IDEO Field Guide to HCD
Inspiration Phase
The Inspiration phase is about learning on the fly, opening yourself up to creative possibilities, and trusting that as long as you remain grounded in desires of the communities you’re engaging, your ideas will evolve into the right solutions. You’ll build your team, get smart on your challenge, and talk to a staggering variety of people.
Frame Your Challenge
Inspriation Phase
Frame Your Challenge
A single sentence that frames what you want to do, drives toward ultimate impact, allow for a variety of solutions, and take into account constraints and context
PDF >>Recruiting Tools
Inspriation Phase
Recruiting Tools
Before you start talking to the people you’re designing for, it’s important to have a strategy around who you talk to, what you ask them, and what pieces of information you need to gather.
PDF >>Secondary Research
Inspriation Phase
Secondary Research
Secondary Research, whether done online, by reading books, or by crunching numbers, can help you ask the right questions.
PDF >>User Interviews
Inspriation Phase
User Interviews
Interviews really are the crux of the Inspiration phase. Human-centered design is about getting to the people you’re designing for and hearing from them in their own words.
PDF >>Define Your Audience
Inspriation Phase
Define Your Audience
You’re bound to learn more once you’re in the eld, but having an idea of your target audience’s needs, contexts, and history will help ensure that you start your research by asking smart questions.
PDF >>Conversation Starters
Inspriation Phase
Conversation Starters
Conversation Starters are a great way to get a reaction and begin a dialogue. The idea here is to suggest a bunch of ideas around a central theme to the people you’re designing for and then see how they react.
PDF >>Immersion
Inspriation Phase
Immersion
The Inspiration phase is dedicated understanding the lives of the people you’re designing for. The best route to gaining that understanding is to talk to them in person, where they live, work, and lead their lives.
PDF >>Analogous Inspiration
Inspriation Phase
Analogous Inspiration
Analogous settings can help you isolate elements of an experience, interaction, or product, and then apply them to whatever design challenge you’re working on.
PDF >>Card Sort
Inspriation Phase
Card Sort
By putting a deck of cards, each with a word or single image, in someone’s hands and then asking them to rank them in order of preference, you’ll gain huge insight into what really counts.
PDF >>UXPin Design Sprint
Unpack Phase (Day One)
The unpacking step consists of focusing on outcomes, assumptive personas, and ‘How might we questions’. Set aside a full day work- shop with the whole project team for all the activities. Consider the outputs from the unpacking stage as a living record of what the group knows and where they’re heading. You will contin- ually revisit the living record as you progress.
Define Top 3 Outcomes
Unpack Phase
Define Top 3 Outcomes
Focus on Outcomes instead of Outputs. An Outcome is something like: “Increase sales by 5% for new users without affecting retention rate for current users”
PDF >>Assumption Generation
Unpack Phase
Assumption Generation
Now it’s time to examine your outcomes from different angles. Write all the questions on big pieces of paper and stick them on the wall. Answer all questions that apply to your project. You can slightly re- word them if necessary.
PDF >>Assumptive Personas
Unpack Phase
Assumptive Personas
Assumptions in design are only bad if you never validate them. Luck- ily, assumptive personas are just an early iteration that we’ll verify with the scheduled interviews.
PDF >>User Research Interviews (Day 2 & 3)
Now you validate your assumptions and identify your persona’s pains and gains. Both Lean UX and traditional UCD advocate user interviews for fast insights. During your scheduled user interviews, ask users about the whole scope of the experience, not just the particular area you're interested in.
Quantitative Research Validation
User Research Interviews
Quantitative Research Validation
Focus on Outcomes instead of Outputs. An Outcome is something like: “Increase sales by 5% for new users without affecting retention rate for current users”
PDF >>Conducting User Interviews
User Research Interviews
Conducting User Interviews
Start your user interview with an introduction of who you are and how the interview will work. It’s a good idea to ask participants questions about themselves early so you can add context to their answers.
PDF >>Running User Surveys
User Research Interviews
Running User Surveys
If you’re on a time crunch, you can skip this step. If the entire sprint spans 3 weeks instead of 7 days, however, you can run a user survey after your user interview to further validate the analytics and interview results.
PDF >>Update Assumptions
User Research Interviews
Update User Personas
Once you’ve analyzed data and interviewed your users, it’s time to hunt for patterns in the following areas: Pains, Gains & Observations
PDF >>Update Your Assumptions
Update Assumptions
Update Your Assumptions
After analyzing your findings, you need to update your assumptions based on the newly validated research.
PDF >>Transforming Insights into Design Questions
Transforming Insights into Design Questions
Transforming Insights into Design Questions
Now you can create ‘How might we’ questions based on the validated findings from user research. We’ll applying traditional design think- ing with a bit of Agile prioritization.
PDF >>Prioritize HMW Questions
Transforming Insights into Design Questions
Prioritize HMW Questions
Prioritize HMW questions by the degree of risk and amount of current knowledge. The highest priority HMW questions pose the highest risk and suffer from the lowest amount of current knowledge.
PDF >>Ideation Phase
In the Ideation phase you’ll share what you’ve learned with your team, make sense of a vast amount of data, and identify opportunities for design. You’ll generate lots of ideas, some of which you’ll keep, and others which you’ll discard. You’ll get tangible by building rough prototypes of your ideas, then you’ll share them with the people from whom you’ve learned and get their feedback. You’ll keep iterating, re ning, and building until you’re ready to get your solution out into the world.
 It’s critical to pay close attention to your teammates’ stories, learnings, and hunches.
PDF >>Share Inspiring Stories
Ideation Phase
Share Inspiring Stories
Over the course of the Inspiration phase, you’ve heard stories or had experiences that stuck with you. Most likely, they won’t be the ultimate solutions to your design challenge, but chances are they’ll resonate with your team as well.
PDF >>Top Five
Ideation Phase
Top Five
This exercise gives you a break from the deep thinking and simply asks, what are the Top Five ideas or themes sticking out to you right now. Not only can answering this question as a team help you strategize, but it can also help uncover themes, isolate key ideas, and reveal opportunities for design.
PDF >>Find Themes
Ideation Phase
Find Themes
Take a good long look across your Interviews, Analogous Inspiration, and other learnings. Have any patterns emerged? Is there a compelling insight you heard again and again?
PDF >>Create Insight Statements
Ideation Phase
Create Insight Statements
Succinct sentences that will point the way forward. Insight statements are incredibly valuable as they’ll help you frame How Might We questions and give shape and form to subsequent Brainstorms.
PDF >>How Might We
Ideation Phase
How Might We
You’ve identified problem areas that pose challenges to the people you’re designing for. Now, try reframing your insight statements as How Might We questions to turn those challenges into opportunities for design.
PDF >>Create Frameworks
Ideation Phase
Create Frameworks
Create Frameworks to help synthesize our learnings and nd clarity in what are often highly complex challenges. Frameworks like 2x2s, relational maps, and journey maps.
PDF >>Brainstorm
Ideation Phase
Brainstorm
Promote openness, lots of ideas, and creativity over immediate feasibility. Brainstorms work best when the group is positive, optimistic, and focused on generating as many ideas as possible.
PDF >>Bundle Ideas
Ideation Phase
Bundle Ideas
Bundling Ideas takes you from strong individual concepts to solutions of substance. Think of it as a game of mix and match, with the end goal of putting the best parts of several ideas together to create more complex concepts.
PDF >>Design Principles
Ideation Phase
Design Principles
Design Principles are the guardrails of your solution—quick, memorable recipes that will help keep further iterations consistent. These principles describe the most important elements of your solution and give integrity and form to what you’re designing.
PDF >>Create A Concept
Ideation Phase
Create A Concept
A concept is more polished and complete than an idea. It’s more sophisticated, something that you’ll want to test with the people you’re designing for, and it’s starting to look like an answer to your design challenge.
PDF >>Co-Creation Session
Ideation Phase
Co-Creation Session
The purpose of a Co-Creation Session is to convene a group of people from the community you’re serving and then get them to design alongside you. You’re not just hearing their voices, you’re empowering them to join the team.
PDF >>Determine What to Prototype
Ideation Phase
Determine What to Prototype
Your idea will have lots of testable components, so be clear about what you need to learn and which components will give you the necessary answers. Prototyping isn’t about being precious. Make simple, scrappy prototypes to not only save time, but to focus on testing just the critical elements.
PDF >>StoryBoard
Ideation Phase
StoryBoard
By visually plotting out elements of your product or service, you can learn a lot about your idea. Not only will this method help you re ne what your idea is, it can also help you understand who will use it, where, and how.
PDF >>Rapid Prototyping
Ideation Phase
Rapid Prototyping
Rapid Prototyping means that you’re building only enough to test your idea, and that you’re right back in there making it better once you’ve gotten feedback.
PDF >>Business Model Canvas
Ideation Phase
Business Model Canvas
This simple sheet asks you key questions like what’s your revenue stream, what are key partnerships you’ll need to forge, and what resources are vital to your operation.
PDF >>Co-Design Sessions (Day 4 & 5)
Co-Design session reduce inefficient conversations and have two steps, Divergent Design and Convergent Design. Each HMW question will go through its own diverge and converge process. Try to make an environment where all ideas are welcome - do not shoot down ideas on sight (we’ve tried that, that will never work, etc)
Divergent Design
Co-Design
Divergent Design
Diverging is when you create as many ideas as possible that aim to answer each HMW question. We don’t know what ideas have value until we explore them all – so the aim is to come up with as many as possible.
PDF >>Collaborative Sketching Session
Co-Design
Collaborative Sketching Session
Now that you’ve generated the broad feature ideas, the collabora- tive sketching session starts with a ‘6-up’. A ‘6-up’ is an A-3 piece of paper that you divide into six boxes – one box for each idea.
PDF >>Convergent Design
Co-Design
Convergent Design
Once everyone has presented their 6-ups, they look over the feedback and consolidate the best designs into one solution each. Remember that no idea belongs to anyone. You’ll get to the best ideas if you steal and build on each other’s.
PDF >>Prototyping the Solution (Day 6 & 7)
Once you’ve passed through the divergent and convergent stages for your HMW questions, you’ll have a pared-down list of focused features that are worth prototyping as a cohesive design.
Create Prototype
Prototype
Create Prototype
For digital projects, get your one or two most experienced designers to distill the co-designed solutions into a prototype–or if you're plan- ning on A/B testing, two prototypes.
PDF >>Usability Testing (Day 8)
Usability testing is the undeniable moment of truth. Now that you’ve created your prototypes, it’s time to get the verdict from users. We’ve never uncovered less than 20 key findings the first time we test a prototype, so prepare yourself for serious feedback.
Usability Testing
Testing
Usability Testing
Try to do the testing (or viewing of) as a team. Encourage the team to write down their own findings on post-it notes for each test participant (Green for positive, Pink for negative, Blue for personal observations) Stick on the wall to make an affinity diagram - cluster the similar findings together and number observations by participant or use a rainbow spreadsheet: Link
PDF >>


