Magic
Magic is the ability to channel Energy into physical manifestations of power.
The most common form of Magic is Spells, which use the Energy of the caster.
Learning a spell
Any character can learn spells, but they must provide a reason for how or why they learned magic. Some common examples are:
- You studied in the Mage College, poring over spell books, where you wrote down interesting ones in a book of your own, which you study and keep updated.
- Your bloodline has intristically been able to tap into mystical forces and gain power naturally.
- You made a deal with a supernatural beast for power in exchange for your allegiance.
- A deity you worship grants you the ability to channel their power as long as you spread word of their deeds.
Each spell a character learns costs 1 Feature Point.
Some characters, as a trait, have difficulty tapping into Magic. You decide if you want your character to fall into this category by not having them learn any spells.
Casting a spell
You have a number of spell slots equal to your Focus Modifier. If your Focus Modifier is 0 or less, you cannot cast spells.
 If no spell slots are free, you cannot cast any additional spells. These Spell Slots are noted in the Spell Slots section of your Character Sheet.
To place a spell in a spell slot, follow these steps:
- Choose a free spell slot and write down the name of a spell that you have learned in the "Spell Name" column. This causes the slot to no longer be free.
- Choose how much Energy you want to channel for the spell, record this number in the "Channeled Energy" column, then add the number to the Total Channeled section of your Character Sheet. See notes about Channeled Energy below.
- Record how many rounds it will take to regain this Channeled Energy, as noted by the "Regen Time" of the spell description, in the "Regen Left" column. If the Regen Time listed is "Concentration", simply mark an in this spot instead.
At the start of each of your turns, do the following:
- Subtract 1 from the "Regen Left" column for each spell that is not Concentration based.
- If the number in the "Regen Left" column becomes 0 after performing the previous step, decrease the Total Channeled by the number in the Channeled Energy column. Then, erase the row, making the spell slot free.
For concentration spells, at any point on your turn you may decide to stop concentrating on the spell. In which case, decrease the Total Channeled by the number in the Channeled Energy column, then erase the row, thus freeing the spell slot. Any effects from the spell immediately cease.
Channeled Energy
All spells have a description for "Energy Cost", which indicates how much Energy must be channeled in order to produce an effect. Typically, the more Energy channeled, the more powerful the spell becomes.
When you place a spell in a spell slot, you determine how much of your own Energy you are willing risk on this spell, and record it in the "Channeled Energy" column.
The sum of the "Channeled Energy" column of every spell, in other words, the "Total Channeled" in the Spell Slots section, cannot exceed your Current Energy.
If your Current Energy goes down, such as for taking damage from an attack, the following happens:
- Your Current Energy is decreased by the Total Channeled in the Spell Slots section.
- All spells are erased from the Spell Slots, freeing all of them. Any effects still in play from those spells cease immediately.
- As a result, Total Channeled is set to 0.


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#include <db_cxx.h>int DbLockTab::close();
The DbLockTab::close method closes the lock table.
The DbLockTab::close method does not release any locks still held by the closing process, providing functionality for long-lived locks. Processes that wish to have all their locks released can do so by issuing the appropriate DbLockTab::vec call.
In addition, if the dir argument to DbLockTab::open was NULL and dbenv was not initialized using DbEnv::appinit, all files created for this shared region will be removed, as if the DbLockTab::unlink method were called.
In multi-threaded applications, only a single thread may call the DbLockTab::close method.
The DbLockTab::close method either returns errno or throws an exception that encapsulates an errno on failure, and 0 on success.
The DbLockTab::close method may fail and throw an exception for any of the errors specified for the following Berkeley DB and C library functions: abort(3), close(3), fcntl(3), fflush(3), fprintf(3), free(3), getpid(3), memset(3), munmap(3), shmctl(3), shmdt(3), strlen(3), unlink(3), vfprintf(3), and vsnprintf(3).


Julia Roberts was suggested to play Harriet Tubman in biopic, writer claims
A Hollywood executive suggested Julia Roberts play slavery abolitionist Harriet Tubman in a film about her life, a screenwriter has claimed.
Tubman, who was black, is one of the best-known women in US history and played a pivotal role in leading enslaved people to freedom on the Underground Railroad.
British actress Cynthia Erivo plays her in the recently released biopic Harriet, which has been warmly received by critics.
However, the film could have been very different, according to its screenwriter Gregory Allen Howard.
Writing in the Los Angeles Times, he said an unnamed president of a Hollywood sub-studio was eager to make the film in 1994 but suggested casting Roberts, who is white, in the lead role.
He reportedly said: “This is a great script. Let’s get Julia Roberts to play Harriet Tubman.”
When it was pointed out that Tubman, who died in 1913, was black, the president is said to have replied: “That was so long ago. No one will know that.”
Had Roberts been cast in Harriet, it would have been another infamous instance of Hollywood “whitewashing”, a term used to describe the habit of having white actors play non-white characters.
The actress Scarlett Johansson was criticised for her role in 2017 sci-fi film Ghost In The Shell, which is based on a Japanese anime series.
Howard, who spent more than two decades trying to get Harriet made, credits the success of Oscar-winning 12 Years A Slave and the Marvel blockbuster Black Panther with opening the door for its success.
He said: “When 12 Years a Slave became a hit and did a couple hundred million dollars worldwide, I told my agent, ‘You can’t say this kind of story won’t make money now.’ Then Black Panther really blew the doors open.”
Harriet, which also stars Leslie Odom Jr, Joe Alwyn, and Janelle Monee is in cinemas now.


What is the average case cost of Shellsort for a good increment series?
\Theta(n \sqrt{n})
\Theta(n^2)
\Theta(n)
\Theta(n \log n)
\Theta(2^n)
\Theta(\log n)
Shellsort does not have the same runtime as most typical sorts.

Shellsort is not \Theta(n \log n)
or \Theta(n^2)


When searching where to host our team’s blog we wanted to stay close to the community that we were trying to give back to. Github Pages immediately stood out and with the cost of $0 it’s perfect. Github Pages allows us to keep using the same tools we use everyday in Development for Blogging.
Where to Start?
The first thing you will want to do is choose a static site generator. To make it easy for you I’ve read through quite a bit material and the top 2 imo are:
- jekyll (ruby)
- cabin (javascript)
Both have their advantages but since I need some material for the next Ruby Meetup we are going to start this blog with using Jekyll.
Getting Started
Github Pages is straight forward and easy to follow.
Jekyll is even easier to install
gem install jekyll
There are a ton of resources and tools from here to get you started with Jekyll. One of the most common is Octopress. But I like starting from something even more basic. That’s where I found Poole.
Even more so let’s start from one of its themes Hyde. Download the source code and extract it into our new project.
Let’s take a look at the file structure. You will see some files/folders with underscores. Every thing without an underscore will get copied into to the _site folder on build.
If you open up /_layouts/post.html you will see handlebars used for injecting content. This is using the Liquid Markup Language.
Configuring Poole
There are few things we will need to do configure Poole
-
First, unelss you are using a custom domain is to remove the CNAME file, otherwise edit it with your domain.
-
Second, let’s set customize the _config.yml to fit our blog.
-
Third, remove the default blog posts under the _posts folder and write your our first post.
That’s it! Now run jekyll build
Commit and push!
You can add Disqus Comments and customize it a ton of different ways. Good luck


{% extends 'all-watch/base.html' %} {% load static %} {% block content %}
business name:{{business_items.name}}
business email:{{business_items.email}}
from :{{business_items.hood.name}}
owner:{{business_items.hood.admin}}


Caitlin Hoffman is a native Californian who loves to code and design beautiful and functionally superb websites. She currently resides in the Bay Area and is open to moving for the right position. Caitlin is looking for new opportunities to create and produce amazing products, so feel free to contact her with any inquiries.


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Template classes can be formed using the template facility described in §8.1.1/140:
template <class type-parameter [, class type-parameter]... > class class-name { ... } ;
creates a template class named class-name
that depends on the given type parameters.
These type-parameter names may be used inside the template wherever a type is
required. In the scope of the class, the template class may be referred to without qualification;
outside the class scope, class-name
must be qualified with the type parameters:
template <class T> Vec<T>& Vec<T>::operator=(const Vec&) { ... }
Users specify the actual types when creating objects of template types: Vec<int>
causes
the implementation to instantiate a version of Vec
binding the type parameter to int
.
Copy control: In general, classes control what happens when objects are created, copied, assigned, or destroyed. Constructors are invoked as a side effect of creating or copying objects; the assignment operator is invoked in expressions involving assignment; and the destructor is run automatically when objects are destroyed or go out of scope.
Classes that allocate resources in a constructor almost invariably must define the copy constructor, the assignment operator, and the destructor. When we write an assignment operator, it is essential for us to check for self-assignment. For consistency with the built-in assignment operators, it is good practice to return a reference to the left-hand operand.
Synthesized operations: If a class defines no constructors, the compiler will synthesize the default constructor. If the class does not explicitly define them, the compiler will synthesize the copy constructor, assignment operator, and/or destructor. The synthesized operations are defined recursively: Each synthesized operator recursively applies the appropriate operation for the data members of the class.
Overloaded operators are defined by defining a function named operator
op, where op
is the operator being defined. At least one parameter must be of class type. When an
operator function is a member of a class, its left-hand operand (if it is a binary operator) or
its only operand (if it is a unary operator) is bound to the object on which it is invoked.
The index operator and the assignment operator must be class members.
11-0. Compile, execute, and test the programs in this chapter.
11-1. The Student_info
structure that we defined in Chapter 9 did not define a copy constructor,
assignment operator, or destructor. Why not?
11-2. That structure did define a default constructor. Why?
11-3. What does the synthesized assignment operator for Student_info
objects do?
11-4. How many members does the synthesized Student_info
destructor destroy?
11-5. Instrument the Student_info
class to count how often objects are created, copied,
assigned, and destroyed. Use this instrumented class to execute the student record
programs from Chapter 6. Using the instrumented Student_info
class will let you see how
many copies the library algorithms are doing. Comparing the number of copies will let
you estimate what proportion of the cost differences we saw are accounted for by the use
of each library class. Do this instrumentation and analysis.
11-6. Add an operation to remove an element from a Vec
and another to empty the entire Vec
.
These should behave analogously to the erase
and clear
operations on vector
s.
11-7. Once you've added erase
and clear
to Vec
, you can use that class instead of vector
in most of the earlier programs in this book. Rewrite the Student_info
programs from
Chapter 9 and the programs that work with character pictures from Chapter 5 to use Vec
s
instead of vector
s.
11-8. Write a simplified version of the standard list
class and its associated iterator.
11-9. The grow
function in §11.5.1/208 doubles the amount of memory each time it needs more.
Estimate the efficiency gains of this strategy. Once you've predicted how much of a difference
it makes, change the grow
function appropriately and measure the difference.


