-
Dianbo Liu
-
Gillian Dunphy
-
Stefan Tomov
-
Thiago Britto-Borges
-
Sarah-Lena Offenburger
-
Fábio Madeira
-
Darya Baranovka
-
Kushal Rugjee
-
Sam Watkins
-
Teodora Maghear
-
Alasdair McGill
-
Yuri Alexandrov
Dianbo Liu is the licence holder for the
upcoming TEDxUniversityofDundee event. He is the leader
of the team and is currently studying for a PhD within the
Computational and Physical Biology department at the
University of Dundee. He is interested in making links
among seemingly unrelated fields, and therefore come up
with innovative and useful inventions.
Gillian Dunphy was born and raised in Glasgow but was
compelled to make the lengthy journey to Dundee after being
lured by its East Coast charm. She is currently studying for
a PhD in Immunology at the University of Dundee. She is
currently managing a team of student volunteers in the areas
of Graphic Design and Stage Design. She is also involved in
marketing and speaker recruitment.
Stefan Tomov is a recent University of Dundee
graduate in the field of Economics and Politics. He comes
from Bulgaria and is currently the Deputy President of
Dundee University Students' Association (DUSA). Stefan is
responsible for securing the logistics required to make
TEDxUniversityofDundee a resounding success.
Thiago Britto-Borges is a Brazilian PhD student within
the Computational Biology department of the University of Dundee
He has an inexhaustible passion for learning and enjoys spreading knowledge. Thiago
is responsible for developing and designing the TEDxUniversityofDundee website.
Sarah-Lena Offenburger is pursuing a PhD in
Neurobiology at the University of Dundee. When Sarah is not
in the lab, you will probably find her doing sports, taking
pictures or just having a coffee with friends. Sarah is a
curator within our team and is responsible for recruiting
and liasing with the speakers of the event.
Originally from Portugal, Fábio Madeira is
currently studying for a PhD within the Cell and Molecular
Biology department at the University of Dundee,
specialising in Computational Biology. Alongside with Thiago, he helps managing and developing the website of the event.
is the president of Enterprise
Gym which is a University of Dundee department where
students learn about entrepreneurship. She comes from the
small and pretty town of Liepaya, which is located on the
Latvian coast of the Baltic Sea. Darya is responsible for
the recruitment of volunteers and organising the logistics
of the event.
moved from the island of
Mauritius to equally sunny Dundee to pursue a PhD in
Microbiology. His role in the team is to co-ordinate the
overall organisation of the TEDx event and to ensure that
progress is being made.
is currently studying Computer Science at the University of Dundee.
He is in his third year now and interested in Web
Programming, Product Development and Entrepreneurship. Sam
is our Social Media Officer, taking care of our Twitter and
Facebook accounts.
is the Enterprise Assistant & Coordinator
at the Enterprise Gym, University of Dundee.
Her perseverance and passion for entrepreneurship and innovation has seen her work her way up in the roles in the department.
She received her BSc in business Economics with marketing from Dundee University in 2014. Dora is actively contributing her
expertise in a number of areas of the project.
is Head of Enterprise &
Entrepreneurial Strategy at the University of Dundee.
Not a man to sit about, Alasdair has a brain that's always
busy and he loves thinking of creative ways to make a
difference. As well as his role at the university, Alasdair
is also a director and founder of several companies in
industries as diverse as retail, software, sports and
financial services. He is a formidable adviser within our
team.
is currently pursuing a master's
degree in Data Engineering. He studies Data because he likes
to observe and find patterns in the environment that
surrounds us. Yuri serves as a curator along with Sarah in
our team. He is involved in the recruitment of speakers.


{% extends "base.html" %} {% block pageTitle %}
{{campaign.title}} ( {{campaign.proposals.count}} proposals)
You have not started any campaigns.


The debugger caught an exception in your WSGI application. You can now
look at the traceback which lead to the error.
If you enable JavaScript you can also use additional features such as code
execution (if the evalex feature is enabled), automatic pasting of the
exceptions and much more.


This class is used to make an object for Task.
Get the associated document IDs of the task.
Array of document IDs.
# File projects/model/Task.rb, line 388 def getAssociateDocumentIds return @associateDocumentIds end
Get the associated forum IDs of the task.
Array of forum IDs.
# File projects/model/Task.rb, line 408 def getAssociateForumIds return @associateForumIds end
Get the comments of the task.
List of Comment object.
# File projects/model/Task.rb, line 368 def getComments return @comments end
Get the id of the user who created the task.
Id of the person who created the task.
# File projects/model/Task.rb, line 88 def getCreatedBy return @createdBy end
Get the name of the person who created the task.
Task created person name.
# File projects/model/Task.rb, line 108 def getCreatedPerson return @createdPerson end
Get the task duration.
Task duration.
# File projects/model/Task.rb, line 288 def getDuration return @duration end
Get the task end date.
task end date.
# File projects/model/Task.rb, line 228 def getEndDate return @endDate end
Get the task end date format.
Task end date format.
# File projects/model/Task.rb, line 248 def getEndDateFormat return @endDateFormat end
Get the end date long.
Task end date long.
# File projects/model/Task.rb, line 268 def getEndDateLong return @endDateLong end
Get the task name.
Task name.
# File projects/model/Task.rb, line 48 def getName return @name end
Get the owner of the task.
List of Owner object.
# File projects/model/Task.rb, line 348 def getOwners return @owners end
Get the task completed percent.
Task complete percent.
# File projects/model/Task.rb, line 148 def getPercentComplete return @percentComplete end
Get the priority of the task.
Task priority.
# File projects/model/Task.rb, line 128 def getPriority return @priority end
Get the task start date.
Task start date.
# File projects/model/Task.rb, line 168 def getStartDate return startDate end
Get the task start date format.
Task start date format.
# File projects/model/Task.rb, line 188 def getStartDateFormat return @startDateFormat end
Get the start date long.
Task start date long.
# File projects/model/Task.rb, line 208 def getStartDateLong return @startDateLong end
Get the subtasks of the task.
List of Task object.
# File projects/model/Task.rb, line 428 def getSubtasks return @tasks end
Get the tasklist of the task.
Tasklist object.
# File projects/model/Task.rb, line 448 def getTasklist return @tasklist end
Get the time sheet URL.
Time sheet URL.
# File projects/model/Task.rb, line 328 def getTimesheetURL return @timesheetUrl end
Get the task URL.
Task URL.
# File projects/model/Task.rb, line 308 def getURL return @url end
Get whether the task is completed or not.
true, if the task is completed else false.
# File projects/model/Task.rb, line 68 def isCompleted return @completed end
Set the associated document IDs of the task.
Document IDs of the task.
# File projects/model/Task.rb, line 378 def setAssociateDocumentIds(associateDocumentIds) @associateDocumentIds = associateDocumentIds end
Set the associated forum IDs.
Forum IDs of the task.
# File projects/model/Task.rb, line 398 def setAssociateForumIds(associateForumIds) @associateForumIds = associateForumIds end
Set the comments of the task.
List of Comment object.
# File projects/model/Task.rb, line 358 def setComments(comments) @comments = comments end
Set whether the task is completed or not.
Is task is completed or not.
# File projects/model/Task.rb, line 58 def setCompleted(completed) @completed = completed end
Set the id of the person who created the task.
Id of the user who created the task.
# File projects/model/Task.rb, line 78 def setCreatedBy(createdBy) @createdBy = createdBy end
Set the task created person name.
Name of the person who created the task.
# File projects/model/Task.rb, line 98 def setCreatedPerson(createdPerson) @createdPerson = createdPerson end
Set the task duration.
Duration of the task.
# File projects/model/Task.rb, line 278 def setDuration(duration) @duration = duration end
Set the task end date.
End date of the task.
# File projects/model/Task.rb, line 218 def setEndDate(endDate) @endDate = endDate end
Set the task end date format.
End date format of the task.
# File projects/model/Task.rb, line 238 def setEndDateFormat(endDateFormat) @endDateFormat = endDateFormat end
Set the end date long.
End date for the task.
# File projects/model/Task.rb, line 258 def setEndDateLong(endDateLong) @endDateLong = endDateLong end
Set the task id.
ID of the task.
# File projects/model/Task.rb, line 18 def setId(id) @id = id end
Set the task name.
Name of the task.
# File projects/model/Task.rb, line 38 def setName(name) @name = name end
Set the owners of the task.
List of Owner object.
# File projects/model/Task.rb, line 338 def setOwners(owners) @owners = owners end
Set the task completed percent.
Task completed percent.
# File projects/model/Task.rb, line 138 def setPercentComplete(percentComplete) @percentComplete = percentComplete end
Set the priority of the task.
Priority of the task.
# File projects/model/Task.rb, line 118 def setPriority(priority) @priority = priority end
Set the task start date.
Start date of the task.
# File projects/model/Task.rb, line 158 def setStartDate(startDate) @startDate = startDate end
Set the task start date format.
Start date format of the task.
# File projects/model/Task.rb, line 178 def setStartDateFormat(startDateFormat) @startDateFormat = startDateFormat end
Set the start date long.
Start date long for the task.
# File projects/model/Task.rb, line 198 def setStartDateLong(startDateLong) @startDateLong = startDateLong end
Set the subtasks of the task.
List of Task object.
# File projects/model/Task.rb, line 418 def setSubtasks(tasks) @tasks = tasks end
Set the tasklist of the task.
Tasklist object.
# File projects/model/Task.rb, line 438 def setTasklist(tasklist) @tasklist = tasklist end
Set the time sheet URL.
URL for the time sheet.
# File projects/model/Task.rb, line 318 def setTimesheetURL(timesheetUrl) @timesheetUrl = timesheetUrl end
Set the task URL.
URL for the task.
# File projects/model/Task.rb, line 298 def setURL(url) @url = url end
Convert the Task object into HashMap.
HashMap object.
# File projects/model/Task.rb, line 458 def toParamMAP() requestBody = Hash.new if owners != nil personResponsible = ""; owners.each do|owner| personResponsible += String(owner.getId)+"," end requestBody["person_responsible"] = personResponsible end if name != nil requestBody["name"] = name end if tasklist != nil requestBody["tasklist_id"] = tasklist.getId end if startDate != nil requestBody["start_date"] = startDate end if endDate != nil requestBody["end_date"] = endDate end if duration != nil requestBody["duration"] = duration end if priority != nil requestBody["priority"] = priority end return requestBody end


Changing QA Data Statuses¶
Every test that is performed in QATrack+ is assigned a Status that indicates what type of data the test is and whether or not it requires review. Details on how to review QA data and/or update the status of a set of tests are given below.
Reviewing QA Data¶
The number of test lists with unreviewed tests is displayed at the top of every QATrack+ page (provided you have the permissions required to review data).
To review data, choose the Unreviewed option from the Review Data dropdown menu at the top of the page.
You will then be presented with a list of QA sessions awaiting review. This list can be filtered/sorted by unit, frequency, date or user.
Click on the Review button of the QA session that you want to review and you can then change the status of the tests as described below.
Repeat those steps for all the QA sessions that you would like to review.
Changing the status of test data¶
On the following page select the status you would like to apply to the tests from the dropdown menu at the top. Click the checkmark next to the dropdown to apply that status to all the tests in the list (Test statuses can also be set individually.) Click the Update Test Statuses to save your changes to the database.


