Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.4.0) |
| Imports: | sampling |
| Published: | 2018-10-08 |
| Author: | Roberto Sichera [aut, cre] |
| Maintainer: | Roberto Sichera <roberto.sichera at unipa.it> |
| BugReports: | https://github.com/rhobis/jipApprox/issues |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | jipApprox results |
| Reference manual: | jipApprox.pdf |
| Package source: | jipApprox_0.1.1.tar.gz |
| Windows binaries: | r-devel: jipApprox_0.1.1.zip, r-release: jipApprox_0.1.1.zip, r-oldrel: jipApprox_0.1.1.zip |
| OS X binaries: | r-release: jipApprox_0.1.1.tgz, r-oldrel: jipApprox_0.1.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=jipApprox to link to this page.


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Darth Vader Lives Here
Something short and leading about the collection below—its contents, the creator, etc. Make it short and sweet, but not too short so folks don’t simply skip over it entirely.
This is a wider card with supporting text below as a natural lead-in to additional content. This content is a little bit longer.
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Symposium Presentations
Here are links to final projects and presentations from the Symposium Spectacular. Congratulations to all the teams for impressive work and engaging presentations! UVA Students and COVID Spread Maxwell Patek, Nate Strawser, Nicolas Izurrategui, Muxuan Yan Pizza-Winning Presentation: Video Project Site: http://people.virginia.edu/~my2pk/infection-simulator/sites/ Source Code: https://github.com/max7patek/infection-simulator COVID-19 Interactive Simulator Will Define, Shen Yan, Christian Paget, Brienne Tyler Pizza-Winning Presentation: Video Project Site: https://wdefine.github.io/mmmfinalproject/ Optimizing your COVID-19 Finances: A Survival Guide Disha Jain, Sindhu Ranga, Jason Quinn, Sameer GuptaSymposium Spectacular
The schedule for the Final Projects Symposium Spectacular is below, starting at 2:00pm on Wednesday, 29 April at https://virginia.zoom.us/j/97366214060.
| Start Time | Project Title | Team |
|---|---|---|
| 2:00 PM | Optimizing your COVID-19 Finances: A Survival Guide | Disha Jain, Sindhu Ranga, Jason Quinn, Sameer Gupta |
| 2:15 PM | NLP for COVID19 | Eric Wang, Serena Jiao, Megan Cheng, Christine Cheng | 2:25 PM | Effect of Contact Tracing on the Spread of COVID-19 | Rebecca Zhou, Avery Burton, Eimara Mirza, Louisa Evola |
| 2:35 PM | Risky Business | An Analysis of Health Risk Factors | Anna Davis, Courtney Jacobs, Sally Greenberg, Chris Puglisi |
| 2:45 PM | Income and Occupation Effects on Social Distancing | Ryan Keller, Zac Bilmen, Ravi Kodali, Jami Park |
| 2:55 PM | UVA Students and COVID Spread | Maxwell Patek, Nate Strawser, Nicolas Izurrategui, Muxuan Yan |
| 3:05 PM | Inside China's Two-Child Policy | Margaret Cheng, Sophia Cheung, Yilin Huang, Yutong Zhong |
| 3:15 PM | Walk Around Your Room Break | |
| 3:30 PM | Soccer Player Ratings | Joe Karaki, Jack Short |
| 3:40 PM | Hotel Occupancy Optimization | Sam Spreen, Arjun Iyer, Ashwin Krishnamurthi, Rohan Malladi |
| 3:50 PM | COVID-19 Stock Exploration and Time Series Analysis | Faisal Refai, Misran Dolan, Hisham Iqbal |
| 4:00 PM | COVID-19 Interactive Simulator | Will Define, Shen Yan, Christian Paget, Brienne Tyler |
| 4:10 PM | COVIM: COVID-19 Intervention Modeling | Kiara Gan, Aaron Ho, Rashid Lasker, Eli Lifland |
| 4:20 PM | Analyzing the Impacts of Covid 19 Through Travel Data | Saeed Razavi, Sharvari Bhatt, Leon Smith, Yi Fan Lim, Lydia Kim |
Class 26: Network Contagion
Chapter 21 of Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg covers many of the topics from today’s class.
Final Plans summaries the plans for the rest of the semester, that were discussed in Class 26.
Final Plans and Project Presentations
We discussed results from the survey and plans for the rest of the semester. Slides on Survey and Plans (PDF) Remaining Classes The scheduled class meetings on Thursday, 23 April and Tuesday, 28 April will be open office hours for discussions about any topic you are interested in, including your final projects. This will be an opportunity to get feedback or advice on any issues for your project, including both techincal and research issues, as well as on planning your presentation.Class 23: Adam Davis
Talk and discussion with Adam Davis,from Upstart, an AI lending platform, on business analytics and his experiences as a new employee at a startup company.
(A video is available for students in the class, but not publicly.)
Class 22: Privacy in the Age of Coronavirus
Slides Class 22 Slides (PDF) Links Video: Coronavirus: From 93 infected to 0, what did this Chinese City do to contain the virus? Google’s COVID-19 Community Mobility Reports Virginia, 29 March 2020 (You can see if you’ve contributed to this at https://www.google.com/maps/timeline.) Kinsa US Health Weather Map Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating Noise to Sensitivity in Private Data Analysis. TCC 2006. (Paper that introduced Differential Privacy.Class 21: Joseph Calandrino
Guest lecture and discussion with Joseph Calandrino, Research Director, Office of Technology Research and Investigation, Federal Trade Commission.
Class 20: Peer Effects
Slides
The video we watched is Esther Duflo, Social experiments to fight poverty.
Class 19: Guest - Tammy Wang
Guest lecture and discussion with Tammy Wang, Vice President of Data Science and Engineering at Riviera Partners.


