text(str, x, y, width=None, height=None, outline=False, plot=True, **options)
Draw a string at x/y (while optionally setting its maximum width and height)
text(x, y, width=None, height=None, str="", **options)
The string can be omitted as the first arg if provided as a keyword argument
text(x, y, width=None, height=None, xml="", **options)
If an xml string is passed as a keyword argument, its tags will be accessible through the select() method and the tag names will be mapped to font definitions in the stylesheet()
text(x, y, width=None, height=None, src="<path or url>", **options)
Load the file pointed to by the src argument and draw its contents
Draws text to the screen, either as a single line or a multi-line block. The first parameter is a string of unicode text to display. The x & y parameters set the location of the text’s baseline. The optional width
parameter specifies the column-width for a multi-line textblock and, if present, the height
parameter sets maximum height for the column. Text in a column is wrapped across as many lines as will fit in the maximum height.
Text will be drawn using the current font() and layout() style, but individual settings can be overridden by passing new values as keyword arguments (the text() command supports all the same keyword arguments that font() and layout() provide). For instance, the text's alignment can be set with the layout() command or the optional align
keyword argument.
Text will be drawn in the current fill() color, but will not reflect the global pen() and stroke() unless drawn as an outline
. If the current fill() is a multi-color gradient, text() will use the first color in its range (unless outlined, in which case it will use the gradient fill).
By default, text is not outlined (which may not be what you want when exporting to PDF). To "trace" the text into outlines, call text() with outline=True
. This will convert the text to a Bezier before adding it to the canvas. Also consider using the textpath() convenience function.
By default, text() will render the entire str
argument in a single style (based on the canvas's current font(), layout(), and fill() state). You can selectively style portions of the string if you format it as XML and use the stylesheet() command to define styles for the tags you use in the markup. Stylesheet definitions behave similarly to CSS declarations in that more deeply nested styles will override attributes of less deeply nested ones.
Note that for PlotDevice to recognize your string as XML it must be passed as the xml
keyword argument – passing it as the first positional argument will display the raw markup verbatim.
The string you pass must be "valid" XML, in particular you must escape the control characters: &
, <
, >
, '
, and "
. Also note that any attributes you add to your tags must be wrapped in double-quotes. PlotDevice diverges from strict XML in two ways:
When drawing an XML string, text() will ignore any tags that don't correspond to a definition in the stylesheet(). Tags that don't correspond to styles can still be useful though since their character location, layout, and attribute data can be retrieved from the TextMatch objects returned by Text.select().
If you pass a src
keyword argument to text(), PlotDevice will treat it as either a file path or (if it begins with "http://"
or "https://"
) a URL to be fetched. If the file name ends with ".xml"
, the contents will be parsed for inline styling; otherwise the text read from the file will be displayed without further processing.
family
weight
size
italic
variant
face
tracking
You can override the current character-style for an individual text() invocation by including any of the keyword arguments supported by the font() command.
lig
sc
osf
tab
vpos
frac
ss
The current OpenType Feature settings from prior calls to the font() command may also be overridden for an individual text() call.
align
hyphenate
margin
indent
leading
spacing
Likewise, the current paragraph-style settings can be overridden for an individual text() invocation by including any of the layout arguments supported by layout().
style
If you have defined text styles using the stylesheet() command, you can select one by passing a style-name as the style
argument. The selected style will be applied to the entire run of text (so use inline markup if you need greater granularity).
plot
By default, the text() command will render to the canvas immediately. Passing plot=False
will return the rendered Text or Bezier object without adding it to the canvas.
fill(0.2) font("Helvetica", 20) text("hello", 10,50) text("goodbye", 10,70, italic=True)


[gecode-users] Propagation of distinct with ICL_DOM
M Dobrogost
michal.dobrogost at gmail.com
Thu May 31 22:03:44 CEST 2007
Hello All,
I came across previous discussions on the list which explained that with
ICL_VAL the distinct propagator blocks until a variable is assigned. Once a
variable is assigned it then restricts all other variables from having that
value. Could someone explain how powerful the propagation achieved by
distinct is with ICL_DOM?
Thanks!
Michal
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The sun is hot. An elephant stomps across the track. It sees a puddle and steps into the mud. The elephant sits in the mud. It lies in the mud. The elephant sucks mud up in its trunk and sprays the mud onto its back. The mud sticks to its skin. Why does an elephant slap mud on its skin" Is it for fun" The mud helps to protect the elephant's skin. It acts as a sunscreen and stops insects and ticks from attacking its skin.
in-context


Woman dies as downpours bring floods across central and northern England
LONDON, Nov 8 — A woman was found dead in flood water today after severe storms brought a month’s rainfall in less than 24 hours to parts of central and northern England.
Police said the woman’s body had been found near a stretch of the River Derwent near the town of Darley Dale in Derbyshire.
“The woman was reported as having been swept away by flood water in Rowsley and the body of what is believed to be the same woman was found in Darley Dale,” police said in a statement.
Dozens of people were trapped in a shopping centre in the city of Sheffield overnight after becoming stranded by rising waters and about 100 others were rescued from a nearby mall by firefighters in dinghies.
The greatest concern was for homes in six areas around the River Don near the town of Doncaster where water levels reached dangerous record highs.
Residents were initially warned to immediately evacuate although the local authority later advised them to instead remain vigilant.
There were more than 150 alerts in other areas of the country where the Environment Agency cautioned that flooding was either expected or possible.
The torrential rain also brought widespread travel disruption with some rail operators issuing a “do not travel” advice to passengers in places around Sheffield and other affected areas while many roads were also closed.
“Awful to see the terrible flooding across the North of England,” Prime Minister Boris Johnson said on Twitter. “Thank you to the emergency staff & volunteers helping families through this difficult time.” — Reuters


Sahitya Roy
Founder Of Eduhub community
Priya Chandak
Founder Eduhub community
Sakshi Mundhra
Social Media Manager/ Content Writer


The number of years before the birth of Jesus Christ.
In 3100 BC the first Egyptian dynasty began; in 509 BC the Roman republic was founded; in 46 BC Caesar changed the calendar.


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flight_ticket.doc
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flight_ticket.ppt
flight_ticket.csv


DataProvider Introduction¶
DataProvider is a module that loads training or testing data into cpu or gpu memory for the following triaining or testing process.
For simple use, users can use Python PyDataProvider
to dynamically reads
the original data in any format or in any form, and then transfer them into a
data format PaddlePaddle requires. The process is extremly flexible and highly
customized, with sacrificing the efficiency only a little. This is extremly
useful when you have to dynamically generate certain kinds of data according to,
for example, the training performance.
Besides, users also can customize a C++ DataProvider
for a more
complex usage, or for a higher efficiency.
The following parameters are required to define in the PaddlePaddle network configuration file (trainer_config.py): which DataProvider is chosen to used, and specific parameters for DataProvider, including training file list (train.list) and testing file list (test.list).
Train.list and test.list are simply two plain text files, which defines path of training or testing data. It is recommended that directly placing them into the training directory, and reference to them by using a relative path ( relative to the PaddePaddle program).
Testing or evaluating will not be performed during training if the test.list is not set or set to None. Otherwise, PaddlePaddle will evaluate the trained model by the specified tesing data while training, every testing period (a user defined command line parameter in PaddlePaddle) to prevent over-fitting.
Each line of train.list and test.list is an absolute or relative path (relative to the PaddePaddle program runtime) of data file. Fascinatingly more, each line can also be a HDFS file path or a SQL connection string. As long as the user assures how to access each file in DataProvider.
Please refer to the following articles for more information about the detail usages of DataProvider and how to implement a new DataProvider,


Chaotic HTTP
Delay
probability (0.0-1.0)
duration (in
time.ParseDuration
format)
Failure
probability (0.0-1.0)
...
◼
delayed
◼
failed
live
clear
X


{%extends "registration/base.html"%} {% load bootstrap4 %} {% block content %}
Some Information Might be Incorrect
Get the App