STORAGE
Storage refers to holding information somewhere. RAM, Random Acess Memory, Short term memory. It is volatile memory because the memory is automatically "erased" when the power is turned off or interrupted. The RAM is located inside the computer case on the motherboard. The motherboard is not the keyboard. The keyboard is what we type with. The motherboard holds RAM memory, electronic circuit and other computer parts including the central processing unit. ROM, Read Only Memory is not volatile meaing the memory is still there when the power is interrupted or turned off. When the computer is turned back on again, ROM memory is still in storage of the internal hard disk.
HOME
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PROCESSING
OUTPUT
STORAGE


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Basketball: Harden, Westbrook shine as Rockets extend win streak
LOS ANGELES: James Harden scored 36 points and Russell Westbrook bagged a triple double as the Houston Rockets extended their red-hot winning streak to eight games with a 132-108 rout of the Portland Trail Blazers on Monday (Nov 18).
Harden, fresh from a 49-point display in Saturday's win over Minnesota, was once again in commanding form against a Portland team who were always second best at Houston's Toyota Center.
The 2017-2018 NBA Most Valuable Player also had six rebounds and five assists as the Blazers struggled to contain the Rockets' multi-pronged offense.
Westbrook tormented the Portland defense, finishing with 28 points, 13 rebounds and 10 assists on a night when all five Houston starters finished in double figures.
Rockets coach Mike D'Antoni praised the contribution of Westbrook.
"Russell had an extra gear tonight, but it's hard to single out one guy," D'Antoni said. "They're all playing well."
The Rockets coach reserved special praise for Harden though, once again repeating his belief that the 30-year-old is the best offensive player he has ever seen.
"I'm lost for words," D'Antoni said.
"What he does is unbelievable... No matter what the other teams try to do, double-team or whatever, he just figures it out. It's incredible what he's doing."
The win saw the Rockets improve to 11-3 at the top of the Western Conference, neck-and-neck with the pace-setting Los Angeles Lakers (11-2).
CJ McCollum led the scoring for Portland with 25 points, but team-mate Damian Lillard was kept quiet with only 13 points. Portland fell to 5-9 with the defeat.
'ALONG FOR THE RIDE'
In Dallas, Luka Doncic scored a career-high 42 points and bagged a triple double to guide the Mavericks to a 117-110 win over the San Antonio Spurs.
The 20-year-old Slovenian is the second youngest player in history to score a 40-point triple double after LeBron James.
"It's not surprising to me," Mavericks coach Rick Carlisle said.
"This guy can do anything he wants to on a basketball court. And you know, he's having just one of those magical runs right now, and it's a phenomenal thing to watch. It's a phenomenal thing to be a part of. His teammates, we're all just kind of along for the ride."
In Los Angeles, Paul George scored 17 points against his former team as the Clippers eked out a nail-biting 90-88 victory over the Oklahoma City Thunder.
Montrezl Harrell was the star for the Clippers with 28 points off the bench in a hard-fought contest that went right down to the buzzer.
Elsewhere Monday, Giannis Antetokounmpo finished with a 33-point haul as the Milwaukee Bucks outgunned the Chicago Bulls 115-101.
Antetokounmpo's points tally was matched by impressive defensive numbers, with the Greek star pulling down 10 rebounds, forcing eight turnovers and making three steals.
Brook Lopez had 19 points while Donte DiVincenzo added 15 for the Bucks, who improved to 10-3 in the Eastern Conference standings.
In Toronto, O.G. Anunoby and Pascal Siakam sparked the Raptors in a 132-96 demolition of the Charlotte Hornets.
Anunoby had 24 points and Siakam 20 as seven Toronto players finished in double figures against an outclassed Charlotte team.
The Boston Celtics remain on top of the Eastern Conference table after grinding out a 99-85 win over the Phoenix Suns.
Jayson Tatum had 26 points and Kemba Walker 19 as the Celtics improved to 11-2.


The components the Shiny application "Exploring the Beta Density" are
- ui.R
- server.R
The components the Shiny application "Exploring the Beta Density" are
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Exploring The Beta Density"),
sidebarPanel(
sliderInput('alpha', 'Alpha',value = 1, min = 0.01, max = 10, step = 0.5,),
sliderInput('beta', 'Beta',value = 1, min = 0.01, max = 10, step = 0.5,)
),
mainPanel(
plotOutput('betaDensity')
)
))
library(shiny)
pvals <<- seq(0.01, 0.99, length = 1000)
shinyServer(
function(input, output) {
output$betaDensity <- renderPlot({
alpha <- max(input$alpha,0.01)
beta <- max(input$beta,0.01)
plot(pvals, dbeta(pvals, alpha, beta), type = "l", lwd = 3, col=4, frame = FALSE)
title(paste0("The Beta distribution with parameters alpha=",alpha," and beta=",beta," has density"))
})
}
)
Finally, we will use Shiny to add interactive controls to the chart we created previously. Suppose that we want to control Alpha
and the Beta
of plot. Let us first add the UI. slidifyUI
behaves almost like shinyUI
except that it outputs a character vector.


Warehouse Freightman Carlon:
You shouldn't carry around unnecessary cargo anymore! Our guild safely delivers packages for our customers, wherever they're needed! You can trust us!
Quest


|
|
|
Disclosure of Contracts Over $10,000
|
Vendor Name: |
KONICA MINOLTA BUSINESS SOLUTIONS | |
Reference Number: |
8019197 | |
Contract Date: |
2008-01-01 | |
Description of work: |
533 RENTAL OF MACHINERY OFFICE FURNITURE AND FIXTURES | |
Contract Period: |
2008-01-01 to 2010-12-31 | |
Contract Value: |
$18,034.41
| |
Comments: |
|
|


EDA Foundations - Final Reflections
17 May 2020
Three new things I've learned about myself and my ego as a result of core learning
- In exploring my personal core values, I learned that they have informed a lot of my life decisions. The times when I had been really unhappy in the past can be attributed to being put in environments that don't affirm my core values.
- After exploring growth mindset further, I realised that my goal-oriented ego can make me oblivious to the importance of the learning process. This ego sets unrealistic expectations and puts pressure on itself to achieve them and so it can get overwhelmed when it can't do so in a short amount of time.
- Following up on the above, I have learned that daily guided mindfulness meditation can help tame my less constructive egos (yes, there's more than one). It has helped bring out the growth mindset attributes I already have, such as persistence and embracing challenges.
What are the roles of values, empathy and self awareness in learning and programming?
In learning and programming, you come across people with various levels of programming experience and diverse backgrounds in various facets of life. Being self-aware and knowing your values can help you understand how you behave and why you respond in certain ways to different things. Having empathy for others helps you respond more appropriately to how others behave around you. These can help you navigate the dynamics and pressures in the learning and programming environments you find yourself in.
What has surprised you the most about the core learning?
I was suprised how much of the core learning covered concepts that I was already familiar with or have at least come across previously. It's great to be part of an environment that values these things. I found the core learning refreshing and they provided me with much needed breaks from the technical components.
What were the most challenging aspects of the core learning?
While I'm familiar with a lot of the core learning materials, it's because I enjoy exploring them that I actually found it challenging to not spend too much time on them. It was also challenging for me to not get trapped into overthinking things with the materials.
Why do you think EDA, a programming school, spends so much time focusing on core learning in a web development bootcamp course?
We're all humans and we bring our conditioning with us everywhere we go. In an intense bootcamp course like EDA, students come from diverse backgrounds and are put under a lot of pressure. We will be working with others heaps at EDA and need to be able to do so well. The core learning material is meant to set us up with the tools and awareness we need to get through bootcamp safely and well. They are also useful for life in general.
Does the time you spent studying core learning feel like a waste of time? Should you have just used that time to practise programming instead? Justify your answer.
As you can probably tell by now, I don't think it's a waste of time; EDA is preaching to the choir when it comes to core learning with me. For starters, employers look to hire people who can self-manage and work well with others. I also think that the tools and habits we pick up from the core learning materials will help us navigate real life circumstances including during bootcamp, at work and in our personal lives.
Vision and long term goal
My big picture vision is to utilise the skills I have gained in web development to contribute towards creating solutions for complex societal and environmental problems such as inequality and climate change. There's a lot of work and steps for me to figure out in order to get there.
The first step would be to start on a smaller scale of long term goal for after I leave EDA. I think front end development is the path I'd like to pursue so I'll be looking for work experience in this area. I think I may also need to dive into the world of design thinking as interdisciplinary crossovers is where creative solutions to complex matters tend to be found.
As for the rest, I'll have to figure it out as I progress in the web development world. One thing for sure, it won't be a linear path and I expect it to involve countless amount of challenges.
Strengths and limitations during bootcamp
I am self-motivated and I follow through on things I said I would do. These things should help me through bootcamp and to develop productive working relationships with others.
During Foundations, I noticed that I have a tendency to overthink what steps may be needed to solve challenges. I often thought I'd missed steps or that a solution would be more complicated than it needs to be. I'll need to be aware when I start overthinking things during bootcamp so I can pull myself back to evaluate my understanding and what steps I could realistically take to solve a problem.
What do you think your biggest non-technical challenge at bootcamp will be?
To be honest, I'm not sure that I'm technically ready for bootcamp, as my ability to code JavaScript is very primary at the moment. Due to this, I think being able to stay on top of the materials will be a challenge for me. Constantly being around other people during bootcamp could be challenging half the time as I need a decent amount of time on my own every day to recharge. The pressures of bootcamp would also be challenging.
What non-technical skills would you like to develop while at EDA?
I'd like to develop my ability to give and receive and evaluate feedback. Pair programming and coding problem solving skills are also things I'd like to develop. I'm also aware that I tend to internalise thoughts and things that happen around me including what others do and say. I'd like to work on verbalising my thoughts more when appropriate during my time at EDA.
Expectations of myself and self-management plan during bootcamp
My primary expectation of myself during bootcamp is that I look after my wellbeing. I believe this is key to managing other expectations I have of myself around putting in effort, being able to apply the materials in practice and improving my coding skills.
On the more practical side, I will plan out slots in my calendar for EDA and coding times, meal times, social breaks, exercise, mindfulness meditation and life maintenance like meal prepping, laundry and grocery shopping. I'll also need to go to sleep and wake up around the same time every day.
This self-management plan will help me see when I may need help as usually one of these things can be out of whack when I'm struggling. Looking after my wellbeing is the best way to ensure I'll be able to work productively and safely with others.