Sheriff: Woman killed by feral hogs outside Texas home
by The Associated PressANAHUAC, Texas — Authorities say feral hogs attacked and killed a woman outside a Southeast Texas home where she worked as a caretaker.
Christine Rollins cared for an elderly couple at their home in the small town of Anahuac (a-nuh-WAK’). Chambers County Sheriff Brian Hawthorne said Monday the 84-year-old woman she cared for was waiting for Rollins to arrive on Sunday. The woman went outside and found the 59-year-old Rollins in the front yard between her car and the front door.
Hawthorne says Rollins had a severe head wound and several other injuries consistent with an animal bite. The coroner in neighbouring Jefferson County ruled Monday that Rollins bled to death after an attack by feral hogs.
Anahuac is just over 40 miles (65 kilometres) east of Houston.
The Associated Press


Reality TV dance teacher pleads guilty to rape, exposing teen to HIV
by Associated PressMEMPHIS, Tenn. — A dance teacher who appeared on a reality TV show has pleaded guilty in Tennessee to raping a teenage student and exposing him to the AIDS virus.
The Shelby County district attorney’s office said Tuesday that 30-year-old John Conner III avoided trial when he pleaded guilty to criminal exposure to HIV, statutory rape by an authority figure and solicitation of a minor.

Prosecutors say Conner did not tell the teen that he had HIV. The boy later tested positive for HIV.
Conner has appeared on the Lifetime show “Bring It.” His sentencing is scheduled on Dec. 20.
Conner has two other cases pending involving sexual partners ages 17 and 24.


--- layout: page title: About header-img: "img/about-bg.png" ---
A Seattleite and California native- traveling the world learning and teaching dance and now, code. I'm a nomad at heart and wanted to see if I can make the new tech dream work with remote and mobile learning. I'm a communication nut and I find that talking to people is one of the hardest things a person can do.


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CodeFirst: Girls
Learning how to code
week 1
week 2
more information to come
Hello cruel world!
start here


Cops seek help to track down Cape Town motorist’s killers
by Ntwaagae SelekaNyanga police have pleaded for assistance to track down perpetrators who shot and killed a motorist in an attempted hijacking.
Police spokesperson Captain FC van Wyk said the incident took place on November 5 at about 16:10 on the corner of Monwood Drive and Sheffield Road in Browns Farm.
Van Wyk said according to reports, the deceased was shot by unknown assailants after he dropped off his employees.
"The suspects wanted to hijack his LDV but the owner sped off and the suspects fired shots at the vehicle wounding him. He was rushed to a nearby hospital where he later succumbed due to the injuries sustained," said Van Wyk.
Anyone with any information or who might have witnessed the incident or might know the suspects is kindly requested to contact the police.


Welcome to Seattle's Salmon Cookies! There is no better escape from Seattle's chilly drizzle than a hot cup of coffee. And nothing goes better with coffee than Seattle's Salmon Cookies. We even made it into Seattle's top 10!! We credit our unique and delicious confections!'
Visit one our 5 cookie stand locations or stop by our flaship store in Capitol Hill @ 310 3rd Avenue!