Workshop PoV is a banner for Design, Architecture, Film making and Allied fields based in New Delhi.
With a young team of dedicated, enthusiastic and discerning professionals with diverse work experiences and interests.
With more than 8 years of working experience in the field of Architecture design and intervention as well as Film Making and Script Writing,
we have developed a good network of professionals to provide you with a complete solution.
Our emphasis on making things that are functional and yet have aesthetic value by virtue of their simplicity. Each project
is rendered unique and goes through its own processes.
STUDIO
FOUNDING PARTNERS
Aditya Mazumdar, the fellow Principal Architect at Workshop PoV, completed his diploma in architecture in 2008, allowing him his first foray into the world of architecture, when he worked as an assistant architect at a boutique architecture firm. Even as he was learning on the job, he completed his B.Arch Degree from Aayojan School of Architecture, Jaipur (affiliated to YCMOU) in 2015. Furthermore, he received an apprenticeship in Earth Construction from Ladhak, India,
in 2013, and did a Master's Program from the Dessau Institute of Architecture, Germany in 2016. His professional repertoire includes various projects of scale ranging from exhibition design to interior design, residntial architecture to institutional and hospital projects, not to mention numerous Master-plan proposals.
Shamsher Singh is an architect who completed his B.Arch course from MEASI Academy of Architecture, Chennai. Since graduating in 2009-10, he practiced as an assistant architect under various architects of repute in Delhi. In Workshop PoV, he serves as one of the two Principal Architects. As a young architect, he has worked at various scales, including exhibition design, interior design, residential architecture, and institutional architecture and Master planning.
Being a process oriented person, he holds strong the belief that rather than dictating terms, architecture, design and built form should create opportunities for the future while being a commentary on the present.
Anurag Sikder has been academically trained in economics and business. Under the aegis of online media and film making organisations, he trained to be a writer and a multi media storyteller. In Workshop PoV, he serves as the lead writer and producer of literary and cinematic productions. His works include short stories, fiction films and documentaries. It is routine to find an impression of his personal beliefs about sustainability and transparency in his creations.


Readers write: A gun owner’s experience with the NRA, and more
by Monitor readersEditor’s note: The Oct. 14 Monitor Weekly cover story, “The gunfight of his life,” elicited many reader responses. Here’s a selection of letters discussing the article.
Gun owner’s NRA experience
The cover story was a great article with a narrative that needed to be told. I am a gun owner. I have never been a member of the National Rifle Association and never will. NRA leadership is too militant for me; they do not seem willing to compromise.
I believe strongly in background checks and also that all gun sales should be regulated. In other words, no sale – including a private one – should occur without filling out the proper forms for a transfer of ownership, just like buying from a licensed dealer.
The NRA apparently doesn’t see it that way, and many of my gun-owning friends say a private sale should remain private. I disagree, but apparently the NRA believes what my friends do.
David Bacon
Fort Huachuca, Arizona
Militias and the Second Amendment
I am not a constitutional scholar by any means, but there is something about the way that many people discuss the Second Amendment that is very puzzling to me. The amendment itself is simply one sentence that contains two ideas. The first idea – “A well regulated Militia, being necessary to the security of a free State” – is almost never mentioned. The only thing people seem to focus on is the second idea: “the right of the people to keep and bear Arms, shall not be infringed.”
Why is that?
The first part of the sentence gives me the impression that people who keep and bear arms are meant to be part of a well-regulated militia – for example, the National Guard.
That would be quite a change. It would mean that people who use guns would need to be registered as part of a militia. That is, they would undergo some sort of group basic training and regular periodic reviews to maintain their skills. In addition, there might be certain requirements concerning what kinds of guns they must have or must not have.
Why is this half of the Second Amendment never discussed and certainly never implemented? Would David Dell’aquila, who was featured in the cover story, enjoy being part of a well-regulated militia in Kentucky? Would it help to end the corruption that is rampant in the NRA?
Marianne Preger-Simon
Whately, Massachusetts
I am a documented immigrant living in the United States. What I never understand about America is why the first half of the Second Amendment is often conveniently forgotten.
Why aren’t all the people who so happily want to own guns required to train in a militia? If they did, they could be deployed to situations like border protection, instead of the U.S. military. I suspect that if many gun owners had to go through a stringent training like the military does, they might not be so casual about owning guns.
Why do so many people have to have multiple guns, anyway? Coming from overseas, the attitude to owning guns here seems totally out of proportion to the need. Yes, farmers might need guns to get rid of vermin and other predators, and some people, like Mr. Dell’aquila, might like to belong to gun clubs. But just owning guns for gun ownership’s sake – potentially in homes where young children can get hold of them – is just plain scary to those of us who do not come from such a culture.
America is no longer a country that needs pioneers to open up the vast expanses of land. More than 50% of the U.S. is urban or suburban, and guns in those environments are doing much more harm than good, in my opinion. Perhaps the wording of the Second Amendment needs to be changed to meet the modern-day needs of this country.
Nina Maynard
Portland, Oregon
Rethinking the narrative
The cover story was a complete waste of space and should never have been featured. It could be summarized in a couple of sentences: NRA members are upset with greed and corruption in the organization. They don’t want to bring it down; they want to reform it.
The article could have been redeemed if Mr. Dell’aquila had explained why NRA members need a 40-round clip or a bump stock or an assault rifle to bring down Bambi. The Christian Science Monitor is a superior newsmagazine, but you missed the mark on this one.
Michael J. Lopez
Nederland, Texas
Related stories
- Readers Respond Readers write: Eco-equine, consumer choices, and grandparents and child care
- Readers Respond Readers write: At peace with semicolons, and rethinking ‘superpower’
- Readers Respond Readers write: India’s growth, education and democracy, and fashion choices
Read this story at csmonitor.com
Become a part of the Monitor community


Internationalization (Project configuration)
If a project needs to be presented in multiple languages, possibly with different presentations and additional, replaced or removed content for each of those locales, it can be "internationalized". Internationalization in Sage is a process of branching content and presentation based on the locale. It enables projects that operate in multiple languages to integrate locale-based differences smoothly and naturally, and it is applied on several different levels. The locales (languages) that the project supports are defined in the internationalization section of the project configuration. Find out more.
internationalization element
Defines the locales to use with this project.
In this example, three locales have been defined; uk
, de
and fr
:
Defines a single locale
Specifies the name of the locale. Any name can be used here but typically this would be a value such as en
or en-US
.
Sets the cultture and date formatting options for the locale.
Specifies the name of the CultureInfo
instance to use with this locale.
Specifies the short date format to use with this locale. This value is optional and should only be used if it is different than the format provided by this locale's CultureInfo
instance. Default value is d
, which is represents the date and time format string that is defined by this locale's CultureInfo
DateTimeFormatInfo.ShortDatePattern property.
Specifies the long date format to use with this locale. This value is optional and should only be used if it is different than the format provided by this locale's CultureInfo
instance. Default value is D
, which is represents the date and time format string that is defined by this locale's CultureInfo
DateTimeFormatInfo.LongDatePattern property.