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| Home | Libraries | People | FAQ | More |
All the code in this library is inside namespace boost::math.
In order to use a distribution my_distribution you will need to include either the header <boost/math/distributions/my_distribution.hpp> or the "include everything" header: <boost/math/distributions.hpp>.
For example, to use the Students-t distribution include either <boost/math/distributions/students_t.hpp> or <boost/math/distributions.hpp>
Each kind of distribution in this library is a class type.
Policies provide fine-grained control of the behaviour of these classes, allowing the user to customise behaviour such as how errors are handled, or how the quantiles of discrete distribtions behave.
| Tip | |
|---|---|
If you are familiar with statistics libraries using functions, and 'Distributions as Objects' seem alien, see the comparison to other statistics libraries. |
Making distributions class types does two things:
Although the distribution classes in this library are templates, there
are typedefs on type double that mostly take the usual
name of the distribution (except where there is a clash with a function
of the same name: beta and gamma, in which case using the default template
arguments - RealType =
double
- is nearly as convenient).
Probably 95% of uses are covered by these typedefs:
using namespace boost::math; // Construct a students_t distribution with 4 degrees of freedom: students_t d1(4); // Construct a double-precision beta distribution // with parameters a = 10, b = 20 beta_distribution<> d2(10, 20); // Note: _distribution<> suffix !
If you need to use the distributions with a type other than double
, then you can instantiate the template
directly: the names of the templates are the same as the double
typedef but with _distribution
appended, for example: Students
t Distribution or Binomial
Distribution:
// Construct a students_t distribution, of float type, // with 4 degrees of freedom: students_t_distribution<float> d3(4); // Construct a binomial distribution, of long double type, // with probability of success 0.3 // and 20 trials in total: binomial_distribution<long double> d4(20, 0.3);
The parameters passed to the distributions can be accessed via getter member functions:
d1.degrees_of_freedom(); // returns 4.0
This is all well and good, but not very useful so far. What we often want is to be able to calculate the cumulative distribution functions and quantiles etc for these distributions.
Want to calculate the PDF (Probability Density Function) of a distribution? No problem, just use:
pdf(my_dist, x); // Returns PDF (density) at point x of distribution my_dist.
Or how about the CDF (Cumulative Distribution Function):
cdf(my_dist, x); // Returns CDF (integral from -infinity to point x) // of distribution my_dist.
And quantiles are just the same:
quantile(my_dist, p); // Returns the value of the random variable x // such that cdf(my_dist, x) == p.
If you're wondering why these aren't member functions, it's to make the library more easily extensible: if you want to add additional generic operations - let's say the n'th moment - then all you have to do is add the appropriate non-member functions, overloaded for each implemented distribution type.
| Tip | |
|---|---|
|
Random numbers that approximate Quantiles of Distributions If you want random numbers that are distributed in a specific way, for example in a uniform, normal or triangular, see Boost.Random. Whilst in principal there's nothing to prevent you from using the quantile function to convert a uniformly distributed random number to another distribution, in practice there are much more efficient algorithms available that are specific to random number generation. |
For example, the binomial distribution has two parameters: n (the number of trials) and p (the probability of success on one trial).
The binomial_distribution
constructor therefore has two parameters:
binomial_distribution(RealType n, RealType
p);
For this distribution the random variate is k: the number of successes observed. The probability density/mass function (pdf) is therefore written as f(k; n, p).
| Note | |
|---|---|
|
Random Variates and Distribution Parameters Random variates and distribution parameters are conventionally distinguished (for example in Wikipedia and Wolfram MathWorld by placing a semi-colon (or sometimes vertical bar) after the random variate (whose value you 'choose'), to separate the variate from the parameter(s) that defines the shape of the distribution. |
As noted above the non-member function pdf
has one parameter for the distribution object, and a second for the random
variate. So taking our binomial distribution example, we would write:
pdf(binomial_distribution<RealType>(n, p), k);
The ranges of random variate values that are permitted and are supported
can be tested by using two functions range
and support
.
The distribution (effectively the random variate) is said to be 'supported' over a range that is "the smallest closed set whose complement has probability zero". MathWorld uses the word 'defined' for this range. Non-mathematicians might say it means the 'interesting' smallest range of random variate x that has the cdf going from zero to unity. Outside are uninteresting zones where the pdf is zero, and the cdf zero or unity.
For most distributions, with probability distribution functions one might
describe as 'well-behaved', we have decided that it is most useful for
the supported range to exclude random variate values like exact zero if the end point is discontinuous. For example,
the Weibull (scale 1, shape 1) distribution smoothly heads for unity as
the random variate x declines towards zero. But at x = zero, the value
of the pdf is suddenly exactly zero, by definition. If you are plotting
the PDF, or otherwise calculating, zero is not the most useful value for
the lower limit of supported, as we discovered. So for this, and similar
distributions, we have decided it is most numerically useful to use the
closest value to zero, min_value, for the limit of the supported range.
(The range
remains from
zero, so you will still get pdf(weibull, 0)
== 0
).
(Exponential and gamma distributions have similarly discontinuous functions).
Mathematically, the functions may make sense with an (+ or -) infinite
value, but except for a few special cases (in the Normal and Cauchy distributions)
this implementation limits random variates to finite values from the max
to min
for the RealType
. (See
Handling
of Floating-Point Infinity for rationale).
| Note | |
|---|---|
|
Discrete Probability Distributions
Note that the discrete
distributions, including the binomial, negative binomial, Poisson
& Bernoulli, are all mathematically defined as discrete functions:
that is to say the functions However, because the method of calculation often uses continuous functions it is convenient to treat them as if they were continuous functions, and permit non-integral values of their parameters.
Users wanting to enforce a strict mathematical model may use The quantile functions for these distributions are hard to specify in a manner that will satisfy everyone all of the time. The default behaviour is to return an integer result, that has been rounded outwards: that is to say, lower quantiles - where the probablity is less than 0.5 are rounded down, while upper quantiles - where the probability is greater than 0.5 - are rounded up. This behaviour ensures that if an X% quantile is requested, then at least the requested coverage will be present in the central region, and no more than the requested coverage will be present in the tails. This behaviour can be changed so that the quantile functions are rounded differently, or return a real-valued result using Policies. It is strongly recommended that you read the tutorial Understanding Quantiles of Discrete Distributions before using the quantile function on a discrete distribtion. The reference docs describe how to change the rounding policy for these distributions. For similar reasons continuous distributions with parameters like "degrees of freedom" that might appear to be integral, are treated as real values (and are promoted from integer to floating-point if necessary). In this case however, there are a small number of situations where non-integral degrees of freedom do have a genuine meaning. |
Often you don't want the value of the CDF, but its complement, which is
to say 1-p
rather than p
.
You could calculate the CDF and subtract it from 1
,
but if p
is very close
to 1
then cancellation error
will cause you to lose significant digits. In extreme cases, p
may actually be equal to 1
, even though the true value of the complement
is non-zero.
In this library, whenever you want to receive a complement, just wrap all
the function arguments in a call to complement(...)
, for example:
students_t dist(5); cout << "CDF at t = 1 is " << cdf(dist, 1.0) << endl; cout << "Complement of CDF at t = 1 is " << cdf(complement(dist, 1.0)) << endl;
But wait, now that we have a complement, we have to be able to use it as
well. Any function that accepts a probability as an argument can also accept
a complement by wrapping all of its arguments in a call to complement(...)
,
for example:
students_t dist(5); for(double i = 10; i < 1e10; i *= 10) { // Calculate the quantile for a 1 in i chance: double t = quantile(complement(dist, 1/i)); // Print it out: cout << "Quantile of students-t with 5 degrees of freedom\n" "for a 1 in " << i << " chance is " << t << endl; }
| Tip | |
|---|---|
|
Critical values are just quantiles Some texts talk about quantiles, others about critical values, the basic rule is: Lower critical values are the same as the quantile. Upper critical values are the same as the quantile from the complement of the probability. For example, suppose we have a Bernoulli process, giving rise to a binomial distribution with success ratio 0.1 and 100 trials in total. The lower critical value for a probability of 0.05 is given by:
and the upper critical value is given by:
which return 4.82 and 14.63 respectively. |
| Tip | |
|---|---|
|
Why bother with complements anyway?
It's very tempting to dispense with complements, and simply subtract
the probability from 1 when required. However, consider what happens
when the probability is very close to 1: let's say the probability expressed
at float precision is Or to look at this another way: consider that we want the risk of falsely rejecting the null-hypothesis in the Student's t test to be 1 in 1 billion, for a sample size of 10,000. This gives a probability of 1 - 10-9, which is exactly 1 when calculated at float precision. In this case calculating the quantile from the complement neatly solves the problem, so for example:
returns the expected t-statistic
raises an overflow error, since it is the same as:
Which has no finite result. |
Sometimes it's the parameters that define the distribution that you need to find. Suppose, for example, you have conducted a Students-t test for equal means and the result is borderline. Maybe your two samples differ from each other, or maybe they don't; based on the result of the test you can't be sure. A legitimate question to ask then is "How many more measurements would I have to take before I would get an X% probability that the difference is real?" Parameter finders can answer questions like this, and are necessarily different for each distribution. They are implemented as static member functions of the distributions, for example:
students_t::find_degrees_of_freedom( 1.3, // difference from true mean to detect 0.05, // maximum risk of falsely rejecting the null-hypothesis. 0.1, // maximum risk of falsely failing to reject the null-hypothesis. 0.13); // sample standard deviation
Returns the number of degrees of freedom required to obtain a 95% probability that the observed differences in means is not down to chance alone. In the case that a borderline Students-t test result was previously obtained, this can be used to estimate how large the sample size would have to become before the observed difference was considered significant. It assumes, of course, that the sample mean and standard deviation are invariant with sample size.
complement(...)
.
complement(...)
.
Now that you have the basics, the next section looks at some worked examples.