Rock, Paper, Scissors, Lizard, Spock
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LSU Defense Receives A Major Boost On Monday
by Alek ArendLSU’s defense has had plenty of problems this season. One thing’s for sure – this isn’t the Tigers’ defense of old.
Coach O’s team allowed 37 points to Ole Miss on Saturday. Due to the Tigers’ elite offensive attack, the big point total wasn’t an issue in regards to the outcome of the game. But there’s legitimate concerns about Ed Orgeron’s squad when facing a good offense, like Clemson or Ohio State.
Fortunately for the Tigers, they received a major boost on the defensive side of the ball on Monday. Linebacker Michael Divinity – who left the team due to personal reasons in the week leading up to the Alabama game – has rejoined the team and is now listed on the official roster.
The news was first reported by 247Sports.
It’s unclear if he’ll play this Saturday for the Tigers against Arkansas. But it’s certainly a major re-addition for a defense needing more play-makers. Prior to his departure, Divinity led the team with three sacks. He also added 23 tackles in games prior to his leave.
The LSU linebacker’s return makes sense given his message announcing he was taking a leave of absence.
“My goal is to get myself back to 100% in time to graduate in December and be back on the field fighting with my brothers soon.”
It looks like Divinity was able to get things sorted out. It’s certainly a major addition for the LSU defense.


Watch: Club for Growth Unveils Second Round of Anti-Impeachment Ads
The conservative Club for Growth launched ads Tuesday attacking some of the House Democrats’ most vulnerable members for backing the impeachment inquiry into President Donald Trump.
by Sean MoranThe Club for Growth launched five ads across five swing districts that Democrats represent, asking voters to urge their representative to stop the impeachment process, and instead focus on solving America’s problems.
The five districts all include freshmen Democrat representatives who flipped seats previously held by Republicans during the 2018 midterm elections.
Club for Growth President David McIntosh said in a statement Tuesday:
Now that socialists have driven the Democratic party over the cliff with the sham impeachment, conservatives have an opportunity, especially in certain districts where recently elected Democrats who campaigned on moderation and independence have to go home and answer questions from voters about why they haven’t done more on issues like jobs and the economy.
The five congressmen include:
Rep. Sean Casten (D-IL)
Rep. Joe Cunningham (D-SC)
Rep. Jared Golden (D-ME)
Rep. Ben McAdams (D-UT)
Rep. Harley Rouda (D-CA)
House Speaker Nancy Pelosi’s (D-CA) move towards impeachment has put many of these vulnerable freshmen Democrats, including Golden, in a bind; they have tried to push their legislative agenda while insisting that impeachment will not fracture the nation.
Golden said on Sunday, “I wish this was not the situation that we’re in, but I do think that we can do this in a way that puts a real focus on you know making sure that it doesn’t pull the country apart.”
Club for Growth ran ads against some of the other most vulnerable House Democrats. The conservative grassroots group ran ads against Reps. Katie Porter (D-CA), Xochitl Torres Small (D-NM), Abigail Spanberger (D-VA), and Lauren Underwood’s (D-IL) congressional districts.
The conservative group also ran ads against Sen. Mitt Romney (R-UT), urging him to stop supporting Democrats and their push towards impeachment.
In an interview with Breitbart News Saturday host Matthew Boyle, McIntosh said that Utah Republicans are “disgusted” with Romney for fighting the impeachment inquiry.
Sean Moran is a congressional reporter for Breitbart News. Follow him on Twitter @SeanMoran3.


interface ProcessBuilderFactoryInterface
Methods
| ProcessBuilder |
create()
Returns a new instance of Symfony ProcessBuilder |
|
| String |
getBinary()
Returns the binary path |
|
| ProcessBuilderFactoryInterface |
useBinary(String $binary)
Sets the binary path |
Details
at line 26
public ProcessBuilder
create()
Returns a new instance of Symfony ProcessBuilder
at line 33
public String
getBinary()
Returns the binary path
at line 44
public ProcessBuilderFactoryInterface
useBinary(String $binary)
Sets the binary path


DataSource Sort Example
Select a sort option and click "GO"
Sort full name in ascending order
Sort full name in descending order
Go
Results:


--- layout: page title: About header-img: "img/about-bg.png" ---
I would venture I'm older than most other code camp students - I like to think of myself as a not so old dog learning new tricks. I've been in the market research business for over 10 years now, but my passion lies in coding. I've used python, javascript and a little CSS3 but am looking to really learn how to be a developer.