The easiest way to understand Classes is as a collection of behaviors (or “methods”) used to give characteristics to, and model the behavior of, various objects. Often classes are used simulate real world things, like dice or pets or the moon, and allow us to interact with them in a digital environment. When we create a class for these objects, we define what their features are (with as little or much specificity as we like), and can then create new versions (“instances”) of them to play with on the computer.
Take the die as an easy example. It only has a couple relevant characteristics that define its behavior — namely, that it has a certain number of sides, and that rolling it will return only one of those sides, at random. If we researchers (or gamblers) who wanted to use the power of computers to simulate a lot of die rolls in a very short amount of time, and have the flexibility to tinker with the number of sides on our die without having to buy a brand new one, we could do this very easily with a new Ruby die class. We would just create the class, ask the user to input the number of sides the die has, and then whenever it is told to “roll” it would just return a random number between 1 and the total number of sides. Simple. But very powerful nonetheless.
It’s important to understand that classes are just blueprints for objects, and objects are the real focal point of Ruby programs. (Note: While Ruby classes are technically objects themselves, this can cause some confusion to beginners, so I will leave it to the textbooks to explain). A class should outline the defining characteristics of an object, but not he specifics of it. It should allow you to define the specifics when you create an instance of that class, and provide you with the toolkit to interact with it as appropriate.
Below is another example of a class you might use, this time showing the syntax you would use to construct it. Here I’m creating a Movie class, which could help me keep track of all the movies I’ve seen. I want the ability to create a new Movie object every time I see a new movie, tell it some key info about that movie, and then call up that information later whenever I want it.
class Movie
attr_accessor :rating
attr_reader :title, :director
@@instance_collector = []
def initialize (title, director, year_released, year_seen, rating)
@@instance_collector << self
@title = title
@director = director
@year_released = year_released
@year_seen = year_seen
@rating = rating
end
Here we start by creating a class called Movie, and use the built-in Ruby methods of
Next we create an initialize method which defines the information required in creating a new instance of the class (title, director, year released, etc.) and what happens automatically as soon as the class instance is created. The first thing we do is push
def summary
puts "#{@title} was directed by #{@director} and was released in
<EMAIL_ADDRESS>puts "You first saw it in #{@year_seen} and rate it #{@rating} out of 5
stars."
end
Here we create a summary method that allows us to get a quick output of all the information we've stored any given movie, in two neatly formatted strings.
def self.rating(rating)
puts "Movies you've rated #{rating} out of 5 stars:"
@@instance_collector.map do |x|
if x.rating == rating
puts "#{x.title}, directed by #{x.director}"
end
end
end
end
Finally, we create a method
Now to test out our new class with some driver test code:
waking_life = Movie.new("Waking Life", "Richard Linklater", 2001, 2005, 5)
magnolia = Movie.new("Magnolia", "P. T. Anderson", 1999, 2004, 4)
magnolia.summary
magnolia.rating = 5
magnolia.summary
Movie.rating(5)
Here we create two instances of the new Movie class for two of my favorite movies.


Climate Change Indicators: Ocean Heat
Key Points
- In three different data analyses, the long-term trend shows that the oceans have become warmer since 1955 (see Figure 1).
- Although concentrations of greenhouse gases have risen at a relatively steady rate over the past few decades (see the Atmospheric Concentrations of Greenhouse Gases indicator), the rate of change in ocean heat content can vary from year to year (see Figure 1). Year-to-year changes are influenced by events such as volcanic eruptions and recurring ocean-atmosphere patterns such as El Niño.
Background
When sunlight reaches the Earth’s surface, the world’s oceans absorb some of this energy and store it as heat. This heat is initially absorbed at the surface, but some of it eventually spreads to deeper waters. Currents also move this heat around the world. Water has a much higher heat capacity than air, meaning the oceans can absorb larger amounts of heat energy with only a slight increase in temperature.
The total amount of heat stored by the oceans is called “ocean heat content,” and measurements of water temperature reflect the amount of heat in the water at a particular time and location. Ocean temperature plays an important role in the Earth’s climate system—particularly sea surface temperature (see the Sea Surface Temperature indicator)—because heat from ocean surface waters provides energy for storms and thereby influences weather patterns.
Increasing greenhouse gas concentrations are trapping more energy from the sun. Because changes in ocean systems occur over centuries, the oceans have not yet warmed as much as the atmosphere, even though they have absorbed more than 90 percent of the Earth’s extra heat since 1955.1,2 If not for the large heat-storage capacity provided by the oceans, the atmosphere would warm more rapidly.3 Increased heat absorption also changes ocean currents because many currents are driven by differences in temperature, which cause differences in density. These currents influence climate patterns and sustain ecosystems that depend on certain temperature ranges.
Because water expands slightly as it gets warmer, an increase in ocean heat content will also increase the volume of water in the ocean, which is one cause of the observed increases in sea level (see the Sea Level indicator).
About the Indicator
This indicator shows trends in global ocean heat content from 1955 to 2015. These data are available for the top 700 meters of the ocean (nearly 2,300 feet), which accounts for just under 20 percent of the total volume of water in the world’s oceans. The indicator measures ocean heat content in joules, which are units of energy.
The National Oceanic and Atmospheric Administration has calculated changes in ocean heat content based on measurements of ocean temperatures around the world at different depths. These measurements come from a variety of instruments deployed from ships and airplanes and, more recently, underwater robots. Thus, the data must be carefully adjusted to account for differences among measurement techniques and data collection programs. Figure 1 shows three independent interpretations of essentially the same underlying data.
Indicator Notes
Data must be carefully reconstructed and filtered for biases because of different data collection techniques and uneven sampling over time and space. Various methods of correcting the data have led to slightly different versions of the ocean heat trend line. Scientists continue to compare their results and improve their estimates over time. They also test their ocean heat estimates by looking at corresponding changes in other properties of the ocean. For example, they can check to see whether observed changes in sea level match the amount of sea level rise that would be expected based on the estimated change in ocean heat.
Data Sources
Data for this indicator were collected by the National Oceanic and Atmospheric Administration and other organizations around the world. The data were analyzed independently by researchers at the National Oceanic and Atmospheric Administration, Australia’s Commonwealth Scientific and Industrial Research Organisation, and the Japan Meteorological Agency’s Meteorological Research Institute.
Technical Documentation
References
1. IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.
2. Levitus, S., J.I. Antonov, T.P. Boyer, O.K. Baranova, H.E. Garcia, R.A. Locarnini, A.V. Mishonov, J.R. Reagan, D. Seidov, E.S. Yarosh, and M.M. Zweng. 2012. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. 39:L10603.
3. Levitus, S., J.I. Antonov, T.P. Boyer, O.K. Baranova, H.E. Garcia, R.A. Locarnini, A.V. Mishonov, J.R. Reagan, D. Seidov, E.S. Yarosh, and M.M. Zweng. 2012. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. 39:L10603.
4. Based on a total global energy supply of 13,541 million tons of oil equivalents in the year 2013, which equates to 5.7 x 1020 joules. Source: IEA (International Energy Agency). 2015. Key world energy statistics. www.iea.org/publications/freepublications/publication/KeyWorld_Statistics_2015.pdf.
5. CSIRO (Commonwealth Scientific and Industrial Research Organisation). 2016 update to data originally published in: Domingues, C.M., J.A. Church, N.J. White, P.J. Gleckler, S.E. Wijffels, P.M. Barker, and J.R. Dunn. 2008. Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature 453:1090–1094. www.cmar.csiro.au/sealevel/thermal_expansion_ocean_heat_timeseries.html.
6. MRI/JMA (Meteorological Research Institute/Japan Meteorological Agency). 2016 update to data originally published in: Ishii, M., and M. Kimoto. 2009. Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr. 65:287–299.
7. NOAA (National Oceanic and Atmospheric Administration). 2016. Global ocean heat and salt content. Accessed May 2016. www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT.


Strauss hopes Flower 'stays involved in English cricket'
Cape Town - Former England captain Andrew Strauss believes Andy Flower still has a huge amount to offer English cricket.
Strauss was the skipper and Flower the coach when they led England to being the No 1 Test side in the world in 2010/11, a year which also saw them winning the Ashes in Australia.
However, Flower's 12-year association with English cricket, where he worked also worked with the England Lions, was ended last month by the ECB, much to the dismay of Strauss.
Strauss told the PA news agency: "Andy was by far the best coach I ever worked with, a great man and had incredible success with the England cricket team.
"We certainly haven't seen the end of Andy Flower yet. I would not be at all surprised if we saw him in a high-profile coaching role again.
"Maybe not with the ECB, but in the English game somewhere. I sincerely hope he stays involved in English cricket, rather than going abroad somewhere."
Strauss, who previously held the role of ECB director of cricket, recently accepted a role as chair of the ECB cricket committee.
It will run alongside his work for the Ruth Strauss Foundation, which was set up in memory of his late wife to fund research into rare forms of lung cancer and to support patients and families affected by the disease.
"It's me dipping my toe back into the game of cricket," he explained.
"The ECB cricket committee has a very broad reach overseeing professional cricket in the country, but also women's cricket and disability cricket and the pathway to the top.
"It keeps me engaged and cricket is in my blood so I want to be involved in some way, but it's about juggling it with the foundation.
"Even more importantly it's juggling it with my boys and making sure I am around to do what they need me to do."
-TeamTalk Media


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--- layout: post status: publish published: true title: Matt Murton is called up by Colorado author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 735 wordpress_url: http://ivyenvy.com/?p=735 date: '2009-04-30 21:34:24 +0000' date_gmt: '2009-05-01 03:34:24 +0000' categories: - Minor Leagues - Former Cubs tags: - Colorado Rockies - Dodger Stadium - Iowa Cubs - Los Angeles - Matt Murton - Jake Fox - Colorado Springs comments: [] ---
I completely missed this news and I'd like to thank Kevin for the e-mail, alerting me. Matt Murton was called up to the Rockies from AAA Colorado Springs. Murton was batting .411 with four homeruns, six doubles and 19 RBI's in 18 games. Matt has also stolen five bases. I'm really happy to see Matt get back up the big leagues and his ability to hit multi-base hits has obviously returned.
I may have to pick up a Murton Rockies shirt when I drive through Denver to Los Angeles this summer. Also, if he is still with the Rockies in July, I will tempted to return to the hell-hole referred to as Dodger Stadium when the Rockies visit.
The Marlins just put up half a dozen on the Cubs in the 10th and the Cubs will soon be under .500. Why comment on that when there is something good happening in baseball? Congrats, Matt.
Speaking of tearing things up in the Minors, how about Jake Fox in Iowa? In 20 games, he's batting .429 with an OBP of .500. Sure, that's impressive. But how about this: 12 HR, 8 doubles, a triple and 31 RBI's...in 20 games!


