| position_dodge {ggplot2} | R Documentation |
Dodging preserves the vertical position of an geom while adjusting the
horizontal position. position_dodge()
requires the grouping variable to be
be specified in the global or geom_*
layer. Unlike position_dodge()
,
position_dodge2()
works without a grouping variable in a layer.
position_dodge2()
works with bars and rectangles, but is
particulary useful for arranging box plots, which
can have variable widths.
position_dodge(width = NULL, preserve = c("total", "single")) position_dodge2( width = NULL, preserve = c("total", "single"), padding = 0.1, reverse = FALSE )
width |
Dodging width, when different to the width of the individual elements. This is useful when you want to align narrow geoms with wider geoms. See the examples. |
preserve |
Should dodging preserve the total width of all elements at a position, or the width of a single element? |
padding |
Padding between elements at the same position. Elements are shrunk by this proportion to allow space between them. Defaults to 0.1. |
reverse |
If |
Other position adjustments:
position_identity()
,
position_jitterdodge()
,
position_jitter()
,
position_nudge()
,
position_stack()
ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar(position = "dodge2") # By default, dodging with `position_dodge2()` preserves the total width of # the elements. You can choose to preserve the width of each element with: ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar(position = position_dodge2(preserve = "single")) ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(position="dodge2") # see ?geom_bar for more examples # In this case a frequency polygon is probably a better choice ggplot(diamonds, aes(price, colour = cut)) + geom_freqpoly() # Dodging with various widths ------------------------------------- # To dodge items with different widths, you need to be explicit df <- data.frame(x = c("a","a","b","b"), y = 2:5, g = rep(1:2, 2)) p <- ggplot(df, aes(x, y, group = g)) + geom_col(position = "dodge", fill = "grey50", colour = "black") p # A line range has no width: p + geom_linerange(aes(ymin = y - 1, ymax = y + 1), position = "dodge") # So you must explicitly specify the width p + geom_linerange( aes(ymin = y - 1, ymax = y + 1), position = position_dodge(width = 0.9) ) # The same principle applies to error bars, which are usually # narrower than the bars p + geom_errorbar( aes(ymin = y - 1, ymax = y + 1), width = 0.2, position = "dodge" ) p + geom_errorbar( aes(ymin = y - 1, ymax = y + 1), width = 0.2, position = position_dodge(width = 0.9) ) # Box plots use position_dodge2 by default, and bars can use it too ggplot(data = iris, aes(Species, Sepal.Length)) + geom_boxplot(aes(colour = Sepal.Width < 3.2)) ggplot(data = iris, aes(Species, Sepal.Length)) + geom_boxplot(aes(colour = Sepal.Width < 3.2), varwidth = TRUE) ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar(position = position_dodge2(preserve = "single")) ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar(position = position_dodge2(preserve = "total"))


Class: Aws::SageMaker::Types::MonitoringAlertSummary
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb |
Overview
Provides summary information about a monitor alert.
Constant Summary
Instance Attribute Summary
-
#actions ⇒ Types::MonitoringAlertActions
rw
A list of alert actions taken in response to an alert going into
InAlert
status. -
#alert_status ⇒ String
rw
The current status of an alert.
-
#creation_time ⇒ Time
rw
A timestamp that indicates when a monitor alert was created.
-
#datapoints_to_alert ⇒ Integer
rw
Within
EvaluationPeriod
, how many execution failures will raise an alert. -
#evaluation_period ⇒ Integer
rw
The number of most recent monitoring executions to consider when evaluating alert status.
-
#last_modified_time ⇒ Time
rw
A timestamp that indicates when a monitor alert was last updated.
-
#monitoring_alert_name ⇒ String
rw
The name of a monitoring alert.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Aws::Structure#to_h. |
Instance Attribute Details
#actions ⇒ Types::MonitoringAlertActions (rw)
A list of alert actions taken in response to an alert going into
InAlert
status.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#alert_status ⇒ String
(rw)
The current status of an alert.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#creation_time ⇒ Time
(rw)
A timestamp that indicates when a monitor alert was created.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#datapoints_to_alert ⇒ Integer
(rw)
Within EvaluationPeriod
, how many execution failures will raise an
alert.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#evaluation_period ⇒ Integer
(rw)
The number of most recent monitoring executions to consider when evaluating alert status.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#last_modified_time ⇒ Time
(rw)
A timestamp that indicates when a monitor alert was last updated.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end
#monitoring_alert_name ⇒ String
(rw)
The name of a monitoring alert.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 29993
class MonitoringAlertSummary < Struct.new( :monitoring_alert_name, :creation_time, :last_modified_time, :alert_status, :datapoints_to_alert, :evaluation_period, :actions) SENSITIVE = [] include Aws::Structure end


The Rise Of The GM And The Case For Variably-Comped Product Managers
The Rise Of The GM And The Case For Variably-Comped Product Managers
In these periodical posts, I opine on prose, products, and profits. I do this for your entertainment and education (and, of course, to shamelessly promote myself and my team, as that’s what you do these days.)
Here’s a surprisingly and seemingly difficult question to answer, particularly in Toronto: who ultimately owns “product P&L” in a start-up?
Is it the COO? Is it the CFO? How about the CPO or the VP of Engineering? Or does it simply default to the CEO?
In my experience (at least as far as Toronto is concerned), most start-ups do a horrible job at assigning and empowering a single point of accountability for product P&L—and their products suffer for it.
Recently, as the city’s start-ups have become scale-ups and established companies (and begin finally taking cues from successful companies outside of our shoulder-chipped echo chamber), the General Manager role is emerging as one answer to that question, particularly as prominent international companies have created Toronto outposts (e.g., we need one right now for Lime so I can get my electric scooter on).
But before a General Manager (or COO-in-waiting) makes sense, who should actually own product P&L?
As you might expect, I have opinions — hastily drawn, poorly thought through, and likely deeply flawed opinions, but opinions that I feel compelled to assert thanks to my need for constant external validation and attention.
(I am a millennial yuppie, after all.)
What is “Product P&L”?

The rise of once-start-ups like Amazon and Facebook may have trained a cohort of entrepreneurs to believe that profit is irrelevant until you hit scale, but for the rest of us (read: those that value businesses which generate more value than they consume), profitability is incredibly important.
The key to building a sustainable business is to invest capital back into the business effectively to stimulate growth; doing so with profit rather than equity (read: VC Scrooge McDuck money-bags) allows the business’ original founders and ESOP’d employees to benefit more substantially for their effort and lost youth.
Built atop that core foundational business tenet is one of my most tightly-held beliefs about building great products: just as a good business manages itself against its holistic P&L, so too should a good product of its own P&L.
So how does one actually calculate a product’s P&L? Here are the two formulas I use:
Top-line Profitability = (Monthly Product Revenue) less (Avg Monthly PD Salary times PD Staff Count)
Unit Profitability = (Avg Customer LTV) less (Avg Monthly PD* Salary)
*PD = Product, Dev, and Design resources
Success on the top-line during the start-up phase is pretty simple: work as fast as possible to get to a greater product-driven revenue than the burn on dev investment into obtaining that product-driven revenue. This is in many ways very similar to the common metric of “revenue per employee”.
As stated, though, this is necessarily product-specific measure and is also necessarily imperfect. Product revenue will be driven by more than just the product, obviously: both sales and marketing will significantly contribute to that number.
That’s why the usage of that top-line metric should be about multiples as soon as break-even is achieved.
After product-market fit is found and the start-up becomes a scale-up, product revenue affected by sales and marketing will be more often than not incremental rather than exponential. It is product development’s job to find the exponential gains and affect product profitability in multiples.
The same is true for the unit profitability. Adding more valuable customers by having sales sell longer commitments will multiply unit product profitability, but that has its limits and eventually only product development can affect unit profitability in multiples.
The Case For Variable Product Manager Comp
So, with that lens of profitability, we come back to the question we started with: who owns product P&L?
The right answer should be the product manager. A VP will own the product P&L of the full portfolio, a Director will own product P&L of a given segment or logically-seperated part of the portfolio, and an individual PM will own product P&L of a given product or part of a product.
A good product manager, in my opinion, is one who can steward a product towards greater product profitability.
 No one will agree to own a metric as sensitive as profitability unless they can be empowered to make decisions, plan the strategy, and execute.
And therein lies the rub, at least based on my experience in Toronto: product managers are rarely given control and more rarely own product P&L.
For the most part, a good product manager in Toronto is one who can work well with dev, talk to sales, schmooze with execs, and ship on time. Nothing more. And somehow, that warrants $120,000+ a year or more in salary.