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We’re At the Precipice of a New World
YOUR PLEDGE is Needed To Take The Leap
We are only days away from One Billion Rising and couldn’t be more
excited! There are now 200 countries signed up to participate in this
world wide event, along with world leaders, celebrities,
organizations, and activists who are ready to Strike, dance, RISE,
proving that everyone is ready to say enough and end violence against
women for good.
One Billion Rising is the beginning of the new world ignited by a new
energy. It is not the end of our struggle but the escalation of it,
and while we are all ready to shake the earth with our dancing on 14
February, we are asking everyone to make One Billion Rising more than
just a single day of action.
By taking the One Billion Rising Pledge, and asking EVERYONE at your
event to do the same, you are helping to finish what we have all
started by making ending violence against women and girls THE central
issue locally, nationally, and internationally. The world is ready to
shift and it begins with you. The One Billion revolution has
started and with your pledge it will continue to grow.
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: STRIKING, DANCING, RISING, TRENDING!
While you’re striking, dancing, and rising on the streets, we want
to be trending online! Wherever you post, be sure to
use the official hashtag,. We want to see your photos,
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happen.
* Twitter user? Please post all your updates using the
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You get the idea.
We look forward to seeing you all RISE on 14 February!
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This library works by reading a cycle (AKA science record) of EDBs 0-59 plus and HDB from the open data file into static memory. Users then read items out of this static memory with data access functions. (Note: for operational reasons, each reading of a cycle actually reads the next two EDBs from the next cycle as well, because they contain data which was accumulated during the current cycle.) Here is a somewhat detailed description of how that is done: After opening a file with smsOpenFile, a call to smsReadCycle sets a chain of events in motion. First, a data record is read from the file. This record is then scanned for H/EDB identifiers. When H/EDB identifiers are found, the H/EDB is assembled from the subsequent subframes. As each H/EDB is returned to smsReadCycle, what happens to it depends on which it is: For EDBs, it is put into a data structure which breaks out the parts of the EDB according to the DPU manual. Its header is decoded and saved. This is the tricky part because EDBs vary based on the number of PHA words formatted for each instrument. For HDBs, they are decoded (in part) into a structure. When the record has been processed, another is read, being careful to deal properly with H/EDBs that span records. Once a complete cycle has been accumulated, smsReadCycle returns to the user's calling program and the user may use the data access functions to read this data out.
Unfortunately, at first glance the function names appear cryptic. However, efforts were made to make them at least partially systematic. Hopefully, that system will be reasonably clear after reading the following principles used for naming: sms -- all user functions in the library start with this sequence. The instrument group is actually referred to as SMS in a number of engineering documents making this a good identifier. It stands for SWICS MASS STICS if that isn't obvious. All user-level data access routines follow the following scheme: sms[g,s,c,d][x,t,m]name[ending] sms -- (see above) g, s, c, or d -- describes type of function g -- get s -- set c -- convert d -- dump (print a bunch of stuff) x, t, or m -- instrument ID x -- SWICS t -- STICS m -- MASS name -- a name which closely matches the name in the DPU manual (big blue book); looking at the description for individual functions should make this name pretty clear ending -- some functions have endings which follow a pattern tab -- table; often used even if only one element is returned s -- functions that return a string end in s, e.g. smsgtimes (get time string)
Many of the global funtions return SMS return codes. Unfortunately,the return codes are not currently defined in perl. (Hopefully there will be pre-defined perl variables with these names in a future release. For now you'll have to either hard code in the numbers or define your own variables and store the values below in them. Here are the return codes: SMSSUCCESS = 0, no problems SMSEOF = -1, end-of-file was found SMSERROR = 1, there was a non-fatal error SMSFATAL = 128, there was a fatal error
There are two main types of messages written to stdout by libsms: 1) informational messages that do not indicate any problem 2) messages that indicate a (potential) problem. For informational messages, the volume and types are controllable by the user. See smssetdbg and smssettrace. Messages indicating a (potential) problem are always preceeded by one of the following: warning, error or fatal. Warning messages are produced when the software finds what it knows to be a non-standard condition. This condition may or may not indicate a problem; that is up to the user to decide. (If this condition were a known problem, the message would be tagged error or fatal.) These messages are controlled (to some extent) by smssetdbg and smssettrace. Error messages are usually produced when the library encounters a situation that it knows is not right but that is not fatal. It usually also indicates a condition that will not be handled correctly but some useful data may still result. Fatal error messages are just that: fatal. They indicate a problem that will prevent any useful data from being accessible and often cause routines to exit immediately.
# open file $result = libsms::smsOpenFile(); # set debug parameters libsms::smssetdbg(0); libsms::smssettrace(0); # read cycles $ncycle = 0; while (libsms::smsReadCycle() = 0) { ncycle++; # get time of cycle printf("%s: cycle time in seconds is %d\n",$thisprog,libsms::smsgtime()); # get time string for cycle printf("%s: Cycle[%3.3d] time is %s\n",$thisprog,$ncycle, libsms::smsgtimes()); # write info commuted over many HDBs into memory for later retrieval if ($ncycle == 1) { libsms::smsDecommHDB(); } for ($nedb=0; $nedb < 60; $nedb++) { printf("%s: EDB[%2.2d] - tnpha=%d\n",$thisprog,$nedb, libsms::smsgtnpha($nedb)); # STICS rates printf("%s: ",$thisprog); for ($j = 0; $j < 3; $j++) { printf("tfsr[%1.1d]=%3.3d ",$j,libsms::smsgtfsr($nedb,$j)); } printf("\n"); # DPPS step printf("%s: ",$thisprog); printf("tDVS[%2.2d]=%2.2d ",$nedb,libsms::smsgtdvs($nedb)); printf("\n"); } } libsms::smsCloseFile();
smsOpenFile(char *);
[ returns int ]
Opens data file, returns one of the SMS return codes.
smsCloseFile();
[ returns int ]
Closes open data file, returning SMSSUCCESS (see above) if no problems were encountered. Prints warning if it encounters a write error.
smsReadCycle();
[ returns int ]
Read one complete cycle from already open data file; incomplete cycles are skipped over. Setting smssettrace(1) causes messages to be output as each cycle is assembled, both complete and incomplete.
smsDecommHDB();
[ returns int ]
Decommute HDB: read a set of 64 HDBs from the file. This is used for the measured voltage tables as well as other things. Note: this behaves strangely if it is run for each cycle (which shouldn't need to be done usually)
smsDecompress(byte,fIsCodeC);
[ returns long ]
Decompress byte -- only needed if you are dealing with raw TM values; rates and such are already decompressed. fIsCodeC is true when decompression according to code C is to be used; false uses code A
smssetdbg(i);
[ returns int ] Set libsms debug level to i. The various values give different amounts of output: level 0 -- no debugging output level 1 -- minimal debugging output, level 2 -- adds more debugging output (not currently well defined) level 3 -- adds output of each H/EDB is found in record (lots of output) level 4 -- adds dumping of H/EDBs -- huge volume of output
smssettrace(i);
[ returns int ]
Set trace flag for libsms; setting to TRUE (1) allows more careful tracking of the data through the processing
smsgtime();
[ returns long ]
Get time for cycle in msec of day. This is the time from the data record header for the first EDB in the cycle. Time for an individual EDB can be approximated by assuming 3s for each EDB.
smsgtimes();
[ returns char * ]
Same as above but returns string of time converted into YYYYDOY HH:MM:SS (msec usec) suitable for printing
smsgyear();
[ returns int ]
Returns year of cycle; see description for smsgtime()
smsgdoy();
[ returns int ]
Returns year of cycle; see description for smsgtime()
smsghms();
[ returns char * ]
Get hours minutes seconds (hms) -- returns hh:mm:ss of current cycle
smsgss1970();
[ returns double ]
Get seconds since 1970 -- returns cycle time in seconds since 1970
smsdedbhdr(int );
[ returns int ]
Dump edb header (for debugging)
smsgscirec();
[ returns long ]
Get science record counter (from HDB) of current cycle. This counter is a unique number for each HDB (and associated EDBs of the science record (AKA cycle).
smsgrevcount(nedb);
[ returns int ]
Get revolution spin counter from EDB[nedb] -- This the is low byte of this counter. It is found in EDB byte 4.
smsgxfsr(nedb);
[ returns long ]
Get SWICS front SEDA (secondary electron detector assembly) rate for EDB[nedb]
smsgxdvs(nedb);
[ returns long ]
Get SWICS deflection voltage step (DVS; AKA DPPS step) for EDB[nedb]
smsgxnpha(nedb);
[ returns long ]
Get SWICS number of PHA words for EDB[nedb]
smsdxpha(nedb,i);
[ returns int ]
Dump (print) raw parts of PHA[i] (first is i=0) from EDB[nedb]
smsgxenergy(nedb,i);
[ returns double ]
Get SWICS energy of PHA[i] from EDB[nedb] in keV
smsgxeoq(nedb,i);
[ returns double ]
Get energy / charge (E/Q) of PHA[i] from EDB[nedb]
smsgxtof(nedb,i);
[ returns double ]
Does not currently work -- Get SWICS TOF (in physical units of ns) of PHA[i] from EDB[nedb]
smsgxessd(nedb,i);
[ returns double ]
Does not currently work -- Get SWICS energy at SSD in keV of PHA[i] from EDB[nedb]
smsgxmass(nedb,i);
[ returns double ]
Does not currently work -- Get SWICS mass in amu PHA[i] from EDB[nedb]
smsgxmoq(nedb,i);
[ returns double ]
Does not currently work -- Get SWICS mass/charge in amu PHA[i] from EDB[nedb]
smsgtmpvtab(i);
[ returns double ]
get STICS measured positive voltage table (volts), element i
smsgtmpvtab_raw(i);
[ returns int ]
get STICS measured positive voltage table (raw), element i
smsgtmnvtab(i);
[ returns double ]
get STICS measured negative voltage table, element i
smsgtmnvtab_raw(i);
[ returns int ]
get STICS measured negative voltage table (raw), element i
smsgthmr(nedb,i);
[ returns long ]
Get STICS high resolution matrix rates, EDB[nedb], element i
smsgtsmr(nedb,i);
[ returns long ]
Get STICS sector matrix rates, EDB[nedb], element i
smsgtbr0(nedb,i);
[ returns long ]
Get STICS basic rates BR0, EDB[nedb], element i
smsgtbr1(nedb,i);
[ returns long ]
Get STICS basic rates BR1, EDB[nedb], element i
smsgtbr2(nedb,i);
[ returns long ]
Get STICS basic rates BR2, EDB[nedb], element i
smsgtomr(nedb,i);
[ returns long ]
Get STICS omnidirectional matrix rates, EDB[nedb], element i
smsgtfsr(nedb,i);
[ returns long ]
Get STICS front SEDA rate for detector i, EDB[nedb]
smsgtrsr(nedb,i);
[ returns long ]
Get STICS rear SEDA rate for detector i, EDB[nedb]
smsgtdcr(nedb,i);
[ returns long ]
Get STICS double coincidence rate for detector i, EDB[nedb]
smsgttcr(nedb,i);
[ returns long ]
Get STICS triple coincidence rate for detector i, EDB[nedb]
smsgtssd(nedb,i);
[ returns long ]
Get STICS solid state detector rate for detector i, EDB[nedb]
smsgtdvs(nedb);
[ returns int ]
Get STICS deflection voltage step (DVS; AKA DPPS step) for EDB[nedb]
smsgtdvstab(i);
[ returns int ] Get STICS deflection voltage step table (dvstab), element i (0 <= i <= 59) Method: This gets a dvs sequence from the current cycle THE FIRST TIME IT IS CALLED. Subsequent calls simply read that table out of static memory. It resets itself when a new file is opened. Therefore, you should be sure to call this routine once (with any legal argument) before you exit an smsReadCycle loop. If this is done, this function will always return a valid stepping table. Note 1: This DOES NOT do checks to be sure that this table does not change during the reading of the file. If what you want is a dvs table from the current cycle, do something like this: for ($i = 0; $i < 60; $i++){ $dvstab[$i] = smsgtdvs(i) } just after a call to smsReadCycle. Note 2: If you want an e/q table, see smsgteqtab.
smsgtmode(nedb);
[ returns int ] Get diagnostic mode id. for EDB[nedb] T ==> diagnostic mode ; F ==> Normal mode. When in diagnostic mode some rates are moved around in the following way: smsgtomr(11) returns FSR 1 smsgtomr(12) returns FSR 2 smsgtomr(13) returns FSR 3 smsgtomr(14) returns FSR 4 smsgtomr(15) returns FSR 5 smsgtomr(16) returns FSR 6 smsgtomr(17) returns UFSR smsgtomr(18) returns URSR smsgtomr(19) returns MFSR smsgtfsr(1) returns MRSR smsgtfsr(2) returns MPF smsgtfsr(3) returns MPR
smsgtmodes(nedb);
[ returns char * ]
Get diagnostic mode identifier string, string version of smsgtmode. Returns 'Normal' and 'Diagnostic'.
smsgtnpha(nedb);
[ returns int ]
Get number pha of words from EDB[nedb]
smsdtpha(nedb,i);
[ returns int ]
Dump (print) raw parts of PHA[i] (first is i=0) from EDB[nedb]
smsgtstopid(nedb,i);
[ returns long ]
Get STICS stop Id of PHA[i] from EDB[nedb]
smsgted(nedb,i);
[ returns long ]
Get STICS energy (digital but decompressed from PHA) of PHA[i] from EDB[nedb]
smsgtsector(nedb,i);
[ returns long ]
Get STICS sector of PHA[i] from EDB[nedb]
smsgtssdid(nedb,i);
[ returns long ]
Get STICS SSD ID of PHA[i] from EDB[nedb]
smsgttofd(nedb,i);
[ returns long ]
Get STICS time of flight (digital; straight from PHA) of PHA[i] from EDB[nedb]
smsgtstart(nedb,i);
[ returns long ]
Get STICS start anode of PHA[i] from EDB[nedb]
smsgtrange(nedb,i);
[ returns long ]
Get STICS range of PHA[i] from EDB[nedb]
smsgteoq(nedb,i);
[ returns double ]
Get energy / charge (E/Q) of PHA[i] from EDB[nedb]
smsgttof(nedb,i);
[ returns double ]
Get TOF (in physical units of ns) of PHA[i] from EDB[nedb]
smsgtessd(nedb,i);
[ returns double ]
Get energy at SSD in keV of PHA[i] from EDB[nedb]
smsgtmass(nedb,i);
[ returns double ]
Get mass in amu of PHA[i] from EDB[nedb]
smsgtmoq(nedb,i);
[ returns double ]
Get mass/charge in amu of PHA[i] from EDB[nedb]
smsgtnm(nedb,i);
[ returns int ]
Get STICS NM (mass classification bin) number for EDB[nedb] PHA[i] -- The mass part of the M-M/Q space is divided into 58 bins which are logarithmically spaced. The bins are calculated by mass{lower bound of Nm in amu} = 0.5*km^(Nm - 1) with km = (95.0/0.5)^1/58 . Nm = 0 for mass zero events.
smsgtnq(nedb,i);
[ returns int ]
Get STICS NQ (mass/charge classification bin) number for EDB[nedb] PHA[i] -- The mass/charge part of the M-M/Q space is divided into 126 bins which are logarithmically spaced. The bins are calculated by M/Q{lower bound of Nq in amu/e} = 0.5*kq^(Nq - 1) with km = (42.0/0.9)^1/126 . Nm = 0 for mass zero events.
smsgteqtab(i);
[ returns double ]
Get STICS energy/charge table (eqtab), element i. NOTE: This just uses smsgtdvstab and smsctdvs2eq.
smsctdvs2eq(dvs);
[ returns double ]
Convert STICS deflection voltage step (dvs) to energy/charge. Note: This is the same energy/charge calculation as smsmgteoq uses (but that one inserts the particular DVS for the PHA word in question).
smsgmfsr(nedb,i);
[ returns long ]
Get MASS front SEDA (secondary electron detector assembly) rate i from EDB[nedb]
smsgmfsrb(nedb);
[ returns long ]
Get MASS front SEDA rate B, EDB[nedb]
smsgmdvs(nedb);
[ returns long ]
Get mass deflection voltage step (DVS; aka DPPS step) from EDB[nedb]
smsgmnpha(nedb);
[ returns int ]
Get MASS number of pha words from EDB[nedb]