The Analyst will be responsible for the successfully completion of selected portions of ICH Method Validation of the Lancaster site’s Methods.
Education: BS in Science
BS with 9 years, or MS with 7 years, or PhD with 4 years of analytical chemistry experience. A focused degree in Chemistry and related field is preferred.
Experience validating HPLC Analytical Methods (prefer Agilent and Waters systems), working knowledge of GMPs, Experience with Chromatography Data Systems (prefer Water's Empower). Worked in Analytical Laboratory with both QC and Validation experience. Knowledge of ICH and USP Validation Requirements, Understanding of Basic Statistics- standard deviation, relative standard deviation and confidence interval, Ability to write validation reports, using word and excel


An Intel 8080 emulator
Controls
- LEFT/RIGHT or A/D: Move left and right
- SPACE, UP or W: Fire
- Z: Single-player
- X: Multi-player
- C: Insert Coin
- R: Reset the CPU
Somewhat substantially based off of Thibault Imbert's implementation.
Somewhat substantially based off of Thibault Imbert's implementation.


Flow State
Overview
Color
Typography
Icons
Forms
Inputs
Buttons
Layout and Grid
Motion
Imagery
Voice and Tone
Motion
Can't wait to see what happens!
iOS
Android
Web
iOS
Some content.
Android
Some content in menu 1.
Web
Some content in menu 2.


The cloud provider uses text mining and image recognition to sell your ad-profile. The photo is not mine, give a try to the excellent https://thispersondoesnotexist.com/ when you need a fake profile photo
For this example I have
The OS sees the account as a remote disk. On the screenshot you can see that both the Rclone normal access to this account and the Rclone encrypted access are "MacOS Volumes". The volume on the left is the encrypted one, while on the right you see what's stored on the cloud. Rclone encrypts on the fly, transparently


Previous: Scope and Purpose, Up: Scope
Lisp is a family of languages with a long history. Early key ideas in Lisp were developed by John McCarthy during the 1956 Dartmouth Summer Research Project on Artificial Intelligence. McCarthy’s motivation was to develop an algebraic list processing language for artificial intelligence work. Implementation efforts for early dialects of Lisp were undertaken on the IBM~704, the IBM~7090, the Digital Equipment Corporation (DEC) PDP-1, the DEC~PDP-6, and the PDP-10. The primary dialect of Lisp between 1960 and 1965 was Lisp~1.5. By the early 1970’s there were two predominant dialects of Lisp, both arising from these early efforts: MacLisp and Interlisp. For further information about very early Lisp dialects, see The Anatomy of Lisp or Lisp 1.5 Programmer’s Manual.
MacLisp improved on the Lisp~1.5 notion of special variables and error handling. MacLisp also introduced the concept of functions that could take a variable number of arguments, macros, arrays, non-local dynamic exits, fast arithmetic, the first good Lisp compiler, and an emphasis on execution speed. By the end of the 1970’s, MacLisp was in use at over 50 sites. For further information about Maclisp, see Maclisp Reference Manual, Revision~0 or The Revised Maclisp Manual.
Interlisp introduced many ideas into Lisp programming environments and methodology. One of the Interlisp ideas that influenced Common Lisp was an iteration construct implemented by Warren Teitelman that inspired the loop macro used both on the Lisp Machines and in MacLisp, and now in Common Lisp. For further information about Interlisp, see Interlisp Reference Manual.
Although the first implementations of Lisp were on the IBM~704 and the IBM~7090, later work focussed on the DEC PDP-6 and, later, PDP-10 computers, the latter being the mainstay of Lisp and artificial intelligence work at such places as Massachusetts Institute of Technology (MIT), Stanford University, and Carnegie Mellon University (CMU) from the mid-1960’s through much of the 1970’s. The PDP-10 computer and its predecessor the PDP-6 computer were, by design, especially well-suited to Lisp because they had 36-bit words and 18-bit addresses. This architecture allowed a cons cell to be stored in one word; single instructions could extract the car and cdr parts. The PDP-6 and PDP-10 had fast, powerful stack instructions that enabled fast function calling. But the limitations of the PDP-10 were evident by 1973: it supported a small number of researchers using Lisp, and the small, 18-bit address space (2^18 = 262,144 words) limited the size of a single program. One response to the address space problem was the Lisp Machine, a special-purpose computer designed to run Lisp programs. The other response was to use general-purpose computers with address spaces larger than 18~bits, such as the DEC VAX and the S-1~Mark~IIA. For further information about S-1 Common Lisp, see "S-1 Common Lisp Implementation."
The Lisp machine concept was developed in the late 1960’s. In the early 1970’s, Peter Deutsch, working with Daniel Bobrow, implemented a Lisp on the Alto, a single-user minicomputer, using microcode to interpret a byte-code implementation language. Shortly thereafter, Richard Greenblatt began work on a different hardware and instruction set design at MIT. Although the Alto was not a total success as a Lisp machine, a dialect of Interlisp known as Interlisp-D became available on the D-series machines manufactured by Xerox—the Dorado, Dandelion, Dandetiger, and Dove (or Daybreak). An upward-compatible extension of MacLisp called Lisp Machine Lisp became available on the early MIT Lisp Machines. Commercial Lisp machines from Xerox, Lisp Machines (LMI), and Symbolics were on the market by 1981. For further information about Lisp Machine Lisp, see Lisp Machine Manual.
During the late 1970’s, Lisp Machine Lisp began to expand towards a much fuller language. Sophisticated lambda lists, setf, multiple values, and structures like those in Common Lisp are the results of early experimentation with programming styles by the Lisp Machine group. Jonl White and others migrated these features to MacLisp. Around 1980, Scott Fahlman and others at CMU began work on a Lisp to run on the Scientific Personal Integrated Computing Environment (SPICE) workstation. One of the goals of the project was to design a simpler dialect than Lisp Machine Lisp.
The Macsyma group at MIT began a project during the late 1970’s called the New Implementation of Lisp (NIL) for the VAX, which was headed by White. One of the stated goals of the NIL project was to fix many of the historic, but annoying, problems with Lisp while retaining significant compatibility with MacLisp. At about the same time, a research group at Stanford University and Lawrence Livermore National Laboratory headed by Richard P. Gabriel began the design of a Lisp to run on the S-1~Mark~IIA supercomputer. S-1~Lisp, never completely functional, was the test bed for adapting advanced compiler techniques to Lisp implementation. Eventually the S-1 and NIL groups collaborated. For further information about the NIL project, see "NIL—A Perspective."
The first effort towards Lisp standardization was made in 1969, when Anthony Hearn and Martin Griss at the University of Utah defined Standard Lisp—a subset of Lisp~1.5 and other dialects—to transport REDUCE, a symbolic algebra system. During the 1970’s, the Utah group implemented first a retargetable optimizing compiler for Standard Lisp, and then an extended implementation known as Portable Standard Lisp (PSL). By the mid 1980’s, PSL ran on about a dozen kinds of computers. For further information about Standard Lisp, see "Standard LISP Report."
PSL and Franz Lisp—a MacLisp-like dialect for Unix machines—were the first examples of widely available Lisp dialects on multiple hardware platforms.
One of the most important developments in Lisp occurred during the second half of the 1970’s: Scheme. Scheme, designed by Gerald J. Sussman and Guy L. Steele Jr., is a simple dialect of Lisp whose design brought to Lisp some of the ideas from programming language semantics developed in the 1960’s. Sussman was one of the prime innovators behind many other advances in Lisp technology from the late 1960’s through the 1970’s. The major contributions of Scheme were lexical scoping, lexical closures, first-class continuations, and simplified syntax (no separation of value cells and function cells). Some of these contributions made a large impact on the design of Common Lisp. For further information about Scheme, see IEEE Standard for the Scheme Programming Language or "Revised^3 Report on the Algorithmic Language Scheme."
In the late 1970’s object-oriented programming concepts started to make a strong impact on Lisp. At MIT, certain ideas from Smalltalk made their way into several widely used programming systems. Flavors, an object-oriented programming system with multiple inheritance, was developed at MIT for the Lisp machine community by Howard Cannon and others. At Xerox, the experience with Smalltalk and Knowledge Representation Language (KRL) led to the development of Lisp Object Oriented Programming System (LOOPS) and later Common LOOPS. For further information on Smalltalk, see Smalltalk-80: The Language and its Implementation. For further information on Flavors, see Flavors: A Non-Hierarchical Approach to Object-Oriented Programming.
These systems influenced the design of the Common Lisp Object System (CLOS). CLOS was developed specifically for this standardization effort, and was separately written up in "Common Lisp Object System Specification." However, minor details of its design have changed slightly since that publication, and that paper should not be taken as an authoritative reference to the semantics of the object system as described in this document.
In 1980 Symbolics and LMI were developing Lisp Machine Lisp; stock-hardware implementation groups were developing NIL, Franz Lisp, and PSL; Xerox was developing Interlisp; and the SPICE project at CMU was developing a MacLisp-like dialect of Lisp called SpiceLisp.
In April 1981, after a DARPA-sponsored meeting concerning the splintered Lisp community, Symbolics, the SPICE project, the NIL project, and the S-1~Lisp project joined together to define Common Lisp. Initially spearheaded by White and Gabriel, the driving force behind this grassroots effort was provided by Fahlman, Daniel Weinreb, David Moon, Steele, and Gabriel. Common Lisp was designed as a description of a family of languages. The primary influences on Common Lisp were Lisp Machine Lisp, MacLisp, NIL, S-1~Lisp, Spice Lisp, and Scheme. Common Lisp: The Language is a description of that design. Its semantics were intentionally underspecified in places where it was felt that a tight specification would overly constrain Common Lisp research and use.
In 1986 X3J13 was formed as a technical working group to produce a draft for an ANSI Common Lisp standard. Because of the acceptance of Common Lisp, the goals of this group differed from those of the original designers. These new goals included stricter standardization for portability, an object-oriented programming system, a condition system, iteration facilities, and a way to handle large character sets. To accommodate those goals, a new language specification, this document, was developed.
Previous: Scope and Purpose, Up: Scope