Histogram¶
Hans Dembinski
Copyright (c) 2016 Hans Dembinski
Distributed under the Boost Software License, Version 1.0, see accompanying file LICENSE.
Description¶
This project contains an easy-to-use powerful n-dimensional histogram class implemented in C++03
, optimized for convenience and excellent performance under heavy duty. Move semantics are supported using boost::move. The histogram has a complete C++ and a Python interface, and can be passed over the language boundary with ease. Numpy is fully supported; histograms can be filled with Numpy arrays at C speeds and are convertible into Numpy arrays without copying data. Histograms can be streamed from/to files and pickled in Python.
My goal is to submit this project to the Boost Libraries.


general-development-workflow.Rmd
Interactive development of new functions and reports is easy using devtools
. Any R code (functions etc) go in the R
folder of the package root. A good convention to follow is to have one file per function, however if you have a collection of related functions it may be best to keep them together. Please see later on how to document and test your functions. The latest code can be loaded into the R session (as if you had built, installed, and attached the package) by using the devtools::load_all()
function. A useful shortcut for this inside RStudio is ctlr+shift+L
.
Here’s an example workflow for adding a function to the package step by step
function_name.R
script in the R
directoryctlr+shift+L
R
sessionOnce you are happy with your addition to the package you can build a source version for others to install and use with the following function.
devtools::build()
If you are are adding or modifying a vignette (built in report) then make sure you build with the following argument
devtools::build(vignettes = TRUE)
The vignettes are slow to build so I suggest only running them when you are working on the vignettes.


The BN-20 is capable of printing high-quality full color images on vinyl stickers and cutting out as custom shapes.
To get started, open Adobe Illustrator and make a new file: File – New… and hit OK.
Click-and-drag your images into the window or open them via File – Open. (You can draw your stickers in Illustrator as well if you’re not working with images).
Arrange and resize your stickers.
Click on your sticker - the “Transform” window at the top shows the dimensions. You can type these manually or scale the sticker by dragging the corners. (If this window isn’t there, select Window – Transform from the top menu)
Check the maximum width of the roll of material that’s in the printer - you don’t want to put images outside the printing area.
Our sticker printer has the special functions of printing metallic silver ink and cutting out custom shapes. You can set these functions in the “Swatches” window.
Note:
If this icon isn’t in the sidebar, you can bring it up by clicking Window - Swatches
In the top-right corner of the Swatches window, click Options - Open Swatch Library - User Defined - Roland VersaWorks
Draw a box or shape around where you want the sticker to be cut out. Select it, then click the first icon – “CutContour” .
The cutting perimeter should turn pink. This color won’t show up on your sticker. (You can move it behind the sticker by right clicking and selecting Arrange – Send to Back if you’d like.)
I drew a rectangle around the edges of my image. I didn’t need a border.
If you’d like something printed in metallic ink, it works on the same principle. Select any shape and apply the RDG_MetallicSilver swatch to it. It will turn grey.
Note:
If you change your mind about something that has a swatch applied to it, you need to manually select another color from the swatch menu. For example, if you decide to remove the cutting command from , instead of using the color menu on the left to select a color, open Window - Swatches and pick a new color there.
Once your file is looking good, we need to save it as an .EPS – File – Save As… and Save as Type “Illustrator EPS”.
Click OK - the default settings are fine.
Open VersaWorks Dual Software from the desktop or bottom dock.
From the file menu, select “Add Job to Queue A” (or B - doesn’t really matter).
Click the “Settings” icon in the bottom left corner.
Under “Layout” we can calculate the material width for our vinyl roll. Make sure the material is sitting between the two metal “feet” and then click “Get Media Width”
Checking that material is sitting correctly between metal feet:
You can set the location of the stickers in the layout menu if it doesn’t look right.
The top left of the preview window (coordinates 0,0) will be the right-side of the printing bed, printing top to bottom.
Moving to the “Quality” tab:
I’m using the standard vinyl.
Under Mode you can select the order in which the inks are printed -
These options can be important if you need the metallic ink to dry first, for example, or if you’re doing more complicated layer work.
Under “Job Management”, select “RIP while Printing” to save time.
Note:
RIP (“Raster Image Processing”) is the process of translating the image file into code that the printer can use. This can be done all at once before you print, which the the default, or while it’s printing.
In the main window, click the name of your job in the queue to the left.
Under “Special Items”, you can double-check that the job is programmed correctly. If you’re using the cutting tool to cut out your sticker, the ‘A’ icon will be here. If you’re using metallic ink, the metal drop icon will be here.
Note:
If these special items are supposed to be here but you can’t see the icons, double-check that the correct swatch was selected in Illustrator (click the shapes that are supposed to be cut / be metallic and select the VersaWorks swatch again.)
Once everything is set up, click the “Print” icon and you’re good to go.


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Back
Select all that apply.
This can be for any child, not just one that you want to claim Carer's Allowance for looking after.
Put the names of every child they mind
Put a total amount and say whether it's each week or month
This can be a company or individual.
These can be anyone who looks after your children or another person you care for.


Aww yeah, you successfully read this important alert message. This example text is going to run a bit longer so that you can see how spacing within an alert works with this kind of content.
Whenever you need to, be sure to use margins to keep things nice and tidy.


%@ taglib prefix="c" uri="http://java.sun.com/jsp/jstl/core" %>
noun: All the Music You Can Play
Music
Messages
User Information
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This is not one of those lame 10 reasons why you must live in New York City article.
In February 2014 I moved to New York City after living in Sydney for 7 years, I am writing this as a reminder of my year and a half in NYC and hopefully to help people about to make the same move. I already had a job lined up in NYC and this post won’t help you with that.
Probing
Before moving I was able to visit NYC twice and get a gut feeling for the massive amount of change I would be signing up for. The first time for two weeks starting at the end of August until September 2013. The second time one week in November 2014. During those two vacations I understood there was a positive balance between things I liked and disliked of New York City so I didn’t have to adjust that much when I moved.
Food
New York City spoiled me with its excellent and wide variety of choices. From the cheap 1$ pizza slice–which is way better then all street pizza I ever got in Sydney–to Indian and Turkish restaurants. In the time I was there I couldn’t find any Thai restaurants as good as Sydney but to be fair I only tried a couple.
Yelp is a widely used app for venue reviews–a good start but since you meet people from all over the planet working in NYC ask them for recommendations about their home country best food in Manhattan for example. When 2 or 3 reply with the same place it’s bound to be good. Even better if they tag along with you!
Food shopping
Coming from Australia food shopping in NYC was shockingly bad–regulations are more permissive so if you care about what you eat you might need to keep your eyes open. Fresh produce most of the time was poor quality and the farmer market I went to in front of Brooklyn Borough Hall was disappointing.
There are higher quality chains like Wholefoods and Trader Joe–you’ll pay the toll and during the weekend they are jammed with people but very well organized and long counter queues move really fast.
Cost of living
Sydney is expensive but NYC is more expensive so prepare to compromise on work proximity, apartment size or number of flatmates. Prices are only going up so check Zillow to get an idea. During contract negotiation I checked the rental prices and asked adjustments to my paycheck to afford the area and category building I wanted.
Finding an apartment
Each New York City neighborhood is extremely different. Sometime one or two blocks distance can mean a lot in terms of noise, good vibe, safety, food venues and transport–without knowing the one you like you will be just guessing.
Be careful not to lease in a location you dislike because the lease is a legal contract that you can’t just terminate like in Sydney–here you are legally bound to pay the rent until the end of the contract or the owners will sue you. People that live in the city don’t consider walking out of a lease an option.
To avoid signing a long term lease you could search on craigslist and commit for 2 or 3 months–you usually still have to give the first month in advance and one month bond. Craigslist is a bit of a zoo but I’ve heard of people finding decent accommodations with limited budget.
I stayed at two airbnb for a total of 5 weeks. This allowed me to triage areas and avoid making a rushed decision on where to move. I decided I wanted to be close to my office in the financial district so I moved in downtown Brooklyn–I had plenty of subways stopping within 2 blocks from me, a nice park nearby and the Brooklyn bridge park and good food shopping at my doorstep.
Another option is to give a real estate agent your budget and requirements and he will show you as many places as possible in a day or two so you can pick one and move in as soon as possible.
Forget washer/dryer
Old buildings have strict regulations that prohibit installing washer/dryer in the unit and because of the general lack of space in New York City they are considered a luxury–if you are lucky the building might have one in the basement or to share with a few floors but don’t worry you have dry cleaners at almost every corner as well as pickup services.
The commute
Get ready to spend about 40 minutes or more each way–a long commute is the easiest way to afford a reasonable rent.
I was one train and less then 20 minutes door to door to my office which in New York City is pretty good considering the average. In about 30 minutes I could get to any mid-town venue, 40 minutes to central Park.
Keep in mind the metro works 24/7 but there are always change of service after 10/11pm and during the weekend–keep an eye out for MTA paper notices taped outside the station or use the CityMapper app that alerts when there are change of service.
Unlike Tokyo or London or any other country I’ve been the NYC Subway stations have not communicating separate entrances for direction. If you get in the wrong direction most of the times you can’t just walk across you need to walk outside and swipe your Metrocard again–if you have a weekly or monthly pass it will flag you as double pass and it won’t let you in and you’ll have to wait around 15 minutes. Keep an eye on the subway entrance sign if it only says “Uptown” or “Downtown” it’s very likely you can’t cross the other direction.
Banking and credit cards
You should open a bank account as soon as possible, without a social security number you will need to provide your ozzie address which is ok. I suggest you wait to apply for a credit card until you get a few paychecks in or you might get declined. Nobody told me that when a credit card application is denied your credit score goes down–if you apply for another card and is declined the credit score goes even lower and as a foreign citizens with no credit history in the US you already start pretty low.
Work culture
I worked as a software engineer and I didn’t see the crazy hours everybody talks about. Most of my ~100 people company was working 9-5. Exception to that was the finance division. I know many interns and white collar were paid peanuts which I bet turned their NYC experience in to a nightmare.
I actually met lots of people on minimal wage struggling to live in NYC but still living there because it’s New York City. Others where unhappily stuck there–even tough they would never admit it–because they couldn’t find a better job elsewhere.
My company had unlimited–subject to approval–paid vacations which was pretty sweet. For other companies it’s usually 10 days.
Why would anyone want to live in New York City?
If you haven’t been to New York City you should visit it–nothing I will say will convince you. Everyone is after different things at different points of their lives–I can tell what I liked about it.
- Having the choice to go to a different events or exhibitions
- The variety of nighlife venues
- The excellent food culture
- The seasons and change of colors: Spring, Autumn the best and the bite of a very cold (-10) white Winter
- The proximity to Europe and North America made it a fabulous starting point for my trips
Things I disliked:
- Walking in Manhattan is like a level 29 douchebag Tetris. But you can fit in.
- The sewerage stench from late Spring till end of Summer
- The garbage on the streets
- Summer 2014 was too humid for me but according to the locals it was much worst the year before
- I could really smell the traffic pollution
I hope you have a bit more information about your next move. If you are in doubt about moving to New York City just do it I doubt you will regret it.