--- layout: pattern category: text name: time usage: Represents a precise hour or date with a machine-readable form specified as value of the datetime attribute. ---
datetime
Today's date is the October 17, 2014.


Defined in generated file: python/ops/gen_array_ops.py
Converts a flat index or array of flat indices into a tuple of
tf.compat.v1.unravel_index
tf.compat.v2.unravel_index
tf.unravel_index( indices, dims, name=None )
coordinate arrays.
indices
: A Tensor
. Must be one of the following types: int32
, int64
.
An 0-D or 1-D int
Tensor whose elements are indices into the
flattened version of an array of dimensions dims.dims
: A Tensor
. Must have the same type as indices
.
An 1-D int
Tensor. The shape of the array to use for unraveling
indices.name
: A name for the operation (optional).A Tensor
. Has the same type as indices
.
Equivalent to np.unravel_index


In this game, use "G" and "O" to flip the paddle to correctly catch the ball. Careful, the ball gets faster as you score more points. You have three lives.
1) Draw a paddle with two colors and put it in the center.
2) Assign two keys to flip the paddle.
3) Create a ball with two colored costumes that starts at the top of the screen.
4) Make the ball move towards the paddle. Once it hits the paddle, have it start back at the top.

7) Make the ball speed up as you score points.


--- layout: default title: Home ---
This mocking workshop we'll have a new take on the code retreat workshop's old friend, Conway's Game of Life. But instead of just writing a rules engine, we're going to implement a physical board, with LEDs that light up to show if a cell is alive or dead.
Because our workstations don't have the same hardware that our fancy art installation Life board does, we need to learn some new techniques to make our code work with this board. In this workshop we'll learn how to test our logic against hardware that we don't have.


Account Management
Instagram is the leading social media in relation to images and videos. Their massive user base is at your disposal if you are able to catch their audience correct.
We lead accounts using proffesional images that are aimed at capturing consumer attention. Increasing their chances of interacting with a company.
However, It does not stop there. Once we gain the consumers attention, the way in which they interact with the company has a crucial part in their long term support.
Therefore, we also prioritise active engagement with the community.
When considering the potential of instagram alongside a tailored social media strategy, the potential results that a company can see has next to no limits.
Basic
Regular Updates
Inclusive of Images
Social strategy developments and creation
Reputation Monitoring
Dedicated account manager
Monthly progress report
Price: £299/per month
Advanced
Regular Updates
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Social Design
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Profile Design
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Bespoke Strategy
Community Interaction
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Price: £496/per month
Enquiries
All packages will be specifically adjusted to client requirements before purchase, thus, they are subject to change.
For all package enquiries please contact us at Info@MoneyShotMedia.com