JNU protest: Entry, exit at three Delhi Metro stations near Parliament reopened
Entry/exit gates for Lok Kalyan Marg are still closed and trains will not be halting at the station.
by PTIThe entry and exit points of three Delhi Metro stations near Parliament have been reopened after a brief closure on Monday due to the ongoing protest by JNU students, authorities said.
“Entry or exit at Udyog Bhawan, Patel Chowk and Central Secretariat have been opened. Trains are halting at all three stations. Entry/exit gates for Lok Kalyan Marg are still closed and trains will not be halting at the station,” the DMRC tweeted.
Thousands of Jawaharlal Nehru University students, carrying placards and chanting slogans, marched towards Parliament on the first day of the Winter Session, but were stopped by police around half a kilometre from the institute’s campus in south Delhi.
Earlier, the entry and exit points of the three stations near the Parliament have been shut down temporarily on Monday, due to the ongoing JNU protest.
“As advised by Delhi Police, trains are not halting at Udyog Bhawan and Patel Chowk. Exit/Entry gates for Udyog Bhawan, Patel Chowk and Central Secretariat have been closed temporarily,” the DMRC tweeted.


Add debug flags to the MSVC toolchain:
DefaultToolchain MSVC {
Compiler CPP {
Flags "-Zi"
}
Linker {
Flags "-Debug"
}
}
Start Visual Studio with e.g. a batch file shown below and choose the appropriate solution. If you don't have a solution yet, check out How to create a solution / projects in Visual Studio.
set PATH=%PATH%;C:\tools\Microsoft Visual Studio 11.0\VC\bin
call "C:\tools\Microsoft Visual Studio 11.0\VC\vcvarsall.bat"
"C:\tools\Microsoft Visual Studio 11.0\Common7\IDE\devenv.exe"
This adds the compiler, linker, etc. to the path. vcvarsall.bat setups the environment and the last line starts Visual Studio. As a test, just execute the first two lines and check if "cl.exe" can be executed without any errors. In Visual Studio, you have to define the executable you like to debug. Add the executable in the project properties:
You can also define command line arguments here.
Start debugging as usual...


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CBPS: Covariate Balancing Propensity Score
Implements the covariate balancing propensity score (CBPS) proposed
by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>. The propensity score is
estimated such that it maximizes the resulting covariate balance as well as the
prediction of treatment assignment. The method, therefore, avoids an iteration
between model fitting and balance checking. The package also implements several
extensions of the CBPS beyond the cross-sectional, binary treatment setting.
The current version implements the CBPS for longitudinal settings so that it can
be used in conjunction with marginal structural models from Imai and Ratkovic
(2015) <doi:10.1080/01621459.2014.956872>, treatments with three- and four-
valued treatment variables, continuous-valued treatments from Fong, Hazlett,
and Imai (2015) <doi:10.1214/17-AOAS1101>, and the
situation with multiple distinct binary treatments administered simultaneously.
In the future it will be extended to other settings including the generalization
of experimental and instrumental variable estimates. Recently we have added the
optimal CBPS which chooses the optimal balancing function and results in doubly
robust and efficient estimator for the treatment effect as well as high dimensional
CBPS when a large number of covariates exist.
| Version: |
0.19 |
| Depends: |
R (≥ 3.4), MASS, MatchIt, nnet, numDeriv, glmnet |
| Suggests: |
testthat |
| Published: |
2018-06-17 |
| Author: |
Christian Fong [aut, cre],
Marc Ratkovic [aut],
Kosuke Imai [aut],
Chad Hazlett [ctb],
Xiaolin Yang [ctb],
Sida Peng [ctb] |
| Maintainer: |
Christian Fong <christianfong at stanford.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
ChangeLog |
| CRAN checks: |
CBPS results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=CBPS
to link to this page.


Jad options
Jad has many options that allow the decompilation and its result to be tweaked. Most
of these options are represented by this configuration view.
Note that some options, such as redirect decompilation output to standard output, are
not available due to the way IntelliJad manipulates these features.
The options available here are (hopefully - please tell me if they're not!) self-explanatory.


Welcome to the Job Hunting Strategy!
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6. Year of Experience you have related to Machine Learning
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7. Which of the following integrated development environments (IDE's) do you use on a regular basis?
Jupyter Notebook
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8. What programming languages do you use on a regular basis?
Python
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9. Which of the following ML algorithms do you use on a regular basis?
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines
Bayesian Approaches
Convolutional Neural Networks
Generative Adversarial Networks
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10. Which of the following machine learning frameworks do you use on a regular basis?
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Other:


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The yacht's ETA should be advised at least 12 hours before arrival.
Contact the Port Authority on VHF.
A pilot boat will meet and escort the yacht to a berth.
Last updated March 2010.
Tripoli is the capital and main port of Libya. It is a modern city with a population of about 1 million. Its old city, " The Medina " is at its centre, next to the harbour. Due mainly to its isolation from western tourists, as well as a greater prosperity, there is a pleasant freedom from persistent salesmen not experienced in neighbouring countries.
If the proposed berth is untenable due to swell, the Port Authority will usually allow anchoring in a more sheltered part of the harbour.
Position 32°54'N, 013°11'E.
Nothing is known, yet, about facilities for yachts.


So I am new to OEM and been looking around and there seems to be a lot of great tools and learning points on here. I do admit I have to learn a bit of the Jargon but that should come easily once I dive a little deeper. I do have experience with Arduino's and a small bit with Raspberry Pi's and doing hobby circuits.
However onto my question, Is there a solution, system, DIY kit, way, Ect to monitor every breaker on our power box (maybe multiple power boxes). We have about 12 breakers/lines/circuits I would like to monitor. I understand typically you only really need to monitor the mains to see how much power you are pulling over all. However I really want to know how much each circuit is pulling so I can monitor our entire space's power and consumption in different areas. In all truth I would love to do even more but that will have to wait until my dream house is built. And I would like to treat this as a learning experience for the future. The hardware I have seen has been 4 monitoring posts and 6 at the most. Which is shy of what I want, and I am not sure if you can stack these systems together or not.
What I want to monitor:
- Main Power box, 10 circuits
- Sub Pannel box, 14 circuits
And I would like to see each circuit's usage and label them. And have the added total for each box's usage (done software side easy enough however maybe double checked by a sensor) I can go for monitoring a single box but would love to do both. Also I know this is all software and I could program it if necessary to have usage graphs for every area of the house and history.
If there is any solution, hacker kit, Ect out there I would be very happy to know about it or a few links in the right direction. I am continuing to look around and have already looked into the following places;
- Here obviously
- The energy detective
- Some Google Fu
Thanks
Re: Full Breaker Monitoring for 12 Breakers
sadly the EmonTx only monitors 4 at a time. This site
http://boredomprojects.net/index.php/projects/home-energy-monitor
has done 12 by creating his own EmonTX - its based on an Ardunio Due so its got 12 bit rather than 10bit A/D !
The labeling of the results is handled in EmonCMS so you know how much power is using.
Have a look on his site its far better explained.
Re: Full Breaker Monitoring for 12 Breakers
Thanks a lot. That looks like a good solution for me. I am looking deeper into it but that project looks like what I want to do. I already have 3 Ardunio Due and a Raspberry Pi in my collection. Thanks again. But I will keep looking around just to see what else is out there.


trimcore/0
reclaims space in all of Prolog's data areas. At any
given time, each data area contains some free space. For example, the
local stack space contains the local stack and some free space for
that stack to grow into. The data area is automatically expanded when
it runs out of free space, and it remains expanded until
trimcore/0
is called, even though the stack may have shrunk
considerably in the meantime. The effect of trimcore/0
is to
reduce the free space in all the data areas as much as possible, and to
give the space no longer needed back to the operating system.
trimcore/0
is called each time Prolog returns to the
top level or the top of a break level.


Court tosses Carl Icahn's suit seeking Occidental acquisition records
HOUSTON: Activist investor Carl Icahn will not have access to Occidental Petroleum Corp's Anadarko takeover records to support his proxy fight against the oil firm's board, a Delaware court judge ruled on Thursday.
Icahn sued Occidental in Delaware Court of Chancery last May seeking financial records and details of its negotiations with Anadarko. He has since committed to a proxy fight to appoint new management and accelerate asset sales to repay debt taken on with the US$38 billion deal.
In a sharply worded decision, Vice Chancellor Joseph Slights wrote Icahn's complaints of Occidental mismanagement "appear to be nothing more than disagreements" with the company's directors and his suit failed to show how the documents would advance his proxy contest.
Neither Icahn nor Occidental immediately responded to requests for comment.
Icahn last week disclosed the sale of 10 million shares of the oil and gas producer, reducing his Occidental holdings by nearly a third, according to a letter to shareholders. His stake, valued at about US$1.6 billion last May, was worth about US$900 million after the sale, he wrote.
Replacing the company's leadership would cause its value to "meaningfully increase," he said in an interview last week. Icahn then declined to say how much he lost on the sale of the 10 million Occidental shares.
Occidental traded at US$37.76 on Thursday, down from US$65.33 on April 12 when the company's interest in Anadarko was first disclosed.


The Repository Interactions API allows people with owner or admin access to temporarily restrict interactions in a public repository. For more information, see "Limiting interactions in your repository."


Kathie Lee Gifford Moved To Nashville Because She Was ‘Dying Of Loneliness’ After Death Of Frank Gifford
by Shakiel MahjouriKathie Lee Gifford was looking for a sense of community following the death of her husband.
The former “Today” show host opened up about her life after Frank Gifford’s passing in an interview with Nashville Tennessean. Gifford, 66, said she yearned for a sense of community in the aftermath of her husband’s death in 2015. That prompted her to move from Connecticut to Nashville.
“I moved here because I was dying of loneliness,” she explained. “It’s a culture of kindness in Nashville,” she continued, describing locals as “authentically kind” and “joyful.” Of her Connecticut mansion, she said, “that huge beautiful memory-filled home was like a morgue to me.”
Gifford also touched on how her family life, or lack thereof, allows her more time to pursue writing and other projects.
“Here’s the bad news – I’m a widow, an orphan, and an empty-nester. The good news is, I have the freedom of a widow, an orphan and an empty-nester,” she said. “I’ve got all the time to spend my days writing. I’m having the life I could’ve only ever dreamed of.”
Frank died of natural causes at his Greenwich, Connecticut home, a week before his 85th birthday. Gifford and her husband share two children: Cody, 29, and Cassidy Erin, 26.