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Multiplicative AR(1) with Seasonal is a stochastic process model built on top of AR(1). The package provides the following procedures for MAR(1)S processes: fit, compose, decompose, advanced simulate and predict.
| Version: | 2.1.1 |
| Depends: | cmrutils, fda, zoo |
| Published: | 2018-08-18 |
| Author: | Andrey Paramonov |
| Maintainer: | Andrey Paramonov <paramon at acdlabs.ru> |
| License: | GPL (≥ 3) |
| URL: | https://github.com/aparamon/mar1s |
| NeedsCompilation: | no |
| In views: | TimeSeries |
| CRAN checks: | mar1s results |
| Reference manual: | mar1s.pdf |
| Package source: | mar1s_2.1.1.tar.gz |
| Windows binaries: | r-devel: mar1s_2.1.1.zip, r-release: mar1s_2.1.1.zip, r-oldrel: mar1s_2.1.1.zip |
| OS X binaries: | r-release: mar1s_2.1.1.tgz, r-oldrel: mar1s_2.1.1.tgz |
| Old sources: | mar1s archive |
Please use the canonical form https://CRAN.R-project.org/package=mar1s to link to this page.


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So you think you know England?
Your results are in!
Instructions
So here's how this works for those of you who've never taken an online quiz before (yeah...right).
Hit start quiz, and the timer starts. You have 12 seconds to answer each question.
Get it right, and you get a point in the correct column.
Get it wrong, or run out of time, and it's a strike in the incorrect column for you good ma'am... or chap.
Best of luck to you!
CORRECT
INCORRECT
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0
10
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0


3D Printing
Watch this video for an introduction to 3D Printing:
- Merrylands High School has a Makerbot Z18 5th Generation (PLA filament)
- Merrylands East Public School has a Makerbot Replicator 2x (ABS filament)
3D Printing Flow
- Design a 3D model (using Google Make, Blender or any 3D CAD program)
- Clean your model using NetFabb cloud service
- Prepare you model using 'Makerbot Desktop'
- Prepare the 3D printer for printing
- Transfer your 3D model to the printer by USB
- Preheat 3D printer
- Print your design
- Allow printer to cool
- Remove your model
3D Printing Across the Curriculum
The opportunities for applying 3D design and printing across the curriculum is endless (and by no means limited to STEM subjects):
- Making custom resources or technical models to support your teaching and learning
- Designing an educational game
- Getting students designing and printing their own designs
- Download ready to print models from Thingiverse
This paper from the English National Curriculum gives a range of ideas and the and the 3D Printing in education wiki is a growing source of new ideas and references that can assist teacher integrate this technology into their teaching and learning.
3D Printing Software
You can model 3D object using Photoshop, Google Make (previously called Google Sketchup), Blender or any CAD program. Google Make requires the addition of the STL import/export extension for exporting a STL file which is readable by Makerbots slicing software Makerbot Desktop.
Blender 101 video tutorial series (don't forget to setup a new project for real world design first)
Google Sketchup 101 video tutorial series
Meshlab is a good free utility or visualising and modifying STL files and detecting build errors.
3D Printing Tips
- Print a low resolution and low infill prototype first to test for build/design flaws
- Ensure you slice the model with rafts and if needed supports
- Prepare the build platform including cleaning body oils and dust from the build platform and leveling the build platform for every print
- If a build fails get advice before reprinting
Makerbot Z18 Generation Help & Support
- How to clear a clogged extruder
- How to prevent a clogged extruder and improve your build quality
- How to resolve a 'homing error'
- Makerbot Z18 Manual


HTML5 Mark Up
Lorem Ipsum.........
CSS Styling
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Graphic Design
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Pair programming is an agile software development technique in which two programmers work together at one workstation. One, the driver, writes code while the other, the observer or navigator, reviews each line of code. The two programmers switch roles frequently.
Lorem Ipsum.........
Lorem Ipsum.........
Lorem Ipsum.........


true = ${true} (boolean)
false = ${false} (boolean)
empty = ${empty} (empty string)
notempty = ${notempty} (string)
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${myvar}


This is my blog where I'm going to record my learning journey at Enspiral Dev Academy. I want this blog to be accessible enough that my friends and family can navigate around it to see what I am learning. I have named this Dodo's blog becasue that is what my niece calls me and I want her to be able to see this blog too so there will probably be some cute cat pics inlcuded along the way.


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JavaScript is the language of the web. In these four lessons, you’ll learn its basics.
These lessons were written with ♥ by Jasmine Greenaway, Christopher Harrison and Chris Noring


Effect consume object
This effect delete an object from the scene. The main usage is accompanied by the action, so it gives the impression that actually the action is being performed.
Create a consume object effect:
- Select the effect: Select the effect on the creation effects panel. Press OK.
- Creation effect panel: afterwards an edition panel will appear:
In the panel we can choose the object to be consumed among the objects of the actual chapter.


Selected value is {{level_value}}
{{var3}}
Image 1
*required File too large {{up.file.size / 1000000|number:1}}MB: max 20M
Image 2


This visualization displays the orbital locations of the Parker Solar Probe, the Solar Orbiter and the Earth through 2027.
Still in Development


Question 1
Given points (X1,Y1) and (X2,Y2) select the formula to find the equation of the given line.
y
=
mx
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2
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y
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2
=
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y
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I'm a UX Designer with a focus on research and strategy to deliver user-centered solutions while optimizing business goals.
I am a UX Designer with an emphasis in research, problem-solving, and story-telling. I grew up in Managua, Nicaragua, where I developed not only a deep love for my country, but a passion for helping people as well. This led me to get a degree in Psychology in Loyola University Chicago as I solidified my understanding of the human psyche in order to improve their lives.
As I expanded my expertise in psychology and research, I began dabbling with computer programming, a curiosity which then prompted me to explore the realms of UX design.
Eureka! I found that I was finally able to implement my enthusiasm for helping others, understanding human behaviors, and applying technology on a daily basis. In this website you can explore some of the work that I have done as a UX designer and, in doing so, understand a bit more about who I am.
Paper or Sketch, I do it all.
Agile approach to test solutions.
Data-driven decisions towards solutions.
A psychologist's empathy in every design.
Have a question about my work or just want to chat? Give me a call or send me an email and I will get back to you as soon as possible.
(312) 888-0644


GLEEN Downloads
Current Release:
- Download GLEEN V0.6.0 beta, for ARQ 2.2
- Pre-built GLEEN library and source code included in archive
- Apache 2.0 open source license
- GLEEN requires additional libraries (jena.jar, arq.jar, and commons-logging-1.1.jar) which are most likely already present in any Jena/ARQ application. However, if you need them, they can be downloaded from the following sites:
- ARQ (tested with version 2.2) [actually, the ARQ distribution includes the subsequent jars listed here]
- Jena (tested with version distributed with ARQ 2.2)
- Commons Logging (tested with version 1.1)
Old Versions:
- GLEEN
V0.5.1 beta
- Download GLEEN V0.5.1 beta, for ARQ 2.1 (zip file includes gleen.jar as well as the ANTLR runtime library 3.0.1)
- GLEEN requires additional libraries (jena.jar, arq.jar, and commons-logging-1.1.jar) which are most likely already present in any Jena/ARQ application. However, if you need them, they can be downloaded from the following sites:
- Notes regarding this version:
- Uses ANTLR runtime (grammar parser was generated using ANTLR parser generator)
- In ARQ version 2.0 and 2.1, the query prefixes were not accessible from a property function. The OnPath property function requires these prefix mappings to expand the prefixes in the path expression. GLEEN V0.5.1 included a work-around, but requires users of the function to add a line of code, within their project, at the point were the QueryExecution is created.
The following utility function puts the query prefix mapping into the Context object.
- edu.washington.sig.gleen.util.ContextUtil.setQueryPrefs(queryExecution, query);
- Add the above line immediately after the creation of the QueryExecution.
- This allows the OnPath property function to expand the prefixes in the path expression.
- If you do not add the above line, you must either use prefixes defined in the data source (and in the query or it won't execute) or you must use full URIs in your path expressio
- There are limits to the complexity of expressions allowed by the grammar. You are basically allowed two "levels" of nesting. In a "level one" expression you can apply the *, +, or ? operator to individual properties or alternations of properties and then concatenate these. In a "level two" expression you can apply the *, +, or ? operator to a level one expression or alternation of level one expressions. You cannot, for example, apply a + operator to a level two expression. This is a deliberate limitation of the complexity of allowed expression.