Summary: Latitude vs. X The purpose of this project was to analyze how weather changes as you get closer to the equator.To accomplish this analysis,we first pulled data from the OpenWeatherMap API to assemble a dataset on over 500 cities.After assembing the dataset, we used Matplotib to plot various aspects of the weather vs.latitude.Factors we looked at include: Temperature, Cloudiness, WindSpeed, and Humidity.This site provides the source data and Visualizations created as part of the analysis, as well as explanations and description of any trendss and correlations witnessed. Visualizations


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Me sitting precariously on a slippery rock, looking constipated (Nahal Amud, near Tzfat)


title: Demonstration of Khaleesi
decorator: basic
slug: index.html
identifier: 11e900509c48c9591c42
‡‡‡‡‡‡‡‡‡‡‡‡‡‡
geek highlighter markdown decorator with “Khaleesi”
{Which theme is your favorite?}
#foreach ($theme : $themes_illustrates)
${theme:content}
#end
{Introduction of Khaleesi}
${page:introduction}
↑ Back to top


This XQuery lists customers' names and addresses for all customers who live in Markham, in alphabetical order.


pytma.transform_data¶
-
pytma.
transform_data
(data)¶ Function that takes experimental data and gives us the dependent/independent variables for analysis.
- Parameters
- dataPandas DataFrame or string.
If this is a DataFrame, it should have the columns contrast1 and answer from which the dependent and independent variables will be extracted. If this is a string, it should be the full path to a csv file that contains data that can be read into a DataFrame with this specification.
- Returns
- xarray
The unique contrast differences.
- yarray
The proportion of ‘2’ answers in each contrast difference
- narray
The number of trials in each x,y condition


Tangrams-indic
Tangram - Indic Maps
View the Project on GitHub
srikanthlogic/tangrams-indic
Download
ZIP File
Download
TAR Ball
View On
GitHub
Openstreetmap Indic Maps - Powered by
@Mapzen
@Tangrams
Bengali
Gujarati
Hindi
Kannada
Malayalam
Marathi
Nepali
Odia
Punjabi
Tamil
Telugu
Urdu


simdf.Rmd
The simdf()
function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
For example, here is the relationship between speed and distance in the built-in dataset cars
.
cars %>%
ggplot(aes(speed, dist)) +
geom_point() +
geom_smooth(method = "lm")
You can create a new sample with the same parameters and 500 rows with the code simdf(cars, 500)
.
simdf(cars, 500) %>%
ggplot(aes(speed, dist)) +
geom_point() +
geom_smooth(method = "lm")
You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset iris
.
iris %>%
ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
geom_point() +
geom_smooth(method = "lm")
And here is a new sample with 50 observations of each species, made with the code simdf(iris, 100, "Species")
.
simdf(iris, 50, "Species") %>%
ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
geom_point() +
geom_smooth(method = "lm")
For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.
simdf(iris, 50, "Species") %>%
mutate_if(is.numeric, round, 1) %>%
ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
geom_point() +
geom_smooth(method = "lm")


I liked:the fast-paced action, cartoonish graphics, excellent music and animation I didn't like:the goalie controls (but you get used to them)
I'm not really a big fan of hockey games, or even sports games in
general. However, I've been much more drawn to 8-bit sports games such
as Tennis, Tecmo Bowl, and Ice Hockey. There's NHL 07 and NHL 2k7 and
all that good stuff, but Ice Hockey makes for a good, 8-bit,
arcade-style game of hockey.
The premise of the game is still ice hockey: you command a four-man
team of players, shootin' the puck into the goal and ice hockey stuff
like that. But what makes this game unique is it's simple-but-deep team
customization system.
You have four men on your team; each can be one of three body types,
skinny, average, or heavy (aka fat). Each of the three builds has
specific strengths and weaknesses: skinny players are very fast and
maneuverable, although their shots are weaker.  Average players are in between, with average speed
and shooting.
The default configuration is Fat / Avg / Avg / Skinny (which works
pretty well), but since you can change around your lineup of players,
it gives the game a surprising amount of depth and replay value. I've
actually found that the "four fatties" make for a pretty good team.  That's not to say
the other two types are just straight up useless, though.
The game itself is basically... hockey. A very FAST game of hockey.
Very fast. When I first picked up the game, it took me a while to get
used to the pacing of the game. You control one player at a time, and
can switch between them if the opposing team is on offense. When you
hit the switching button your control shifts to whoever is closest to
the puck, which isn't always clear. You get used to the pacing of the
game after a while though, and you realize it couldn't be played any
slower.
The control itself is very tight. Two buttons is all you need for this
game, on offense or defense. On offense there's just shoot and pass,
and on defense a button to switch players and one to steal the puck.
You basically mash on the A button to steal the puck (if you have a
human opponent, they should be too). If the skirmish lasts for more
than a couple second, a field-wide fight breaks out, with the other six
players on both teams jumping into your fight. A little comic "fight
cloud" forms around them until the ref jumps in and breaks up the
fight. Then someone is sent to the penalty box for a while.
Controlling the goalie is one of the more unorthadox parts of the game.
The goalie is basically always in your control, moving around his
little box. He's only controlled with the D-Pad, but since you command
him and your own player while on defense, you have to shift your
priorities to either actively controlling the goalie to stop a puck, or
actively controlling a defensive player to steal the puck and shift to
offense.
The game can be played in one- or two-player games of three quarters,
seven, ten, or fifteen minutes each. The one player mode is pretty
good, with a fairly challenging AI, albeit an AI you learn to trounce
easily after about your third or fourth game. But the 2-player mode is
an absolute blast, and definetly one of the best multiplayer
experiences on the NES, if not just one of the best simultaneous
multiplayer sports games for the system. In short, it's a lot of fun to
play with a friend.
Graphically, the game holds up well. The players animate well, although
they're presented in a much more cartoony, comical style than some
gritty "realstic" 8-bit graphics, like NES games used to try and have
back in the day.
The games soundtrack consists of four main tunes, one of which you
memorize quickly, which is the game's main theme. One song plays
continuously during gameplay, although it's not one that every gets old
or annoying or anything. The sound effects are your basic skating,
shooting, fighting, referee-whistling kind of stuff, which is great.
In short, if you're into NES sports, you'll definetly like this game.
This coming from someone who isn't really much into sports games in the
least.