Before Change
An observation is an array the velocities for each vehicle
self.obs_var_labels = ["Velocity"]
return Box(low=-np.inf, high=np.inf, shape=(self.scenario.num_vehicles, ))
def apply_action(self, car_id, action):
See parent class (base_env)
After Change
See parent class
An observation is an array the velocities for each vehicle
speed = Box(low=-np.inf, high=np.inf, shape=(self.scenario.num_vehicles,))
absolute_pos = Box(low=0., high=np.inf, shape=(self.scenario.num_vehicles,))
return Product([speed, absolute_pos])
def apply_action(self, car_id, action):
See parent class (base_env)


Cindey Caine
Project Portfolio
Cindey is a Computer Science student at UCSD. She transfered from City College of San Francisco. She is originally from the bay area.
Projects
- Data Analysis - PCA,Naive Bayes,Clustering
- Trie Structures
- File Compression - Encode/Decode using Huffman Code
- Graphs with Searching


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The time limit for searching words is three minutes.
When the timer starts, all players search for the valid words.
A player can search a word starting from a letter tile to an adjacent letter tile.
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--- layout: post title: "DI-Friendly Library" description: "How to create a Dependency Injection-friendly software library." date: 2014-05-19 8:20 UTC tags: [Software Design, Dependency Injection] image: "/content/binary/fluent-builder-intellisense-discoverability.png" image_alt: "Intellisense and Fluent Builder combined enhances discoverability" --- {% include JB/setup %}
{{ page.description }}
In my book, I go to great lengths to explain how to develop loosely coupled applications using various Dependency Injection (DI) patterns, including the Composition Root pattern. With the great emphasis on applications, I didn't particularly go into details about making DI-friendly libraries. Partly this was because I didn't think it was necessary, but since one of my highest voted Stack Overflow answers deal with this question, it may be worth expanding on.
In this article, I will cover libraries, and in a later article I will deal with frameworks. The distinction I usually make is:
Most well-designed libraries are already DI-friendly - particularly if they follow the SOLID principles, because the Dependency Inversion Principle (the D in SOLID) is the guiding principle behind DI.
Still, it may be valuable to distil a few recommendations.
If your library consists of several collaborating classes, define proper interfaces between these collaborators. This enables clients to redefine part of your library's behaviour, or to slide cross-cutting concerns in between two collaborators, using a Decorator.
Be sure to define these interfaces as Role Interfaces.
An example of a small library that follows this principle is Hyprlinkr, which defines two interfaces used by the main RouteLinker class:
public interface IRouteValuesQuery { IDictionary<string, object> GetRouteValues( MethodCallExpression methodCallExpression); }
and
public interface IRouteDispatcher { Rouple Dispatch( MethodCallExpression method, IDictionary<string, object> routeValues); }
This not only makes it easier to develop and maintain the library itself, but also makes it more flexible for users.
Favour the Constructor Injection pattern over other injection patterns, because of its simplicity and degree of encapsulation.
As an example, Hyprlinkr's main class, RouteLinker, has this primary constructor:
private readonly HttpRequestMessage request; private readonly IRouteValuesQuery valuesQuery; private readonly IRouteDispatcher dispatcher; public RouteLinker( HttpRequestMessage request, IRouteValuesQuery routeValuesQuery, IRouteDispatcher dispatcher) { if (request == null) throw new ArgumentNullException("request"); if (routeValuesQuery == null) throw new ArgumentNullException("routeValuesQuery"); if (dispatcher == null) throw new ArgumentNullException("dispatcher"); this.request = request; this.valuesQuery = routeValuesQuery; this.dispatcher = dispatcher; }
Notice that it follows Nikola Malovic's 4th law of IoC that Injection Constructors should be simple.
Although not strictly required in order to make a library DI-friendly, expose every injected dependency as an Inspection Property - it will make the library easier to use when composed in one place, but used in another place. Again, Hyprlinkr does that:
public IRouteValuesQuery RouteValuesQuery { get { return this.valuesQuery; } }
and so on for its other dependencies, too.
Sometimes, your library must create short-lived objects in order to do its work. Other times, the library can only create a required object at run-time, because only at run-time is all required information available. You can use an Abstract Factory for that.
The Abstract Factory doesn't always have to be named XyzFactory; in fact, Hyprlinkr's IRouteDispatcher interface is an Abstract Factory, although it's in disguise because it has a different name.
public interface IRouteDispatcher { Rouple Dispatch( MethodCallExpression method, IDictionary<string, object> routeValues); }
Notice that the return value of an Abstract Factory doesn't have to be another interface instance; in this case, it's an instance of the concrete class Rouple, which is a data structure without behaviour.
If some objects are difficult to construct, because their classes have complex constructors, consider supplying a Facade with a good default combination of appropriate dependencies. Often, a simple alternative to a Facade is Constructor Chaining:
public RouteLinker(HttpRequestMessage request) : this(request, new DefaultRouteDispatcher()) { } public RouteLinker(HttpRequestMessage request, IRouteValuesQuery routeValuesQuery) : this(request, routeValuesQuery, new DefaultRouteDispatcher()) { } public RouteLinker(HttpRequestMessage request, IRouteDispatcher dispatcher) : this(request, new ScalarRouteValuesQuery(), dispatcher) { } public RouteLinker( HttpRequestMessage request, IRouteValuesQuery routeValuesQuery, IRouteDispatcher dispatcher) { if (request == null) throw new ArgumentNullException("request"); if (routeValuesQuery == null) throw new ArgumentNullException("routeValuesQuery"); if (dispatcher == null) throw new ArgumentNullException("dispatcher"); this.request = request; this.valuesQuery = routeValuesQuery; this.dispatcher = dispatcher; }
Notice how the Routelinker class provides appropriate default values for those dependencies it can.
However, a Library with a more complicated API could potentially benefit from a proper Facade. One way to make the API's extensibility points discoverable is by implementing the Facade as a Fluent Builder. The following RouteLinkerBuilder isn't part of Hyprlinkr, because I consider the Constructor Chaining alternative simpler, but it could look like this:
public class RouteLinkerBuilder { private readonly IRouteValuesQuery valuesQuery; private readonly IRouteDispatcher dispatcher; public RouteLinkerBuilder() : this(new ScalarRouteValuesQuery(), new DefaultRouteDispatcher()) { } private RouteLinkerBuilder( IRouteValuesQuery valuesQuery, IRouteDispatcher dispatcher) { this.valuesQuery = valuesQuery; this.dispatcher = dispatcher; } public RouteLinkerBuilder WithValuesQuery(IRouteValuesQuery newValuesQuery) { return new RouteLinkerBuilder(newValuesQuery, this.dispatcher); } public RouteLinkerBuilder WithDispatcher(IRouteDispatcher newDispatcher) { return new RouteLinkerBuilder(this.valuesQuery, newDispatcher); } public RouteLinker Create(HttpRequestMessage request) { return new RouteLinker(request, this.valuesQuery, this.dispatcher); } public IRouteValuesQuery ValuesQuery { get { return this.valuesQuery; } } public IRouteDispatcher Dispatcher { get { return this.dispatcher; } } }
This has the advantage that it's easy to get started with the library:
var linker = new RouteLinkerBuilder().Create(request);
This API is also discoverable, because Intellisense helps users discover how to deviate from the default values:
It enables users to override only those values they care about:
var linker = new RouteLinkerBuilder().WithDispatcher(customDispatcher).Create(request);
If I had wanted to force users of Hyprlinkr to use the (hypothetical) RouteLinkerBuilder, I could make the RouteLinker constructor internal
, but I don't much care for that option; I prefer to empower my users, not constrain them.
Any application that uses your library can compose objects from it in its Composition Root. Here's a hand-coded example from one of Grean's code bases:
private static RouteLinker CreateDefaultRouteLinker(HttpRequestMessage request) { return new RouteLinker( request, new ModelFilterRouteDispatcher( new DefaultRouteDispatcher() ) ); }
This example is just a small helper method in the Composition Root, but as you can see, it composes a RouteLinker instance using our custom ModelFilterRouteDispatcher class as a Decorator for Hyprlinkr's built-in DefaultRouteDispatcher.
However, it would also be easy to configure a DI Container to do this instead.
If you follow SOLID, and normal rules for encapsulation, your library is likely to be DI-friendly. No special infrastructure is required to add 'DI support' to a library.
Comments
I found great library for in process messaging made by Jimmy Bogard - MediatR, but it uses service locator. Implemented mediator uses service locator to lookup for handlers matching message type registered in container. Source.
What would be best approach to eliminate service locator in this case? Would it be better to pass all handler instances in mediator constructor and then lookup for matching one?
Maris, thank you for writing. Hopefully, this article answers your question.


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The personal head tax was four reales or two chickens and a sack of rice. And besides, this new naming pattern made it easy to trace the lineage of all the tulisanes and highway bandits who roamed the countryside.
Claveria in 1849 called a meeting of all the provincial governors in the country.  The parish priests helped.
He tore pages of the Catalogo and gave them to those present. Consequently, what happened was unexpected. Since the lists of surnames were listed in alphabetical order, provinces appeared to have surnames beginning with a certain letter of the alphabet. An appropriate example is like tearing several pages from a telephone book. The likelihood of having the same beginning letter is high.
In my province of Mindoro alone, in Lubang community, the surnames of most people begin with “V” -- Ventura, Villamin, Villaraza, Villarosa, Villarica, Villavicencio,” Villaclara,” etc. In my hometown of Calapan, Oriental Mindoro, the surnames in my class included Acedillo, Acedera, Abaca, Abadilla, Abierto, Acera, Aboboto, Agay, Acasio, Adeva, Alvaro, Alfalfaro, Alcancia, Abolencia, Abadejo, Abad and so on.
In the town of Miagao, Iloilo, all surnames began with “M.”