gridfit: estimates a surface on a 2d grid, based on scattered data Replicates are allowed. All methods extrapolate to the grid boundaries. Gridfit uses a modified ridge estimator to generate the surface, where the bias is toward smoothness. Gridfit is not an interpolant. Its goal is a smooth surface that approximates your data, but allows you to control the amount of smoothing. usage #1: zgrid = gridfit(x,y,z,xnodes,ynodes); usage #2: [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes); usage #3: zgrid = gridfit(x,y,z,xnodes,ynodes,prop,val,prop,val,...); Arguments: (input) x,y,z - vectors of equal lengths, containing arbitrary scattered data The only constraint on x and y is they cannot ALL fall on a single line in the x-y plane. Replicate points will be treated in a least squares sense. ANY points containing a NaN are ignored in the estimation xnodes - vector defining the nodes in the grid in the independent variable (x). xnodes need not be equally spaced. xnodes must completely span the data. If they do not, then the 'extend' property is applied, adjusting the first and last nodes to be extended as necessary. See below for a complete description of the 'extend' property. If xnodes is a scalar integer, then it specifies the number of equally spaced nodes between the min and max of the data. ynodes - vector defining the nodes in the grid in the independent variable (y). ynodes need not be equally spaced. If ynodes is a scalar integer, then it specifies the number of equally spaced nodes between the min and max of the data. Also see the extend property. Additional arguments follow in the form of property/value pairs. Valid properties are: 'smoothness', 'interp', 'regularizer', 'solver', 'maxiter' 'extend', 'tilesize', 'overlap' Any UNAMBIGUOUS shortening (even down to a single letter) is valid for property names. All properties have default values, chosen (I hope) to give a reasonable result out of the box. 'smoothness' - scalar - determines the eventual smoothness of the estimated surface. A larger value here means the surface will be smoother. Smoothness must be a non-negative real number. Note: the problem is normalized in advance so that a smoothness of 1 MAY generate reasonable results. If you find the result is too smooth, then use a smaller value for this parameter. Likewise, bumpy surfaces suggest use of a larger value. (Sometimes, use of an iterative solver with too small a limit on the maximum number of iterations will result in non-convergence.) DEFAULT: 1 'interp' - character, denotes the interpolation scheme used to interpolate the data. DEFAULT: 'triangle' 'bilinear' - use bilinear interpolation within the grid (also known as tensor product linear interpolation) 'triangle' - split each cell in the grid into a triangle, then linear interpolation inside each triangle 'nearest' - nearest neighbor interpolation. This will rarely be a good choice, but I included it as an option for completeness. 'regularizer' - character flag, denotes the regularization paradignm to be used. There are currently three options. DEFAULT: 'gradient' 'diffusion' or 'laplacian' - uses a finite difference approximation to the Laplacian operator (i.e, del^2). We can think of the surface as a plate, wherein the bending rigidity of the plate is specified by the user as a number relative to the importance of fidelity to the data. A stiffer plate will result in a smoother surface overall, but fit the data less well. I've modeled a simple plate using the Laplacian, del^2. (A projected enhancement is to do a better job with the plate equations.) We can also view the regularizer as a diffusion problem, where the relative thermal conductivity is supplied. Here interpolation is seen as a problem of finding the steady temperature profile in an object, given a set of points held at a fixed temperature. Extrapolation will be linear. Both paradigms are appropriate for a Laplacian regularizer. 'gradient' - attempts to ensure the gradient is as smooth as possible everywhere. Its subtly different from the 'diffusion' option, in that here the directional derivatives are biased to be smooth across cell boundaries in the grid. The gradient option uncouples the terms in the Laplacian. Think of it as two coupled PDEs instead of one PDE. Why are they different at all? The terms in the Laplacian can balance each other. 'springs' - uses a spring model connecting nodes to each other, as well as connecting data points to the nodes in the grid. This choice will cause any extrapolation to be as constant as possible. Here the smoothing parameter is the relative stiffness of the springs connecting the nodes to each other compared to the stiffness of a spting connecting the lattice to each data point. Since all springs have a rest length (length at which the spring has zero potential energy) of zero, any extrapolation will be minimized. Note: I don't terribly like the 'springs' strategy. It tends to drag the surface towards the mean of all the data. Its been left in only because the paradigm interests me. 