 That’s why I’ve been trying to figure out how to fundamentally change the way that we run, and comp, Product teams.
Proposed Comp Structure
Rather than taking smart people, giving them a huge salary, and sticking them in a chair to take orders from stakeholders, I have been mulling a move towards a model wherein a Product Manager is incented to more immediately (and necessarily) share in the profit but is required to more actively and literally “manage” the product as a business.
Critical to this plan, though, is the complimentary “give” to that take: we need to empower PMs to have the autonomy (read: exec buy in) to steward the product towards profitability based on their “boots-on-the-ground” view of the battlefield.
Ergo, I believe PMs should be compensated something like this:
- Base salary = today’s base * 0.75
- Quarterly bonus = (today’s base * multiple based on lift to product profitability) / 4
As an example, say my direct report PM is making $100k annually today and we’re doing $50k in MRR.
We decide, for simplicity’s sake, that the multiple will be 10X the percentage increase.
Beginning in Q3, her salary is dropped to $75k, and during Q3, she is able to ship a feature which positively affect profitability by 5%. At the end of the quarter, she is awarded a bonus of $12,500 ($100k * 0.5 / 4).
Assuming MRR stays flat, the incremental benefit of her work has saved us $2,500 in operating cost for that quarter. That means her bonus has a payback period of just 5 months, even though the value of her work will begin to generate ongoing net profit 5 months from now. Those are good economics.
Thanks to her efforts, this PM earned effectively the same salary as she did before the change, but she was able to completely deprioritize and more easily ship product given her new focus on a single top-line metric that everyone cares about: product profitability.
What This Solves
I see a few major advantages to this approach:
- Sales, Marketing, and Product are immediately more aligned, as their success is measured on money coming into the business
- Exec is made more comfortable ceding control of direction to PMs because their impact can be clearly quantified
- The business benefits from having a single point of accountability for driving product profitability
Where This Fails
Ironically, most PMs would probably hate this.
The majority would be immediately paid a ton less and would suddenly be so much more concretely measured and pressured. Many would wash out (myself included, most likely), as increasing product profitability is really hard and only gets harder over time.
Many of today’s tech companies would be unable to accommodate such a model, too. The Product Manager would need to have significantly more control over (or rather alignment with) Dev, as any misalignment between the two parties would set the PM up to fail.
(N.B., A real-world analog here would be Sales Managers whose comp is a function of their team members’ performance and comp.)
A lot of execs would likely reject the model, too. Giving any one employee who isn’t heavily vested complete control is risky in even the most ego-free start-up environments, let alone those wherein the senior leadership need to feel the heroes and #mansplain how business is done to all subordinates in sight.
To be fair, a few companies in Toronto use bonus programs and quarterly goals to attempt to achieve similar results, though I’ve yet to see this effectively implemented for product managers in my experience as an individual contributor PM.
The Takeaway
Admittedly, as I’ve worked through the process of learning how to manage product managers, I’ve struggled to create the environment for my team that I feel I’ve been deprived as I grew up a PM. I suppose that’s very paternal.
One of the reasons I’ve grown so fatigued of the PM role in my own career as I’ve aged is that the role, generally, meanders between the business and the build-out in a manner that often necessitates the relegation of the role’s actor to a position subordinate to the loudest voice in the org — perhaps a heavy-handed “product” CEO who isn’t ready to let go, or a domineering sales team more concerned about this quarter than the next, or even (as a best-worse-case) a vocal minority of old customers who the business is too scared to evolve beyond.
Transforming the comp model, and indeed the focus of the role, toward something more concrete and directly tied to a business metric that can be objectively measured and managed, feels like the salve for my team to the pains I’ve suffered before them. It feels like the trojan horse to create a world in which PMs aren’t just project managers or “tech translators” or “idea” people. It’s not perfect, admittedly, but it’s a move from current state to better state.
Alas, I fear the only product managers who would want such a comp model and such autonomy and such responsibility are those that will simply become entrepreneurs themselves, where they’ll own product profitability by necessity rather than design.
There must be a better way. The hunt continues.
What do you think about product profitability? How would you, as PM, like to be measured and managed? Have you seen something amazing that we should learn from? I want to hear your thoughts. Comment here, or reach me at http://www.frankcaron.com.


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We have some utilities available for input and output of sparse matrices; the interfaces to these routines are available in the module psb_util_mod.
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Dear Alex
<%= @contact.name %> is contacting you. Please contact him/her at <%= @contact.email %>
regards


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Welcoming people
(and other hard problems)
Slides and prose:
github.com/tute/talk-welcoming-people
Tute Costa - @tutec
“The major problems of our work are not so much technological as sociological in nature”
Peopleware: Productive Projects and Teams (1987)
Tom DeMarco and Timothy Lister
“We are humans working with humans
to develop software
for the benefit of humans”
http://humanedevelopment.org/
## Welcoming people
* **Why care?**
* Feedback
* Onboarding to your OSS project
* Working with our biased brains
* Make this expected (and normal!)
Why care? As professionals
“It’s not about adding diversity for the sake of diversity, it’s about subtracting homogeneity for the sake of realism.”
https://twitter.com/MaryRobinette/status/545428674812465152
Why care? As humans
When someone has been discriminated against or is a minority, the status quo is in and on itself unfair.
[...] If you are picking names out of a hat that's not sufficient because they are in the minority, they have less chance to be picked out of the hat. And so it does require you to change your mindset, and make an effort to reach out to people who are in a minority status in the group, and make sure they are included.
Chad Pytel, developer and CEO of thoughtbot
http://betweenpipes.com/2015/06/25/Pytel-Fernandez/
## Welcoming people
* Why care?
* **Feedback**
* Onboarding to your OSS project
* Working with our biased brains
* Make this expected (and normal!)

* Feedback
* **Onboarding to your OSS project**
* Working with our biased brains
* Make this expected (and normal!)
## Onboarding to your OSS project
“Landing page” for your projects. Show/link to:
* Communication channels
* Installation instructions
* Usage examples
* Contributing guidelines
## During code reviews
* Try to respond to every question and comment
* If you disagree strongly, consider responding in a while
* Don’t assume people share your context. Avoid words like
“basically”, “simply”, “clearly”, “obviously”, etc
* Previous comments on "feedback" apply
## Welcoming people
* Why care?
* Feedback
* Onboarding to your OSS project
* **Working with our biased brains**
* Make this expected (and normal!)

* Feedback
* Onboarding to your OSS project
* Micro-agressions
* Working with our biased brains
* **Make this expected (and normal!)**
# Common knowledge
## Stating something publicly
## (even when obvious)
## can change how a group of people
## feel and behave
## Let's carefuly talk about salaries,
## about microagressions,
## about gender and race ratios


9
1 However, those in Judah who were distressed will not continue to suffer. Previously, Yahweh humbled the people in the land where the tribes of Zebulun and Naphtali live. But in the future he will honor the people who live in the region of Galilee, along the road between the Jordan River and the Mediterranean Sea, where many foreigners live.
2 Some day in the future, it will be as though the people who walked in darkness have seen a bright light.
Yes, a bright light will shine on those who live in a land where they now have great troubles.
3 Yahweh, you will cause us people in Israel to rejoice;
we will become very happy.
We will rejoice about what you have done
like people rejoice when they harvest their crops,
or like soldiers rejoice
when they divide up among themselves the things that they have captured in battle.
4 You will cause us to no longer be slaves of those who captured us;
you will lift the heavy burdens from our shoulders.
It will be as though you have broken the weapons of those who oppressed us,
like you did when you destroyed the army of the Midian people group.
5 The boots that the enemy soldiers wore
and their clothing which has stains of blood on them
will all be burned up;
they will be fuel for a big fire.
6 Another reason that we will rejoice is that a special child will be born for us,
a woman will give birth to a son,
and he will be our ruler.
And his names will be 'Wonderful Counselor,' 'Mighty God,' 'Our Everlasting Father,' and 'King who causes us to have Peace.'
7 His rule and the peace that he brings will never end.
He will rule fairly and justly,
like his ancestor King David did.
This will happen because Yahweh, Commander of the angel armies, greatly desires that it happen.
8 The Lord has warned the descendants of Jacob;
he has said that he will punish Israel.
9-10 And all the people in Samaria and other places in Israel will know that,
but they are now very proud and arrogant.
They said, "Our city has been destroyed,
but we will take away the broken bricks from the ruins
and replace them with carefully cut stones.
Our sycamore fig trees have been cut down by our enemies,
but we will plant cedar trees in their place."
11 But Yahweh will bring the armies of Assyria, the enemies of King Rezin of Aram, to fight against Israel
and incite other nations to attack Israel.
12 The army of Aram will come from the east,
and the army of Philistia will come from the west,
and they will destroy Israel
like a wild animal tears another animal apart and devours it.
But even after that happened, Yahweh will still be very angry with them.
He will be ready to strike them with his fist again.
13 But even though Yahweh will punish his people like that,
they still will not return to him and worship him.
They still will not request Yahweh, Commander of the angel armies, to assist them.
14 Therefore, in one day Yahweh will get rid of those who are like Israel's head and those who are like its tail;
the ones who are like the top of the palm tree and the ones who are like the bottom.
15 The leaders of Israel are the head,
and the prophets who tell lies are the tail.
16 The leaders of the people have misled them;
they have caused the people that they are ruling to be confused.
17 For that reason, Yahweh is not pleased with the young men of Israel,
and he does not even act mercifully toward the widows and orphans,
because they are all ungodly and wicked,
and they all say things that are foolish.

They will become like fuel for that great fire,
and no one will try to rescue even his own brother from that fire.
20 The Israelite people will attack their neighbors who live in houses at the right to get food from them,
but they will still be hungry.

But even after that happens, Yahweh will still be very angry with them;
He will be ready to strike them with his fist again.