(For a full list of publications and patents see below or go to Google Scholar, ReseachGate ID)
We performed whole-exome sequencing on tumor samples from 486 patients of the JUPITER-06 study and develop a copy number alteration-corrected tumor mutational burden that depicts immunogenicity more precisely and predicts chemo+anti-PD-1 efficacy. An esophageal cancer genome-based immuno-oncology classification (EGIC) scheme incorporating these immunogenic features and oncogenic alterations is established. EGIC may guide future individualized treatment strategies and inform mechanistic biomarker research for chemo+anti-PD-1 treatment in patients with advanced ESCC.
Yan-Xing Chen†, Zi-Xian Wang†, Ying Jin†, Qi Zhao†, Ze-Xian Liu, Zhi-Xiang Zuo, Huai-Qiang Ju, Chengxu Cui, Jun Yao, Yanqiao Zhang, Mengxia Li, Jifeng Feng, Lin Tian, Xiao-Jun Xia, Hui Feng, Sheng Yao, Feng-Hua Wang, Yu-Hong Li, Feng Wang, Rui-Hua Xu*
Cancer Cell. 2023 May 8;41(5):919-932.e5.
VSOLassoBag is a variable-selection oriented LASSO bagging algorithm for biomarker development in omic-based translational research, providing a bagging LASSO framework for selecting important variables from multiple models. In this package, we introduced several methods, namely (1) curve elbow point detection, (2) parametrical statistical test and (3) permutation test to help determine the cut-off point for variables selection.
Jiaqi Liang†, Chaoye Wang†, Di Zhang†, Yubin Xie, Yanru Zeng, Tianqin Li, Zhixiang Zuo, Jian Ren, Qi Zhao*
Journal of Genetics and Genomics. 2023 Mar;50(3):151-162.
We developed a practical computational framework for comprehensively exploring the MPT from multiregion sequencing (MRS) experiments.To verify the utility of MPTevol, we performed whole-exome MRS for 33 samples of a rare patient with triple-primary tumors and three metastatic sites and systematically investigated clonal dynamics and metastatic routines.
Qingjian Chen†, Qi-Nian Wu†, Yu-Ming Rong†, Shixiang Wang, Zhixiang Zuo, Long Bai, Bei Zhang, Shuqiang Yuan*, Qi Zhao*
Briefings in Bioinformatics, bbac175, 2022
We perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. we find that mitochondrial DNA copy number is an independent factor for predicting the survival outcome of CRCs. Overall, our results provide CRC-related molecular features for clinical practice and a valuable resource for translational research.
Qi Zhao†, Feng Wang†*, Yan-Xing Chen†, Shifu Chen†, Yi-Chen Yao, Zhao-Lei Zeng, Teng-Jia Jiang, Ying-Nan Wang, Chen-Yi Wu, Ying Jing, You-Sheng Huang, Jing Zhang, Zi-Xian Wang, Ming-Ming He, Heng-Ying Pu, Zong-Jiong Mai, Qi-Nian Wu, Renwen Long, Xiaoni Zhang, Tanxiao Huang, Mingyan Xu, Miao-Zheng Qiu, Hui-Yan Luo, Yu-Hong Li, Dong-Shen Zhang, Wei-Hua Jia, Gong Chen, Pei-Rong Ding, Li-Ren Li, Zheng-Hai Lu, Zhi-Zhong Pan & Rui-Hua Xu *
We present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS.
Mengni Liu, Jianyu Chen, Xin Wang, Chengwei Wang, Xiaolong Zhang, Yubin Xie, Zhixiang Zuo, Jian Ren, Qi Zhao*
GigaScience, 10(5), 2021, giab036
In this work, through transcriptomic profiling analysis of 21 paired tumor and normal samples, we identified a novel colorectal cancer–related lncRNA, MNX1-AS1.
Qi-Nian Wu, Xiao-Jing Luo, Jia Liu, Yun-Xin Lu, Yun Wang, Jingjing Qi, Ze-Xian Liu, Qi-Tao Huang, Ze-Kun Liu, Jia-Bin Lu, Ying Jin, Heng-Ying Pu, Pei-Shan Hu, Jia-Bo Zheng, Zhao-Lei Zeng, Huai-Qiang Ju, Dan Xie, Qi Zhao*, Ruihua Xu*
Cancer Research, 2021, 81(10):2636-2650
In this work, we reviewed current Genetic and Epigenetic Biomarkers of Prognostic and Predictive Significance in Stage II/III Colorectal Cancer”
Luo, Xiao-Jing, Qi Zhao†, Jia Liu, Jia-Bo Zheng, Miao-Zhen Qiu, Huai-Qiang Ju, and Rui-Hua Xu*.
Molecular Therapy, 2021 Feb 3;29(2):587-596.
In this study, transcriptomic aberrance and immune microenvironment of small‐cell carcinoma of the esophagus (SCCE) was comprehensively characterized with transcriptome and immunohistochemistry analysis. Assessment of immune checkpoint blockade (ICB) treatment biomarkers provides a rationale for use of ICB treatment in patients with SCCE.
Qi Zhao, Yan-Xing Chen†, Qi-Nian Wu†, Chao Zhang, Min Liu Ying-Nan Wang, Yan-Fen Feng, Jia-Jia Hu, Jian-Hua Fu, Hong Yang, Jing-Jing Qi, Zi-Xian Wang, Yun-Xin Lu, Hui Sheng, Ze-Xian Liu, Zhi-Xiang Zuo, Jian Zheng, Jing-Ping Yun, Jin-Xin Bei, Wei-Hua Jia, Dong-Xin Lin, Rui-hua Xu, Feng Wang*.
Clinical & Translational Immunology, 2020 Oct 5;9(10):e1173
(See also Google Scholar; † for equal contribution; * for corresponding authors)
[36]. Latent class analysis-derived classification improves the cancer-specific death stratification of molecular subtyping in colorectal cancer
Wen Zhou†, Ming-Ming He†, Feng Wang, Rui-Hua Xu, Fang Wang* & Qi Zhao*.
NPJ Precis Oncol. 2023 Jun 23;7(1):60.
[35]. Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins
Miao-Zhen Qiu†, Qingjian Chen†, Dan-Yang Zheng†, Qi Zhao†, Qi-Nian Wu†, Zhi-Wei Zhou, Li-Qiong Yang, Qiu-Yun Luo, Yu-Ting Sun, Ming-Yu Lai, Sha-Sha Yuan, Feng-Hua Wang, Hui-Yan Luo, Feng Wang, Yu-Hong Li, Hui-Zhong Zhang, and Rui-Hua Xu*.
Cell Reports. 2023. DOI:https://doi.org/10.1016/j.celrep.2023.112576
[34]. Unveiling the interplay between mutational signatures and tumor microenvironment: a pan-cancer analysis
Li-Zhi Luo†, Sheng Li†, Chen Wei, Jiao Ma, Li-Mei Qian, Yan-Xing Chen, Shi-Xiang Wang* and Qi Zhao*.
Front. Immunol., 22 May 2023
[33]. An immunogenic and oncogenic feature-based classification for chemotherapy plus PD-1 blockade in advanced esophageal squamous cell carcinoma
Yan-Xing Chen†, Zi-Xian Wang†, Ying Jin†, Qi Zhao†, Ze-Xian Liu, Zhi-Xiang Zuo, Huai-Qiang Ju, Chengxu Cui, Jun Yao, Yanqiao Zhang, Mengxia Li, Jifeng Feng, Lin Tian, Xiao-Jun Xia, Hui Feng, Sheng Yao, Feng-Hua Wang, Yu-Hong Li, Feng Wang, Rui-Hua Xu*
Cancer Cell. 2023 May 8;41(5):919-932.e5.
[32]. Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy.
Kunxiang Liu, Bo Liu, Yuhong Zhang, Qinian Wu, Ming Zhong, Lindong Shang, Yu Wang, Peng Liang, Weiguo Wang, Qi Zhao*, Bei Li*.
Computational and Structural Biotechnology Journal, 2022, 21, 802-811.
[31]. Whole-Exome Sequencing Among Chinese Patients With Hereditary Diffuse Gastric Cancer.
Ze-Xian Liu†, Xiao-Long Zhang†, Qi Zhao†, Yungchang Chen, Hui Sheng, Cai-Yun He, Yu-Ting Sun, Ming-Yu Lai, Min-Qing Wu, Zhi-Xiang Zuo, Wei Wang, Zhi-Wei Zhou, Feng-Hua Wang, Yu-Hong Li, Rui-Hua Xu*, Miao-Zhen Qiu*.
JAMA Network Open. 2022 Dec 1;5(12):e2245836.
[30]. VSOLassoBag: a variable-selection oriented LASSO bagging algorithm for biomarker discovery in omic-based translational research
Jiaqi Liang†, Chaoye Wang†, Di Zhang†, Yubin Xie, Yanru Zeng, Tianqin Li, Zhixiang Zuo, Jian Ren, Qi Zhao*
Journal of Genetics and Genomics. 2023 Mar;50(3):151-162.
[29]. Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol
Qingjian Chen†, Qi-Nian Wu†, Yu-Ming Rong†, Shixiang Wang, Zhixiang Zuo, Long Bai, Bei Zhang, Shuqiang Yuan*, Qi Zhao*
Briefings in Bioinformatics, bbac175, 2022
[28].  [Review]Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis
Kunxiang Liu, Qi Zhao, Bei Li*, and Xia Zhao*
Front. Bioeng. Biotechnol (2022).
[26]. Driver mutations in ADGRL3 are involved in the evolution of ependymoma