{% extends 'main/base.html' %} {% load staticfiles %} {% block content %} {% comment %}
You may use the cell counter without logging in, but you will be unable to save any changes to your keyboard map.
At present this site is optimised for Firefox or Chromium-based web browsers, and display sizes of at least 1024x768. See the About section for more information.
{% endblock %} {% block additional_scripts %} {% endblock %}


Process
The following provides a high-level overview of the process from raw-dataset to great visualisations:
Step 1: Analyse dataset and make decisions — Checking dataset and determining how to leverage these data in order to visualise them to audience.
Step 2: Parse, clean and enrich the dataset — Using Excel and OpenRefine to find errors in the given dataset and fixed them.
Step 3: Build the visualisations — Combining dc.js , d3.js and other libraries to work out two basic visualisations.
Step 4: Enhance and refine visualisations — Adding other functions and adjust color elements(parameters) to improve the visualisations.


Ingredients
1 serving
- 335g Lean Beef Mince Meat
- 2 tsp mixed herbs
- 2 tsp smoked paprika
- 14g coconut oil
- 70g chopped red onion
- 1 clove minced garlic
- 85g mushrooms
-
2 tbsp Waldens Farm BBQ Sauce – Can purchase from Amazon.
- 180g raw Spinach/kale/beans/asparagus or broccoli
- 1 egg
Method
For mince: In a bowl combine mince, mixed herbs and paprika. Heat a medium sized frying pan and add half the coconut oil. Make mince into a big flat burger and brown on both sides.
Heat another non-stick pan to a medium heat add the rest of the coconut oil, gently fry the onions and mushrooms for 4-5 mins before adding the garlic. Break up the burger into the vegetables and add a splash of water and Waldens BBQ sauce.
To serve: Serve mince with greens and one fried egg.
Tips: By cooking the mince separate to the vegetables you build better flavours and will save meat drying out.


Features
Run anything
Tsuru goes beyond 12 factor apps. Run any application written in any language or framework.
Deploy fast and safe
The deploy process is really simple with just one commmand.
Scale
Grow your application dynamically allocating resources with ease.
Developer Velocity
Let your developers code and understand the business instead of solving infrastructure problems or handling large configuration files.
CNCF Integrated
We built the platform as a service atop of battle tested technologies from Cloud Native Computing Foundation stack.
Multi Cluster/Region
Manage your distributed apps in many kubernetes clusters/regions with a single point of control.


A successful project requires many people to play many roles. Some members write code or documentation, while others are valuable as testers, submitting patches and suggestions.
The project team is comprised of Members and Contributors. Members have direct access to the source of a project and actively evolve the code-base. Contributors improve the project through submission of patches and suggestions to the Members. The number of Contributors to the project is unbounded. Get involved today. All contributions to the project are greatly appreciated.
The following is a list of developers with commit privileges that have directly contributed to the project in one way or another.
| Image | Id | Name | URL | Roles | Time Zone | |
|---|---|---|---|---|---|---|
| vfro | Volodymyr Frolov |<EMAIL_ADDRESS>| https://github.com/vfro | architect, developer | -5 |


