZE_IN_LIST (default: true).Optimize IN(...) and IN(SELECT ...) comparisons. This includes optimization for SELECT, DELETE, and UPDATE. optimizeInSelectDatabase setting
OPTIMIZE_IN_SELECT (default: true).Optimize IN(SELECT ...) comparisons. This includes optimization for SELECT, DELETE, and UPDATE. optimizeInsertFromSelectDatabase setting
OPTIMIZE_INSERT_FROM_SELECT
(default: true).Insert into table from query directly bypassing temporary disk storage. This also applies to create table as select. optimizeIsNullDatabase setting
OPTIMIZE_IS_NULL (default: false).Use an index for condition of the form columnName IS NULL. optimizeOrDatabase setting
OPTIMIZE_OR (default: true).Convert (C=? OR C=?) to (C IN(?, ?)). optimizeTwoEqualsDatabase setting
OPTIMIZE_TWO_EQUALS (default: true).Optimize expressions of the form A=B AND B=1. In this case, AND A=1 is added so an index on A can be used. optimizeUpdateDatabase setting
OPTIMIZE_UPDATE (default: true).Speed up inserts, updates, and deletes by not reading all rows from a page unless necessary. pageStoreInternalCountDatabase setting
PAGE_STORE_INTERNAL_COUNT
(default: false).Update the row counts on a node level. pageStoreMaxGrowthDatabase setting
PAGE_STORE_MAX_GROWTH
(default: 128 * 1024).The maximum number of pages the file grows at any time. pageStoreTrimDatabase setting
PAGE_STORE_TRIM (default: true).Trim the database size when closing. queryCacheSizeDatabase setting
QUERY_CACHE_SIZE (default: 8).The size of the query cache, in number of cached statements. Each session has it's own cache with the given size. The cache is only used if the SQL statement and all parameters match. Only the last returned result per query is cached. Only SELECT statements are cached (excluding UNION and FOR UPDATE statements). This works for both statements and prepared statement. recompileAlwaysDatabase setting
RECOMPILE_ALWAYS (default: false).Always recompile prepared statements. reconnectCheckDelayDatabase setting
RECONNECT_CHECK_DELAY (default: 200).Check the .lock.db file every this many milliseconds to detect that the database was changed. The process writing to the database must first notify a change in the .lock.db file, then wait twice this many milliseconds before updating the database. rowIdDatabase setting
ROWID (default: true).If set, each table has a pseudo-column _ROWID_. selectForUpdateMvccDatabase setting
SELECT_FOR_UPDATE_MVCC
(default: true).If set, SELECT .. FOR UPDATE queries lock only the selected rows when using MVCC. shareLinkedConnectionsDatabase setting
SHARE_LINKED_CONNECTIONS
(default: true).Linked connections should be shared, that means connections to the same database should be used for all linked tables that connect to the same database. |


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from the book
creating a alert and variables.
all the variables used here goes here
functions
if statment
Introduction
changing html by js
hey this the actual html text
hey click me!


3
Great Is Your Faithfulness
1 I am the man who has seen affliction
under the rod of his wrath;
2 he has driven and brought me
into darkness without any light;
3 surely against me he turns his hand
again and again the whole day long.
4 He has made my flesh and my skin waste away;
he has broken my bones;
5 he has besieged and enveloped me
with bitterness and tribulation;
6 he has made me dwell in darkness
like the dead of long ago.
7 He has walled me about so that I cannot escape;
he has made my chains heavy;
8 though I call and cry for help,
he shuts out my prayer;
9 he has blocked my ways with blocks of stones;
he has made my paths crooked.
10 He is a bear lying in wait for me,
a lion in hiding;
11 he turned aside my steps and tore me to pieces;
he has made me desolate;
12 he bent his bow and set me
as a target for his arrow.
13 He drove into my kidneys
the arrows of his quiver;
14 I have become the laughingstock of all peoples,
the object of their taunts all day long.
15 He has filled me with bitterness;
he has sated me with wormwood.
16 He has made my teeth grind on gravel,
and made me cower in ashes;
17 my soul is bereft of peace;
I have forgotten what happiness is;
18 so I say, “My endurance has perished;
so has my hope from the LORD.”
19 Remember my affliction and my wanderings,
the wormwood and the gall!
20 My soul continually remembers it
and is bowed down within me.
21 But this I call to mind,
and therefore I have hope:
22 The steadfast love of the LORD never ceases;
his mercies never come to an end;
23 they are new every morning;
great is your faithfulness.
24 “The LORD is my portion,” says my soul,
“therefore I will hope in him.”
25 The LORD is good to those who wait for him,
to the soul who seeks him.
26 It is good that one should wait quietly
for the salvation of the LORD.
27 It is good for a man that he bear
the yoke in his youth.
28 Let him sit alone in silence
when it is laid on him;
29 let him put his mouth in the dust—
there may yet be hope;
30 let him give his cheek to the one who strikes,
and let him be filled with insults.
31 For the Lord will not
cast off forever,
32 but, though he cause grief, he will have compassion
according to the abundance of his steadfast love;
33 for he does not willingly afflict
or grieve the children of men.
34 To crush underfoot
all the prisoners of the earth,
35 to deny a man justice
in the presence of the Most High,
36 to subvert a man in his lawsuit,
the Lord does not approve.
37 Who has spoken and it came to pass,
unless the Lord has commanded it?
38 Is it not from the mouth of the Most High
that good and bad come?
39 Why should a living man complain,
a man, about the punishment of his sins?
40 Let us test and examine our ways,
and return to the LORD!
41 Let us lift up our hearts and hands
to God in heaven:
42 “We have transgressed and rebelled,
and you have not forgiven.
43 “You have wrapped yourself with anger and pursued us,
killing without pity;
44 you have wrapped yourself with a cloud
so that no prayer can pass through.
45 You have made us scum and garbage
among the peoples.
46 “All our enemies
open their mouths against us;
47 panic and pitfall have come upon us,
devastation and destruction;
48 my eyes flow with rivers of tears
because of the destruction of the daughter of my people.
49 “My eyes will flow without ceasing,
without respite,
50 until the LORD from heaven
looks down and sees;
51 my eyes cause me grief
at the fate of all the daughters of my city.
52 “I have been hunted like a bird
by those who were my enemies without cause;
53 they flung me alive into the pit
and cast stones on me;
54 water closed over my head;
I said, ‘I am lost.’
55 “I called on your name, O LORD,
from the depths of the pit;
56 you heard my plea, ‘Do not close
your ear to my cry for help!’
57 You came near when I called on you;
you said, ‘Do not fear!’
58 “You have taken up my cause, O Lord;
you have redeemed my life.
59 You have seen the wrong done to me, O LORD;
judge my cause.
60 You have seen all their vengeance,
all their plots against me.
61 “You have heard their taunts, O LORD,
all their plots against me.
62 The lips and thoughts of my assailants
are against me all the day long.
63 Behold their sitting and their rising;
I am the object of their taunts.
64 “You will repay them, O LORD,
according to the work of their hands.
65 You will give them dullness of heart;
your curse will be on them.
66 You will pursue them in anger and destroy them
from under your heavens, O LORD.”