INTERIOR: DEATH STAR
Ben hurries along one of the tunnels leading to the hangar where the pirate ship waits. Just before he reaches the hangar, Darth Vader steps into view at the end of the tunnel, not ten feet away. Vader lights his saber. Ben also ignites his and steps slowly forward.
VADER: I've been waiting for you, Obi-Wan. We meet again, at last. The circle is now complete.
Ben Kenobi moves with elegant ease into a classical offensive position. The fearsome Dark Knight takes a defensive stance.
VADER: When I left you, I was but the learner; now I am the master.

The two Galactic warriors stand perfectly still for a few moments, sizing each other up and waiting for the right moment. Ben seems to be under increasing pressure and strain, as if an invisible weight were being placed upon him. He shakes his head and, blinking, tries to clear his eyes. Ben makes a sudden lunge at the huge warrior but is checked by a lightning movement of The Sith. A masterful slash stroke by Vader is blocked by the old Jedi. Another of the Jedi's blows is blocked, then countered. Ben moves around the Dark Lord and starts backing into the massive starship hangar. The two powerful warriors stand motionless for a few moments with laser swords locked in mid-air, creating a low buzzing sound.
VADER: Your powers are weak, old man.
BEN: You can't win, Darth. If you strike me down, I shall become more powerful than you can possibly imagine.
Vader and Ben Kenobi continue their powerful duel. As they hit their light sabres together, lightning flashes on impact. Troopers look on in interest as the old Jedi and Dark Lord of the Sith fight. Suddenly Luke spots the battle from his group's vantage point.
LUKE: Look!6>
Luke, Leia, Han, and Chewie look up and see Ben and Vader emerging from the hallways on the far side of the docking bay. Solo, Chewie, Luke, and Leia tensely watch the duel. The troops rush toward the battling knights.
HAN: Now's our chance! Go!
They start for the Millennium Falcon. Ben sees the troops charging toward him and realizes that he is trapped. Vader takes advantage of Ben's momentary distraction and brings his mighty light sabre down on the old man. Ben manages to deflect the blow and swiftly turns around. The old Jedi Knight looks over his shoulder at Luke, lifts his sword from Vader's then watches his opponent with a serene look on his face. Vader brings his sword down, cutting old Ben in half. Ben's cloak falls to the floor in two parts, but Ben is not in it. Vader is puzzled at Ben's disappearance and pokes at the empty cloak. As the guards are distracted, the adventurers and the robots reach the star ship. Luke sees Ben cut in two and starts for him. Aghast, he yells out.
LUKE: No!


This is a sample application to demonstrate the usage of ANgular 2 in creating a single page application
Click here to know more!!


Your project will add these blocks to the
More Blocks
tab in Scripts.
{{ scratchblocks.label }}
Put {{ project.type }} in the input for this, and it will return the label that your machine learning model recognises it as.
{{ scratchblocks.confidence }}
This will return how confident your machine learning model is that it recognises the type of {{ project.type }}. (As a number from 0 - 100).
{{label}} :: custom reporter
These blocks represent the labels you've created in your project, so you can use their names in your scripts.
costume image :: looks reporter
This block is in the Looks palette for Sprites and will return the image of the currently selected costume.
This means you can do something like this:
{{ scratchblocks.sample }}
It will look something like this - except with the name of your project.
The coloured circle next to your project name tells you if your machine learning model is okay.
means your model is trained and ready to go
means your model hasn't finished training yet
means something went wrong. Go back to the Learn & Test page to see what went wrong with training.


Your system doesn't have a webcam or you have not allowed access to the webcam! Kindly switch to a new machine with a webcam or allow access to the webcam and reload the page.