'solver' - character flag - denotes the solver used for the resulting linear system. Different solvers will have different solution times depending upon the specific problem to be solved. Up to a certain size grid, the direct \ solver will often be speedy, until memory swaps causes problems. What solver should you use? Problems with a significant amount of extrapolation should avoid lsqr. \ may be best numerically for small smoothnesss parameters and high extents of extrapolation. Large numbers of points will slow down the direct \, but when applied to the normal equations, \ can be quite fast. Since the equations generated by these methods will tend to be well conditioned, the normal equations are not a bad choice of method to use. Beware when a small smoothing parameter is used, since this will make the equations less well conditioned. DEFAULT: 'normal' '\' - uses matlab's backslash operator to solve the sparse system. 'backslash' is an alternate name. 'symmlq' - uses matlab's iterative symmlq solver 'lsqr' - uses matlab's iterative lsqr solver 'normal' - uses \ to solve the normal equations. 'maxiter' - only applies to iterative solvers - defines the maximum number of iterations for an iterative solver DEFAULT: min(10000,length(xnodes)*length(ynodes)) 'extend' - character flag - controls whether the first and last nodes in each dimension are allowed to be adjusted to bound the data, and whether the user will be warned if this was deemed necessary to happen. DEFAULT: 'warning' 'warning' - Adjust the first and/or last node in x or y if the nodes do not FULLY contain the data. Issue a warning message to this effect, telling the amount of adjustment applied. 'never' - Issue an error message when the nodes do not absolutely contain the data. 'always' - automatically adjust the first and last nodes in each dimension if necessary. No warning is given when this option is set. 'tilesize' - grids which are simply too large to solve for in one single estimation step can be built as a set of tiles. For example, a 1000x1000 grid will require the estimation of 1e6 unknowns. This is likely to require more memory (and time) than you have available. But if your data is dense enough, then you can model it locally using smaller tiles of the grid. My recommendation for a reasonable tilesize is roughly 100 to 200. Tiles of this size take only a few seconds to solve normally, so the entire grid can be modeled in a finite amount of time.  If your data is so sparse than some tiles contain insufficient data to model, then those tiles will be left as NaNs. DEFAULT: inf 'overlap' - Tiles in a grid have some overlap, so they can minimize any problems along the edge of a tile. In this overlapped region, the grid is built using a bi-linear combination of the overlapping tiles. The overlap is specified as a fraction of the tile size, so an overlap of 0.20 means there will be a 20% overlap of successive tiles. I do allow a zero overlap, but it must be no more than 1/2. 0 <= overlap <= 0.5 Overlap is ignored if the tilesize is greater than the number of nodes in both directions. DEFAULT: 0.20 'autoscale' - Some data may have widely different scales on the respective x and y axes. If this happens, then the regularization may experience difficulties. autoscale = 'on' will cause gridfit to scale the x and y node intervals to a unit length. This should improve the regularization procedure. The scaling is purely internal. autoscale = 'off' will disable automatic scaling DEFAULT: 'on' Arguments: (output) zgrid - (nx,ny) array containing the fitted surface xgrid, ygrid - as returned by meshgrid(xnodes,ynodes) Speed considerations: Remember that gridfit must solve a LARGE system of linear equations. There will be as many unknowns as the total number of nodes in the final lattice. While these equations may be sparse, solving a system of 10000 equations may take a second or so. Very large problems may benefit from the iterative solvers or from tiling. Example usage: x = rand(100,1); y = rand(100,1); z = exp(x+2*y); xnodes = 0:.1:1; ynodes = 0:.1:1; g = gridfit(x,y,z,xnodes,ynodes); Note: this is equivalent to the following call: g = gridfit(x,y,z,xnodes,ynodes, ... 'smooth',1, ... 'interp','triangle', ... 'solver','normal', ... 'regularizer','gradient', ... 'extend','warning', ... 'tilesize',inf); Author: John D'Errico e-mail address<EMAIL_ADDRESS>Release: 2.0 Release date: 5/23/06