Last updated: 2021-09-24
Checks: 7 0
Knit directory: myTidyTuesday/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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The data this week was compiled by Shel Kariuki from figures released by the Kenya National Bureau of Statistics in February 2020, originally published in four different pdf files (Volume 1 - Volume 4). Beyond the sample #TidyTuesday
sets, I’ve pulled additional agricultural figures from her web data portal and rKenyaCensus
package.
the Tidytuesday tweet:
#TidyTuesday Week04
— Jim Gruman📚🚵♂⚙ (@jim_gruman) January 20, 2021
Poultry 🐔farming 👩🌾in Kenya 🇰🇪
code: https://t.co/ARmfFPwRm6 #RStats #R4DS
Thanks to @Shel_Kariuki for her awesome work on the #rKenyaCensus package pic.twitter.com/gSaaWjiAYY
shapefiles <- rKenyaCensus::KenyaCounties_SHP %>%
sf::st_as_sf() %>%
st_simplify(dTolerance = 1000)
# Distribution of area (hectares) of Agricultural land and Farming Households by purpose of production, County and Sub-County
ag_land <- rKenyaCensus::V4_T2.25 %>%
dplyr::filter(AdminArea == "County") %>%
select(
County, LandSize_Subsistence, LandSize_Commercial,
No.FHS_Subsistence, No.FHS_Commercial
) %>%
mutate(
AvgArea_Subsistence =
if_else(LandSize_Subsistence > 0.1,
No.FHS_Subsistence / LandSize_Subsistence,
NA_real_
),
AvgArea_Commercial =
if_else(LandSize_Commercial > 0.1,
No.FHS_Commercial / LandSize_Commercial,
NA_real_
)
) %>%
pivot_longer(-County,
names_sep = "_",
names_to = c("Metric", "FarmType")
)
ag_area_sf <- shapefiles %>%
inner_join(ag_land, by = "County") %>%
mutate(County = stringr::str_to_title(County))
landsize_dorling <-
cartogram_dorling(
x = filter(ag_area_sf, Metric == "LandSize"),
weight = "value",
k = 0.5,
itermax = 100
)
families_dorling <-
cartogram_dorling(
x = filter(ag_area_sf, Metric == "No.FHS"),
weight = "value",
k = 0.5,
itermax = 100
)
subs <- sf::st_drop_geometry(landsize_dorling) %>%
group_by(FarmType) %>%
summarize(Total = sum(value))
landsize_dorling %>%
left_join(subs, by = "FarmType") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 8, County, NA_character_)) %>%
ggplot(aes(fill = value)) +
geom_sf(color = NA) +
geom_sf(
data = filter(ag_area_sf, Metric == "LandSize"),
color = "grey30", fill = NA, alpha = 0.3
) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
color = "gray50",
segment.color = "gray50",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
geom_text(aes(label = paste0(scales::comma_format()(Total), " ha")),
x = st_bbox(landsize_dorling)[1] + 0.05 * (st_bbox(landsize_dorling)[3] - st_bbox(landsize_dorling)[1]),
y = st_bbox(landsize_dorling)[2],
hjust = 0,
family = "Roboto Condensed",
size = 4
) +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5,
label = scales::comma_format(),
trans = "log10", guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Hectares",
title = "Crop Farming in Kenya, by County",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
subs <- sf::st_drop_geometry(families_dorling) %>%
group_by(FarmType) %>%
summarize(Total = sum(value))
families_dorling %>%
left_join(subs, by = "FarmType") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 7, County, NA_character_)) %>%
ggplot(aes(fill = value)) +
geom_sf(color = NA) +
geom_sf(
data = dplyr::filter(ag_area_sf, Metric == "No.FHS"),
color = "grey30", fill = NA, alpha = 0.3
) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
colour = "gray50",
segment.colour = "gray50",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
geom_text(aes(label = paste0(scales::comma_format()(Total), " households")),
x = st_bbox(landsize_dorling)[1] + 0.05 * (st_bbox(landsize_dorling)[3] - st_bbox(landsize_dorling)[1]),
y = st_bbox(landsize_dorling)[2],
hjust = 0,
family = "Roboto Condensed",
size = 4
) +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
label = scales::comma_format(),
limits = c(5e4, 2.5e5), n.breaks = 5,
guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Households",
title = paste0("Farmers ", emojifont::emoji("woman_farmer"), " raising crops in Kenya, by County"),
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
ag_area_sf %>%
filter(Metric == "AvgArea") %>%
mutate(CountyLabel = if_else(ntile(value, 10) > 8, County, NA_character_)) %>%
ggplot() +
geom_sf(aes(fill = value), color = "grey30", alpha = 0.8) +
ggrepel::geom_text_repel(
aes(label = CountyLabel, geometry = geometry),
stat = "sf_coordinates",
nudge_x = 0.5,
nudge_y = 0.5,
box.padding = 1,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20,
arrow = arrow(length = unit(0.02, "npc")),
family = "Roboto Condensed",
size = 4,
min.segment.length = 0,
colour = "black",
segment.colour = "black",
na.rm = TRUE
) +
coord_sf(label_axes = "----") +
facet_wrap(~FarmType) +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5,
label = scales::label_number(), guide = "legend"
) +
labs(
x = NULL, y = NULL, fill = "Average \nFarm Size \nHectares",
title = "Crop Farming in Kenya, by County",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
Let’s take a close look at the livestock datasets, and focus first on poultry. As a general rule, broilers are breeds raised commercially for meat and layers are raised commercially for eggs. The meat and eggs are packaged and subsequently sold at markets, often in more urban areas. Indigenous chicken breeds are often fed and raised in small numbers as part of a household.
livestock <- rKenyaCensus::V4_T2.24 %>%
filter(AdminArea == "County") %>%
select(-SubCounty, -AdminArea) %>%
rename(
"Dairy" = "ExoticCattle_Dairy",
"Beef" = "ExoticCattle_Beef",
"Layers" = "ExoticChicken_Layers",
"Broilers" = "ExoticChicken_Broilers"
) %>%
rename_with(~ paste0(., "_stock"), Farming:FishCages)
livestock <- rKenyaCensus::V4_T2.23 %>%
filter(AdminArea == "County") %>%
select(-SubCounty, -AdminArea) %>%
rename(
"Dairy" = "ExoticCattle_Dairy",
"Beef" = "ExoticCattle_Beef",
"Layers" = "ExoticChicken_Layers",
"Broilers" = "ExoticChicken_Broilers"
) %>%
rename_with(~ paste0(., "_households"), Farming:FishCages) %>%
inner_join(livestock, by = "County") %>%
pivot_longer(-County,
names_sep = "_",
names_to = c("Industry", "Metric")
) %>%
pivot_wider(
names_from = "Metric",
values_from = "value"
) %>%
mutate(per_household = stock / households) %>%
remove_missing() %>%
filter(Industry != "Farming") %>% # remove the County subtotals
complete(County, Industry, fill = list(
households = 1,
stock = 1,
per_household = 0.01
))
livestock <- livestock %>%
filter(Industry %in% c("IndigenousChicken", "Layers", "Broilers")) %>%
mutate(Industry = str_replace_all(Industry, "([a-z])([A-Z])", "\\1 \\2"))
stock_ls_dorling <- cartogram_dorling(
x = shapefiles %>%
inner_join(livestock, by = "County"),
weight = "stock",
k = 1,
itermax = 100
)
shapefiles %>%
inner_join(livestock, by = "County") %>%
mutate(Metric = "per_household") %>%
group_by(Industry, Metric) %>%
mutate(CountyLabel = if_else(ntile(per_household, 10) > 8, County, NA_character_)) %>%
ungroup() %>%
ggplot() +
geom_sf(aes(fill = per_household), color = "grey30") +
scale_fill_stepsn(
colors = terrain.colors(5, rev = TRUE),
n.breaks = 5, limits = c(0, 100),
label = scales::label_number(), guide = "legend"
) +
geom_sf(
data = stock_ls_dorling,
fill = "midnightblue",
# color = NULL,
alpha = 0.3
) +
coord_sf(label_axes = "----") +
guides(size = guide_legend()) +
facet_wrap(~Industry, ncol = 3) +
labs(
x = NULL, y = NULL, fill = "Average count \nper farming household",
title = paste0("Poultry ", emojifont::emoji("chicken"), " Farming in Kenya"),
subtitle = "Many egg laying chickens are produced near Nairobi. Elsewhere, households keep \nsmall numbers of indigenous birds. The circle size represents the total birds.",
caption = "2019 Kenya Population and Housing Census"
) +
theme(
panel.grid.major = element_blank(),
legend.position = "bottom"
)
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rKenyaCensus_0.0.2 spdep_1.1-11 spData_0.3.10 sp_1.4-5
[5] cartogram_0.2.2 sf_1.0-2 tweetrmd_0.0.9 tidytuesdayR_1.0.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 tidyverse_1.3.1
[17] hrbrthemes_0.8.0 waffle_0.7.0 ggplot2_3.3.5 systemfonts_1.0.2
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 proto_1.0.0 R.utils_2.10.1
[4] tidyselect_1.1.1 grid_4.1.1 pROC_1.18.0
[7] munsell_0.5.0 codetools_0.2-18 ragg_1.1.3
[10] units_0.7-2 dials_0.0.10 future_1.22.1
[13] withr_2.4.2 colorspace_2.0-2 highr_0.9
[16] knitr_1.34 rstudioapi_0.13 wk_0.5.0
[19] Rttf2pt1_1.3.9 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.28.0 DiceDesign_1.9 farver_2.1.0
[25] rprojroot_2.0.2 coda_0.19-4 parallelly_1.28.1
[28] LearnBayes_2.15.1 vctrs_0.3.8 generics_0.1.0
[31] ipred_0.9-12 xfun_0.26 R6_2.5.1
[34] lhs_1.1.3 cachem_1.0.6 assertthat_0.2.1
[37] showtext_0.9-4 promises_<IP_ADDRESS> scales_1.1.1
[40] nnet_7.3-16 emojifont_0.5.5 gtable_0.3.0
[43] globals_0.14.0 timeDate_3043.102 rlang_0.4.11
[46] workflows_0.2.3 splines_4.1.1 extrafontdb_1.0
[49] yardstick_0.0.8 broom_0.7.9 s2_1.0.6
[52] yaml_2.2.1 modelr_0.1.8 backports_1.2.1
[55] httpuv_1.6.3 extrafont_0.17 tools_4.1.1
[58] lava_1.6.10 usethis_2.0.1 infer_1.0.0
[61] ellipsis_0.3.2 raster_3.4-13 jquerylib_0.1.4
[64] RColorBrewer_1.1-2 proxy_0.4-26 Rcpp_1.0.7
[67] parsnip_<IP_ADDRESS>0 plyr_1.8.6 classInt_0.4-3
[70] rpart_4.1-15 deldir_0.2-10 viridis_0.6.1
[73] haven_2.4.3 ggrepel_0.9.1 fs_1.5.0
[76] here_1.0.1 furrr_0.2.3 magrittr_2.0.1
[79] gmodels_2.18.1 reprex_2.0.1 GPfit_1.0-8
[82] whisker_0.4 R.cache_0.15.0 packcircles_0.3.4
[85] hms_1.1.0 evaluate_0.14 readxl_1.3.1
[88] gridExtra_2.3 compiler_4.1.1 KernSmooth_2.23-20
[91] crayon_1.4.1 R.oo_1.24.0 htmltools_0.5.2
[94] later_1.3.0 tzdb_0.1.2 expm_0.999-6
[97] tidymodels_0.1.3 lubridate_1.7.10 DBI_1.1.1
[100] dbplyr_2.1.1 MASS_7.3-54 boot_1.3-28
[103] Matrix_1.3-4 cli_3.0.1 R.methodsS3_1.8.1
[106] gdata_2.18.0 parallel_4.1.1 gower_0.2.2
[109] pkgconfig_2.0.3 recipes_0.1.16 xml2_1.3.2
[112] foreach_1.5.1 bslib_0.3.0 hardhat_0.1.6
[115] prodlim_2019.11.13 rvest_1.0.1 digest_0.6.27
[118] showtextdb_3.0 rmarkdown_2.11 cellranger_1.1.0
[121] gdtools_0.2.3 curl_4.3.2 gtools_3.9.2
[124] lifecycle_1.0.1 nlme_3.1-152 jsonlite_1.7.2
[127] viridisLite_0.4.0 tune_0.1.6 fansi_0.5.0
[130] pillar_1.6.2 lattice_0.20-44 fastmap_1.1.0
[133] httr_1.4.2 survival_3.2-11 glue_1.4.2
[136] conflicted_1.0.4 iterators_1.0.13 class_7.3-19
[139] stringi_1.7.4 sass_0.4.0 rematch2_2.1.2
[142] textshaping_0.3.5 rsample_0.1.0 styler_1.6.1
[145] e1071_1.7-8 future.apply_1.8.1 sysfonts_0.8.5


#include <SharedMemoryImp.hpp>
◆ SharedMemoryImp() [1/2]
| geopm::SharedMemoryImp::SharedMemoryImp |
( |
| ) |
|
◆ ~SharedMemoryImp()
| geopm::SharedMemoryImp::~SharedMemoryImp |
( |
| ) |
|
|
virtual |
Destructor destroys and unlinks the shared memory region.
◆ SharedMemoryImp() [2/2]
◆ attach_memory_region()
| void geopm::SharedMemoryImp::attach_memory_region |
( |
const std::string & |
shm_key, |
|
|
unsigned int |
timeout |
|
) |
| |
Takes a key and attempts to attach to a inter-process shared memory region. This version of the constructor tries to attach multiple times until a timeout is reached.
- Parameters
-
| [in] | shm_key | Shared memory key to attach to the region. |
| [in] | timeout | Length in seconds to keep retrying the attachment process to a shared memory region. |
◆ chown()
| void geopm::SharedMemoryImp::chown |
( |
const unsigned int |
uid, |
|
|
const unsigned int |
gid |
|
) |
| const |
|
override |
Modifies the shared memory to be owned by the specified gid.
- Parameters
-
| [in] | uid | User ID to become owner. |
| [in] | gid | Group ID to become owner. |
◆ construct_shm_path()
| std::string geopm::SharedMemoryImp::construct_shm_path |
( |
const std::string & |
key | ) |
|
|
static |
Construct the file path to use for the provided key.
◆ create_memory_region()
| void geopm::SharedMemoryImp::create_memory_region |
( |
const std::string & |
shm_key, |
|
|
size_t |
size, |
|
|
bool |
is_secure |
|
) |
| |
Takes a key and a size and creates an inter-process shared memory region.
- Parameters
-
| [in] | shm_key | Shared memory key to create the region. |
| [in] | size | Size of the region to create. |
| [in] | is_secure | Disallow group and world r/w if true. |
◆ get_scoped_lock()
| std::unique_ptr< SharedMemoryScopedLock > geopm::SharedMemoryImp::get_scoped_lock |
( |
void |
| ) |
|
|
override |
◆ key()
| std::string geopm::SharedMemoryImp::key |
( |
void |
| ) |
const |
|
override |
Retrieve the key to the shared memory region.
- Returns
- Key to the shared memory region.
◆ operator=()
◆ pointer()
| void * geopm::SharedMemoryImp::pointer |
( |
void |
| ) |
const |
|
override |
Retrieve a pointer to the shared memory region.
- Returns
- Void pointer to the shared memory region.
◆ size()
| size_t geopm::SharedMemoryImp::size |
( |
void |
| ) |
const |
|
override |
◆ unlink()
| void geopm::SharedMemoryImp::unlink |
( |
void |
| ) |
|
|
override |
The documentation for this class was generated from the following files:


Naturally Sourced Butylene Glycol
That's Better For Good.
Naturally sourced Brontide™ (Butylene Glycol) is a high performance ingredient used in a variety of personal care and cosmetic applications. It's a great humectant and boosts preservative efficacy. It also can be used to add texture to a lotion or as a stabilizer for extracts. Natural without sacrifice.
Brontide is produced from renewable plant sugars, eliminating the need to use crude oil feedstock.
Renewable plant sugars are fermented by specialized microorganisms into butylene glycol. A purification step then completes the process and creates the most pure butylene glycol available.
Brontide butylene glycol can improve a variety of application formulations including personal care, pharmaceutical, home care, flavors and extracts.Get all the great performance you expect in a conventionally-produced ingredient and know that its derived from natural raw materials.
Whether you’re on a mission to create products that are good for people and planet or you’re taking steps to get there, Brontide can help.