You can parse/analyse your Dotah Tooh replays!
Source code available on GitHub
Beneath the covers, all of the heavy lifting is being done by Dotabuff's open-source replay parser written in Go, called Manta.
Dota 2 is owned by Valve
Hope this helps someone!
— Sincerely,
An Archon Five


{% load staticfiles %}
Isaiah Torain's Blog
Just a blog created by a software engineer.
{% block navlist %} {% endblock %}
{% block content %} {% endblock %}


The Qiskit Elements¶
Terra¶
Terra, the ‘earth’ element, is the foundation on which the rest of Qiskit lies. Terra provides a bedrock for composing quantum programs at the level of circuits and pulses, to optimize them for the constraints of a particular device, and to manage the execution of batches of experiments on remote-access devices. Terra defines the interfaces for a desirable end-user experience, as well as the efficient handling of layers of optimization, pulse scheduling and backend communication.
Qiskit Terra is organized in six main moudles:
- Circuit
- A quantum circuit is a model for quantum computing in which a computation is done by performing a sequence of quantum operations (usually gates) on a register of qubits. A quantum circuit usually starts with the qubits in the \(|0,…,0>\) state and these gates evolve the qubits to states that cannot be efficiently represented on a classical computer. To extract information on the state a quantum circuit must have a measurement which maps the outcomes (possible random due to the fundamental nature of quantum systems) to classical registers which can be efficiently represented.
- Pulse Schedule
- A pulse schedule is set of pulses which are sent to a quantum experiment that are applied to a channel (experimental input line). This is a lower level than circuits and requires each gate in the circuit to be represented as a set of pulses. At this leavel the experiments can be designed to reduce errors (dynamical decoupling, error mitigation, and optimal pulse shapes).
- Transpiler
- A major part of research on quantum computing is working out how to run a quantum circuits on real devices. In these devices, experimental errors and decoherence introduce errors during computation. Thus, to obtain a robust implementation it is essential to reduce the number of gates and the overall running time of the quantum circuit. The transpiler introduces the concept of a pass manager to allow users to explore optimization and find better quantum circuits for their given algorithm. We call it a transpiler as the end result is still a circuit.
- Poviders
Once the user has made the circuits to run on the backend they need to have a convenient way of working with it. In Terra we do this using four parts:
- A Provider is an entity that provides access to a group of different backends (for example, backends available through the IBM Q Experience). It interacts with those backends to, for example, find out which ones are available, or retrieve an instance of a particular backend.
- Backend represent either a simulator or a real quantum computer and are responsible for running quantum circuits and returning results. They have a run method which takes in a qobj as input and returns a BaseJob object. This object allows asynchronous running of jobs for retrieving results from a backend when the job is completed.
- Job instances can be thought of as the “ticket” for a submitted job. They find out the execution’s state at a given point in time (for example, if the job is queued, running, or has failed) and also allow control over the job.
- Result. Once the job has finished Terra allows the results to be obtained from the remote backends using result = job.result(). This result object holds the quantum data and the most common way of interacting with it is by using result.get_counts(circuit). This method allows the user to get the raw counts from the quantum circuit and use them for more analysis with quantum inofrmation tools provided by Terra.
- Quantum Information
- To perform more advanced algorithms and analysis of the circuits run on the quantum computer, it is important to have tools to implement simple quantum information tasks. These include methods to both estimate metrics and generate quantum states, operations, and channels.
- Visualization
- In Terra we have many tools to visualize a quantum circuit. This allows a quick inspection of the quantum circuit to make sure it is what the user wanted to implement. There is a text, python and latex version. Once the circuit has run it is important to be able to view the output. There is a simple function (plot_histogram) to plot the results from a quantum circuit including an interactive version. There is also a function plot_state and plot_bloch_vector that allow the plotting of a quantum state. These functions are usually only used when using the statevector_simulator backend but can also be used on real data after running state tomography experiments (Ignis).
Aer¶
Aer, the ‘air’ element, permeates all Qiskit elements. To really speed up development of quantum computers we need better simulators, emulators and debuggers. Aer helps us understand the limits of classical processors by demonstrating to what extent they can mimic quantum computation. Furthermore, we can use Aer to verify that current and near-future quantum computers function correctly. This can be done by stretching the limits of simulation, and by simulating the effects of realistic noise on the computation.
Aer provides a high performance simulator framework for quantum circuits using the Qiskit software stack. It contains optimized C++ simulator backends for executing circuits compiled in Terra. Aer also provides tools for constructing highly configurable noise models for performing realistic noisy simulations of the errors that occur during execution on real devices.
Qiskit Aer includes three high performance simulator backends:
- Qasm Simulator
Allows ideal and noisy multi-shot execution of qiskit circuits and returns counts or memory. There are multiple methods that can be used that simulate different cirucits more efficiently. These inlude:
- statevector - Uses a dense statevector simulation.
- stabilizer - Uses a Clifford stabilizer state simulator that is only valid for Clifford circuits and noise models.
- extended_stabilizer - Uses an approximate simulator that decomposes circuits into stabilizer state terms, the number of which grows with the number of non-Clifford gates.
- matrix_product_state - Uses a Matrix Product State (MPS) simulator.
- Statevector Simulator
- Allows ideal single-shot execution of qiskit circuits and returns the final statevector of the simulator after application.
- Unitary Simulator
- Allows ideal single-shot execution of qiskit circuits and returns the final unitary matrix of the circuit itself. Note that the circuit cannot contain measure or reset operations for this backend.
Ignis¶
Ignis, the ‘fire’ element, is dedicated to fighting noise and errors and to forging a new path. This includes better characterization of errors, improving gates, and computing in the presence of noise. Ignis is meant for those who want to design quantum error correction codes, or who wish to study ways to characterize errors through methods such as tomography, or even to find a better way for using gates by exploring dynamical decoupling and optimal control.
Ignis provides code for users to easily generate circuits for specific experiments given a minimal set of user input parameters. Ignis code contains three fundamental building blocks:
- Circuits
- The circuits module provides the code to generate the list of circuits for a particular Ignis experiment based on a minimal set of user parameters. These are then run on Terra or Aer.
- Fitters
- The results of an Ignis experiment are passed to the Fitters module where they are analyzed and fit according to the physics model describing the experiment. Fitters can plot the data plus fit and output a list of parameters.
- Filters
- For certain Ignis experiments, the fitters can output a Filter object. Filters can be used to mitigate errors in other experiments using the calibration results of an Ignis experiment.
Qiskit Ignis is organized into three types of experiments that can be performed:
- Characterization
- Characterization experiments are designed to measure parameters in the system such as noise parameters (T1, T2-star, T2), Hamiltonian parameters such as the ZZ interaction rate and control errors in the gates.
- Verification
- Verification experiments are designed to verify gate and small circuit performance. Verification includes state and process tomography, quantum volume and randomized benchmarking (RB). These experiments provide the information to determine performance metrics such as the gate fidelity.
- Mitigation
- Mitigation experiments run calibration circuits that are analyzed to generate mitigation routines that can be applied to arbitrary sets of results run on the same backend. Ignis code will generate a list of circuits that run calibration measurements. The results of these measurements will be processed by a Fitter and will output a Filter than can be used to apply mitigation to other results.
Aqua¶
Aqua, the ‘water’ element, is the element of life. To make quantum computing live up to its expectations, we need to find real-world applications. Aqua is where algorithms for quantum computers are built. These algorithms can be used to build applications for quantum computing. Aqua is accessible to domain experts in chemistry, optimization, finance and AI, who want to explore the benefits of using quantum computers as accelerators for specific computational tasks.
Problems that may benefit from the power of quantum computing have been identified in numerous domains, such as Chemistry, Artificial Intelligence (AI), Optimization and Finance. Quantum computing, however, requires very specialized skills. To address the needs of the vast population of practitioners who want to use and contribute to quantum computing at various levels of the software stack, we have created Qiskit Aqua.


Daesh leader arrested in Ukraine
by MENAKIEV, Nov 16 (MENA) - A leader of Daesh terrorist group was arrested in the Ukrainian capital Kiev, Sputnik news agency quoted the Ukrainian Security Service as saying.
The leader was arrested in a joint special operation of the Security Service of Ukraine, the Ministry of Interior of Georgia and the US Central Intelligence Agency, added the security service.


Raise money for over 1.5 million charities with personal fundraisers, events, races and corporate philanthropy.
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City leaders discuss new proposed gun range in Cape Coral in council meeting
by WFTX Digital TeamCAPE CORAL, Fla. — City leaders are discussing an $8 million-gun range for the Cape Coral Police Department.
In June, council members approved to use police impact fee reserves for the training facility at the cost of $650,000.
The Governor vetoed the funding request.
The police department still wanted to move forward with the project.
City council said they wanted to see more research before making any decisions.
They are expected to vote on funding again tonight.
The facility would be built on the corner of Andalusia and Durden Parkway.


The predicate calculus has a number of theorems and axioms for proving logical statements. Here are the main symbols used in predicate calculus:
P(x) – proposition – a logical statement in the condition x.
x – any condition in the set of possible conditions.
c – a particular condition in the set of possible conditions.
" – "For every"
$ – "Exists"
® – Implication
Ù – Conjunction (logical and)
Ú – Disjunction (logical or)