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Angular 11 - база резюме сотрудников компании
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birthday
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| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one | |
| 3 | * or more contributor license agreements. See the NOTICE file | |
| 4 | * distributed with this work for additional information | |
| 5 | * regarding copyright ownership. The ASF licenses this file | |
| 6 | * to you under the Apache License, Version 2.0 (the | |
| 7 | * "License"); you may not use this file except in compliance | |
| 8 | * with the License. You may obtain a copy of the License at | |
| 9 | * | |
| 10 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 11 | * | |
| 12 | * Unless required by applicable law or agreed to in writing, | |
| 13 | * software distributed under the License is distributed on an | |
| 14 | * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
| 15 | * KIND, either express or implied. See the License for the | |
| 16 | * specific language governing permissions and limitations | |
| 17 | * under the License. | |
| 18 | */ | |
| 19 | package org.apache.empire.db.exceptions; | |
| 20 | ||
| 21 | import java.sql.SQLException; | |
| 22 | ||
| 23 | import org.apache.empire.commons.ErrorType; | |
| 24 | import org.apache.empire.db.DBObject; | |
| 25 | import org.slf4j.Logger; | |
| 26 | import org.slf4j.LoggerFactory; | |
| 27 | ||
| 28 | public class ConstraintViolationException extends EmpireSQLException | |
| 29 | { | |
| 30 | // Logger | |
| 31 | private static final Logger log = LoggerFactory.getLogger(ConstraintViolationException.class); | |
| 32 | | |
| 33 | /** | |
| 34 | * Comment for serialVersionUID | |
| 35 | */ | |
| 36 | private static final long serialVersionUID = 1L; | |
| 37 | | |
| 38 | public static final ErrorType errorType = new ErrorType("error.db.statementFailed", "Error executing statement {0}.\r\nNative error is: {1}"); | |
| 39 | | |
| 40 | public ConstraintViolationException(DBObject obj, String sqlCmd, SQLException cause) | |
| 41 | { | |
| 42 | super(ConstraintViolationException.errorType, new String[] { sqlCmd, messageFromSQLException(driverFromObject(obj), cause) }, 1, cause); | |
| 43 | } | |
| 44 | | |
| 45 | /** | |
| 46 | * log the error | |
| 47 | */ | |
| 48 | @Override | |
| 49 | protected void log() | |
| 50 | { | |
| 51 | if ( log.isErrorEnabled() ) | |
| 52 | log.error(getMessage()); | |
| 53 | else | |
| 54 |
1
1. log : removed call to org/apache/empire/db/exceptions/EmpireSQLException::log → NO_COVERAGE |
super.log(); |
| 55 | } | |
| 56 | } | |
Mutations | ||
| 54 |
1.1 |


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Manual Install
Prefer to install and start an Ionic app manually from the command line? Follow the manual install below.
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ionic start myApp tabs
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For v1 projects, use the
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cd myApp
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Shane got a visit from Grandma Connie and Papa Morris.
Shane took some swimming lessons at the Schenley Park pool. He also had a good time wearing his bathing suit while playing in the driveway.
Shane went to the Carnegie museum. He has been busy tending the garden. We planted tomatos and pumpkins and lots of coleus cuttings (all taken from Shane's little plant that he got at Phipps last year).
Shane and Maya visited Grandma Judy and Papa Mike.
Maya and Shane stayed with Grandma Connie and Papa Morris while Mom and Dad went to Edinburgh. They had a great time playing in the wading pool, going to the playground, and spending a day at Dutch Wonderland.
Maya and Shane went to Cousin Justin's 4th birthday party on their way back home to Pittsburgh.
Shane and Maya really like to drink milk. Right before we left for vacation we had the outside of our house painted. Shane thinks the front entrance looks pretty spiffy now with its "orange" trim. Maya and Shane like to play in Mom's sewing room. Mom lets them use her scraps to design quilts on the wall and the floor.
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Poems on Various Subjects, Religious and Moral
by Phillis Wheatley
"An Hymn to Humanity. To S. P. G. Esq."
Additional Information
- Year Published: 1773
- Language: English
- Country of Origin: United States of America
- Source: Wheatley, P. (1773). Poems on Various Subjects, Religious and Moral.London, England: A. Bell.
-
Readability:
- Flesch–Kincaid Level: 2.5
- Word Count: 234
- Genre: Poetry
- Keywords: 18th century literature, american authors, poetry
- ✎ Cite This
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LO! for this dark terrestrial ball
Forsakes his azure–paved hall
A prince of heav'nly birth!
Divine Humanity behold,
What wonders rise, what charms unfold
At his descent to earth!
II.
The bosoms of the great and good
With wonder and delight he view'd,
And fix'd his empire there:
Him, close compressing to his breast,
The sire of gods and men address'd,
"My son, my heav'nly fair!
III.
"Descend to earth, there place thy throne;
"To succour man's afflicted son
"Each human heart inspire:
"To act in bounties unconfin'd
"Enlarge the close contracted mind,
"And fill it with thy fire."
IV.
Quick as the word, with swift career
He wings his course from star to star,
And leaves the bright abode.
The Virtue did his charms impart;
Their G–––––! then thy raptur'd heart
Perceiv'd the rushing God:
V.
For when thy pitying eye did see
The languid muse in low degree,
Then, then at thy desire
Descended the celestial nine;
O'er me methought they deign'd to shine,
And deign'd to string my lyre.
VI.
Can Afric's muse forgetful prove?
Or can such friendship fail to move
A tender human heart?
Immortal Friendship laurel–crown'd
The smiling Graces all surround
With ev'ry heav'nly Art.


GaborFilterDesighnNik Help.
In order to successfully use this GUI please follow the simple instructions beneath:
1. Define the filter, by changing the Mother Gabor parameters or, by loading a previously saved filter (you can view your filter in both time and frequency domains)
2. Load an image
3. Finally, apply the filter
4. See the filtering result sing the "Show" and "Filtered Image" button combination.
Enjoy.


deep learning assessment
Q1. In the following code snippet?
def runtime(x):
n_length = len(x)
if n_length <= 1:
return 13
return runtime(x[:n_length//2] + runtime(x[n_length//2:]))
- O(log n)
- O(n)
- O(n log n)
- O(n^2)
- O(2^n)
Q2. A tourist in Madrid (root node of a tree) wants to visit many places. She wants to start from Madrid and visit cities with direct paths to Madrid first and indirect cities to Madrid sequentially after. What algorithm should she use?
- Depth First Search
- Breadth First Search
- Trim’s algorithm
- Kruskal’s algorithm
Q3. A bag contains 6 yellow chips (labeled A – F) and 6 orange chips(labeled A - F). If two chips are picked one by one, calculate the probability that a yellow chip and an orange chip with the same label will be chosen if the first chip selected is yellow.
- 1/36
- 1/22
- 1/11
- 1/2
- 1/121
Q4. Identify the correct answer in the following statements regarding principal component analysis (PCA).
- The sum of the eigenvalues is exactly the same as the sum of the variance of the variables
- PCA can be used for qualitative variables
- PCA can be used for either qualitative or quantitative variables
- The variables used in PCA do not need to have the same dimensions
- All principal components are equally important and interpretable
Q5. Which of the following statements is not true about tree-based algorithms?
- Tree-based models can handle both linear and non-linear relationships
- Tree-based models work well for both categorical and continuous variables
- Tree-based algorithms have a strong assumption of independence
- Overfitting is one of the common drawbacks of tree-based models
- Random forest is an example of tree-based algorithms that is an ensemble learning
Q6. Ensembling is a combination of various models to achieve a single powerful model. Which of the following statements is correct?
- Ensembling is done separately from regularization in tree-based models
- Voting classifier gives a highest prediction result based on the prediction of all the sub-models
- Bagging is the process that applies classifiers on small partitions of the dataset and take the average of all the predictions
- Boosting focuses on correct guesses to maximize the accuracy
Q7. One potential issue of using ReLu as the activation function is that it is susceptible to vanishing gradient and slow learning rate.
- True
- False
Q8. How does dropout work as a regularizer in deep learning?
- Dropout is a form of model averaging
- has a regularizing effect by preventing feature co-adaption
- Dropout adds noise to the learning process
- Dropout leads to more sparsity in the hidden units
- All of the above
Q9. Select one correct answer in the following statements.
- Variational autoencoders (VAE) are better for reconstruction compared to autoencoders
- One cannot use deep learning models if the length of feature vector is not uniform
- In graphical neural networks (GNN), message passing is a convolution or aggregation operation on graphs that is used to generate the embeddings by getting the messages
- Grid search is the only way to tune multiple hyperparameters in deep learning
Q10. What is not a regularization method in neural networks?
- Dropout
- Batch normalization
- Early stopping
- Data augmentation
- Data standardization
Q11. What is not a major component of reinforcement learning?
- Agent
- Environment
- Reward function
- Value function
- None of the above
Q12. Which of the following statements is a correct characteristic of skip connection?
- Skip connection does not learn residues
- Skip connection must go through the intermediate layers
- Skip connection has no effect on the convergence of model training
- Skip connection directly passes the output from early layers to later layers
- Skip connection is limited to only convolutional neural networks (CNN)
Q13. In deep learning, a larger batch size is always preferred.
- True
- False
Q14. In Long short-term memory (LSTM), the first step is to decide which information to retain or discard from cell state, and this decision is made through a sigmoid layer.
- True
- False
Q15. How do you assess the uncertainty in deep learning models?
- Cross validation
- Node dropout
- Injected data noise
- None of the above
- All of the above
Q16. One can apply data augmentation to the testing dataset.
- True
- False


5
1 This is the record of the descendants of Adam. On the day that God created mankind, he made them in his own likeness.
2 Male and female he created them. He blessed them and named them mankind, when they were created.
3 When Adam had lived 130 years, he became the father of a son in his own likeness, after his image, and he called his name Seth.
4 After Adam became the father of Seth, he lived eight hundred years. He became the father of more sons and daughters.
5 Adam lived 930 years, and then he died.
6 When Seth had lived 105 years, he became the father of Enosh.
7 After he became the father of Enosh, he lived 807 years and became the father of more sons and daughters.
8 Seth lived 912 years, and then he died.
9 When Enosh had lived ninety years, he became the father of Kenan.
10 After he became the father of Kenan, Enosh lived 815 years. He became the father of more sons and daughters.
11 Enosh lived 905 years, and then he died.
12 When Kenan had lived seventy years, he became the father of Mahalalel.
13 After he became the father of Mahalalel, Kenan lived 840 years. He became the father of more sons and daughters.
14 Kenan lived 910 years, and then he died.
15 When Mahalalel had lived sixty-five years, he became the father of Jared.
16 After he became the father of Jared, Mahalalel lived 830 years. He became the father of more sons and daughters.
17 Mahalalel lived 895 years, and then he died.
18 When Jared had lived 162 years, he became the father of Enoch.
19 After he became the father of Enoch, Jared lived eight hundred years. He became the father of more sons and daughters.
20 Jared lived 962 years, and then he died.
21 When Enoch had lived sixty-five years, he became the father of Methuselah.
22 Enoch walked with God three hundred years after he became the father of Methuselah. He became the father of more sons and daughters.
23 Enoch lived 365 years.
24 Enoch walked with God, and then he was gone, for God took him.
25 When Methuselah had lived 187 years, he became the father of Lamech.
26 After he became the father of Lamech, Methuselah lived 782 years. He became the father of more sons and daughters.
27 Methuselah lived 969 years. Then he died.
28 When Lamech had lived 182 years, he became the father of a son.
29 He called his name Noah, saying, "This one will give us rest from our work and from the toil of our hands, which we must do because of the ground that Yahweh has cursed."
30 Lamech lived 595 years after he became the father of Noah. He became the father of more sons and daughters.
31 Lamech lived 777 years. Then he died.
32 After Noah had lived five hundred years, he became the father of Shem, Ham, and Japheth.