Video v0.2 Beta
Today we are announcing the first stable beta for Video. To install it, you can first install Racket, and then run:
raco pkg install video-testing
Alternatively, within DrRacket, go to File -> Install Package
, and type in video-testing
.
Either way, make sure you installed video-testing
, the video
package points to the latest stable build of Video, which is v0.1.1 at the time of this release.
The documentation for this version can be viewed on the Racket documentation website. Unlike with the Alpha builds, the documentation has been updated to reflect the new version. As such:
Video v0.2-beta is now mature enough for programmers to reliably use it without segfaults.
The above sentence is significant because it means Video is now one of the most stable open source video production tools available.
You can also download the source code for the build directly on Video’s github project page.
We would also like to thank David Christiansen and Benjamin Chung for their extensive use and testing of Video.
A detailed list of changes can be found in the source code’s LOG
file, and are listed here:
- Documentation Improvements to match the v0.2 API.
- Create stable/testing/unstable(master) branches.
- Fix deadlocks with the player, also solves sever segfaults
- Increased stability of Video (it no longer seems to segfault).
- Add infrastructure to lift ffmpeg logs into Racket logs (require libvid), disabled for this release.
- Audio works in live preview
- Introduced concept of
merge
for multitrack, replaces transition - Transitions now only appear in playlists.
- Add CI testing for windows builds
- Add initial opengl bindings for windows. (The Racket ones don’t)
- Add legacy opengl support (for opengl v2.1+)
- Remove now deprecated MLT bindings.
- Fix many bugs related to multitracks and the way it processes transitions
- Fix concurrency bugs with live player.
- Reintroduce most of the unit tests from the v0.1 build.
- Add video/version to track the version more accurately than Racket’s info.rkt system.
- Add video-log and ffmpeg-log to print out debug messages. ffmpeg-log not yet hooked up.
- Fix a few minor bugs preventing Video from running on Windows and Linux. We have now tested it on all three platforms.
- Initial work on live preview. The player has now been updated to show the video in the player window. Additionally Video can now seek video outputs (as well as inputs).


quotes: none;
quotes: initial;
quotes: "'" "'";
quotes: "„" "“" "‚" "‘";
quotes: "«" "»" "‹" "›";
Show us the wonder-working Brothers, let them come out publicly—and we will believe in them!
Brothers,


Development
A lot of the research and consulting work I do involves intensive statistical computing. I also dabble a bit in R development.
Statistical Computing
If I had to give myself an academic label, it would probably be “computational statistician”. A great deal of my work revolves around statistical computing. In my methodological research, I use Monte Carlo simulation as the primary tool by which to explore the performance of the methods I’m developing or evaluating. As these methods tend to be some flavor of multiple imputation—which is already computationally intensive—these simulations often place prohibitive demands on computing resources. Hence, I have become a serial abuser of high-performance computing (HPC) clusters such as the Lisa Compute Cluster. My eternal gratitude goes out to the folks at SURF who administer Lisa and many similar services and provide excellent support for their products.
Likewise, most of my collaborative work is computational. In my experience, substantive researchers don’t need statistical experts to help them run t-tests or simple linear models, but these simple methods are often inadequate. As my coauthors and I argue in this paper, plausible representations of modern social-scientific theories—and reasonable tests of the associated hypotheses—often require advanced modeling approaches such as structural equation modeling, multilevel modeling, conditional process analysis, or Bayesian modeling. Similarly, when working with messy real-world data, the asymptotic assumptions underlying simple, traditional statistical tools are often grossly violated. In such cases, computationally intensive methods like cross-validation or resampling methods (e.g., bootstrapping, permutation tests) are often required to support reasonable statistical modeling, inference, and prediction.
Development
As a natural consequence of spending so much of my time immersed in statistical computation, I also dabble in software development, particularly R package development. I created my first R package (MIBRR) after realizing that my life would be much easier if I packaged the code I was using to implement the imputation algorithm I was developing for my PhD project. During my postdoc at Texas Tech University, one of my primary tasks was developing the PcAux R package. After a few more people joined the PcAux project, I took on the role of development team lead.
I have also done a few non-statistical programming projects such as this utility that I developed for the Tilburg University Student Administration to merge the results from the online and on-campus versions of hybrid exams.
Pretty much all of my potentially interesting programming/development work lives on my GitHub page. If you would like to contribute to any of my projects, feel free to submit a pull request.


Snookes1516.github.io
With my dedication to keeping kitchen kitchens clean and for, along with my acquired skills in food preparation and cover maintenance, my qualifications prepare Cuisinart 5922-30HR Elements Open Skillet with Helper.
69 best Letter H Activities images on Pinterest Basic Kitchen Helper Cover Letter Samples and Templates. Oct 10, 2018- Explore JanaMarie Thompson's board "Letter H Activities" on Pinterest. See more ideas about Day Care, Preschool and Learning. Dear Sir/Madam, Application for a Kitchen Assistant. I have recently seen your advert for a kitchen assistant and I would like to submit my CV in application Kitchen Helper Resume Sample – Best Format.
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 They come from Nigella sativa, a small plant with pale purple, blue, or white flowers that grows in Eastern Europe, Western Asia, and the Middle East. Black seed oil has shown promise in treating some of the most common health conditions, including high blood pressure and asthma.
It's usually lightly toasted and then ground or used whole to add flavor to bread or curry dishes. Some people also eat the seeds raw or mix them with honey or water. It can also be used for hair and skin care.


A comprehensive lesson plan / guide for those with little-to-no coding experience who want to learn how to program (and program well)!
Before you can get to coding, there are a few things you can do to setup your coding environment. Investing in some quick setup tools can go a long way as you practice and improve your abilities. So, let's jump in!
Download a text editor
What is a text editor? A text editor is a program in which you'll write most of your code. In the context of coding, think of it as a Microsoft Word, but for coding languages.
There are many great text editors you can download. The ones I've listed below are free and my two favorites. I would recommend purchasing Sublime if you end up using it a lot and like it, but you'll never be required to. You only need one text editor, so no need to download both. Also, if there is another text editor out there you like better, you should download that one (and email me so I can add it to the list!)
Once you have the text editor downloaded, you can open the program (again, like you would a new Word doc), type up some code and save it. The coolest part about these text editors is that, after you save the file and let the editor know what coding language that file is in, these editors will highlight important aspects of the code in the language you are using. This will make it much easier to code!
But how do you let the editor know what language you are using? The answer to this question wasn't very obvious to me! You do this when you save the file by adding your own extension. For example if I am writing an html document and I want to name it "hello", I would type hello.html where the file tells me to name the file as I save it. Here is a screenshot:
The extension will be different depending on what language the file is in. Here is a stack overflow article with a few of the most common extensions by language.
Now you have an easy way to write and save your code! I recommend creating a folder in Documents or on your Desktop where you can save all of your new coding files. Now, before you get started, is a prime time to do this!
Next: Command Line!