Changeset 83
- Timestamp:
- 01/26/09 16:54:02 (7 years ago)
- Files:
-
- 9 edited
-
python/detools.py (modified) (2 diffs)
-
python/differential_evolution.py (modified) (2 diffs)
-
tests/sam_corana2.py (modified) (1 diff)
-
tests/sam_mogi.py (modified) (2 diffs)
-
tests/sam_rosenbrock.py (modified) (1 diff)
-
tests/sam_zimmermann.py (modified) (1 diff)
-
tests/test_mogi.py (modified) (1 diff)
-
tests/test_rosenbrock.py (modified) (1 diff)
-
tests/test_zimmermann.py (modified) (1 diff)
Legend:
- Unmodified
- Added
- Removed
-
python/detools.py
r77 r83 24 24 25 25 def ChangeOverGeneration(tolerance = 1e-6, generations = 30): 26 def _(hist): 26 def _(inst): 27 hist = inst.energy_history 27 28 lg = len(hist) 28 29 return lg > generations and (hist[-generations]-hist[-1]) < tolerance … … 30 31 31 32 def VTR(tolerance = 0.005): 32 def _(hist): 33 def _(inst): 34 hist = inst.energy_history 33 35 return hist[-1] < tolerance 34 36 return _ -
python/differential_evolution.py
r77 r83 228 228 self.energy_history.append(self.bestEnergy) 229 229 230 if detools.EARLYEXIT or termination(self .energy_history):230 if detools.EARLYEXIT or termination(self): 231 231 break 232 232 … … 323 323 self.energy_history.append(self.bestEnergy) 324 324 325 if detools.EARLYEXIT or termination(self .energy_history):325 if detools.EARLYEXIT or termination(self): 326 326 break 327 327 -
tests/sam_corana2.py
r82 r83 45 45 solver = fmin(2) 46 46 solver.SetRandomInitialPoints([0,0],[2,2]) 47 killme = IRE(1e-4,1e-4) 48 solver.Solve(Corana2, termination=killme, StepMonitor = simplex) 47 solver.Solve(Corana2, termination=IRE(), StepMonitor = simplex) 49 48 sol = solver.Solution() 50 49 -
tests/sam_mogi.py
r82 r83 66 66 solver = fmin(len(xinit)) 67 67 solver.SetInitialPoints(xinit) 68 killme = IRE(1e-4,1e-4) 69 solver.Solve(cost_function, termination=killme, StepMonitor = simplex) 68 solver.Solve(cost_function, termination=IRE() StepMonitor = simplex) 70 69 sol = solver.Solution() 71 70 print sol … … 85 84 solver = fmin(len(xinit)) 86 85 solver.SetInitialPoints(xinit) 87 killme = IRE(1e-4,1e-4) 88 solver.Solve(cost_function, termination=killme, StepMonitor = simplex) 86 solver.Solve(cost_function, termination=IRE(), StepMonitor = simplex) 89 87 sol = solver.Solution() 90 88 print sol -
tests/sam_rosenbrock.py
r82 r83 45 45 solver = fmin(len(xinit)) 46 46 solver.SetInitialPoints(xinit) 47 killme = IRE(1e-4,1e-4) 48 solver.Solve(rosen, termination=killme, StepMonitor = simplex) 47 solver.Solve(rosen, termination=IRE(), StepMonitor = simplex) 49 48 sol = solver.Solution() 50 49 -
tests/sam_zimmermann.py
r82 r83 42 42 solver = fmin(2) 43 43 solver.SetRandomInitialPoints([0,0],[7,7]) 44 killme = IRE(1e-4,1e-4) 45 solver.Solve(CostFunction, termination=killme, StepMonitor = simplex) 44 solver.Solve(CostFunction, termination=IRE(), StepMonitor = simplex) 46 45 sol = solver.Solution() 47 46 -
tests/test_mogi.py
r82 r83 128 128 solver = fmin(len(point)) 129 129 solver.SetInitialPoints(point) 130 killme = IRE(1e-4,1e-4) 131 solver.Solve(cost_function, killme, EvaluationMonitor = esow, StepMonitor = simplex) 130 solver.Solve(cost_function, IRE(), EvaluationMonitor = esow, StepMonitor = simplex) 132 131 sol = solver.Solution() 133 132 -
tests/test_rosenbrock.py
r82 r83 51 51 solver = fmin(len(xinit)) 52 52 solver.SetInitialPoints(xinit) 53 killme = IRE(1e-4,1e-4) 54 solver.Solve(rosen, killme, EvaluationMonitor = esow, StepMonitor = ssow) 53 solver.Solve(rosen, IRE(), EvaluationMonitor = esow, StepMonitor = ssow) 55 54 sol = solver.Solution() 56 55 print sol -
tests/test_zimmermann.py
r82 r83 72 72 solver = fmin(len(xinit)) 73 73 solver.SetInitialPoints(xinit) 74 killme = IRE(1e-4,1e-4) 75 solver.Solve(CostFunction, killme, EvaluationMonitor = esow, StepMonitor = simplex) 74 solver.Solve(CostFunction, IRE(), EvaluationMonitor = esow, StepMonitor = simplex) 76 75 sol = solver.Solution() 77 76 print "solution: ", sol
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--- layout: post authors: ["Greg Wilson"] title: "Announcing the Creation of the Software Carpentry Foundation" date: 2014-10-08 time: "11:00:00" tags: ["Announcements", "Community", "Software Carpentry Foundation"] favorite: true ---
In order to foster Software Carpentry's continued growth, we are pleased to announce that we are creating an independent Software Carpentry Foundation (SCF). Like other non-profit open source foundations, it will decide Software Carpentry's overall scope and direction, manage its finances, and hold its intellectual property.
In order to work through the details have assembled an interim board drawn from a wide cross-section of our community:
This group's mandate is to draft the SCF's initial bylaws and get the foundation legal standing, then arrange the transition to the first permanent board some time early in 2015. Until then, we will continue to do what we have always done: teach scientists and engineers how to use computers to do more research in less time and with less pain.
Our thanks to the Alfred P. Sloan Foundation, the Mozilla Foundation, the Software Sustainability Institute, Lawrence Berkeley Laboratory, Research Bazaar, and everyone else listed on our partners page for their sponsorship, and to all of our volunteer instructors for making Software Carpentry possible.


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Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
Makes 6 pasties
These delicious little lamb and mint pasties make great picnic food or a lunchbox treat.
Equipment and preparation: for this recipe you will need a 13cm/5in circular template, such as a saucer or a small bowl.
For the rough puff pastry, put the butter and lard in a bowl and briefly mix. Put the flour and salt in a large mixing bowl and, using your fingertips, briefly rub about a quarter of the butter and lard mixture into the flour (you should still see lumps of butter).
Add 50ml/2¾ fl oz cold water and the lemon juice and mix until it comes together to form a dough. Roughly shape the dough into a rectangle.
On a lightly floured work surface, roll out the dough into a rectangle about the thickness of a pound coin. With a short end of the rectangle facing you, scatter the remaining lard and butter over the top two thirds of the dough. Fold the bottom third over the butter, then fold the top third down. Rotate the dough 90 degrees, then roll out the dough into a rectangle as before and repeat the folding. Turn, roll and fold the dough another two times, then wrap in cling film and refrigerate for 20 minutes.
For the mint sauce, sprinkle the mint leaves with a little salt and finely chop. Transfer to a jug and pour over about 100ml/3½fl oz boiling water. Add the sugar, stir well and leave to cool. When cooled, stir in the white wine vinegar.
For the rough puff pastry, take the cooled pastry from the fridge and repeat the rolling and folding one more time, then return it to the fridge for another 20 minutes.
For the filling, in a medium bowl combine the lamb, garlic and dried rosemary. Add half of the mint sauce and season with salt and pepper. Cover with cling film and set aside.
Add the onion, sweet potato, carrots, parsley and oregano to a separate bowl. Add the rest of the mint sauce, season with salt and pepper and mix well. Finally add the frozen peas and mix again.
Preheat the oven to 180C/350F/Gas 4 and line two large baking trays with baking parchment.
Roll out the pastry to about 3mm thickness. Cut out 6 circles, approximately 13cm/5in in diameter using a small saucer or bowl as a guide. Any offcuts can be rolled out again to make more circles.
Put a few spoonfuls of the vegetable filling to one side of each circle and top with a few pieces of lamb – you need enough to fill the pastry but not so full that the pastry will split open. Fold the pastry over the filling (to encase the filling) and use a little water to help the edges of the pastry stick. Crimp the edges using your fingers and place on a baking tray. Repeat until each baking sheet has six pasties on them.
Brush each pasty with beaten egg and bake for about 40 minutes, or until golden-brown. When cooked, use a fish slice to lift the pasties onto wire racks to cool before serving.


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Welcome to the qooxdoo SDK! It is easy to create your own impressive and
versatile applications. For detailed information on how to create such
apps, for instance a typical rich internet application (RIA), please see
the manual to
get started (needs generation).
get started.
For the impatient, here is a short version:
- Use the
./create-application.py
script to set
up a new application. This will create a minimal directory structure of
an application which is ready to be built and run.
- Within the newly created application folder run the command
generate.py
to generate a development version of your app.
- Open
source/index.html
in your browser and see your
first application in action.
- You can then go on and extend it into an application of your liking by changing the
Application.js
class file below the source/class/
folder.
- The command
generate.py build
generates an optimized
and compressed version of your application, located at
build/index.html
.
Open the included API viewer to
display and search the entire API reference of the framework.
Also have a look at the
readme
readme
and license text.
Demo Applications
Please note that the following applications, except for the
Showcase, are not
pre-built. To create working applications locally use the appropriate commands
generate.py source
or generate.py build
in
the corresponding application's directory.
-
Highlights some of qooxdoo's main features and provides a good overview of what can be achieved.
Source (needs generation)
Build (ready to run)(*)
-
Interactive tutorial app to learn the basics of qooxdoo.
Source |
Build (both need generation)
-
Shows the widgets and themes available in qooxdoo.
Desktop: Source |
Build (both need generation)
Website: Source |
Build (both need generation)
-
Demo Browser (Online)
Contains many examples and tests for widgets, layouts and other framework functionality.
Source |
Build (both need generation)
-
Playground (Online)
Explore qooxdoo programming interactively: edit qooxdoo code in one pane, and see the result running in another.
Source |
Build (both need generation)
-
A hybrid application for displaying RSS feeds, available as a RIA, a trimmed-down web site, or a mobile application (Smartphones: Android, iOS; Desktop: Chrome, IE 11, Firefox, Safari), all from the same code base.
Desktop: Source |
Build (both need generation)
Website: Source |
Build (both need generation)
Mobile: Source |
Build (all need generation)
-
A DOM-oriented application using offline and templating functionality.
Source (needs generation)
-
Mobile Showcase (Online)
An experimental showcase for mobile widgets (Smartphones: Android, iOS; Desktop: Chrome, IE 11, Firefox, Safari).
Source |
Build
(all need generation)


“WOW..YOU ARE REALLY TALENTED..SUPERB! ” — Ms.  ”— Mr. Jitendrabhai Sukhadia
“BEAUTIFUL RANGOLI!!!! KEEP IT UP!!    ”— Mr Bharat Dangar (Mayor Vadodara)


RT @rachelle_mandik: -Can you describe the jellyfish that stung you?