Jing Wang†*, Shao-yan Xi†, Qi Zhao†, Yun-fei Xia, Qun-ying Yang, Hai-ping Cai, Fang Wang, Yi-ying Zhao, Huan-jing Hu, Zhi-hui Yu, Fu-rong Chen, Peng-Fei Xu, Ri-zhen Xu, Jian Wang, Ji Zhang, Chao Ke, Xiang-heng Zhang, Fu-hua Lin, Cheng-cheng Guo, Yan-chun Lv, Cong Li, Hai-tao Xie, Qian Cui, Hong-mei Wu, Yan-hui Liu, Zhi Li, Hong-kai Su, Jing Zeng, Fu Han, Zhao-jie Li, Ke Sai* & Zhong-ping Chen*
Lab Invest (2022).
[25]. POLE/POLD1 mutation in non-exonuclease domain matters for predicting efficacy of immune-checkpoint-inhibitor therapy
Yan-Xing Chen, Zi-Xian Wang, Shu-Qiang Yuan, Teng-Jia Jiang, You-Sheng Huang, Rui-Hua Xu, Feng Wang*, Qi Zhao*
Clin Transl Med. 2021 Sep;11(9):e524.
[24]. MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations
Mengni Liu, Jianyu Chen, Xin Wang, Chengwei Wang, Xiaolong Zhang, Yubin Xie, Zhixiang Zuo, Jian Ren, Qi Zhao*
GigaScience, 10(5), 2021, giab036
[23]. MYC-activated lncRNA MNX1-AS1 promotes the progression of colorectal cancer by stabilizing YB1
Qi-Nian Wu, Xiao-Jing Luo, Jia Liu, Yun-Xin Lu, Yun Wang, Jingjing Qi, Ze-Xian Liu, Qi-Tao Huang, Ze-Kun Liu, Jia-Bin Lu, Ying Jin, Heng-Ying Pu, Pei-Shan Hu, Jia-Bo Zheng, Zhao-Lei Zeng, Huai-Qiang Ju, Dan Xie, Qi Zhao*, Ruihua Xu*
Cancer Research, 2021, 81(10):2636-2650
[22]. Novel Genetic and Epigenetic Biomarkers of Prognostic and Predictive Significance in Stage II/III Colorectal Cancer
Luo, Xiao-Jing, Qi Zhao†, Jia Liu, Jia-Bo Zheng, Miao-Zhen Qiu, Huai-Qiang Ju, and Rui-Hua Xu*.
Molecular Therapy, 2021 Feb 3;29(2):587-596.
[21]. Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks
Hongyu Li†, Qi Zhao†, Yihua Zhang†, Ke Sai, Lunshan Xu, Yonggao Mou, Yubin Xie, Jian Ren*, Xiaobing Jiang*.
Computational and Structural Biotechnology Journal, 2021 May 14;19:3077-3086.
[20]. Evaluation of POLE / POLD1 Variants as Potential Biomarkers for Immune Checkpoint Inhibitor Treatment Outcomes—Reply
Feng Wang , Qi Zhao, Rui-Hua Xu
JAMA oncology, 2020 Mar 5.
[19]. Systematic analysis of the aberrances and functional implications of ferroptosis in cancer.
Zekun Liu†, Qi Zhao†, Zhi-Xiang Zuo†, Shu-Qiang Yuan†, Kai Yu, Qingfeng Zhang, Xiaolong Zhang, Hui Sheng, Huai-Qiang Ju, Han Cheng, Feng Wang, Rui-Hua Xu, Ze-Xian Liu
iScience, 2020 Jul 24;23(7):101302.
[18]. p.P476S mutation of RBPJL inhibits the efficacy of anti-PD-1 therapy in esophageal squamous cell carcinoma by blunting T-cell responses.
Lei Miao†, Xiao-Li Wei†, Qi Zhao†, JingJing Qi, Chao Ren, Qi-Nian Wu, Da-Liang Wei, Jia Liu, Feng-Hua Wang, Rui-Hua Xu
Clinical & Translational Immunology, 2020 Sep 16;9(9):e1172
[17]. Systematic analysis of the transcriptome in small-cell carcinoma of the esophagus reveals its immune microenvironment
Qi Zhao, Yan-Xing Chen†, Qi-Nian Wu†, Chao Zhang, Min Liu Ying-Nan Wang, Yan-Fen Feng, Jia-Jia Hu, Jian-Hua Fu, Hong Yang, Jing-Jing Qi, Zi-Xian Wang, Yun-Xin Lu, Hui Sheng, Ze-Xian Liu, Zhi-Xiang Zuo, Jian Zheng, Jing-Ping Yun, Jin-Xin Bei, Wei-Hua Jia, Dong-Xin Lin, Rui-hua Xu, Feng Wang*.
Clinical & Translational Immunology, 2020 Oct 5;9(10):e1173
[16]. autoRPA: A web server for constructing cancer staging models by recursive partitioning analysis
Xie, Yubin, Xiaotong Luo, Huiqin Li, Qingxian Xu, Zhihao He, Qi Zhao*, Zhixiang Zuo*, and Jian Ren*
Computational and Structural Biotechnology Journal, 2020 Nov 10;18:3361-3367.
[15]. Germline mutational profile of Chinese patients under 70 years old with colorectal cancer.
Teng-Jia Jiang†, Fang Wang†, Ying-Nan Wang†, Jia-Jia Hu, Pei-Rong Ding, Jun-Zhong Lin, Zhi-Zhong Pan, Gong Chen, Jian-Yong Shao, Rui-hua Xu, Qi Zhao*, Feng Wang*.
Cancer Communications, 2020 Nov;40(11):620-632
[14]. MUC4, MUC16 and TTN genes mutation correlated with prognosis, and predicted tumor mutation burden and immunotherapy efficacy in gastric cancer and pan-cancer.
Yue Yang†, Jieyun Zhang†, Yanxing Chen†, Rui-Hua Xu, Zhao Qi*, Weijian Guo*.
Clinical and Translational Medicine, 2020 Aug; 10(4):e155
[13]. Targeting the STING pathway in tumor-associated macrophages regulates innate immune sensing of gastric cancer cells
Miao Lei†, Jingjing Qi†, Qi Zhao†, Qi-Nian Wu, Da-Liang Wei, Xiao-Li Wei, Jia Liu, Jun Chen, Zhaolei Zeng, Huaiqiang Ju, Huiyan Luo, Rui-hua Xu
Theranostics, 2020 Jan 1;10(2):498-515
[12]. BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers
Zuo, Zhixiang†, Huanjing Hu†, Qingxian Xu, Xiaotong Luo, Di Peng, Kaiyu Zhu, Qi Zhao, Yubin Xie, Jian Ren*
Nucleic Acids Research, 2020 Jan 8;48(D1):D789-D796
[11]. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer
Huiyan Luo†, Qi Zhao†, Wei Wei†, Lianghong Zheng†, Shaohua Yi†, Gen Li, Wenqiu Wang, Hui Sheng, Hengying Pu, Haiyu Mo, Zhixiang Zuo, Zexian Liu, Chaofeng Li, Chuanbo Xie, Zhaolei Zeng, Weimin Li, Xiaoke Hao, Yuying Liu, Sumei Cao, Wanli Liu, Sarah Gibson, Kang Zhang, Guoliang Xu, Rui-hua Xu*
Science Translational Medicine, 2020 Jan 1;12(524):eaax7533
[10]. Tumor mutational and indel burden: a systematic pan-cancer evaluation as prognostic biomarkers
Hao-Xiang Wu† , Zi-Xian Wang† , Qi Zhao† , Dong-Liang Chen , Ming-Ming He , Lu-Ping Yang , Ying-Nan Wang , Ying Jin , Chao Ren , Hui-Yan Luo , Zhi-Qiang Wang , Feng Wang
Annals of Translational Medicine, 2019 Nov;7(22):640
[9]. Dysregulation, functional implications, and prognostic ability of the circadian clock across cancers
Zekun Liu†, Kai Yu†, Jian Zheng†, Huan Lin†, Qi Zhao†, Xiaolong Zhang, Weiyi Feng, Liyu Wang, Jianjun Xu, Dawei Xie, Zhi‐Xiang Zuo, Ze‐Xian Liu, Qichang Zheng
Cancer Medicine, 2019 Apr;8(4):1710-1720.
[8]. CrossICC: Iterative consensus clustering of cross-platform gene expression data without adjusting batch effect.
Qi Zhao†, Yu Sun†, Zekun Liu†, Hongwan Zhang, Xingyang Li, Kaiyu Zhu, Ze-Xian Liu, Jian Ren, Zhixiang Zuo*.
Briefings in Bioinformatics, 2020 Sep 25;21(5):1818-1824
[7].  Evaluation of POLE and POLD1 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types
Feng Wang†, Qi Zhao†, Ying-Nan Wang, Ying Jin, Ming-Ming He, Zexian Liu, Rui-Hua Xu*
JAMA oncology, 2019 Aug 15;5(10):1504–6
[5]. LncPipe: A Nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data
Qi Zhao, Yu Sun, Dawei Wang, Hongwan Zhang, Kai Yu, Jian Zheng, Zhixiang Zuo
Journal of Genetics and Genomics, 2018 Jul 20;45(7):399-401
[4]. qPhos: a database of protein phosphorylation dynamics in humans
Kai Yu†, Qingfeng Zhang†, Zekun Liu†, Qi Zhao†, Xiaolong Zhang, Yan Wang, Zi-Xian Wang, Ying Jin, Xiaoxing Li, Ze-Xian Liu, Rui-Hua Xu
Nucleic Acids Research, 10/2018;47(Databaseissue)
[3]. The genomic landscape of small cell carcinoma of the esophagus
Feng Wang, Dong-Bing Liu, Qi Zhao†, Gang Chen, Xin-Ming Liu, Ying-Nan Wang, Hong Su, Yan-Ru Qin, Yi-Fu He, Qing-Feng Zou, Yan-Hui Liu, You-En Lin, Ze-Xian Liu, Jin-Xin Bei, Rui-Hua Xu
Cell Research, 2018 Jul;28(7):771-774.
[2]. VirusMap: A visualization database for the influenza A virus
Yubin Xie, Xiaotong Luo, Zhihao He, Yueyuan Zheng, Zhixiang Zuo, Qi Zhao*, Yanyan Miao*, Jian Ren*
Journal of Genetics and Genomics, 2017 May 20;44(5):281-284.
[1]. GPS-SUMO: A tool for the prediction of sumoylation sites and SUMO-interaction motifs
Qi Zhao, Yubin Xie, Yueyuan Zheng, Shuai Jiang, Wenzhong Liu, Weiping Mu, Zexian Liu, Yong Zhao, Yu Xue, Jian Ren
Nucleic Acids Research, 2014 Jul;42(Web Server issue):W325-30