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class: center, middle, inverse, title-slide # Introduction ## Machine Learning in Economics ### Hüseyin Taştan ### Yildiz Technical University --- class: my-medium-font <style type="text/css"> .remark-slide-content { font-size: 28px; padding: 1em 4em 1em 4em; } .my-large-font { font-size: 40px; } .my-small-font { font-size: 20px; } .my-medium-font { font-size: 30px; } </style> # Outline - [Introduction](#intro) - [What is machine learning?](#whatsml) - [Artifical Intelligence, Data Science, Data Mining](#buzzwords) - [Supervised vs. Unsupervised Learning, Reinforcement learning, Deep learning](#sl) - [Econometrics vs machine learning](#econometrics) - [Prediction policy problems](#policy) - [References](#references) --- name: intro # Introduction - The purpose of this course is to introduce you to fundamental methods, concepts, and algorithms that are widely used in machine learning applications. Our focus will be potential application areas in social sciences especially in economics. * Textbook: James, Gareth, D. Witten, T. Hastie, R. Tibshirani (2017), An Introduction to Statistical Learning with Applications in R, Springer. Freely available at: [https://statlearning.com/](https://statlearning.com/) * Github link to class materials: [https://github.com/htastan/](https://github.com/htastan/) * Software platform: R and RStudio - Please enroll in the class email list. --- name: whatsml # What is machine learning? - There are several definitions of machine learning. In the following, I am going to cover a few of these that I find particularly useful. - One of the pioneers in artificial intelligence [Arthur Samuel](https://en.wikipedia.org/wiki/Arthur_Samuel) defines machine learning as follows: > “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.” - Arthur Samuel - He developed a computer checkers program in which computer played a large number of games against itself and learned about which moves were best. --- # Learning Problem - Tom Mitchell, in his textbook Machine Learning, describes machine learning in terms of a **well-defined learning problem**: > "A computer program is said to learn from experience **E** with respect to some task **T** and some performance measure **P**, if its performance on **T**, as measured by **P**, improves with experience **E**." - A computer “learns” by improving its performance at some task through experience (i.e., data) > “For example, a computer program that learns to play checkers might improve its performance as measured by its ability to win at the class of tasks involving playing checkers games, through experience obtained by playing games against itself” (Mitchell, 1997, p. 2). --- # Checkers learning problem > “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”. - **T**ask: playing checkers - **E**xperience: computer playing practice games against itself - **P**erformance measure: percent of games won against opponents. --- # Learning Problem: Spam Email Imagine an email program that follows your interactions, which emails you answer, which you mark as spam. Following you over time it can learn which emails to classify as important and which ones as unwanted spam. In this problem: - **T**ask: classifying emails as spam or not spam - **E**xperience: watching the user classifying as spam or not - **P**erformance: the proportion of emails classified correctly as spam --- # What is machine learning? > “Machine learning is programming computers to optimize a performance criterion using example data or past experience”. Alpaydın (2018, s.3) - Machine Learning uses statistical theory to develop descriptive or predictive models (algorithms) using training data or past experience - Example (Alpaydın, 2018): a retail-marketer (a supermarket chain, say) may be interested in finding associations between products bought by customers: - If people who buy X typically also buy Y, and if there is a customer who buys X and does not buy Y, he or she is a potential Y customer. Once we find such customers, we can target them for cross-selling. --- # Example: Marketing problem - The task is to estimate the conditional probability of the form `\(P(Y|X)\)` where `\(Y\)` is the product conditional on `\(X\)`, where `\(X\)` is the product the customer has already purchased. - This problem requires a training data set at the customer level where we observe their individual characteristics and shopping behavior. - After we obtain a good estimate of `\(P(Y|X)\)` based on the past customer data, we can design targeted marketing and advertising campaigns. --- # What is machine learning? > “...Machine learning is a field that develops algorithms designed to be applied to datasets, with the main areas of focus being prediction (regression), classification, and clustering or grouping tasks” Susan Athey (2018). - This definition is more specific in terms of the tasks. - The focal point is still “prediction” --- # Statistical Learning > “Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning.” James, Witten, Hastie ve Tibshirani (2017). - This is close to S. Athey’s definition. - Although there are subtle differences between machine learning and statistical learning, they generally refer to the same set of algorithms designed to understand (learn from) data. - In this course we will use machine learning (ML) and statistical learning (SL) interchangeably. The point is that "learning" is based on a random data set using a rule (or an algorithm) that optimizes a criterion function. --- name: buzzwords # Related concepts and buzzwords - **Artificial Intelligence**: a field of study that aims to develop tools for making machines (computers) to behave intelligently and autonomously. + Currently, we see a widespread use of a narrow definition of artificial intelligence where a certain task is completed successfully rather than an artificial intelligence that behaves like humans. + For example: autonomus vehicles, Siri, Alexa and similar devices, Google search, Dianosing certain diseases etc. --- # Related concepts and buzzwords - **Data Science**: a multidisciplinary area of study at the intersection of statistics, computer science, mathematics and related field of study that aims to develop algorithms to turn data into useful knowledge. - **Data Mining**: A field of study that aims to discover useful patterns and relationships in large data sets. --- background-image: url("img/bigpic.PNG") background-size: cover --- name: sl # Supervised Learning - ML algorithms can be classified into two broad types: supervised ML and unsupervised ML. - In supervised learning, we have a set of inputs (predictors or features) and an observed outcome. - The goal is to find the best model to predict the outcome based on a data set. - As an example, consider predicting the probability of default based on a set of features of mortgage applicant. - A bank may develop a statistical model of default probability from past applications and then use it to predict new applicants (out-of-sample prediction). - Two types: Regression and classification problems --- # Unsupervised Learning - In unsupervised learning, on the other hand, there is no outcome variable or “label” that are attached to inputs or predictors. - Unsupervised learning consists of two general problems: clustering, and dimensionality reduction. - In clustering, we try to identify homogenous groups in the data based on a set of features (for example, clusters of similar consumers, types of patients, voters of a political party, etc.) --- # Reinforcement Learning - In some cases, supervised learning is not possible. - Output is not a single action but a sequence of actions. A single action by itself may not be important but applying the correct sequence of actions is. - The sequence of correct actions to reach the goal is called “policy”. There is no single best action. But an action is good if it is a part of good policy. - Example: game playing (eg, chess) where a single move by itself is not that important; it is the sequence of right moves that is good. A move is good if it is part of a good game playing policy. - Example: A robot navigating in an environment in search of a goal location is another application area of reinforcement learning. For details, see Alpaydın (2018, p. 10 and p. 383) --- # Deep Learning * Deep learning is a subfield in machine learning that focuses on developing artificial neural networks (ANN) algorithms based on the functioning of the brain. * An artificial neural network consists of neurons and their connections just like in the brain. * A typical ANN problem consists of multiple layers such as input layer, intermediate layers and output layer. (see the graph below) * Application areas: image recognition, natural language processing, voice recognition, etc. .center[![:scale 50%](img/ann1.PNG)] --- name: econometrics # Econometrics vs Machine Learning - A typical supervised machine learning problem can be written as follows: `$$y = f(x_1, x_2, \ldots, x_p) + \epsilon$$` where `\(y\)` is the outcome variable (or labels), and `\(\{x_1, x_2, \ldots, x_p\}\)` are a set of features (characteristics). `\(\epsilon\)` is a random error term. - `\(f(\cdot)\)` represents unknown functional form. - The model's prediction can be written as `$$\hat{y} = f(x_1, x_2, \ldots, x_p)$$` - The purpose in machine learning is to minimize the prediction error `\(y-\hat{y}\)`, as much as possible. This is very similar to the framework that we use in econometrics. --- # Econometrics vs Machine Learning - Let's assume that `\(f(\cdot)\)` is linear in parameters: `$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 +\ldots+ \beta_p x_p + \epsilon$$` We obtained the linear regression model that we frequently use in econometric applications. - We can represent the model in a more compact way using matrix algebra: `$$\underbrace{\mathbf{y}}_{n\times 1} = \underbrace{\mathbf{X}}_{n\times (p+1)} \underbrace{\mathbf{\beta}}_{(p+1)\times 1} + \underbrace{\mathbf{u}}_{n\times 1}$$` There ae `\(p+1\)` unknown `\(\beta\)` parameters (including the intercept) in this model. --- # Econometrics vs Machine Learning * The focus of **econometrics** is to estimate the parameter vector, `\(\mathbf{\beta}\)`, in an unbiased or consistent and efficient manner. * Econometricians particularly focus on the economic interpretation of the model. * Estimation: Under certain assumptions Ordinary Least Squares (OLS) will be the best linear unbiased estimator (BLUE) according to the Gauss-Markov theorem. * Estimation is possible if the number of observations, `\(n\)`, is significantly larger than the number of variables in `\(\mathbf{X}\)`, i.e., `\(p\)`. This requirement is sometimes written as: `\(n>>p\)` * OLS will not work if `\(p>n\)` * What about `\(p=n\)`? --- # Econometrics vs Machine Learning * The difference between Machine Learning (ML) and econometrics can be summarized as follows: > Machine learning focuses on the prediction of the outcome variable, that is `\(\hat{y}\)`. On the other hand, the focus of econometrics is `\(\hat{\beta}_j\)`, `\(j=1,2,\ldots,p\)`. (Mullainathan ve Spiess, 2017) * Econometrics: What is the causal impact of `\(x_j\)` on `\(y\)` (economic measurement problem) * Econometrics: using economic theory as the guide, aims to estimate the causal effects in an unbiased/consistent and efficient manner. * **ML**: focuses on finding the most successful models in predicting `\(y\)` --- # ML and econometrics - We note that the lines between ML and econometrics cannot be drawn strictly. * Prediction problems may be useful for causal inference. Also, an econometric model (or an economic model) may provide valuable information for predictive modeling. - Therefore, this distinction is somewhat blurred; machine learning tools can be useful for econometric analysis that focuses on causal (ceteris paribus) analysis. - Recent literature focuses on combining ML and econometrics methods to identify causal relationships in large data sets (for details, refer to Athey and Imbens (2019)). --- # ML and econometrics - Unlike econometric methods, ML algorithms can be used is the number of features (variables) larger than the number of observations. - Empirical problems can be decomposed into two parts: prediction and causality - ML is very good at data-driven model selection that gives the best prediction - Data set is divided into two parts: training and test. - Training data is used to estimate the model whereas test data is used to evaluate the performance - We will learn the details of this approach in this course. --- background-image: url("img/ML-workflow.PNG") background-size: contain # A Typical Machine Learning Workflow --- name: policy # Prediction Policy Problems - Econometrics puts great emphasis on causal inference because policy research often requires an understanding of what would happen if the policy is not implemented (so-called the counterfactual). - As argued by Kleinberg et al. (2015), the causal inference may not be necessary for some policy applications. - Instead of a careful elaboration of the counterfactual and a detailed discussion of identification (of the parameters of a structural model), some policy problems may only need good predictions. - They argue that machine learning can be useful in providing these predictions. --- # Prediction Policy Problems: Examples (Kleinberg et al. 2015) * health policy: predicting which surgeries will be futile using data available at the time of surgery. * criminal justice: predicting the arrestee's probability of committing a crime. * education: predicting which teacher will have the greatest value-added. * labor market policy: predicting unemployment spell length to help workers to decide on savings rates and job search strategies. * social policy: predicting highest risk youth for targeting interventions. * finance: lenders identifying the underlying credit-worthiness of potential borrowers. --- name: references class: my-small-font # References Alpaydın, Ethem (2018), *Yapay Öğrenme*, 4. Baskı (Ethem Alpaydın, *Introduction to Machine Learning*, 2. baskıdan çeviri), Boğaziçi Üniversitesi Yayınevi, İstanbul. Athey, S. (2018), ''The Impact of Machine Learning on Economics'', Stanford University, unpublished paper. (https://projects.iq.harvard.edu/files/pegroup/files/athey2018.pdf), printed as: (https://www.nber.org/chapters/c14009). Athey, S. ve Imbens, G.W. (2019), ''Machine Learning Methods That Economists Should Know About'', *Annual Review of Economics*, 11: 685-725. James, Gareth, Witten D., Hastie T. ve Tibshirani R, (2017), *Introduction to Statistical Learning*, corrected 8th printing, Springer, New York. Kleinberg, J., Ludwig, J., Mullainathan, J., ve Obermeyer, Z. (2015), "Prediction Policy Problems", *American Economic Review, Papers and Proceedings*, 105(5): 491-495. (http://dx.doi.org/10.1257/aer.p20151023) Mullainathan, S. ve Spiess, J. (2017) ''Machine Learning: An Applied Econometric Approach'', *Journal of Economic Perspectives*, 31(2), 87-106. Samuel, A. L. (1959), ''Some studies in machine learning using the game of checkers'', *IBM Journal*, 3: 210–229. Varian, H.R. (2014), ''Big Data: New Tricks for Econometrics'', *Journal of Economic Perspectives*, 28 (2): 3–28. nocite: '@*' <!-- --- --> <!-- # Videolar --> <!-- [S. Mullainathan, Machine Learning and Prediction in Economics and Finance](https://www.youtube.com/watch?v=xl3yQBhI6vY) --> <!-- [S. Mullainathan, Machine Intelligence and Public Policy](https://www.youtube.com/watch?v=W7izsrYGhdU) --> <!-- [Susan Athey, Machine Learning and Causal Inference for Policy Evaluation](https://www.youtube.com/watch?v=Yx6qXM_rfKQ) --> <!-- [T. Hastie, Statistical learning with big data. A talk by Trevor Hastie](https://www.youtube.com/watch?v=0EWJZIC4JxA) --> <!-- [Artificial Intelligence, the History and Future - with Chris Bishop](https://www.youtube.com/watch?v=8FHBh_OmdsM) --> <!-- [İstatistiksel Öğrenme Dersleri YouTube Oynatma Listesi: Introduction to Statistical Learning by Robert Tibshirani, Trevor Hastie, and Daniela Witten.](https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) -->