0001 function [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes,varargin) 0002 % gridfit: estimates a surface on a 2d grid, based on scattered data 0003 % Replicates are allowed. All methods extrapolate to the grid 0004 % boundaries. Gridfit uses a modified ridge estimator to 0005 % generate the surface, where the bias is toward smoothness. 0006 % 0007 % Gridfit is not an interpolant. Its goal is a smooth surface 0008 % that approximates your data, but allows you to control the 0009 % amount of smoothing. 0010 % 0011 % usage #1: zgrid = gridfit(x,y,z,xnodes,ynodes); 0012 % usage #2: [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes); 0013 % usage #3: zgrid = gridfit(x,y,z,xnodes,ynodes,prop,val,prop,val,...); 0014 % 0015 % Arguments: (input) 0016 % x,y,z - vectors of equal lengths, containing arbitrary scattered data 0017 % The only constraint on x and y is they cannot ALL fall on a 0018 % single line in the x-y plane. Replicate points will be treated 0019 % in a least squares sense. 0020 % 0021 % ANY points containing a NaN are ignored in the estimation 0022 % 0023 % xnodes - vector defining the nodes in the grid in the independent 0024 % variable (x). xnodes need not be equally spaced. xnodes 0025 % must completely span the data. If they do not, then the 0026 % 'extend' property is applied, adjusting the first and last 0027 % nodes to be extended as necessary. See below for a complete 0028 % description of the 'extend' property. 0029 % 0030 % If xnodes is a scalar integer, then it specifies the number 0031 % of equally spaced nodes between the min and max of the data. 0032 % 0033 % ynodes - vector defining the nodes in the grid in the independent 0034 % variable (y). ynodes need not be equally spaced. 0035 % 0036 % If ynodes is a scalar integer, then it specifies the number 0037 % of equally spaced nodes between the min and max of the data. 0038 % 0039 % Also see the extend property. 0040 % 0041 % Additional arguments follow in the form of property/value pairs. 0042 % Valid properties are: 0043 % 'smoothness', 'interp', 'regularizer', 'solver', 'maxiter' 0044 % 'extend', 'tilesize', 'overlap' 0045 % 0046 % Any UNAMBIGUOUS shortening (even down to a single letter) is 0047 % valid for property names. All properties have default values, 0048 % chosen (I hope) to give a reasonable result out of the box. 0049 % 0050 % 'smoothness' - scalar - determines the eventual smoothness of the 0051 % estimated surface. A larger value here means the surface 0052 % will be smoother. Smoothness must be a non-negative real 0053 % number. 0054 % 0055 % Note: the problem is normalized in advance so that a 0056 % smoothness of 1 MAY generate reasonable results. If you 0057 % find the result is too smooth, then use a smaller value 0058 % for this parameter. Likewise, bumpy surfaces suggest use 0059 % of a larger value. (Sometimes, use of an iterative solver 0060 % with too small a limit on the maximum number of iterations 0061 % will result in non-convergence.) 0062 % 0063 % DEFAULT: 1 0064 % 0065 % 0066 % 'interp' - character, denotes the interpolation scheme used 0067 % to interpolate the data. 0068 % 0069 % DEFAULT: 'triangle' 0070 % 0071 % 'bilinear' - use bilinear interpolation within the grid 0072 % (also known as tensor product linear interpolation) 0073 % 0074 % 'triangle' - split each cell in the grid into a triangle, 0075 % then linear interpolation inside each triangle 0076 % 0077 % 'nearest' - nearest neighbor interpolation. This will 0078 % rarely be a good choice, but I included it 0079 % as an option for completeness. 0080 % 0081 % 0082 % 'regularizer' - character flag, denotes the regularization 0083 % paradignm to be used. There are currently three options. 0084 % 0085 % DEFAULT: 'gradient' 0086 % 0087 % 'diffusion' or 'laplacian' - uses a finite difference 0088 % approximation to the Laplacian operator (i.e, del^2). 0089 % 0090 % We can think of the surface as a plate, wherein the 0091 % bending rigidity of the plate is specified by the user 0092 % as a number relative to the importance of fidelity to 0093 % the data. A stiffer plate will result in a smoother 0094 % surface overall, but fit the data less well. I've 0095 % modeled a simple plate using the Laplacian, del^2. (A 0096 % projected enhancement is to do a better job with the 0097 % plate equations.) 0098 % 0099 % We can also view the regularizer as a diffusion problem, 0100 % where the relative thermal conductivity is supplied. 0101 % Here interpolation is seen as a problem of finding the 0102 % steady temperature profile in an object, given a set of 0103 % points held at a fixed temperature. Extrapolation will 0104 % be linear. Both paradigms are appropriate for a Laplacian 0105 % regularizer. 0106 % 0107 % 'gradient' - attempts to ensure the gradient is as smooth 0108 % as possible everywhere. Its subtly different from the 0109 % 'diffusion' option, in that here the directional 0110 % derivatives are biased to be smooth across cell 0111 % boundaries in the grid. 0112 % 0113 % The gradient option uncouples the terms in the Laplacian. 0114 % Think of it as two coupled PDEs instead of one PDE. Why 0115 % are they different at all? The terms in the Laplacian 0116 % can balance each other. 0117 % 0118 % 'springs' - uses a spring model connecting nodes to each 0119 % other, as well as connecting data points to the nodes 0120 % in the grid. This choice will cause any extrapolation 0121 % to be as constant as possible. 