Next: About this document ...
Up: Fast, Approximate Error Prediction
Previous: Conclusion
- 1
-
C. Constantinescu.
Trends and challenges in vlsi circuit reliability.
IEEE Micro, 23(4):14-19, 2003.
- 2
-
E. Normand.
Single event upset at ground level.
IEEE Transactions on Nuclear Science, 43(6):2742-2750, 1996.
- 3
-
P. Bose J. Rivers J. Srinivasan, S. V. Adve and C. K. Hu.
RAMP: A model for reliability aware microprocessor design.
IBM Research Report, 2003.
- 4
-
D. Kammler, J. Guan, G. Ascheid, R. Leupers and H. Meyr.
A fast and flexible Platform for Fault Injection and Evaluation in
Verilog-based Simulations.
In Proc. 3rd IEEE International Conference on Secure Software
Integration and Reliability Improvement (SSIRI '09), Shanghai, China,
Jul 2009.
- 5
-
M. Sugihara, T. Ishihara, M. Muroyama and K. Hashimoto.
A Simulation-Based Soft Error Estimation Methodology for Computer
Systems.
In ISQED, pages 196-203, 2006.
- 6
-
M. Li, P. Ramachandran, S. K. Sahoo, S. V. Adve, V. S. Adve and Y. Zhou.
Understanding the Propagation of Hard Errors to Software and
Implications for Resilient System Design.
In In Proc. Intl. Conf. on Architectural Support for
Programming Languages and Operating Systems(ASPLOS), 2008.
- 7
-
Z. Wang, C. Chen and A. Chattopadhyay.
Fast reliability exploration for embedded processors via high-level
fault injection.
In ISQED, pages 265-272, 2013.
- 8
-
H. Cho, S. Mirkhani, C. Cher, J. A. Abraham, and S. Mitra.
Quantitative evaluation of soft error injection techniques for robust
system design.
In DAC, page 101, 2013.
- 9
-
S. S. Mukherjee, C. T. Weaver, J. S. Emer, S. K. Reinhardt, and T. M. Austin.
A systematic methodology to compute the architectural vulnerability
factors for a high-performance microprocessor.
In MICRO, pages 29-42, 2003.
- 10
-
A. Biswas, P. Racunas, J. S. Emer, and S. S. Mukherjee.
Computing Accurate AVFs using ACE Analysis on Performance Models: A
Rebuttal.
Computer Architecture Letters, 7(1):21-24, 2007.
- 11
-
S. Rehman, M. Shafique, F. Kriebel, and J. Henkel.
Reliable software for unreliable hardware: embedded code generation
aiming at reliability.
In CODES+ISSS, pages 237-246, 2011.
- 12
-
S. Rehman, M. Shafique, F. Kriebel and J. Henkel.
RAISE: Reliability-Aware Instruction SchEduling for unreliable
hardware.
In ASP-DAC '12, pages 671 -676, 30 2012-feb. 2 2012.
- 13
-
Z. Wang, K. Singh, C. Chen and A. Chattopadhyay.
Accurate and efficient reliability estimation techniques during
ADL-driven embedded processor design.
In Proceedings of the Conference on Design, Automation and Test
in Europe, DATE, pages 547-552, 2013.
- 14
-
D. Ernst, N. S. Kim, S. Das, S. Pant, R. R. Rao, T. Pham, C. H. Ziesler,
D. Blaauw, T. M. Austin, K. Flautner, and Trevor N. Mudge.
Razor: A low-power pipeline based on circuit-level timing
speculation.
In MICRO, 2003.
- 15
-
J. Srinivasan, S. V. Adve, P. Bose and J. A. Rivers.
The Case for Lifetime Reliability-Aware Microprocessors.
SIGARCH Comput. Archit. News, 32(2):276, 2004.
- 16
-
A. Kahng, S. Kang, R. Kumar, and J. Sartori.
Designing Processors from the Ground up to Allow Voltage/Reliability
Tradeoffs.
In HPCA, 2010.
- 17
-
S. K. Reinhardt and S. S Mukherjee.
Transient Fault Detection via Simultaneous Multithreading.
ISCA '00, pages 25-36, 2000.
- 18
-
A. DeHon, H. M. Quinn, and N. P. Carter.
Vision for cross-layer optimization to address the dual challenges of
energy and reliability.
In DATE, pages 1017-1022. IEEE, 2010.
- 19
-
R. Hegde and N.R. Shanbhag.
Energy-efficient Signal Processing via Algorithmic Noise-tolerance.
ISLPED '99.
- 20
-
V. Gupta, D. Mohapatra, A. Raghunathan, and K. Roy.
Low-power digital signal processing using approximate adders.
IEEE Trans. on CAD of Integrated Circuits and Systems,
32(1):124-137, 2013.
- 21
-
H. Cho, L. Leem and S. Mitra.
ERSA: Error Resilient System Architecture for Probabilistic
Applications.
IEEE Trans. on CAD of Integrated Circuits and Systems,
31(4):546-558, 2012.
- 22
-
K. Palem and A. Lingamneni.
Ten years of building broken chips: The physics and engineering of
inexact computing.
ACM Trans. Embed. Comput. Syst., 12(2s):87:1-87:23, May 2013.
- 23
-
M. Willems H. Keding, F. Hürtgen and M. Coors.
Transformation of floating-point into fixed-point algorithms by
interpolation applying a statistical approach.
In Proc. Int. Conf. on Signal Processing Application and
Technology (ICSPAT), Toronto, sep 1998.
- 24
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S. Krishnaswamy, G. F. Viamontes, I. L. Markov and J. P. Hayes.
Probabilistic transfer matrices in symbolic reliability analysis of
logic circuits.
ACM Trans. Design Autom. Electr. Syst., 13(1), 2008.
- 25
-
Synopsys.
Processor Designer
http://www.synopsys.com/Systems/BlockDesign/processorDev.
- 26
-
A. Chattopadhyay, H. Meyr and R. Leupers.
LISA: A Uniform ADL for Embedded Processor Modelling,
Implementation and Software Toolsuite Generation, chapter 5, pages 95-130.
Morgan Kaufmann, jun 2008.
- 27
-
T. Huang, G. Yang, and G. Tang.
A fast two-dimensional median filtering algorithm.
IEEE Trans. Acoust., Speech, Signal Processing, 27(1):13-18,
1979.
Zheng Wang A Chattopadhyay
2014-07-16


Attraction - Must Visit
Indiana Memorial Union - The iconic mansion.
Places that many people would love to Visit Many times
Bloomington is an Indiana city, southwest of Indianapolis. The WonderLab science museum has hands-on kids’ exhibits, plus insects and reptiles. Indiana University’s Eskenazi Museum of Art has paintings, sculptures and decorative arts from around the world. The 1835 Wylie House is now a museum with period furnishings and an heirloom garden. Southeast, trails lace Hoosier National Forest’s Charles C. Deam Wilderness.
Bloomington is a city in and the county seat of Monroe County in the southern region of the U.S. state of Indiana.[6] It is the seventh-largest city in Indiana and the fourth-largest outside the Indianapolis metropolitan area. According to the Monroe County History Center, Bloomington is known as the "Gateway to Scenic Southern Indiana." The city was established in 1818 by a group of settlers from Kentucky, Tennessee, the Carolinas, and Virginia who were so impressed with "a haven of blooms" that they called it Bloomington.
Alumni Hall
Museum for Artists
MotherBear's


eosDAC tools
No Scatter Found
Read Only!
Key Tools
Generate
EOS SYSTEM ACTIONS
Create Account
RAM Market
SMART CONTRACTS
Deploy
Load & Interact
TOKENS
Get Balance
Test function
x


~>
tlock.dev█
Find me at: Army of Bees | Github | Furano Avalanche Center
Tech I care about:
Languages I am bullish on:
- Go
- Elixir
- Kotlin
- Rust
- Dart (Well, sort of. Its complicated)
Languages I am using in production:
@ front-end
- Typescript
- Javascript
- CSS
- Sass
- PostCSS
@ back-end
- SQL/plpgsql
- Go
- Python
- Typescript
- Ruby
- PHP
Tech / Frameworks / Platforms etc I am using in production:
@ front-end
- Svelte
- React
- MobX + vanilla JS
@ back-end
- Node
- Deno
- FastAPI
@ cloud db / serverless
- Supabase
- Firebase
- AWS ...too many to list
- Hasura
@ ssg / headless
- Hugo
- Netlify CMS
- Gatsby
@ monolith
- Ruby on Rails
- Wordpress
- Kirby CMS
@ toolchain
- Vite
- Webpack
- Rollup
- Parcel


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import _root_.models.Address @import _root_.utils.AtedSubscriptionUtils @import config.ApplicationConfig @import views.html.helpers._ @import utils._ @import _root_.utils.AuthUtils.isAgent @import controllers.auth._ @import views.html.helper.form @this(atedMain: atedMain, formHelper: FormWithCSRF) @(businessAddressForm: Form[BusinessAddress], businessAddress: Address, backLink: Option[String] = None)(implicit request: Request[_], authContext: AtedSubscriptionAuthData, messages: Messages, atedSubUtils: AtedSubscriptionUtils, appConfig: ApplicationConfig) @implicitFormInstance = @{ Some(businessAddressForm) } @header = @{if(isAgent) {messages("ated.registered-business-address.agent.header")} else {messages("ated.registered-business-address.header")}} @atedMain(title = if(isAgent) { messages("ated.registered-business-address.agent.title") } else { messages("ated.registered-business-address.title") }) { @pageHeadersAndError(backLink, "business-registered-text", if(isAgent) {messages("ated.registered-business-address.agent.sub-header")} else {messages("ated.registered-business-address.sub-header")}, "registered-business-address-header", header, Some(atedErrorSummary(businessAddressForm, "ated.business-address-error.general"))) @if(isAgent) {