Signum is an American Sign Language (ASL) translator built using computer vision and machine learning
An ASL Translator using CV and ML.
In order to try Signum for yourself, you will need to clone this GitHub repository to your local machine, download the related packages and run the recognition program
$ git clone https://github.com/Utsav22G/ASL-Translator.git
$ cd ASL-Translator
$ pip install -r requirements.txt
$ python3 live_demo.py
Once you've gotten this far, you're ready to get going. The program will prompt you to choose a camera; you can use an external camera if you want, but if you are using your computer's default camera you should enter 0
.
You can then follow the prompt to set the background, and begin testing signs to see the program recognize it.
Signum is a program that can convert symbols from the ASL alphabet into speech.
Signum uses OpenCV to interact with your computer's camera to see what hand genstures are being shown.
In order to recognize what a hand gesture is in the ASL alphabet, we trained a neural net with a set of over 7,000 images of hands showing what every letter looks like. Using this training set of data, Signum is able to recognize similar shapes and classify them as whatever letter it is closest to.
Once the algorithm has decided on what is being shown, we use Google's text-to-speech library (gTTS) to convert the text into spoken words.


Models
-
class
blog.models.
Task
(id, author, content, date_created) -
exception
DoesNotExist
-
exception
MultipleObjectsReturned
Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
content
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
date_created
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_next_by_date_created
(*, field=<django.db.models.fields.DateTimeField: date_created>, is_next=True, **kwargs)
-
get_previous_by_date_created
(*, field=<django.db.models.fields.DateTimeField: date_created>, is_next=False, **kwargs)
-
id
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>
-
exception
-
class
users.models.
CustomUser
(id, password, last_login, is_superuser, username, first_name, last_name, is_staff, is_active, date_joined, mobile_number, address, email) -
exception
DoesNotExist
-
exception
MultipleObjectsReturned
-
address
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
emailaddress_set
Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
get_next_by_date_joined
(*, field=<django.db.models.fields.DateTimeField: date_joined>, is_next=True, **kwargs)
-
get_previous_by_date_joined
(*, field=<django.db.models.fields.DateTimeField: date_joined>, is_next=False, **kwargs)
-
groups
Accessor to the related objects manager on the forward and reverse sides of a many-to-many relation.
In the example:
class Pizza(Model): toppings = ManyToManyField(Topping, related_name='pizzas')
Pizza.toppings
andTopping.pizzas
areManyToManyDescriptor
instances.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
id
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
logentry_set
Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
mobile_number
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
task_set
Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
user_permissions
Accessor to the related objects manager on the forward and reverse sides of a many-to-many relation.
In the example:
class Pizza(Model): toppings = ManyToManyField(Topping, related_name='pizzas')
Pizza.toppings
andTopping.pizzas
areManyToManyDescriptor
instances.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
exception
-
class
users.models.
userdata
(id, name, email, content) -
exception
DoesNotExist
-
exception
MultipleObjectsReturned
-
content
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
email
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
id
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
name
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>
-
exception


Sylvanian Families Angeles' Bedtime Set
From the box:
Angeles Bearbury is a pretty little girl. She has routines she adheres to rigidly from the moment she gets up till bedtime. At 7 o'clock on the dot she kisses everyone goodnight, then she cleans her teeth, and puts on a fresh pair of pyjamas and her slippers before climbing the stairs. She and her bear Teddy carefully select a book to read in bed, then snuggle up to sleep.


Roomwise Vacancy
| Bhawan | Room | No. of Participants | Details | {% for room in object_list %}
|---|---|---|---|
| {{ room.bhavan.name }} | {{ room.room }} | {{ room.regsoftdetails_set.all | length }} | Details |
{% extends "regsoft/base.html" %} {% block body %} {% load staticfiles %}
| Bhawan | Room | No. of Participants | Details | {% for room in object_list %}
|---|---|---|---|
| {{ room.bhavan.name }} | {{ room.room }} | {{ room.regsoftdetails_set.all | length }} | Details |


Kappad Beach has historically played an important role in the history of Kerala. On these shores, over 500 years ago in 1498, 170 men led by Vasco- da-Gama first stepped onto Kerala. The entire State, especially the Malabar Coast, would never be the same again. A visit to Kozhikode is incomplete without a visit to this legendary site. The spice route flourished through this Beach. A walk in and around the place gives one an idea of itshistorical relevance. The rocks and small hills add to its charm. The nearby shacks are always stocked with excellent local delicacies and tea. Migratory birds can be spotted occasionally as well. Kappad Beach is a truly majestic destination, which will always hold an important place in our history.
Beypore beach is located 15 km away from Kadavu, at the jaws of Chaliyar River. The charming beach is just like a treasure of peace and tranquility with its picturesque atmosphere, wide stretches of golden sands and fascinating coconut trees; swaying tenderly in the cool breeze. This beach is pristine and perfect for a quiet escapade. Take a short break from your busy life and embark on Beypore beach where you can pamper yourself eternally with Ayurvedic massages, indulge in various adventurous water sports activities, or simply strolling along the shoreline. So, relax and take part in all activities and enjoy holidaying in Beypore with full pleasure.


Mountain Confluence
Frozen Valley
North River
Twin Lakes
Chill Lake
The Northlands
Challenging
Grow and evolve your settlement in a harsh environment.
In the northern regions, long winters, short summers and sparse resources made survival a lot harder, and only the most determined humans could thrive.
Protein Hoarding
Accumulate 5 units of Cured meat and Dry Fish
Northern Survival
Survive 5 years
Winter Resistance
Accumulate 10 Skins Outfits
Winter Hunt
Hunt a Mammoth a Woolly Rhino and a Cave Bear
Winter Domestication
Have 10 Sheep at one time
Extra Warmth
Unlock Brewing Tech
Technocracy
Research all techs
North Settlement
Build 30 Roundhouses and achieve a population of 150


The friendliest collaborative 3D pixel editor!
Meteor Blocks uses the Meteor web framework and X3DOM together to create a collaborative 3D scene editor.
When you want to collaborate, just send the URL of the scene you are editing to someone else and they can edit it with you. When you're done and you want to save a version of your scene, just click Publish and it's enshrined in history.
Written by Sashko Stubailo.


I am a senior at Bishop Chatard High School. I am a computer enthusiast. I always enjoy learning and experiencing new things. I especially love learning about computers and how they work. I learn in a variety of ways, from watching videos to trial and error.
I have been interested into the coding world for some time now. I have watched video series on many languages. I completed Codecademy courses, though the most substantial course I have taken is the Eleven Fifty .Net Foundations course, which I completed the summer before my junior year. I have gone back as an intern this year to be a TA for the JavaScript Cohort.
I also have academic successes too. I was acknowledged for scoring in the top 2% in my class on a biology ECA and National Science League Chemestry and Physics exam. I was chosen to be one of the two leaders of a retreat called Sudent Leadership Program from a series of applications, and all students from six different schools were in the running for the position.


Gilt an Official Sponsor of the PostgreSQL Conference Europe 2013
Gilt has officially become a Silver Sponsor of this year’s PostgreSQL Conference Europe, which will take place October 29 through November 1 in Dublin, Ireland (where we have an office). PGConf.EU, as it’s otherwise known, is the largest PostgreSQL conference in Europe and involves most of Europe’s user groups. In addition to talks in English, German, Dutch and other languages, the conference will include a full day of short PostgreSQL training sessions. Registration for the conference hasn’t begun yet, but if you’re interested in presenting, you’re in luck: the deadline for talk proposals runs through July 22, 2013.