Traits As Interfaces
Table of Contents
Beginning
This is a look at Scala traits. The traits look sort of like abstract base classes, but they are used here to define "parts" for classes that can then be implemented to create a specific type.
Middle
Some Traits
These are some traits to make up an animal. Viewed in this context, the name "traits" makes sense - each trait defines a different aspect of the animal class.
trait Tail {
def wag(): Unit
def stop_wagging(): Unit
}
Note: I originally called those methods start
and stop
but when you create the class all the methods are going to get a concrete definition, so you don't want them to be too generic or they might clash across different traits.
trait Legs {
def move(): Unit
def stand(): Unit
}
trait Voice {
def speak(): String
}
Now when you create the class you can extend one of the traits and add more using the with
keyword, implementing the methods in a way that makes sense for the concrete class.
class Cow extends Tail with Legs with Voice {
// Tail trait
def wag(): Unit = {println("I'm wagging my tail")}
def stop_wagging(): Unit = {println("my tail is stilled")}
// Legs trait
def move(): Unit = {println("trotting")}
def stand(): Unit = {println("standing still")}
// Voice trait
def speak(): String = "moo"
}
Create a Cow
val cow = new Cow()
cow.wag
cow.move
cow.stop_wagging
cow.stand
println(cow.speak)
I'm wagging my tail trotting my tail is stilled standing still moo
One thing to note here is that I had a misspelling in that previous code block and ob-ipython failed to execute the block but didn't tell me what the error was. Luckily if you open the REPL you can start poking around (it opens an ammonite REPL) and maybe figure out what's failing.
End
So that's traits. There's a lot more to them, I'm sure, but that's enoug for now.


The Revelation to Saint John
1
1 This is the revelation of Jesus Christ that God gave him to show his servants what must soon take place. He made it known by sending his angel to his servant John.
2 John bore testimony to everything he saw concerning the word of God and to the testimony given about Jesus Christ.
3 Blessed is the one who reads aloud—and all those who hear—the words of this prophecy and obey what is written in it, because the time is near.
4 John, to the seven churches in Asia: Grace to you and peace from him who is, and who was, and who is to come, and from the seven spirits who are before his throne,
5 and from Jesus Christ, who is the faithful witness, the firstborn of the dead, and the ruler of the kings of the earth. To him who loves us and has released us from our sins by his blood,
6 he has made us a kingdom, priests to God and his Father—to him be the glory and the power forever and ever. Amen.
7 Look, he is coming with the clouds;
every eye will see him,
including those who pierced him.
And all the tribes of the earth will mourn over him.
Yes, Amen.
8 "I am the Alpha and the Omega," says the Lord God, "the one who is, and who was, and who is to come, the Almighty."
9 I, John—your brother and the one who shares with you in the suffering and kingdom and patient endurance that are in Jesus—was on the island called Patmos because of the word of God and the testimony about Jesus.
10 I was in the Spirit on the Lord's day. I heard behind me a loud voice like a trumpet.
11 It said, "Write in a book what you see, and send it to the seven churches—to Ephesus, to Smyrna, to Pergamum, to Thyatira, to Sardis, to Philadelphia, and to Laodicea."
12 I turned around to see whose voice was speaking to me, and as I turned I saw seven golden lampstands.
13 In the middle of the lampstands there was one like a Son of Man, wearing a long robe that reached down to his feet, and a golden belt around his chest.
14 His head and hair were as white as wool—as white as snow, and his eyes were like a flame of fire.
15 His feet were like burnished bronze, like bronze that had been refined in a furnace, and his voice was like the sound of many rushing waters.
16 He had in his right hand seven stars, and coming out of his mouth was a sharp two-edged sword. His face was shining like the sun at its strongest shining.
17 When I saw him, I fell at his feet like a dead man. He placed his right hand on me and said, "Do not be afraid. I am the First and the Last,
18 and the one who lives. I was dead, but look, I live forever! And I have the keys of death and of hades.
19 Therefore write down what you have seen, what is now, and what will take place after this.
20 As for the hidden meaning about the seven stars you saw in my right hand, and the seven golden lampstands: the seven stars are the angels of the seven churches, and the seven lampstands are the seven churches."


| Ticket ID: 409788 | |
|---|---|
| Name: | Parker Seidel |
| No Active Support | |
| Email: |<EMAIL_ADDRESS>|
| Response Time: | 72hours, 60hours left |
| Last Action: | Never been locked |
| Link to public post | |
| Status: | Not answered |
| Ticket ID: 409788 | |
|---|---|
| Name: | Parker Seidel |
| No Active Support | |
| Email: |<EMAIL_ADDRESS>|
| Response Time: | 72hours, 60hours left |
| Last Action: | Never been locked |
| Link to public post | |
| Status: | Not answered |


Daniel P Doherty
Democrat
Retired since February, 2017. Past Academic Dean, Past Institutional Researcher in Program Development/Closure at Aims Community College (2007-2017). Past Dean of Instruction at Western Nebraska Community College (1999-2007), Past Instructor of Communication and Theatre at Eastern Wyoming College (1975-1999)
BA in Speech/Theatre from Fort Hays Kansas State College (1973), MA in Theatre from University of Wyoming (1979), ED.D in Educational Administration and Leadership from UW (2003).
80634
Minimal involvement. Small donations this year (total less than $100.00) on some state and federal campaigns.
Nothing about n the last 5 years. I have been a board member of the Union Colony Civic Center and of the Centennial Area Health Education Center between about 2009 and 2015.
Other than the two boards just mentioned...none. No significant work with either of then on advocating action...essentially oversight and operations.
Strong general interest, concern about balance/structure of current state political districts, time available
Career let ng experience in committee structures and involvement, open to hearing and considering differing points of view. Strong belief in resolution by discussion leading to consensus.
Taught and judged competitive public speaking, including research-based extemporaneous speaking. Judged local and state high school public speaking events (including debate and research-based extemporaneous speaking).
Male


LEV/L simplifies game development for educators by dividing the labor required to build a game.
Faculty focus on the 'script,' expressed in a Game Design Document. This document is designed to be relatively simple to create and edit. The format is very flexible; you can specify as much or as little complexity as you want.
CNDLS student assistants and other support staff create scripts and style documents that describe the look and feel of the game. The game is just a Web page where elements are added or removed to create scenes where the game takes place. You can style these with ordinary Web stylesheets. By following a simple structure, the game scripts can both respond to and trigger events that happen during game play. The game scripts can also be integrated with HTML5 game or animation libraries to create more compelling effects.
CNDLS provides the framework code that assembles the Game Design Document and the custom scripts and styles to generate the game experience and to record student interactions.
Based on the player's actions, the faculty-designed game can:
- award or take back points,
- skip forward or loop back to other rounds,
- enforce time limits,
- trigger animations,
- make resources available to the player,
- and many other options...


List of all members.
Public Member Functions |
|
(void) | - reset |
|
(void) | - addHEADOperation: |
|
(void) | - go |
Static Public Member Functions |
|
(id) | + queue |
Properties |
|
id | uploadProgressDelegate |
|
id | downloadProgressDelegate |
|
SEL | requestDidStartSelector |
|
SEL | requestDidReceiveResponseHeadersSelector |
|
SEL | requestWillRedirectSelector |
|
SEL | requestDidFinishSelector |
|
SEL | requestDidFailSelector |
|
SEL | queueDidFinishSelector |
|
BOOL | shouldCancelAllRequestsOnFailure |
|
id | delegate |
|
BOOL | showAccurateProgress |
|
int | requestsCount |
|
NSDictionary * | userInfo |
|
unsigned long long | bytesUploadedSoFar |
|
unsigned long long | totalBytesToUpload |
|
unsigned long long | bytesDownloadedSoFar |
|
unsigned long long | totalBytesToDownload |
The documentation for this class was generated from the following file:


Information
Current URL:
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Open in environment
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${competition.name}
(Participating in Incognito)
- Input Upload: ${h.ftime(competition.input_upload_begins, competition.input_upload_ends)}
- Output Upload: ${h.ftime(competition.output_upload_begins, competition.output_upload_ends)}
-
Verification: ${h.ftime(competition.verification_begins, competition.verification_ends)}
-
Resolution: ${h.ftime(competition.resolution_begins, competition.resolution_ends)}
-
Open Verification: ${h.ftime(competition.open_verification_begins, competition.open_verification_ends)}
-
Evaluation: ${h.ftime(competition.evaluation_begins, competition.evaluation_ends)}
Open Verification is now available. View the
rankings table to participate.
Switch Stage (admin only):


You can remove items from the cache from anywhere in your application using Remove
and RemoveAsync
methods. Include the following
namespace in your project:
[Serializable]
class Customer
{
...
}
Cache _cache = NCache.InitializeCache("myCache");
_cache.Clear();
Customer customer = new Customer ();
customer.name = "David" ;
customer.customerID = 1001;
customer.City = "London" ;
customer.ContactNumber = "+44-xxx-xxxx-xxxx" ;
customer.IsOrderInProcess = true ;
// Add Item to Cache
_cache.Add("Customer:David:1001" , customer);
// Removes key from Cache and returns the attached object with it
Object removedObject = _cache.Remove("Customer:David:1001");
Customer customerInformation = (Customer)removedObject;
Console.WriteLine("Customer ID: " + customerInformation.customerID);
Console.WriteLine("Name: " + customerInformation.name);
Console.WriteLine("City: " + customerInformation.City);
Console.WriteLine("Contact Number: " + customerInformation.ContactNumber);
Console.WriteLine("Order placed: " + customerInformation.IsOrderInProcess);
Initialize Cache | Adding Items | Finding items | Dispose Cache | Start Cache | Stop Cache


"Hij ging met de kippen op stok"
The Dutch phrase "Hij ging met de kippen op stok" literally translates into "He went to roost with the chickens", but it means that he went to bed early.