Suppose your are going to transfer money abroad or make an international payment.
Basically, it is a simple exchange ... let's say to exchange Euro with Yuan.
How long does it take with the conventional methods?
3 days ? 5 days ? a weeK? or even more!
What if you do it in less than 24 hours?
That's what we do ...
Let's take a look at our solution.
page1


Research
My primary fields are Macroeconomics and International Finance. My secondary field is International Trade.
Job Market Paper
- “Clustered Sovereign Default”
(paper)
Abstract
--FILL IN--
Working Papers
- "Credit Constrained Households in Emerging Markets and their Effect on Consumption Volatility”
(paper)
Abstract
--FILL IN--
- “Creditor Rights During a Financial Crisis", (with Sudip Gupta and Krishnamurthy Subramanian)
(paper)
Abstract
--FILL IN--


|
Xenomai
3.0.4
|
Lightweight FIFO IPC mechanism. More...
Data Structures | |
| struct | RT_BUFFER_INFO |
| Buffer status descriptor. More... | |
Macros | |
| #define | B_PRIO 0x1 /* Pend by task priority order. */ |
| Creation flags. More... | |
Functions | |
| int | rt_buffer_create (RT_BUFFER *bf, const char *name, size_t bufsz, int mode) |
| Create an IPC buffer. More... | |
| int | rt_buffer_delete (RT_BUFFER *bf) |
| Delete an IPC buffer. More... | |
| ssize_t | rt_buffer_write_timed (RT_BUFFER *bf, const void *ptr, size_t size, const struct timespec *abs_timeout) |
| Write to an IPC buffer. More... | |
| static ssize_t | rt_buffer_write_until (RT_BUFFER *bf, const void *ptr, size_t size, RTIME timeout) |
| Write to an IPC buffer (with absolute scalar timeout). More... | |
| static ssize_t | rt_buffer_write (RT_BUFFER *bf, const void *ptr, size_t size, RTIME timeout) |
| Write to an IPC buffer (with relative scalar timeout). More... | |
| ssize_t | rt_buffer_read_timed (RT_BUFFER *bf, void *ptr, size_t size, const struct timespec *abs_timeout) |
| Read from an IPC buffer. More... | |
| static ssize_t | rt_buffer_read_until (RT_BUFFER *bf, void *ptr, size_t size, RTIME timeout) |
| Read from an IPC buffer (with absolute scalar timeout). More... | |
| static ssize_t | rt_buffer_read (RT_BUFFER *bf, void *ptr, size_t size, RTIME timeout) |
| Read from an IPC buffer (with relative scalar timeout). More... | |
| int | rt_buffer_clear (RT_BUFFER *bf) |
| Clear an IPC buffer. More... | |
| int | rt_buffer_inquire (RT_BUFFER *bf, RT_BUFFER_INFO *info) |
| Query buffer status. More... | |
| int | rt_buffer_bind (RT_BUFFER *bf, const char *name, RTIME timeout) |
| Bind to an IPC buffer. More... | |
| int | rt_buffer_unbind (RT_BUFFER *bf) |
| Unbind from an IPC buffer. More... | |
Lightweight FIFO IPC mechanism.
A buffer is a lightweight IPC mechanism, implementing a fast, one-way producer-consumer data path. All messages written are buffered in a single memory area in strict FIFO order, until read either in blocking or non-blocking mode.
Message are always atomically handled on the write side (i.e. no interleave, no short writes), whilst only complete messages are normally returned to the read side. However, short reads may happen under a well-defined situation (see note in rt_buffer_read()), albeit they can be fully avoided by proper use of the buffer.
| #define B_PRIO 0x1 /* Pend by task priority order. */ |
Creation flags.
| int rt_buffer_bind | ( | RT_BUFFER * | bf, |
| const char * | name, | ||
| RTIME | timeout | ||
| ) |
Bind to an IPC buffer.
This routine creates a new descriptor to refer to an existing IPC buffer identified by its symbolic name. If the object does not exist on entry, the caller may block until a buffer of the given name is created.
| bf | The address of a buffer descriptor filled in by the operation. Contents of this memory is undefined upon failure. |
| name | A valid NULL-terminated name which identifies the buffer to bind to. This string should match the object name argument passed to rt_buffer_create(). |
| timeout | The number of clock ticks to wait for the registration to occur (see note). Passing TM_INFINITE causes the caller to block indefinitely until the object is registered. Passing TM_NONBLOCK causes the service to return immediately without waiting if the object is not registered on entry. |
| int rt_buffer_clear | ( | RT_BUFFER * | bf | ) |
Clear an IPC buffer.
This routine empties a buffer from any data.
| bf | The buffer descriptor. |
| int rt_buffer_create | ( | RT_BUFFER * | bf, |
| const char * | name, | ||
| size_t | bufsz, | ||
| int | mode | ||
| ) |
Create an IPC buffer.
This routine creates an IPC object that allows tasks to send and receive data asynchronously via a memory buffer. Data may be of an arbitrary length, albeit this IPC is best suited for small to medium-sized messages, since data always have to be copied to the buffer during transit. Large messages may be more efficiently handled by message queues (RT_QUEUE).
| bf | The address of a buffer descriptor which can be later used to identify uniquely the created object, upon success of this call. |
| name | An ASCII string standing for the symbolic name of the buffer. When non-NULL and non-empty, a copy of this string is used for indexing the created buffer into the object registry. |
| bufsz | The size of the buffer space available to hold data. The required memory is obtained from the main heap. |
| mode | The buffer creation mode. The following flags can be OR'ed into this bitmask, each of them affecting the new buffer: |
This parameter also applies to tasks blocked on the buffer's write side (see rt_buffer_write()).
| int rt_buffer_delete | ( | RT_BUFFER * | bf | ) |
Delete an IPC buffer.
This routine deletes a buffer object previously created by a call to rt_buffer_create().
| bf | The buffer descriptor. |
| int rt_buffer_inquire | ( | RT_BUFFER * | bf, |
| RT_BUFFER_INFO * | info | ||
| ) |
Query buffer status.
This routine returns the status information about the specified buffer.
| bf | The buffer descriptor. |
| info | A pointer to the returnbuffer" to copy the information to. |
|
inlinestatic |
Read from an IPC buffer (with relative scalar timeout).
This routine is a variant of rt_buffer_read_timed() accepting a relative timeout specification expressed as a scalar value.
| bf | The buffer descriptor. |
| ptr | A pointer to a memory area which will be written upon success with the received data. |
| len | The length in bytes of the memory area pointed to by ptr. |
| timeout | A delay expressed in clock ticks. |
References rt_buffer_read_timed().
| ssize_t rt_buffer_read_timed | ( | RT_BUFFER * | bf, |
| void * | ptr, | ||
| size_t | len, | ||
| const struct timespec * | abs_timeout | ||
| ) |
Read from an IPC buffer.
This routine reads the next message from the specified buffer. If no message is available on entry, the caller is allowed to block until enough data is written to the buffer, or a timeout elapses.
| bf | The buffer descriptor. |
| ptr | A pointer to a memory area which will be written upon success with the received data. |
| len | The length in bytes of the memory area pointed to by ptr. Under normal circumstances, rt_buffer_read_timed() only returns entire messages as specified by the len argument, or an error value. However, short reads are allowed when a potential deadlock situation is detected (see note below). |
| abs_timeout | An absolute date expressed in clock ticks, specifying a time limit to wait for a message to be available from the buffer (see note). Passing NULL causes the caller to block indefinitely until enough data is available. Passing { .tv_sec = 0, .tv_nsec = 0 } causes the service to return immediately without blocking in case not enough data is available. |
writer thread > rt_write_buffer(&bf, ptr, 1, TM_INFINITE); (one byte to read, 1023 bytes available for sending) writer thread > rt_write_buffer(&bf, ptr, 1024, TM_INFINITE); (writer blocks - no space for another 1024-byte message) reader thread > rt_read_buffer(&bf, ptr, 1024, TM_INFINITE); (short read - a truncated (1-byte) message is returned)
In order to prevent both threads to wait for each other indefinitely, a short read is allowed, which may be completed by a subsequent call to rt_buffer_read() or rt_buffer_read_until(). If that case arises, thread priorities, buffer and/or message lengths should likely be fixed, in order to eliminate such condition.
Referenced by rt_buffer_read(), and rt_buffer_read_until().
|
inlinestatic |
Read from an IPC buffer (with absolute scalar timeout).
This routine is a variant of rt_buffer_read_timed() accepting an absolute timeout specification expressed as a scalar value.
| bf | The buffer descriptor. |
| ptr | A pointer to a memory area which will be written upon success with the received data. |
| len | The length in bytes of the memory area pointed to by ptr. |
| abs_timeout | An absolute date expressed in clock ticks. |
References rt_buffer_read_timed().
| int rt_buffer_unbind | ( | RT_BUFFER * | bf | ) |
Unbind from an IPC buffer.
| bf | The buffer descriptor. |
This routine releases a previous binding to an IPC buffer. After this call has returned, the descriptor is no more valid for referencing this object.
|
inlinestatic |
Write to an IPC buffer (with relative scalar timeout).
This routine is a variant of rt_buffer_write_timed() accepting a relative timeout specification expressed as a scalar value.
| bf | The buffer descriptor. |
| ptr | The address of the message data to be written to the buffer. |
| len | The length in bytes of the message data. |
| timeout | A delay expressed in clock ticks. |
References rt_buffer_write_timed().
| ssize_t rt_buffer_write_timed | ( | RT_BUFFER * | bf, |
| const void * | ptr, | ||
| size_t | len, | ||
| const struct timespec * | abs_timeout | ||
| ) |
Write to an IPC buffer.
This routine writes a message to the specified buffer. If not enough buffer space is available on entry to hold the message, the caller is allowed to block until enough room is freed, or a timeout elapses, whichever comes first.
| bf | The buffer descriptor. |
| ptr | The address of the message data to be written to the buffer. |
| len | The length in bytes of the message data. Zero is a valid value, in which case the buffer is left untouched, and zero is returned to the caller. |
| abs_timeout | An absolute date expressed in clock ticks, specifying a time limit to wait for enough buffer space to be available to hold the message (see note). Passing NULL causes the caller to block indefinitely until enough buffer space is available. Passing { .tv_sec = 0, .tv_nsec = 0 } causes the service to return immediately without blocking in case of buffer space shortage. |
Referenced by rt_buffer_write(), and rt_buffer_write_until().
|
inlinestatic |
Write to an IPC buffer (with absolute scalar timeout).
This routine is a variant of rt_buffer_write_timed() accepting an absolute timeout specification expressed as a scalar value.
| bf | The buffer descriptor. |
| ptr | The address of the message data to be written to the buffer. |
| len | The length in bytes of the message data. |
| abs_timeout | An absolute date expressed in clock ticks. |
References rt_buffer_write_timed().