package actors
Type Members
-
class
Convoy extends Actor with ActorLogging
Pseudo-agent providing between-agent communication for Vehicle agents
-
class
Vehicle extends Actor with ActorLogging
The agent controlling a vehicle in convoy
-
class
World extends Actor with ActorLogging
Pseudo-agent providing the world state and simulation process orchestration


Algortihm
An Algorithm is essentially a finite set of instructions, which when executed produces a desired result. The calculation it perform could require some input data, or it could also require access to some resources depending on the application it serves. Algorithms are not new, as many people would perceive them to be. They date back to atleast ancient Babylonion era (around 2500 BC.) if not prior. As per wikipedia,
"Arithmetic algorithms, such as a division algorithm, was used by ancient Babylonian mathematicians c. 2500 BC and Egyptian mathematicians c. 1550 BC. Greek mathematicians later used algorithms in the sieve of Eratosthenes for finding prime numbers"
As of today, there are a ton of algorithms out there. Speaking specifically of programming language C++, if you have gone through the STL library, there are at least 105 major algorithms in STL itself. The thing with Algorithms is that you need to practice a lot to feel confidant. However, for the beginners, the most difficult conundrum is to determine where to start and which algorithms to master first. The plethora of algorithms out there for every niche tends to baffle the beginners. But there’s a good news for those already intimidated beginners. You surely need to learn a lot of algorithms, but you don’t need to know all of them to start working. We can classify all the algorithms into some bunch of sections. What you can do instead, is learn a few algorithms from each section and thus you’ll not be marooned on the island when someone asks you about algorithms.
Below is a list of ten algorithm classes in which all the algorithms from the STL library can be classified into and under each class, there are a handful of algorithms that you can befriend with for the time being. Also mentioned are the situations under which you will find those algorithms useful.
So, to start with, lets go through the list of ten algorithms classes (and one bonus class) that we will discuss here and then we will elaborate each one.
- Heap
- Sort
- Partition
- Permutations
- Query a Value
- Query a Property
- Search a Value or a Range
- Sets
- Movers
- Modifiers
Heap
Heap, in real life which can be represented as a tree where every node is smaller than its child or the node below it.  However, a heap in C++ is slightly different. Unlike in real life, heap in C++ can be represented like a tree where parent node is always larger than its children, as shown in figure 1(b).
To represent Heap in Memory, we can use an Array. To initialize an array, we simply add elements row-wise. Heap initialization is shown in figure 2. As seen in the figure, for a heap which appears like a tree, elements are stored in array in a row-wise manner.
At the moment, this array is merely a collection of integers (in given example). To make it a heap, we can call the STL algo make_heap.
To add a new element to the heap and bubble its way to its appropriate position, we use push_back(element) and then push_heap as shown below.
To pop something from a heap, we can only remove elements from one end which can only be largest element. To do so, we swap its position with the last element of the heap (which is probably the smallest element of the heap) & then pop it out. We use pop_heap and then pop_back as shown below.
NOTEIf pop_back is not called, then calling pop_heap() will cause all the largest elements fall in order at the end of the heap. This will cause heap to be sorted and this is the actual mechansm of sort_heap
Sort
Sorting a collection is basically rearranging the elements of a collection in a certain order depending on the criteria. There are lots of sorting algorithms. In STL, simplest sorting algorithm is Sort. This sorts the collection in ascending order.
When we need to sort only a subset of a collection, we can use partial_sort. This algorithm takes in, the beginning of the collection and the point up to where collection needs to be sorted.
When we need to know an element that would have been there, had the collection been sorted, we can do so by using nth_element algorithm.
When there are two sorted sub-collections that are adjacent to each other, however the entire collection as a whole is not sorted, we can use inplace_merge.
Partitioning
It is basically organizing a collection based on a predicate. One example could be a situation where a collection has mixed values of zeroes and ones but we need to partition the collection into two parts based on whether the element is a zero or a one as shown below.
we can use partition to put all the zeroes in the beginning and all the ones at the end. The point which creates the divide between the two regions is called “Partition Point”.
The algorithm which retrieves the partitioning point is called partition_point.
Permutations
These are the algorithms which change the order of the elements of a collection without changing their values.
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Rotate It is one algorithm where elements in the middle and end goes to the beginning and that is decided by where the middle points to as shown in the figure below.
std::rotate() -
ShuffleIt uses a random generator and reorganizes the collection randomly.
std::shuffle() -
Next_permutationElements can be compared to each other depending on their order which is similar to order of alphabets that appears in a Dictionary. By calling this algorithm, it will rearrange the elements of the collection in such a way that the new collection order is just the next order of permutation in respect of order of elements.
The figure shown below clarifies what the order means in this context.
std::next_permutation() -
Prev_permutationJust like Next_permutation, prev_permutation gives the previous order.
std::prev_permutation() -
Finally, reverse simply reverses the order of collection entirely.
std::reverse()
Query a Value
Querying forms a fundamental class in algorithms as many times, we only need to know what value or property certain variable or objects have. This class has numeric algorithms which return a specific value (or No value as No values is also technically a value)
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Count This algorithm counts the occurrence of an input value in a collection of elements and returns the occurrence as integer value. As shown in the figure, count(a) returns 3 as there are three times ‘a’ has occurred in the collection.
std::count() -
Accumulate / (Transform_) reduceAccumulate find the sum of all the elements of a collection and returns it. Reduce works just like accumulate except for the fact that it doesn’t take initial value (which certainly saves some overhead) and this algorithm can be parallelized which means, it can be programmed to utilize multiple cores of a microprocessor simultaneously (Obviously, if the microprocessor has multiple cores in the first place. No points for guessing that. :p).
Following figure shows how accumulate works.
std::accumulate() -
Transform_reduceTransform_reduce works same as reduce but it works on functions and templates. Its like the cousin twin brother who also happen to go and live abroad with his rich parent (poor reduce couldn’t afford all that ).
std::transform_reduce() -
Partial_sumPartial_sum is used to calculate sum of adjacent elements upto a given position in a collection of elements.
For instance, the element at position three will constitute of the sum of first two elements elements and the third element (which basically means sum of first three elements) as shown below.
std::partial_sum() -
Inclusive_scan / Transform_Inclusive_scanInclusive_scan performs the same functionality as partial_sum but can be parallelized. Exlusive_scan, on the other hand, doesn’t include the current element while calculating the sum upto that position. It only calculates up until that position as shown below.
std::inclusive_scan()Transform_inclusive_scan and Transform_exclusive_scan are applied on the functions and objects.
std::transform_inclusive_scan() -
Inner_productInner_product calculates product of corresponding elements of two collection before adding the sum. This could be hard to comprehend. To understand this, have a look on the figure below. First the corresponding elements of both the collections are multiplied to create a single collection having elements as the result of multiplication and then the sum of all the elements of that collection is taken which is given by inner_product algorithm.
std::inner_product() -
Adjacent_differenceThis algorithm calculates the difference of adjacent elements of a collection.
std::adjacent_difference() -
SampleThis algorithm randomly pick out specified number of elements from a collection using a random generator.
std::sample()
Query a Property
This class of algorithm proves extremely useful for day to day basis. One can query a property on one collection as well as on two ranges.
Following is a list of algorithms used to query a property on one collection.
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All_ofTakes a predicate and a collection and returns TRUE (boolean value) if all the elements satisfy the property (predicate)
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Any_ofReturns TRUE if any of the element of the collection satisfy the condition (property/predicate)
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None_ofReturns TRUE if none of the element of the collection satisfy the property.
Following is a list of algorithms used to query property on two ranges where the idea is to compare two ranges.
-
EqualIf, given two ranges, all the elements are same in same order in both the ranges (or collection) and both the collections have same size, the algorithm returns a boolean true
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Is_permutationIf two collections have same size and same elements but Not in same order, then this algorithm returns a boolean true.
-
Lexicographical_compareGiven two collection, this algorithm returns true if the first collection is smaller in order than the second collection.
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MismatchThis algorithm matches two collections and stops at the point where two collections differ which is a way gives back the first point of mismatch.
Search Value or Range
When searching for a value, algorithms are broadly classified into two groups – Sorted and Unsorted.
Unsorted
This group consist of two algorithms namely find and adjacent_find which are both briefed below.
FindWhen we need to find something in a collection, we can call this algorithm and it returns the position of the searched item in the collection if the searched item is present in the collection. Else, it returns Null.
Adjacent_findWhen we need to find two consecutive occurrence of a value, we can use this algorithm. For instance, if we have to find where in a collection of numbers, two consecutive ‘3’ appears, we can use this algorithm. This algorithm returns the position of first element and the next element is adjacent to the returned position ( quite obviously)
Sorted
In a sorted collection, all the occurrences of a same value are going to lie together, adjacent to each other. Hence, they form like a subrange.
Equal_rangeThis algorithm returns the position of of the sub-range that we intend to find in a sorted collection.
Lower_bound / upper_boundIf we have to add another element of a value which already exist in a sorted collection, we can either add the element on the left of the existing element ( or group of elements) or on the right of element. To add an element to the left of an existing element, we can use this algorithm which returns the position where to insert the new element. Upper_bound returns the position which lies on right of the existing element. This can be better explained using figure shown below.
Binary_searchThis algorithm returns a boolean True if it finds the item we are looking for in the collection. It performs Binary Search to find the item but it doesn’t return the position of the item, if the item is found. It only returns a confirmation in the form of boolean True or False.
The Search can be performed for a range of values (also called as sub-range) as well.
SearchThis algorithm returns the first occurrence of a sub-range in a collection. It basically gives the position where the sub-range starts.
Find_endThis algorithm returns the Last occurrence of the sub-range in a collection. In a way, this is opposite of search algorithm.
Find_first_ofIf you want to search the first occurrence of any of the value of the small sub-range in a collection, we can use find_first_of.
Sets
A set in C++ is actually just a sorted collection. It can be a Suit-set which happen to be sorted or it can be sorted-vector. The operation on sets look very similar to mathematical operation on sets.
Set_differenceThis algorithm returns what elements which are present in first collection but Not in second collection. This has linear complexity
Set_intersectionThis algorithm returns the elements which are common to both first and second collection.
Set_unionThis algorithm returns the combined elements of both first and second collection.
Set_symmetric_differenceThis algorithm returns the elements which are exclusive to each other which means elements as shown in Figure below.
IncludesThis algorithm returns a boolean indicating whether all the elements of second collection form a sub set of first collection as shown in the figure below.
MergeThis algorithm takes in two sorted collections and returns one sorted collection having member elements of both the individual sorted algorithms used as input.
Movers
This section has probably some of the easiest algorithms which you can find useful on a day-to-day basis.
CopyThis algorithm copies the elements from source collection to destination collection defined by the address.
MoveThis algorithm moves the elements from source collection to destination collection which means it copies the elements from source collection to destination collection and then deletes from the original source location.
swap_rangesThis algorithm interchanges the contents of two collections.
copy_backwardsThis algorithm copies the elements starting from back. One use case could be when we need to copy some elements of the source collection partially to overwrite other elements in the same collection as shown in the figure below.
Move_backwardsThis algorithm works similar to copy_backwards, but it deletes elements from source once it has copied successfully to destination.
Modifiers
Modifiers simply change the value of the elements of a collection.
FillTo fill all elements of a collection with a specific value, we can use this algorithm.
GenerateThis algorithm is used to generate random numbers up until the input number.
Bonus: Prefix/Suffix
There also exist some keywords which are used as prefix and some as suffix to the already existing algorithms changing the algorithm’s functionality slightly. Following is the list of prefix and suffix used :-
All these algorithms are easy to understand and can be used in day to day programming which essentially make tasks easier to execute. You can practice with just a few algorithms from each section and soon you will find all the other algorithms easy to comprehend. This will also instill the knowledge of writing your own algorithms.


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Homer, Iliad with scholia and an interlinear paraphrase of Books I to XII, Genève, Bibliothèque de Genève, MS. gr. 44
Albert Einstein, The principle of relativity; original papers
Romance of Alexander, Alexander and Dindimus, Li Livres du Graunt Caam; Inside upper cover, MS. Bodl. 264
Wunder der Vererbung
Book of Hours, Walters Ms. W.168
The Life of Buddha, first book (Shaka no Honji, jō), Cod. Bodmer 600a
Wellcome Apocalypse
William Shakespeare, The tragedy of Hamlet Prince of Denmarke
Principles of Geology: Being an attempt to explain the former changes of the Earth's Surface, by reference to causes now in operation.
First World War posters [20th cent.]
Chronique du monde depuis la création, et des rois de France et d'Angleterre, jusqu'à l'an 1461 : manuscript, [ca. 1461], MS Typ 41.
The Works of Charles Dickens. Household edition. [With illustrations.].
Abraham Cresques , Atlas de cartes marines , dit [Atlas catalan], Espagnol 30, BnF
Letters and notes on the manners, customs, and condition of the North American Indians
Urania's mirror, or a view of the heavens
Composite manuscript of content related to astronomy, Basel, Universitätsbibliothek, F III 25
Petrus Lombardus, Liber sententiarum I-IV, Cod. S II 72
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