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Gorgeous stuff from Jorge Quinteros taken at the Museum of Modern Art in New York.


providing Good health without stress, also available for emergency situations, At cheaper rate
LIVE HEALTHY
Good Life Just A CLick Away
Reliable Home Service


About
Diverse Sources is a searchable database of underrepresented experts in the areas of science, health and the environment. Anyone who considers themselves underrepresented and is willing to respond to journalists on deadline is encouraged to join (including but not limited to appearance, ethnicity, gender expression, gender identity, language, mental health experience, nationality, physical abilities, race, religion, sex, sexual orientation, etc.).
This database aims to make it easy for journalists and others to include a wider range of backgrounds, experiences and perspectives in their work. By doing so, we can improve our coverage and better reflect the world we cover.
Questions? Feedback? Want to help? Contact us!
Testimonials
"I told some friends of mine about this, and one signed up and was just quoted in the Washington Post. Keep doing great work!" —Sally, bioengineer
"I spoke with NPR recently, and credit it to diversesources. Thanks!" —Liz, social psychologist
"Working on an article about the uses of VR and got some help from @DiverseSources on finding a couple good ones." —Andrew, freelance journalist
"I love Diverse Sources. It makes including new voices in my science stories so much easier—even when writing on deadline. And I believe that our stories get better when we don’t quote from the same small pool of experts again and again." —Carla, science journalist
"Right now I’m working on two [science stories] and grateful this database exists." — Natalie Daher
Team
- Mollie Bloudoff-Indelicato / project director
- Greg Linch / web developer
Advisory board
- Bethany Brookshire
- Terra Hall
- Cindy Ramjeawan
- Nicholas St. Fleur
Technical advisers
Special thanks to
- Nsikan Akpan
- Laura Helmuth
- Evelyn Lamb
- Robin Lloyd
- Ashley Smart


For the next four articles, I am going to review some basic but important topics such as Loss function, evaluation method, regularization and so on. After that I will move to review Deep learning and start to learn NLP.
Started from my first machine learning article, I have emphasized loss function's importance. So after covering most of basic ML models, it is time to summarize all these loss functions.
1. What is loss function
1.1 Definition
A loss function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. Basically, it is a measure of how good a prediction model does in terms of being able to predict the expected outcome. Thus it is very natural to always minimize a loss function for a machine learning model.
1.2 Terms Comparison
I believe you must have also seen some terms like Cost Function or Objective Function when you are reading some machine learning articles and books. But what are theirs differences comparing with loss function?
Basically, they are not very strict terms and highly related. However:
Loss function is usually a function defined on a data point, prediction and label, and measures the penalty. For example: * square loss \(l(f(x_i|\theta),y_i) = (f(x_i|\theta)-y_i)^2\)Cost function is usually more general. It might be a sum of loss functions over your training set plus some model complexity penalty (regularization). For example: * Mean Squared Error \(MSE(\theta) = \frac{1}{N}\sum_{i=1}^N(f(x_i|\theta)-y_i)^2\)
Objective function is the most general term for any function that you optimize during training. For example, when you perform MLE, the likelihood function will be the objective function you are going to maximize.
To make long story short, a loss function is a part of cost function which is a type of an objective function.
2. Different type of Loss Function
After understanding what is loss function, then let's talk about each of loss function one by one in detail. Generally speaking, Loss functions can be broadly categorized into 2 types: Regression Loss and Classification Loss.
2.1 Regression Loss Function
Regression model aims to return a continuous target value. Mathematically, a regression model can be formulated as: \[min_{f(x)}\sum_{I=1}^nl(y_i-f(x_i)) + R_{\lambda}(f)\] Where \(f(x_i)\) is the regression model, \(y_i-f(x_i)\) represents the deviation between \(f(x_i)\) and target value, \(l(r)\) represents a loss function that measures the loss generated by deviation, \(R_{\lambda}(f)\) is the regularization term for reducing the risk of overfitting.
2.1.1 Mean Squared Error/L2 Loss
- \(MSE = \frac{1}{n}\sum_{i=1}^{n}(y_i-f(x_i))^2\)
- Is the most common loss function for regression problem.
- Easy to calculate gradients
- Penalized heavily for far away values
2.1.2 Mean Absolute Error/L1 Loss
- \(MAE = \frac{1}{n}\sum_{i=1}^{n}|y_i-f(x_i)|\)
- Unlike MSE, MAE's derivatives are not continuous, and the gradient is large, making it inefficient and difficult to find the solution. (Can use dynamic learning rate)
- More robust to outlier since it does not use of square
2.1.3 Huber Loss/Smooth Mean Absolute Error
- \(L_{\delta}(y,f(x)) = \lbrace^{\frac{1}{2}(y-f(x))^2 \ \ for \ |y-f(x)|\le \delta}_{\delta|y-f(x)|-\frac{1}{2}\delta^2 \ \ otherwise}\)
- The combination of MAE and MSE to learn theirs advantages:
- Differentiable at 0
- Less sensitive to outlier
- \(\delta\) is the tuning parameter that determines what you're willing to consider as an outlier
2.1.4 Quantile Loss
- \(L_{\gamma}(y,f(x))= \sum_{I=f(x_i)<y_i}(\gamma -1)|f(x_i) - y_i| + \sum_{I=f(x_i) \ge y_i}(\gamma)|f(x_i) - y_i|\) where \(\gamma\) is the required quantile and has value between 0 and 1
- It is very obvious that \(\gamma\) controls which part of data we want to penalize on. If \(\gamma\) equals to 0.5, then quantile loss equals to MAE.
- From practical perspective, sometimes we want to know about the range of predictions as opposed to only point estimates
2.2 Classification Loss Function
Classification model aims to return a discrete value. A binary classification model can be formulated as: \[min_{f(x)}\sum_{I=1}^{n}l(y_if(x_i))+R_{\lambda}(f)\] Where \(y_i\) is the label of \(x_i\) and \(y_if(x_i)\) (margin) represents the deviation between \(f(x_i)\) and hyperplane. \(f(x_i)\) = 0 is the hyperplane.
The classification rule is that: with positive margin \(y_if(x_i)>0\), it is classified correctly; whereas margin \(y_if(x_i)<0\) means misclassified. The goal of the classification algorithm is to produce positive margins as frequently as possible.
2.2.1 0-1 Loss/Misclassification Error
\(\sum_{i=1}^{n}I(y_if(x_i)<0)\) or \(\sum_{i=1}^{n}I(y_i \neq f(x_i))\)
Non-convex, Non differentiable
Gives unit penalty for negative margin values, and no penalty at all for positive ones
2.2.2 Logistic loss/Binomial Deviance
From my previous post, Logloss is deduced by using the notation \(y\in\{0,1\}\). This is the most natural one cause it derives from Bernoulli probability model. But this notation make it hard for logloss to compare with other loss functions. Actually, logloss has another formula. This article has a very good explanation. Basically, by using notation \(y\in \{-1,1\}\), we integrate the label and the prediction together into the probability function. And then get the loss function by MLE.
- \(BD = log(1+e^{-y_if(x_i)})\)
- Differentiable
- Will always have loss
2.2.3 Cross Entropy Loss/Log Loss
Use the alternative label convention we get the logloss for Logistic Regression:
- \(Logloss = -y_iln(f(x_i))-(1-y)ln(1-f(x_i))\)
- Logloss is just the special case of Cross Entropy: -\(\sum_{j=1}^{c}p_j log p_j\) where j represent a class
- Gini index is another impurity measure close to Cross Entropy: Gini = \(\sum_{j=1}^{c}1 - p_j^2\) where j represent a class
- Related to information theory and multi-classification problem which I will cover later on
2.2.4 Exponential Loss
- \(Exp\ Loss = e^{-y_if(x_i)}\)
- Penalize incorrect predictions more and has a larger gradient
2.2.5 Hinge Loss
- \(Hinge = max\{1-y_if(x_i),0\}\)
- Convex, upper bound on 0-1 loss. Not differentiate at 1
- Only have 'margin error' when \(y_if(x_i)<1\)
Notice: Here I have only listed the loss function related to binary classification.
3. Loss Function Comparison
So after reviewing all these loss functions, I think we are able to see the differences among them. Basically, they have two major differences:
- Differentiable or not
- Robust to outlier or not
Then Let's compare them in detail.
3.1 Regression Loss Function Comparison
Above is the graph for comparing all the regression losses. Here are some summaries:
- MSE is sensitive to outliers but gives a stable and closed for solution
- MAE is useful for data is corrupted with outliers, but difficult to learn
- Huber Loss is the combination of MAE and MSE, but the choice of \(\delta\) is critical. And we need to tune this parameter which increase the computation cost.
- Quintile Loss is mainly used when you want an interval prediction. It is still mainly MAE, with different quantile value, it will decide to panelize on the upper bound value or lower bound.
3.2 Classification Loss Function Comparison
Here are the summaries for classification loss functions:
- 0-1 loss is not that sensitive to outlier since it gives unit penalty to all the misclassified data. But in another word, it is not sensitive to the data changes as well. So for tree growing model we may use other loss function like cross entropy.
- Both the exponential and deviance loss can be viewed as monotone continuous approximations
to misclassification loss. They continuously penalize increasingly negative margin values more heavily than they reward increasingly positive ones.
The difference between them is in degree. The penalty associated with binomial deviance increases linearly, whereas the exponential criterion increases the influence of such observations
exponentially. Not to say that exponential loss will be more sensitive to outliers.
3.3 Summary
- The loss function \(l(r)\) with an appropriate upper bound when \(r\) takes a large value is generally robust to the outliers.
- There is not a single loss function that works for all kind of data
Here is the link of Jupyter Notebook for loss function
Reference:
The Element of Statistical Learning
https://rohanvarma.me/Loss-Functions/
https://davidrosenberg.github.io/ml2015/docs/3a.loss-functions.pdf
An investigation for loss functions widely used in machine learning
https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0
https://towardsdatascience.com/common-loss-functions-in-machine-learning-46af0ffc4d23