DnD Toolbox
Character Name:
Level & Class:
Race:
Alignment:
Background:
Edit
Strength
Dexterity
Constitution
Intelligence
Wisdom
Charisma
Inspiration
Proficiency Bonus
+0
Strength
+0
Dexterity
+0
Constitution
+0
Intelligence
+0
Wisdom
+0
Charisma
+0
Acrobatics
+0
Animal Handling
+0
Arcana
+0
Athletics
+0
Deception
+0
History
+0
Insight
+0
Intimidation
+0
Investigation
+0
Medicine
+0
Nature
+0
Perception
+0
Performance
+0
Persuasion
+0
Religion
+0
Sleight of Hand
+0
Stealth
+0
Survival
Armor Class
Initiative
Speed
remove
Health
add
remove
Temp Health
add
Hit Dice
Successes
Failures
Death Saves
Attacks
PP:
GP:
SP:
CP:
Equipment
Spells:
refresh
Spell Level
Spell
Dice
Spells cannot be edited until the character is created
Roll Some Dice!
What would you like to roll?
Results:
Add Spell
×
Add level
spell:
Name
Dice
Should be either empty or of the form XdY (roll X Y-sided dies) or XdY+Z (roll X Y-sided dies and add Z to each).
Edit Spell
×
Name
Level
The level must exist.
Dice
Should be either empty or of the form XdY (roll X Y-sided dies) or XdY+Z (roll X Y-sided dies and add Z to each).
Add Spell Level
×
Level
Maximum Spell Points
Leave empty to have no maximum.
Edit Spell Level
×
Level
Maximum Spell Points
Leave empty to have no maximum.
Current Spell Points
If empty, duplicates the value of the maximum.
No More Spell Points
×
You have no remaining spell points for level
spells.


Tokens¶
Create token (authenticate)¶
In order to generate a token, you must pass in the JSON template that is sent to the API. This is because Rackspace’s operation expects a slightly different entity body than OpenStack Keystone.
To do this, and then generate a token:
$json = $client->getCredentials();
/** @var $response Guzzle\Http\Message\Response */
$response = $service->generateToken($json);
$jsonBody = $response->json();
When a token is generated by the API, there are a few things returned:
- a service catalog outlining all of the services you can interact with, including their names, service types, and endpoint URLs. Which services make up your catalog, and how your catalog is structured, will depend on your service provider.
- details about your token, such as its ID, created and expiration date
- details about your user account
- details about your tenant
Interacting with the service catalog¶
Once you have the $jsonBody, you can construct a Catalog object for easier interaction:
$data = $jsonBody->access->serviceCatalog;
$catalog = OpenCloud\Common\Service\Catalog::factory($data);
foreach ($catalog->getItems() as $service) {
/** @param $service OpenCloud\Common\Service\CatalogItem */
printf("Catalog item: Name [%s] Type [%s]\n", $service->getName(), $service->getType());
foreach ($service->getEndpoints() as $endpoint) {
printf(" Endpoint provided: Region [%s] PublicURL [%s] PrivateURL [%s]\n",
$endpoint->getRegion(), $endpoint->getPublicUrl(), $endpoint->getPrivateUrl());
}
}
Interacting with tokens¶
$data = $jsonBody->access->token;
$token = $service->resource('Token', $data);
printf("Token ID: %s - Token expiry %s", $token->getId(), $token->getExpires());
if ($token->hasExpired()) {
// ...
}
Interacting with users¶
$data = $jsonBody->access->user;
$user = $service->resource('User', $data);
To see which methods you can call on $user (which implements OpenCloud\Identity\Resource\User), see our user documentation which accompanies this guide.
Interacting with tenants¶
$data = $jsonBody->access->tenant;
$tenant = $service->resource('Tenant', $data);
To see which methods you can call on $tenant (which implements OpenCloud\Identity\Resource\Tenant), see our user documentation which accompanies this guide.
Revoke token (destroy session)¶
$service->revokeToken('{tokenId}');


|
Alcohol addiction, also known as alcoholism, is a disease that affects people of all walks of life. Experts have tried to pinpoint factors like genetics, sex, race, or socioeconomics that may predispose someone to alcohol addiction. But it has no single cause. Psychological, genetic, and behavioral factors can all contribute to having the disease. It’s important to note that alcoholism is a real disease. It can cause changes to the brain and neurochemistry, so a person with an alcohol addiction may not be able to control their actions. Alcohol addiction can show itself in a variety of ways. The severity of the disease, how often someone drinks, and the alcohol they consume varies from person to person. Some people drink heavily all day, while others binge drink and then stay sober for a while. Regardless of how the addiction looks, someone typically has an alcohol addiction if they heavily rely on drinking and can’t stay sober for an extended period of time. |
|
Alcohol addiction can be difficult to recognize. Unlike cocaine or heroin, alcohol is widely available and accepted in many cultures. It’s often at the center of social situations and closely linked to celebrations and enjoyment. Drinking is a part of life for many people. When is it common in society, it can be hard to tell the difference between someone who likes to have a few drinks now and then and someone with a real problem. Some symptoms of alcohol addiction are: 。increased quantity or frequency of use 。high tolerance for alcohol, or lack of “hangover” symptoms 。drinking at inappropriate times, such as first thing in the morning, or in places like church or work 。wanting to be where alcohol is present and avoiding situations where there is none 。changes in friendships; someone with an alcohol addiction may choose friends who also drink heavily 。avoiding contact with loved ones 。hiding alcohol, or hiding while drinking 。dependence on alcohol to function in everyday life 。increased lethargy, depression, or other emotional issues 。legal or professional problems such as an arrest or loss of a job As an addiction tends to get worse over time, it’s important to look for early warning signs. If identified and treated early, someone with an alcohol addiction may be able to avoid major consequences of the disease. If you’re worried that someone you know has an alcohol addiction, it’s best to approach them in a supportive way. Avoid shaming them or making them feel guilty. This could push them away and make them more resistant to your help.
|
|
Alcohol addiction can result in heart disease and liver disease. Both can be fatal. Alcoholism can also cause: 。ulcers 。diabetes complications 。birth defects 。bone loss 。vision problems 。increased risk of cancer 。suppressed immune function If someone with an alcohol addiction takes dangerous risks while drinking, they can also put others at risk. According to the Centers for Disease Control and Prevention (CDC), drunk driving, for example, takes 28 lives every day in the United States. Drinking is also associated with an increased incidence of suicide and homicide. These complications are reasons why it’s important to treat alcohol addiction early. Nearly all risks involved with alcohol addiction may be avoidable or treatable, with successful long-term recovery.
|
|
Alcohol may be regarded as a ‘social lubricant’, especially in festivity. But in fact many people are having a great time with non-alcoholic beverages (e.g. alcohol-free punches, juices, teas and coffees) as alternatives. However, if you choose to drink alcohol, you need to be aware that 。Do not drink on an empty stomach to prevent quick absorption 。Know your limits and the alcohol content of different kinds of drinks. Choose drinks with lower alcohol content. Reduce alcohol content by adding ice 。Take sips rather than gulps to prevent blood alcohol level from rising quickly 。Do not get into rounds. Drink alcohol-free beverages in between (such as water, fruit juice or soft drinks) 。Never drink and drive. Plan your transport home ahead. Use public transport or designate a non-drinking driver. |


This city lies at the southern tip of Argentina, close to the Chilian border. It sits on the southern bank of the Gallegos river and is an important military base. It is about 50nm north of the Beagle Channel.
Position 51° 35.4'S, 68° 57.2'W.
For full details on clearing into and out of the country see Argentina Formalities.
Nothing is known about any facilities, anchorages or berths for yachts here.
Last udated October 2011.