D.2 General Comments
I show, for each test, how the sustainable throughput varies over
time. The y-axis shows the throughput (responses per second). The
x-axis is the ratio of fill-traffic volume to cache size. Because
each test takes a different amount of time, this is a nice way to
normalize all the results. The test is over when the cache has been
filled twice.
In most traces, you'll see that sustainable
throughput decreases over time. At the beginning of the test, the
throughput is very high. Here, the disks are empty, and Squid
doesn't need to replace old objects. The throughput
for a full cache is usually worse than for an empty cache. This is a
common characteristic of proxy benchmarks and emphasizes the
importance of reaching steady-state conditions.
Don't be fooled by impressive results from short
tests.
The Throughput, Response Time, and Hit Ratio values given in the
summary tables are taken from the last 25% of the test. Here, between
1.5 and 2.0 on the x-axis, the throughput is more or less stable and
flat. I report the mean of the throughput, response time, and hit
ratio values in this range from the trace data.
Throughput is the most interesting metric in these tests. It is given
in responses per second. The rows in each summary table are sorted by
throughput.
The response time numbers are less interesting because they are all
about the same. I decided to report them to show that, indeed, the
results stay within the response time window defined by the workload.
The target response time is around 1.5 seconds, but the actual
response time varies depending on the particular test.
The response hit ratio values are also not particularly interesting.
The ideal hit ratio for this workload is about 58%. Due to an as-yet
unresolved Polygraph bug, however, the hit ratio decreases slightly
as the test progresses.
Keep in mind that these results are meant to demonstrate the relative
performance of different options, rather than the absolute values.
You'll get different numbers if you repeat the tests
on different hardware.
|


class Bundler::SystemRubyVersion
A subclass of RubyVersion that implements version, engine and #engine_version based upon the current information in the system. It can be used anywhere a RubyVersion object is expected, and can be compared with a RubyVersion object.
Public Class Methods
new(*)
click to toggle source
# File lib/bundler/ruby_version.rb, line 76 def initialize(*) # override the default initialize, because # we will implement version, engine and # engine_version dynamically end
Public Instance Methods
engine()
click to toggle source
# File lib/bundler/ruby_version.rb, line 90 def engine if defined?(RUBY_ENGINE) RUBY_ENGINE.dup else # not defined in ruby 1.8.7 "ruby" end end
engine_version()
click to toggle source
# File lib/bundler/ruby_version.rb, line 99 def engine_version case engine when "ruby" RUBY_VERSION.dup when "rbx" Rubinius::VERSION.dup when "jruby" JRUBY_VERSION.dup else raise BundlerError, "RUBY_ENGINE value #{RUBY_ENGINE} is not recognized" nil end end
gem_version()
click to toggle source
# File lib/bundler/ruby_version.rb, line 86 def gem_version @gem_version ||= Gem::Version.new(version) end
patchlevel()
click to toggle source
# File lib/bundler/ruby_version.rb, line 113 def patchlevel RUBY_PATCHLEVEL.to_s end
version()
click to toggle source
# File lib/bundler/ruby_version.rb, line 82 def version RUBY_VERSION.dup end


K-Nearest Neighbors
- 1. KNN algorithm
- 2. Decision Boundary
- 3. Curse of Dimensionality
- 4. Python Implementation: Classification
1. KNN algorithm
The nearest-neighbors algorithm considers the $K$ nearest neighbors of a datapoint $x$ to predict its label. In the figure below, we have represented a binary classification problem (colors red and green for classes 0,1 respectively) with datapoints living in a 2-dimensional feature space.
The algorithm consists in attributing the majority class amongts the $K$-nearest neighbors. In the example above we consider the 3 nearest neighbors using euclidean distances. Mathematically the predictor $\hat{y}$ is given by
\[\hat{y}(x)=\text{argmax}_{0,1}\{n_0(x),n_1(x): x\in D_K(x)\}\]where $D_K(x)$ is the set of $K$-nearest neighbors and $n_{0,1}(x)$ are the number of neighbors in $D_K$ with class $0,1$ respectively. The ratio $n_{0,1}/K$ are the corresponding probabilities. For a multiclass problem the predictor follows a similar logic except that we choose the majority class for which $n_i(x)$ is the maximum, with $i$ denoting the possible classes.
A probabilistic approach to nearest neighbors is as follows. We consider the distribution
\[p(x|c)=\frac{1}{N_c\sqrt{2\pi\sigma^2}^{D/2}}\sum_{n\in\text{class c},n=1}^{n=N_c}e^{-\frac{\|x-\mu_n\|^2}{2\sigma^2}}\]with $N_c$ the number of points with class $c$ which have coordinates $\mu_c$, and $x$ lives in $D$ dimensions. The probabilities $p(c)$ are determined from the observed frequencies, that is,
\[p(c=0)=\frac{N_0}{N_0+N_1},\;p(c=1)=\frac{N_1}{N_0+N_1}\]The ratio of the likelihoods is then
\[\frac{p(c=1|x)}{p(c=0|x)}=\frac{p(x|c=1)p(c=1)}{p(x|c=0)p(c=0)}=\frac{\sum_{n=1}^{n=N_1}e^{-\frac{\|x-\mu_n\|^2}{2\sigma^2}}}{\sum_{n=1}^{n=N_0}e^{-\frac{\|x-\mu_n\|^2}{2\sigma^2}}}\]Take $d(x)$ as the largest distance within the set of $K$-nearest neighbors of the datapoint $x$. If the variance $\sigma$ is of order $\sim d$ then the exponentials with arguments $|x-\mu|^2>d^2$ can be neglected while for $|x-\mu|^2<d^2$ the exponential becomes of order one, and so we approximate
\[\frac{\sum_{n=1}^{n=N_1}e^{-\frac{\|x-\mu_n\|^2}{2\sigma^2}}}{\sum_{n=1}^{n=N_0}e^{-\frac{\|x-\mu_n\|^2}{2\sigma^2}}}\simeq \frac{\sum_{i\in D_K^1(x)} e^{-\frac{\|x-\mu_i\|^2}{2\sigma^2}}}{\sum_{j\in D_K^0(x)} e^{-\frac{\|x-\mu_j\|^2}{2\sigma^2}}}\sim\frac{\#i}{\#j}\]where $D^{0,1}_K(x)$ are the nearest neihgbors of $x$ with classes $0,1$ respectively, and $#i+#j=K$. In theory this would reproduce the K-nearest neighbors predictor. However, this would require that for each $x$ the threshold $d$ is approximately constant, which may not happen in practice. The algorithm is however exact as $\sigma\rightarrow 0$ for which only the nearest neighbor is picked.
In regression we calculate instead the average of $K$-nearest neighbor targets. That is,
\[\hat{y}(x)=\frac{1}{K}\sum_{i\in D_K(x)}y_i\]Consider different datasets whereby the positions of the datapoints $x$ do not change but the target $y$ is drawn randomly as $f+\epsilon$ where $f$ is the true target and $\epsilon$ is a normally distributed random variable with mean zero and variance $\sigma^2$. The bias is thus calculated as
\[\text{Bias}(x)=f(x)-\text{E}[\hat{f}(x)]=f(x)-\frac{1}{K}\sum_{i\in D_K(x)}f(x_i)\]For $K$ small the nearest neighbors will have targets $f(x_i)$ that are approximately equal to $f(x)$, by continuity. As such, the bias is small for small values of $K$. However, as $K$ grows we are probing datapoints that are farther and farther away and thus more distinct from $f(x)$, which in general will make the bias increase.
On the other hand, the variance at a point $x$, that is,
\[\text{Var}(\hat{f})|_x=\text{E}[(\hat{f}(x)-\text{E}[\hat{f}(x)])^2]\]becomes equal to
\[\text{Var}(\hat{f})=\frac{\sigma^2}{K}\]Therefore, for large values of $K$ the variance decreases, while it is larger for smaller values of $K$.
2. Decision Boundary
In the picture below, we draw the decision boundary for a $K=1$ nearest neighbor. For any point located inside the polygon (hard lines), the nearest neighbor is $P_1$, and so the predicted target is $f(P_1)$ in that region.
To construct the decision boundary, we draw lines joining each point to $P_1$, and for each of these, we draw the corresponding bisector. For example, consider the points $P_1$ and $P_2$. For any point along the bisector of $\overline{P_1P_2}$, the distance to $P_1$ is the same as the distance to $P_2$. Therefore, the polygon formed by drawing all the bisectors bounds a region where the nearest point is $P_1$.
For $K>1$, we have to proceed slightly differently. First, we construct the $K=1$ decision boundary- this determines the nearest neighbor. Call this point $N_1$, the first neighbor. Second, we pretend that the point $N_1$ is not part of the dataset and proceed as in the first step. The corresponding nearest neighbor $N_2$ is then the second nearest neighbor while including $N_1$. We proceed iteratively after $K$ steps. The decision boundary is then determined by joining the $K=1$ polygons of each $N_1,N_2,\ldots N_K$.
3. Curse of Dimensionality
In this section, we discuss the K-nearest neighbors algorithm in higher dimensions. Consider a sphere of radius $r=1$ in $d$ dimensions. We want to calculate the probability of finding the nearest neighbor at a distance $r<=1$ from centre. This probability density is calculated as follows. Let $p_r$ be the probability of finding a point at a distance $r$ and $p_{>r}$ the probability of finding a point at a distance $>r$. Then the probability that we want can be written as
\[\begin{equation*}\begin{split}&Np_r p_{>r}^{N-1}+\frac{1}{2}N(N-1)p_r^2p_{>r}^{N-2}+\ldots\\ &=(p_r+p_{>r})^N-p_{>r}^N\end{split}\end{equation*}\]which is the probability of finding at least one point at $r$ and none for $< r$. The probability $p_r$ is infinitesimally small, since
\[p_r=r^{d-1}dr\]while $p_{>r}=(1-r^d)$. Hence, we can expand the expression above and determine the probability density
\[\frac{dP(r)}{dr}=N r^{d-1}(1-r^{d})^{N-1}\]Take $d\gg1$. The probability density has a maximum at
\[r^*=\frac{1}{N^{1/d}}\]For the K-nearest neighbors algorithm to perform well, there should exist at each point a sufficiently large number of neighbors at distances $\epsilon\ll 1$, so one can use the continuity property of a smooth function. Therefore if we insist that $r^*=\epsilon\ll 1$, this implies that $N$ must be exponentially large as a function of $d$. In other words, for higher dimensions, the probability of finding a neighbor at distance $\epsilon$ is smaller because there is more “space” available. To compensate for that, we need an exponentially larger number of datapoints.
4. Python Implementation: Classification
Define KNN class with fit and call methods. The fit method memorizes the training data and the call method retrieves the predictor.
class KNN:
def __init__(self,k):
self.k=k
self.x=None
self.y=None
self.classes=None
def fit(self,x,y):
self.x=x
classes=sorted(set(y))
self.classes={a:b for b,a in enumerate(classes)}
self.y=np.zeros((y.shape[0],len(classes)))
for i,a in enumerate(y):
j=self.classes[a]
self.y[i][j]=1
def __call__(self,x):
if len(x.shape)==1:
t=x.reshape(1,-1)
else:
t=x
y_pred=np.zeros((t.shape[0],1))
for i,z in enumerate(t):
z=z.reshape(1,-1)
norm=self.x-z
norm=np.linalg.norm(norm,axis=1)
args=np.argpartition(norm,self.k)[:self.k]
ypred=self.y[args].mean(0)
ypred=ypred.argmax()
y_pred[i]=ypred
return y_pred
As an example, load Iris dataset and also the built-in SKlearn K-nearest neighbors algorithm.
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris=load_iris()
features=iris['data']
target=iris['target']
#train & test split
indices=np.arange(0,features.shape[0])
np.random.shuffle(indices)
l=int(0.2*features.shape[0])
xtrain=features[indices][:-l]
xtest=features[indices][-l:]
ytrain=target[indices][:-l]
ytest=target[indices][-l:]
knn=KNN(30) #the class above
Kneighbor=KNeighborsClassifier(n_neighbors=30) #the SKlearn class
knn.fit(xtrain,ytrain)
accuracy_score(ytest,knn(xtest))
Kneighbor.fit(xtrain,ytrain)
accuracy_score(ytest,Kneighbor.predict(xtest))
Retrieving exactly the same accuracy.