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Please fill in as many fields as you can. This will help us match with items more efficiently.
Field
Value
New Value
Username
Address
Phone
First Name
Last Name
Nickname
Personal Email
Country
Gender
Marital Status


The contextmenu
plugin enables a contextual menu to be shown, when the user right-clicks a node (or when triggered programatically by the developer).
Whether to select the right clicked node when showing the context menu. If this is set to true
and the node is not selected all currently selected nodes will be deselected.
Whether to show the context menu just below the node, or at the clicked point exactly.
Expects an object or a function, which should return an object. If a function is used it fired in the tree's context and receives one argument - the node that was right clicked. The object format is:
{ // Some key "rename" : { // The item label "label" : "Rename", // The function to execute upon a click "action" : function (obj) { this.rename(obj); }, // All below are optional "_disabled" : true, // clicking the item won't do a thing "_class" : "class", // class is applied to the item LI node "separator_before" : false, // Insert a separator before the item "separator_after" : true, // Insert a separator after the item // false or string - if does not contain `/` - used as classname "icon" : false, "submenu" : { /* Collection of objects (the same structure) */ } } /* MORE ENTRIES ... */ }
Shows the contextmenu next to a node. Triggered automatically when right-clicking a node.
mixed
node
This can be a DOM node, jQuery node or selector pointing to an element within the tree.
number
x
The X-coordinate to show the menu at - may be overwritten by show_at_node
. If you omit this the menu is shown aligned with the left of the node.
number
y
The Y-coordinate to show the menu at - may be overwritten by show_at_node
. If you omit this the menu is shown just below the node.


ToDo
Factorization of multi-variate polynomials over algebraic field
Factorization over polynomial ring over Euclidian ring
Geometry


Scikit-Learn Exporter Module¶
-
skl_to_pmml.
any_in
(seq_a, seq_b)[source]¶ Checks for common elements in two given sequence elements
- seq_a : list
- A list of items
- seq_b : list
- A list of items
-
skl_to_pmml.
avgPathLength
(n)[source]¶ Generates average path length for Isolation forest models
- n : int
- Number of samples
-
skl_to_pmml.
get_PMML_kwargs
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns all the pmml elements.
- model : Scikit-learn model object
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the target column .
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_anomaly_detection_output
(model)[source]¶ Generates output for anomaly detection models
- model :
- Scikit-learn’s model object
-
skl_to_pmml.
get_anomalydetection_model
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Creates xml elements for anomaly detction models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_bayes_inputs
(model, derived_col_names)[source]¶ It returns the Bayes Input element of the naive bayes model .
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing.
-
skl_to_pmml.
get_bayes_output
(model, target_name)[source]¶ It returns the Bayes Output element of the model
- model :
- An instance of Scikit-learn model.
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_classificationMethod
(model)[source]¶ It returns the Classification method name for SVM models.
- model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_cluster_num
(model)[source]¶ Returns number of cluster for clustering models
- model :
- An instance of Scikit-learn model.
-
skl_to_pmml.
get_cluster_vals
(model, counts)[source]¶ Generates cluster information for clustering models
- model :
- An instance of Scikit-learn model.
-
skl_to_pmml.
get_clustering_flds
(col_names)[source]¶ Generates cluster fields for clustering models
- col_names :
- Contains list of feature/column names.
-
skl_to_pmml.
get_clustering_model
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for clustering models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_comp_measure
()[source]¶ Generates comparison measure information for clustering models
-
skl_to_pmml.
get_comparison_measure
(model)[source]¶ It return the Comparison measure element for nearest neighbour model.
- model :
- An instance of Scikit-learn model.
-
skl_to_pmml.
get_data_dictionary
(model, feature_names, target_name, categoric_values)[source]¶ It returns the Data Dictionary element.
- model :
- A Scikit-learn model instance.
- feature_names : List
- Contains the list of feature/column name.
- target_name : List
- Name of the Target column.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_dtype
(feat_value)[source]¶ It return the data type of the value.
- feat_value :
- Contains a value for finding the its data type.
-
skl_to_pmml.
get_ensemble_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elemenets for ensemble models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_entire_string
(pipe0)[source]¶ Creates a String containing the whole Pipeline’s information
- pipe0 :
- The Pipeline object
-
skl_to_pmml.
get_funct
(sk_model)[source]¶ It returns the activation fucntion for a neural network model.
- model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_inline_table
(model)[source]¶ It Returns the Inline Table element of the model.
- model :
- An instance of Scikit-learn model.
-
skl_to_pmml.
get_inner_segments
(model, derived_col_names, col_names, index)[source]¶ It returns the segments of a Segmentation.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- index : Integer
- The index of the estimator for the model
-
skl_to_pmml.
get_instance_fields
(derived_col_names, target_name)[source]¶ It returns the Instance field element.
- derived_col_names : List
- Contains column names after preprocessing.
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_kernel_type
(model)[source]¶ It returns the kernel type element.
- model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_knn_inputs
(col_names)[source]¶ It returns the KNN Inputs element.
- col_names : List
- Contains list of feature/column names.
-
skl_to_pmml.
get_mining_buildtask
(pipeline)[source]¶ Create MiningBuildTask for the given pipeline
- pipeline :
- Pipeline object
-
skl_to_pmml.
get_mining_func
(model)[source]¶ It returns the name of the mining function of the model.
- model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_mining_schema
(model, feature_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Mining Schema of the model.
- model :
- A Scikit-learn model instance.
- feature_names : List
- Contains the list of feature/column name.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_model_kwargs
(model, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns all the model element for a specific model.
- model :
- An instance of Scikit-learn model.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_multiple_model_method
(model)[source]¶ It returns the type of multiple model method for MiningModels.
- model :
- A Scikit-learn model instance
-
skl_to_pmml.
get_naiveBayesModel
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for naive bayes models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_nearestNeighbour_model
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for nearest neighbour model
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_neural_layer
(model, feature_names, target_name)[source]¶ It returns the Neural Layer and Neural Ouptput element.
- model :
- A Scikit-learn model instance.
- feature_names : List
- Contains the list of feature/column name.
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_neural_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for neural network models
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_neuron_input
(feature_names)[source]¶ It returns the Neural Input element.
- feature_names : List
- Contains the list of feature/column name.
-
skl_to_pmml.
get_node
(model, features_names, main_model=None)[source]¶ It return the Node element of the model.
- model :
- An instance of the estimator of the tree object.
- features_names : List
- Contains the list of feature/column name.
- main_model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_outer_segmentation
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Segmentation element of a MiningModel.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_output
(model, target_name)[source]¶ It returns the output element of the model.
- model :
- A Scikit-learn model instance.
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_output_for_clustering
(values)[source]¶ Generates output for clustering models
- model :
- An instance of Scikit-learn model.
-
skl_to_pmml.
get_reg_mining_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Creates xml elements for multi-class linear models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_reg_tab_for_reg_mining_model
(model, col_names, index, categorical_values)[source]¶ Generates Regression Table for multi-class linear models
- model :
- An instance of Scikit-learn model.
- col_names : List
- Contains list of feature/column names.
index : int categoric_values : tuple
Contains Categorical attribute names and its values
-
skl_to_pmml.
get_regrs_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for linear models
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_regrs_tabl
(model, feature_names, target_name, categoric_values)[source]¶ It returns the Regression Table element of the model.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- target_name : String
- Name of the Target column.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_segments
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Segment element of a Segmentation.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_segments_for_gbc
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns list of Segments element of a Segmentation.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_super_cls_names
(model_inst)[source]¶ It returns the set of Super class of the model.
- model_inst :
- Instance of the scikit-learn model
-
skl_to_pmml.
get_supportVectorMachine
(model)[source]¶ Generates PMML elements for support vector machine models
- model :
- A Scikit-learn model instance.
-
skl_to_pmml.
get_supportVectorMachine_models
(model, derived_col_names, col_names, target_names, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for support vector machine models
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_names : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_targets
(model, target_name)[source]¶ It returns the Target element of the model.
- model :
- A Scikit-learn model instance.
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_training_instances
(model, derived_col_names, target_name)[source]¶ It returns the Training Instance element.
- model :
- An instance of Scikit-learn model.
- derived_col_names : List
- Contains column names after preprocessing
- target_name : String
- Name of the Target column.
-
skl_to_pmml.
get_tree_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ Generates PMML elements for tree models
- model :
- A Scikit-learn model instance.
- derived_col_names :
- Contains column names after preprocessing.
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the Target column.
- mining_imp_val : tuple
- Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
get_vectorDictionary
(model, derived_col_names, categoric_values)[source]¶ It return the Vector Dictionary element.
- model :
- A Scikit-learn model instance.
- derived_col_names : List
- Contains column names after preprocessing.
- categoric_values : tuple
- Contains Categorical attribute names and its values
-
skl_to_pmml.
has_target
(model)[source]¶ Checks whether a given model has target or not
- model :
- Scikit-learn’s model object
-
skl_to_pmml.
skl_to_pmml
(pipeline, col_names, target_name='target', pmml_f_name='from_sklearn.pmml',model_name=None,description=None)[source]¶ Exports scikit-learn pipeline object into pmml
- pipeline :
- Contains an instance of Pipeline with preprocessing and final estimator
- col_names : List
- Contains list of feature/column names.
- target_name : String
- Name of the target column. (Default=’target’)
- pmml_f_name : String
- Name of the pmml file. (Default=’from_sklearn.pmml’)
- model_name: String (optional)
- Name of the model
- description : string (optional)
- Description of the model


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