I'm a 21 year old developer from Dublin, Ireland living in San Francisco, California
I ship code @ Intercom
I sometimes take photos too. You can find them over on Flickr
You can also find me on GitHub and Tumblr


I have gone through some school.
At primary level
i went to our lady of nazareth and at high school i joined BH11 school.
I was very happy to join moringa school.
it was my pleasure
i didnt have any background for coding but i think i am somewhere no thanks to
MORINGA SCHOOL.
I want to be the best developer.
it is my dream
one of the best developers that ever existed. am currently shapenning my skills at
MORINGA SCHOOL.
Email<EMAIL_ADDRESS>hotmail number:+2547454883461
Location:NAIROBI/KENYA


Symmetry, Automorphisms, and
Visual Group Theory
Steven H. Cullinane, March 2, 2010
From the conclusion
of Weyl's Symmetry –
One example of Weyl's "structure-endowed entity" is a partition of a six-element set into three disjoint two-element sets– for instance, the partition of the six faces of a cube into three pairs of opposite faces.
The automorphism group of this faces-partition contains an order-8 subgroup that is isomorphic to the abstract group C2×C2×C2 of order eight--
The action of Klein's simple group of order 168 on the Cayley diagram of C2×C2×C2 in the following note on Carter's 2009 book Visual Group Theory furnishes an example of Weyl's statement that
"… one may ask with respect to a given abstract group: What is the group of its automorphisms…?"
|
Visual Group Theory
Note of March 2, 2010 The current article on group theory at Wikipedia has a Rubik's Cube as its logo–
The article quotes Nathan C. Carter on the question "What is symmetry?" This naturally suggests the question "Who is Nathan C. Carter?" A search for the answer yields the following set of images… Click image for some historical background. Carter turns out to be a mathematics professor at Bentley University. His logo– an eightfold-cube labeling (in the guise of a Cayley graph)– is in much better taste than Wikipedia's. |


CHAPTER 394
MANPOWER DEVELOPMENT AND TRAINING
Section
394-1 Purpose
394-2 Establishment of programs
394-3 Contracts; public and private sectors
394-4 Compensation
394-5 Administration
394-6 Funds
394-7 Type of training
394-8 New industry training program
394-9 Eligibility requirements


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TVBEATS
0
Game of Thrones
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Nine noble families fight for control over the lands of Westeros, while an ancient enemy returns after being dormant for millennia.
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Band of Brothers
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The story of Easy Company of the U.S. Army 101st Airborne Division and their mission in World War II Europe, from Operation Overlord to V-J Day.
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Breaking Bad
20$
A high school chemistry teacher diagnosed with inoperable lung cancer turns to manufacturing and selling methamphetamine in order to secure his family's future.
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Chernobyl
12$
In April 1986, an explosion at the Chernobyl nuclear power plant in the Union of Soviet Socialist Republics becomes one of the world's worst man-made catastrophes.
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Dark
14$
When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.
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Dexter
10$
He's smart. He's lovable. He's Dexter Morgan, America's favorite serial killer, who spends his days solving crimes and nights committing them. Golden Globe winner Michael C. Hall stars in the hit SHOWTIME Original Series.
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Hannibal
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Explores the early relationship between renowned psychiatrist Hannibal Lecter and a young FBI criminal profiler who is haunted by his ability to empathize with serial killers.
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La casa de
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An unusual group of robbers attempt to carry out the most perfect robbery in Spanish history - stealing 2.4 billion euros from the Royal Mint of Spain.
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Mr. Robot
18$
Elliot, a brilliant but highly unstable young cyber-security engineer and vigilante hacker, becomes a key figure in a complex game of global dominance when he and his shadowy allies try to take down the corrupt corporation he works for.
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Peaky Blinders
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Sherlock
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A modern update finds the famous sleuth and his doctor partner solving crime in 21st century London.
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The Blacklist
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The Queen's Gambit
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Vikings transports us to the brutal and mysterious world of Ragnar Lothbrok, a Viking warrior and farmer who yearns to explore - and raid - the distant shores across the ocean.
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Rangy Core Demo
Please use your mouse and/or keyboard to make selections from the sample content below and use the buttons on the left hand size to manipulate the selection.
Association football is a sport played between two teams. It is usually called football, but in some countries, such as the United States, it is called soccer. In Japan, New Zealand, South Africa, Australia, Canada and Republic of Ireland, both words are commonly used.
Each team has 11 players on the field. One of these players is the goalkeeper, and the other ten are known as "outfield players." The game is played by kicking a ball into the opponent's goal. A match has 90 minutes of play, with a break of 15 minutes in the middle. The break in the middle is called half-time.
Competitions (this section is editable)
There are many competitions for football, for both football clubs and countries. Football clubs usually play other teams in their own country, with a few exceptions. Cardiff City F.C. from Wales for example, play in the English leagues and in the English FA Cup.
Who plays football (this section is editable and in pre-formatted text)
Football is the world's most popular sport. It is played in more countries than any other game. In fact, FIFA (the Federation Internationale de Football Association) has more members than the United Nations. It is played by both males and females.
Text adapted from Simple Wikipedia page on Association Football, licensed under the Creative Commons Attribution/Share-Alike License.


|
My Project
|
This class encapsulates an arbitrary number of cells which apply the logic (rules) for the Game of Life documented at http://www.bitstorm.org/gameoflife/. More...
Classes | |
| class | CellCoordinate |
| This class encapsulates the coordinates of a cell in the Game of Life. More... | |
| class | DataCopyPositions |
| This inner class exists only to enable pre and post condition verification. | |
| class | DataCopyTable |
| This inner class exists only to enable pre and post condition verification. | |
Public Member Functions | |
| void | update () |
| Update the state of all encapsulated cells. More... | |
| Collection< CellCoordinate > | getPositions () |
| boolean | debugEquals (Object other) |
| Everything from here on exists exclusively to verify pre and post conditions. More... | |
| GameOfLifeModel | debugClone () |
This class encapsulates an arbitrary number of cells which apply the logic (rules) for the Game of Life documented at http://www.bitstorm.org/gameoflife/.
Cells conceptually exist in a regular 2D grid using signed integer coordinates to identify each grid position that may be occupied by a cell. At all times, there is at most one cell at each grid position.
This implementation supports an "infinite" game area limited only by the use of integer cell coordinates and the amount of available memory. This implementation does not use concurrency, but the rules for evaluating the presence of neighbors simulate simultaneous evaluation for every cell. In other words, upon each update of the game, all cells evaluate the same environment prior to any cells modifying the environment. As a result, the order in which cells evaluate the environment is immaterial.
This implementation has no dependencies on the way a Game of Life model may or may not be displayed to users. After construction, call the update() method periodically to update the game state. The frequency of calls to update() does not affect game logic (rules).
Note: to enable assertions, make sure the application is compiled with DEBUG enabled. In Android Studio, use the Build->Edit Build Types... menu. Select the "Debug" build type and make sure the "debuggable" option is set to true.
When assertions are enabled for this file, the maximum update rate achievable for a GameOfLifeModel is approximately 1/20 the rate achievable with assertions disabled.
| boolean com.wsu.erikbuck.gameoflife.GameOfLifeModel.debugEquals | ( | Object | other | ) |
Everything from here on exists exclusively to verify pre and post conditions.
Indicates whether some other object is "equal to" this one. This method is exclusively used to support pre and post condition validation.
| other | the object to compare to the receiver. |
| Collection<CellCoordinate> com.wsu.erikbuck.gameoflife.GameOfLifeModel.getPositions | ( | ) |
Begin actual real implementation of method
End actual real implementation of method
| void com.wsu.erikbuck.gameoflife.GameOfLifeModel.update | ( | ) |
Update the state of all encapsulated cells.
As each cell is updated, the cell applies the logic (rules) of the Game of Life based on the presence or absence of cells in adjacent grid positions to the cell being updated. Cells change their respective internal states, and may call the private spawnCellAt() of this class.
Begin actual real implementation of method
End actual real implementation of method
Begin actual real implementation of method
End actual real implementation of method


Oh no, 404
Page not found! :(
After 60 seconds, back to home page
Take a walk on the home page or check the nav bar to find what you want!
Or you can directly go to the course you are looking for. Say MATH 135, then simply go to https://n.sibp.ro/math135
or https://notes.sibeliusp.com/math135
.