The Team
manager
Manager
ID: 1
Email: man@man
Office number: 205
engi
Engineer
ID: 2
Email: engi@engi
GitHub: engi
intern
Intern
ID: 3
Email: intern@intern
School: ucf


index
module components.merging.sputlink.utils
CompositionTable
class CompositionTable
Inherits from: object
Implements the 28 by 28 composition table. Takes care of loading
the table from file and retrieving compositions from the table.
Public Functions
__init__(self, compositions_file)
Read the compositions file and load all compositions into the
data instance variable.
compose_rels(self, rel1, rel2)
Looks up the composition of rel1 and rel2 and returns it.
pp(self, filename)
Print an html table to filename.
module functions
compare_id(a, b)
html_graph_prefix(fh)
intersect_lists(l1, l2)
Returns the intersection of two lists. The result will not contain
duplicate elements and list order is not preserved.
intersect_relations(rels1, rels2)
Returns the intersection of two relation sets. Returns None if both
of the two sets are None.
massage(str)
Prepare the string representation of a relation set for printing in
html.


The package implements ANFIS Type 3 Takagi and Sugeno's fuzzy if-then rule network with the following features: (1) Independent number of membership functions(MF) for each input, and also different MF extensible types. (2) Type 3 Takagi and Sugeno's fuzzy if-then rule (3) Full Rule combinations, e.g. 2 inputs 2 membership funtions -> 4 fuzzy rules (4) Hibrid learning, i.e. Descent Gradient for precedents and Least Squares Estimation for consequents (5) Multiple outputs.
| Version: | 0.99.1 |
| Depends: | R (≥ 3.0), methods, parallel |
| Published: | 2015-01-16 |
| Author: | Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez |
| Maintainer: | Cristobal Fresno <cfresno at bdmg.com.ar> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.bdmg.com.ar |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | anfis results |
| Reference manual: | anfis.pdf |
| Package source: | anfis_0.99.1.tar.gz |
| Windows binaries: | r-devel: anfis_0.99.1.zip, r-release: anfis_0.99.1.zip, r-oldrel: anfis_0.99.1.zip |
| OS X binaries: | r-release: anfis_0.99.1.tgz, r-oldrel: anfis_0.99.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=anfis to link to this page.


{% extends '_base.html' %} {% block content %}
coach_s_name:{{ sport.coach_s_name }}
description: {{ sport.description }}


Dear {{name}},
You have requested to have your password reset for your Winter Wishes account. Please visit this url to reset your password: {{url}} If you didn't make this request, you can ignore this email.


Verify that well-written security code is reused.
Reusing security code creates a more-secure application for these reasons:
When working with various programming and scripting languages, create new versions of reusable security code as required for each programming language, such as Java, PHP, Python, Ruby and .NET. This includes routines for authentication, cryptographic, and logging code, for example.
To verify that well-written security code is reused:
Identify well-written security code. Look through your applications for security code that is well-written and can be reused. Examples of security code that can be reused include authentication, authorization, cryptographic, data validation and logging code.
Examine reusable security code. Check whether well-written security code is the same in multiple applications - this would indicate that security code is being reused. Interview developers and ask them whether they maintain a library of security code. Examine the library of reusable code to determine its quality.
The development of security code will generally parallel your software development lifecycle, and will follow these general steps.
Identify requirements and perform analysis. Identify requirements which security code must address.
Design software architecture. Develop specific code parameters to work within the application architecture boundary. Include inputs and outputs from the requirements specification. Use data flow diagrams and other illustrations to transform use cases into pseudo code.
Write software code. Actual coding is developed at this time. This is the longest phase of the cycle.
Test security code. Test the security code against the requirements by using standard test methods.
Deploy security code. Add the security code to current projects.
Maintain software library. When the security code needs updating due to deficiencies noticed in actual use, use change control procedures to revise or add requirements. Submit the code for security testing, and add to the code security library when your change control sign-offs are completed.


Psalms 104:6
Thou coveredst it with the deep as with a garment: the waters stood above the mountains.


{{> ../layouts/head.html }} Pending Unreleased Not Confirmed Requests Purchase Orders Receipts Feedback Invoices {{> ../layouts/footer.html }}


A simple app to help you keep track of all your coding accomplishments. CodeBook allows you to compete and compare your progress with your classmates and coworkers. Help celebrate small or large victories to encourage your friends on their coding journey!
We are a team of five developers currently attending the UCI Full Stack coding bootcamp. We have learned so much in the last seven weeks and are eager to showcase our skills with this project.
This app allows you to showcase your accomplishments with other developers as well as get help with your programming questions. This project demonstrates our abilities in creating a full stack web application after programming for just 6 weeks. We are very excited for you to give our application a try!
Register » Login »Register or login above to begin posting in our feed. Start connecting with others now!
Take a look at our team members! Here you will find each of our Github's, Linkedin's, and Portfolio's.