Running a Single Model¶
The model communications interface consists of 2 pairs of very simple functions - one pair to create channels and one pair to accomplish data tranfer. Each pair represents a read-side and a write-side.
in = CisInput(name) # create a named input channel
out = CisOutput(name) # create a named output channel
in.send(data) # send a message through the managed channel
flag, data = out.recv(name) # receive message from channel - block if empty channel
Python-style is shown, but as these interfaces are thin wrappers around System V IPC message queues, equivalent thin-wrappers are implemented easily in almost any language. Models may create and use as many channels as necessary, depending on the number of interfaces that they require.
The only other model requirement is to provide a YAML specification file that declares the channels that the model uses, and the system command required to start the model executing.
model:
name: hellopython
driver: PythonModelDriver
args: hellopython.py
inputs:
- name: inFile
driver: FileInputDriver
args: input.txt
- name: helloQueueIn
driver: RMQInputDriver
args: helloQueue
outputs:
- name: helloQueueOut
driver: RMQOutputDriver
args: helloQueue
- name: outFile
driver: FileOutputDriver
args: /tmp/output.txt
This example tells the system to run a Python model defined in the script “hellopython.py” and to connect the file inputdata/input.txt to the cis_input named ‘inFile’ in your model, so when you recv() on it, you’ll get the file contents. Likewise when you send(data_to_send) to the channel you made in your code with cis_output(‘outFile’) the data will go to the associated files.
In additon to reading/writing to/from files on disk, models can send/recv messages to/from generic message queues that other models can connect to. In the example above, there is an output queue named ‘helloQueueOut’ messages sent to the channel cis_output(‘helloQueueOut’) can be received by any model with a corresponding input channel. In this example, the model accesses this queue through the channel cis_input(‘helloQueueIn’). These two channels are connected because they share the same argument ‘helloQueue’ in their YAML blocks.
The final part is the model runner, cisrun - to which you pass your yml file. The model runner reads the specification, arranges for your inputs and outputs to come and go as you’ve specified, and then calls your model with the arguments as specified in the model stanza. The model runs, using the send and recv calls as needed, and when the model exits, the communications get cleaned up by the runner. The examaple hellopython.py under examples is run like this (in examples/python):
cisrun hellopython.yml
The code in hellopython.py uses the channels to read data from the file input.txt, sends the data to the output queue, receives it from the input queue, and then sends it to the output file /tmp/output.txt:
inf = CisInput('inFile')
outf = CisOutput('outFile')
inq = CisInput('helloQueueIn')
outq = CisOutput('helloQueueOut')
buf = inf.recv()
outq.send(buf)
buf = inq.recv()
outf.send(buf)
This example is very simple and only shows how to run a single model which could be accomplished by just handling input/output within the script. The real advantage to using the CiS framework comes when you need two models in different languages to communicate.


Getting the correct data is most of the challenge. You’ll need the ‘assessment roll’ and the ‘sales roll’. Every jurisdiction releases this data slightly differently (ranging from three thousand page pdfs to csv files on open data portals) so this guide tries to capture most situations.
In order to conduct a sales ratio study, data is required to have at least three columns: Sale Year, Sale Price, and Assessed Value. We want to compare the sale price to the assessed value at the time of sale. Here is an example:
head(cmfproperty::example_data)
#> PIN SALE_YEAR SALE_PRICE ASSESSED_VALUE
#> 1<PHONE_NUMBER>1118 2015 53000 33860
#> 2<PHONE_NUMBER>1010 2018 80000 60390
#> 3<PHONE_NUMBER>1042 2018<PHONE_NUMBER>00
#> 4<PHONE_NUMBER>1015 2017<PHONE_NUMBER>0
#> 5<PHONE_NUMBER>0000 2018 3705000 3670740
#> 6<PHONE_NUMBER>0000 2016<PHONE_NUMBER>80
Each row in this dataset should match a sale and an assessment from the same year for a single property.
After gathering data in this form, we have to identify the three required columns (sales, assessments, and sale year) for the package using reformat_data
.
library(cmfproperty)
df <- cmfproperty::example_data
ratios <-
cmfproperty::reformat_data(
df,
sale_col = "SALE_PRICE",
assessment_col = "ASSESSED_VALUE",
sale_year_col = "SALE_YEAR",
)
#> [1] "Filtered out non-arm's length transactions"
#> [1] "Inflation adjusted to 2019"
head(as.data.frame(ratios))
#> PIN SALE_YEAR SALE_PRICE ASSESSED_VALUE TAX_YEAR RATIO
#> 1<PHONE_NUMBER>1118 2015 53000 33860 2015 0.6388679
#> 2<PHONE_NUMBER>1010 2018 80000 60390 2018 0.7548750
#> 3<PHONE_NUMBER>1042 2018<PHONE_NUMBER>00 2018 0.9177966
#> 4<PHONE_NUMBER>1015 2017<PHONE_NUMBER>0 2017 0.6948207
#> 5<PHONE_NUMBER>0000 2018 3705000 3670740 2018 0.9907530
#> 6<PHONE_NUMBER>0000 2016<PHONE_NUMBER>80 2016 0.7747246
#> arms_length_transaction SALE_PRICE_ADJ ASSESSED_VALUE_ADJ
#> 1 1 59209.48 37827.04
#> 2 1 82313.03 62136.05
#> 3 1 121411.71 111431.26
#> 4 1 132854.54 92310.09
#> 5 1 3812122.01 3776871.45
#> 6 1 376080.37 291358.73
Our data is now in the appropriate form for further analysis. More information on the output of reformat_data
can be found on the Getting started page.
Let’s look at an example. The Cook County Assessor’s Office, which is the assessor for Cook County, Illinois, has an excellent open data portal which includes all the necessary information and is the source of the example_data
from this package.
Cook County releases both the sales roll and the assessment roll on their open data portal. Sales were found here and assessments were found here. These files can be downloaded manually or via RSocrata
.
Here we download the files manually:
library(data.table)
library(tidyverse)
sales <- fread("~/../Downloads/Cook_County_Assessor_s_Residential_Sales_Data.csv",
colClasses = "character") #from 2013 to 2019
assessments <- fread("~/../Downloads/Cook_County_Assessor_s_Residential_Assessments.csv",
colClasses = "character") #from 2015 to 2019
Then filter the data to select only the columns we need and to remove some sales which are clearly not between two independent parties:
sales <- sales %>% select(PIN, `Sale Year`, `Sale Price`, `Deed No.`) %>%
filter(`Sale Year` > 2014)
assessments <- assessments %>% select(PIN, YEAR, CERTIFIED)
# Filtering data to remove duplicate sales and low value sales
sales <- sales %>% distinct(`Deed No.`, .keep_all = TRUE) %>% select(-`Deed No.`)
sales <- sales %>% filter(as.numeric(`Sale Price`) > 2500)
Finally, we conduct a left join of assessments on sales matching sales to assessment by unique identifier (PIN) and year.
# Join assessments to sales based on PIN (a unique identifier) and Year.
joined <- sales %>% left_join(assessments, by=c("PIN"="PIN", "Sale Year"="YEAR"))
# Remove spaces from column names (not necessary)
joined <- joined %>% rename(SALE_YEAR = `Sale Year`, SALE_PRICE = `Sale Price`, ASSESSED_VALUE = CERTIFIED)
This dataset is then identical to example_data
:
head(cmfproperty::example_data)
#> PIN SALE_YEAR SALE_PRICE ASSESSED_VALUE
#> 1<PHONE_NUMBER>1118 2015 53000 33860
#> 2<PHONE_NUMBER>1010 2018 80000 60390
#> 3<PHONE_NUMBER>1042 2018<PHONE_NUMBER>00
#> 4<PHONE_NUMBER>1015 2017<PHONE_NUMBER>0
#> 5<PHONE_NUMBER>0000 2018 3705000 3670740
#> 6<PHONE_NUMBER>0000 2016<PHONE_NUMBER>80


The Thought and Necessity of starting Njiiri School was born in the year 1952 - through the initiative of the late Ex-Senior Chief Njiiri Wa Karanja. The School is situated in Kigumo Sub-County of Murang’a County, then known as ‘Fort Hall District’ in the Central Province of Kenya. It was the ‘first high school’ - ever to be established in the district.
During the late 1940s’ and early 1950s’ the late Ex- Senior Chief Njiiri wa Karanja who was the area Chief of Location 2, where the School is situated, requested the District Commissioner, Fort Hall, to start a “Government African Secondary School” (GASS) - for boys in the district. This was because Murang’a had none, while neighbouring Kiambu had Alliance & Mang’u, and Nyeri had Kagumo & Nyeri high schools. His request was granted - subject to availability of a suitable site anywhere in the district. This, in effect, meant going around the whole District from Kangema, Kiharu, Kandara/Gatanga and Kigumo for the search of available land.
Four Senior Chiefs in the district namely, Michuki Njoroge, Ignatius Murai, Ndung’u Kagori and Njiiri Wa Karanja were provided with the D.C’s Land Rover and a driver. They travelled the whole day; eventually ending up at the present school site – (Kihaaro Kia ‘Ndumo’ na Baraza ya Chief ) – not far from Chief Njiiri’s home – late in the evening. That far, they had not found/agreed on any site as yet. Through persuasion of Chief Njiiri (who was the oldest in the team), they unanimously settled for the ‘final site’ as the most suitable for a school. Their recommendation was forwarded to the D.C. thereafter. Early in the year 1952, the District Commissioner gave the go-ahead for the building works to commence so as to admit the first Form One class the following year. A team of building construction ‘Apprentices/trainees’, from Thika Technical & Trade School of the Native Industrial Training Depot (N.I.T.D) was dispatched to the site to start construction works. Two Staff-houses, a Classroom block and a Dormitory for 25 boys were nearing completion by October 1952 - when, the then Governor of the British Colonial government, Sir Patrick Renison, declared the ‘State of Emergency’ due to the Mau Mau Movement – culminating in a vicious fight between freedom-fighters and the government. Consequently, the site and buildings were converted to a ‘Home-guards’ base/camp during the seven years Mau Mau war period. When tension eased-off after the Emergency period, the original idea of the school was revived and building works re-commenced during the second-half of mid -1957.


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One might ask the question why bother to map some of the features in Scala to Java.
Why not use Scala? If you like me walk between the Java/Scala you'll miss some of Scala's features when programming in pure Java. It's for these situations this project has been created. To allow plain Java users use some of goodies in Scala but without actually using Scala.
The project will supply features such as:
Copyright 2015 Peter Nerg.
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.


This is the configuration Panel for your NINSS Minecraft server. Console is here Plugins here and Configuration can be done here For help using NINSS visit our github page here You can post Issues here