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[t]Thank you[/t]
[t]We have added your change to our Crowdfunding portal. You will receive an e-mail with a confirmation.[/t]


Here is a short, quickstart guide to using pynmea
Using the NMEA part of this library is simple:
from pynmea import nmea
# This is a GPGGA sentence
data = '$GPGGA,064746.000,4925.4895,N,00103.9255,E,1,05,2.1,-68.0,M,47.1,M,,0000*4F'
# Create the object
gpgga = nmea.GPGGA()
# Ask the object to parse the data
gpgga.parse(data)
That’s it. All of the data from the gpgga sentence is now accessible on the object. So gpgga.latitude is ‘4925.4895’ and gpgga.num_sats is ‘05’.
This is only of limited use however. Splitting up and parsing the raw data is one of the more tedious jobs. For this reason the NMEAStreamer was created:
from pynmea.streamer import NMEAStreamer
with open('example_data_file.txt', 'r') as data_file:
streamer = NMEAStreamer(data_file)
next_data = streamer.get_strings()
data = []
while next_data:
data += next_data
next_data = streamer(read)
This code snippet would read an entire NMEA data file and output the contents into data, which is a list of sentences.
You may also chose to return a list of NMEA objects rather than plain text strings:
from pynmea.streamer import NMEAStreamer
with open('example_data_file.txt', 'r') as data_file:
streamer = NMEAStreamer(data_file)
next_data = streamer.get_objects()
data = []
while next_data:
data += next_data
next_data = streamer(read)
You may also feed the streamer raw data from memory:
from pynmea.streamer import NMEAStreamer
streamer = NMEAStreamer()
raw_data = '$GPGGA,064746.000,4925.4895,N,00103.9255,E,1,05,2.1,-68.0,M,47.1,M,,0000*4F\n$GPGGA,064746.000,4925.4895,N,00103.9255,E,1,05,2.1,-68.0,M,47.1,M,,0000*4F\n$GPGGA,064746.000,4925.4895,N,00103.9255,E,1,05,2.1,-68.0,M,47.1,M,,0000*4F'
data_obs = streamer.get_objects(data=raw_data)
# Remember to make sure you feed some empty data to flush the last of the data out
data_obs += streamer.get_objects(data='')
data is then a list of nmea objects. The same can be done with get_string(data=data) to retrieve a list of strings.


public interface IVersionedContent
IVersionedContentManager
or its component specific subclass,
and this description is the identity of the bytes.| Modifier and Type | Method and Description |
|---|---|
ContentHash |
getHash()
Returns the hash for the content as stored on the server.
|
IVersionableHandle |
getOriginalContainingState()
Returns the state handle that this content is stored in on the server.
|
long |
getSize()
Returns the uncompressed size for the content as stored on the server.
|
boolean |
sameContent(IVersionedContent other)
Returns whether the receiver represents semantically the same content
as the one passed in.
|
ContentHash getHash()
long getSize()
IVersionableHandle getOriginalContainingState()
boolean sameContent(IVersionedContent other)


You will be given a random number at the start of the game.
There are four crystals below. By clicking on crystal you will add a specific amount of points to your total score.
You will win the game by matching your total score to a random number, you loose the game if your total score goes above the random number.
The value of each crystal is hidden from you until you click on it.
Each time the game starts, the game wil change the values of each crystal.


To generate your MadLibs, please enter the following:
An honorific title (e.g. Mister or Colonel):
Your full name:
An adjective (i.e. a word used to describe something):
A vehicle (e.g. plane, boat, etc.):
A part of the vehicle you just named:
Where someone would park the aforementioned vehicle:
A precious object:
Something that makes noise:
Something that people do together as a group:
Another adjective (i.e. a word used to describe something):
A third adjective (i.e. a word used to describe something):
A color:
A family relationship (e.g. sister, father, etc.):
An organ (e.g. heart, liver, spleen, etc.):


{{ mainmenu.render }}
{% block page_content %}
{{ tmpl.render }}
{% endblock page_content %}
{% comment %} Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %}


Holiday shoppers are in the driver's seat now--here's what they're crashing into
Article - Nov 2017
4 principles for brooding to your favorite podcasts in shoulder pads
work Case Study - Nov 2017
How Nestle Waters convinced people it wasn't evil
work Case Study - Nov 2017
Look at something on a table with your friends
work Case Study - Month 2017
Are you worth it? Experts don't seem to think so
Perspective - Month 2017
Holiday shoppers want more help in a hurry--here's how to profit from their insatiable lust for consumption this season
Article - Oct 2017


CLI Interface¶
Go-Web has a built-in command-line interface that makes easy for developers to use the framework. Before using the CLI, the developer needs to compile the project by running command: go build goweb.go
After compiling the project, the CLI can be used to view all commands supported by Go-Web ./goweb show:commands
The following listing table shows commands presented to the user by show:command:
| Commands | Descriptions |
|---|---|
| database:seed <model_name> | Executes seeder (all available models if is not specified) |
| show:commands | Shows all custom CLI commands |
| server:daemon | Run Go-Web server as a daemon |
| server:run | Run Go-Web server normally |
Create custom commands¶
Go-Web command line interface (CLI) can be extended by running command
alfred -mCMD <command_name>
Before being available to Go-Web, commands must be registered in register.go. The following listing shows the registration of command Greetings:
Warning
Command must be registered in the register.go file located in project root directory.
Commands = gwf.CommandRegister{
List: map[string]interface{}{
"queue:failed": &console.QueueFailed{},
"queue:run": &console.QueueRun{},
"greetings": &console.Greetings{}, // new
// Here is where you've to register your custom commands
},
}
The command registration Commands variable is used by Go-Web to recognize and list supported commands.


Green Grass
Redraw
Toggle Greyscale
leafCountFactor:
minWidth:
maxWidth:
minHeight:
topRadius:
colorMax:
colorMin:


An icon is a glyph used to represent something else
Icons can represent types of content found on websites
_alarm icon_
_alarm slash icon_
_alarm outline icon_
_alarm slash outline icon_
_at icon_
_browser icon_
_bug icon_
_calendar outline icon_
_calendar icon_
_cloud icon_
_comment icon_
_comments icon_
_comment outline icon_
_comments outline icon_
_copyright icon_
_dashboard icon_
_dropdown icon_
_external square icon_
_external icon_
_eyedropper icon_
_feed icon_
_find icon_
_heartbeat icon_
_history icon_
_home icon_
_idea icon_
_inbox icon_
_lab icon_
_mail icon_
_mail outline icon_
_mail square icon_
_map icon_
_options icon_
_paint brush icon_
_payment icon_
_phone icon_
_phone square icon_
_privacy icon_
_protect icon_
_search icon_
_setting icon_
_settings icon_
_shop icon_
_sidebar icon_
_signal icon_
_sitemap icon_
_tag icon_
_tags icon_
_tasks icon_
_terminal icon_
_text telephone icon_
_ticket icon_
_trophy icon_
_wifi icon_
Icons can represent common actions a user can take
_adjust icon_
_add user icon_
_add to cart icon_
_archive icon_
_ban icon_
_bookmark icon_
_call icon_
_call square icon_
_cloud download icon_
_cloud upload icon_
_compress icon_
_configure icon_
_download icon_
_edit icon_
_erase icon_
_exchange icon_
_external share icon_
_expand icon_
_filter icon_
_flag icon_
_flag outline icon_
_forward mail icon_
_hide icon_
_in cart icon_
_lock icon_
_pin icon_
_print icon_
_random icon_
_recycle icon_
_refresh icon_
_remove bookmark icon_
_remove user icon_
_repeat icon_
_reply all icon_
_reply icon_
_retweet icon_
_send icon_
_send outline icon_
_share alternate icon_
_share alternate square icon_
_share icon_
_share square icon_
_sign in icon_
_sign out icon_
_theme icon_
_translate icon_
_undo icon_
_unhide icon_
_unlock alternate icon_
_unlock icon_
_upload icon_
_wait icon_
_wizard icon_
_write icon_
_write square icon_


GO
Select a picture here and provide a passphrase, then click the button. When the result is displayed, you can save it by touch&hold or right click, or select another picture by touch or click on it.


Learn Android from scratch creating twitter client
Start learning android practising. Minimum theory maximum practise skills. Register for courses right now. This part is absolutely free. After finishing them you will be able to write client for popular api like
vk, facebook, instagram.


| bookings/__init__.py |
0 |
0 |
0 |
100% |
| bookings/admin.py |
9 |
0 |
0 |
100% |
| bookings/apps.py |
3 |
3 |
0 |
0% |
| bookings/forms.py |
74 |
1 |
0 |
99% |
| bookings/helper_functions.py |
23 |
3 |
0 |
87% |
| bookings/high_demand.py |
3 |
3 |
0 |
0% |
| bookings/migrations/0001_initial.py |
9 |
0 |
0 |
100% |
| bookings/migrations/0002_auto_20171204_0233.py |
5 |
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| bookings/migrations/0003_auto_20171204_0511.py |
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| bookings/migrations/0011_auto_20171218_0350.py |
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| bookings/migrations/0017_auto_20171220_1725.py |
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| bookings/migrations/0035_auto_20180113_1624.py |
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| bookings/migrations/0036_auto_20180114_1614.py |
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| bookings/migrations/0038_auto_20180120_1609.py |
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| bookings/migrations/0039_auto_20180120_1611.py |
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| bookings/migrations/0040_auto_20180124_2337.py |
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| bookings/migrations/0041_auto_20180127_2106.py |
6 |
0 |
0 |
100% |
| bookings/migrations/__init__.py |
0 |
0 |
0 |
100% |
| bookings/models.py |
118 |
30 |
0 |
75% |
| bookings/oldcode.py |
73 |
73 |
0 |
0% |
| bookings/tests/__init__.py |
0 |
0 |
0 |
100% |
| bookings/tests/soon_test_select_bus_link_view.py |
19 |
19 |
0 |
0% |
| bookings/tests/test_view_home.py |
70 |
0 |
0 |
100% |
| bookings/tests/tests_forms/__init__.py |
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| bookings/tests/tests_forms/test_form_priceform.py |
20 |
0 |
0 |
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| bookings/urls.py |
5 |
0 |
0 |
100% |
| bookings/utils.py |
40 |
40 |
0 |
0% |
| bookings/views.py |
315 |
254 |
0 |
19% |
| manage.py |
13 |
6 |
0 |
54% |
| pb_config/__init__.py |
0 |
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| pb_config/settings.py |
71 |
11 |
0 |
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| pb_config/storage_backends.py |
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| pb_config/wsgi.py |
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| users/__init__.py |
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| users/admin.py |
1 |
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3 |
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| users/migrations/__init__.py |
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| users/models.py |
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1 |
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| users/urls.py |
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| users/views.py |
5 |
5 |
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