0122 % 0123 % Here the smoothing parameter is the relative stiffness 0124 % of the springs connecting the nodes to each other compared 0125 % to the stiffness of a spting connecting the lattice to 0126 % each data point. Since all springs have a rest length 0127 % (length at which the spring has zero potential energy) 0128 % of zero, any extrapolation will be minimized. 0129 % 0130 % Note: I don't terribly like the 'springs' strategy. 0131 % It tends to drag the surface towards the mean of all 0132 % the data. Its been left in only because the paradigm 0133 % interests me. 0134 % 0135 % 0136 % 'solver' - character flag - denotes the solver used for the 0137 % resulting linear system. Different solvers will have 0138 % different solution times depending upon the specific 0139 % problem to be solved. Up to a certain size grid, the 0140 % direct \ solver will often be speedy, until memory 0141 % swaps causes problems. 0142 % 0143 % What solver should you use? Problems with a significant 0144 % amount of extrapolation should avoid lsqr. \ may be 0145 % best numerically for small smoothnesss parameters and 0146 % high extents of extrapolation. 0147 % 0148 % Large numbers of points will slow down the direct 0149 % \, but when applied to the normal equations, \ can be 0150 % quite fast. Since the equations generated by these 0151 % methods will tend to be well conditioned, the normal 0152 % equations are not a bad choice of method to use. Beware 0153 % when a small smoothing parameter is used, since this will 0154 % make the equations less well conditioned. 0155 % 0156 % DEFAULT: 'normal' 0157 % 0158 % '\' - uses matlab's backslash operator to solve the sparse 0159 % system. 'backslash' is an alternate name. 0160 % 0161 % 'symmlq' - uses matlab's iterative symmlq solver 0162 % 0163 % 'lsqr' - uses matlab's iterative lsqr solver 0164 % 0165 % 'normal' - uses \ to solve the normal equations. 0166 % 0167 % 0168 % 'maxiter' - only applies to iterative solvers - defines the 0169 % maximum number of iterations for an iterative solver 0170 % 0171 % DEFAULT: min(10000,length(xnodes)*length(ynodes)) 0172 % 0173 % 0174 % 'extend' - character flag - controls whether the first and last 0175 % nodes in each dimension are allowed to be adjusted to 0176 % bound the data, and whether the user will be warned if 0177 % this was deemed necessary to happen. 0178 % 0179 % DEFAULT: 'warning' 0180 % 0181 % 'warning' - Adjust the first and/or last node in 0182 % x or y if the nodes do not FULLY contain 0183 % the data. Issue a warning message to this 0184 % effect, telling the amount of adjustment 0185 % applied. 0186 % 0187 % 'never' - Issue an error message when the nodes do 0188 % not absolutely contain the data. 0189 % 0190 % 'always' - automatically adjust the first and last 0191 % nodes in each dimension if necessary. 0192 % No warning is given when this option is set. 0193 % 0194 % 0195 % 'tilesize' - grids which are simply too large to solve for 0196 % in one single estimation step can be built as a set 0197 % of tiles. For example, a 1000x1000 grid will require 0198 % the estimation of 1e6 unknowns. This is likely to 0199 % require more memory (and time) than you have available. 0200 % But if your data is dense enough, then you can model 0201 % it locally using smaller tiles of the grid. 0202 % 0203 % My recommendation for a reasonable tilesize is 0204 % roughly 100 to 200. Tiles of this size take only 0205 % a few seconds to solve normally, so the entire grid 0206 % can be modeled in a finite amount of time. The minimum 0207 % tilesize can never be less than 3, although even this 0208 % size tile is so small as to be ridiculous. 0209 % 0210 % If your data is so sparse than some tiles contain 0211 % insufficient data to model, then those tiles will 0212 % be left as NaNs. 0213 % 0214 % DEFAULT: inf 0215 % 0216 % 0217 % 'overlap' - Tiles in a grid have some overlap, so they 0218 % can minimize any problems along the edge of a tile. 0219 % In this overlapped region, the grid is built using a 0220 % bi-linear combination of the overlapping tiles. 0221 % 0222 % The overlap is specified as a fraction of the tile 0223 % size, so an overlap of 0.20 means there will be a 20% 0224 % overlap of successive tiles. I do allow a zero overlap, 0225 % but it must be no more than 1/2. 0226 % 0227 % 0 <= overlap <= 0.5 0228 % 0229 % Overlap is ignored if the tilesize is greater than the 0230 % number of nodes in both directions. 0231 % 0232 % DEFAULT: 0.20 0233 % 0234 % 0235 % 'autoscale' - Some data may have widely different scales on 0236 % the respective x and y axes. If this happens, then 0237 % the regularization may experience difficulties. 0238 % 0239 % autoscale = 'on' will cause gridfit to scale the x 0240 % and y node intervals to a unit length. This should 0241 % improve the regularization procedure. The scaling is 0242 % purely internal. 0243 % 0244 % autoscale = 'off' will disable automatic scaling 0245 % 0246 % DEFAULT: 'on' 0247 % 0248 % 0249 % Arguments: (output) 0250 % zgrid - (nx,ny) array containing the fitted surface 0251 % 0252 % xgrid, ygrid - as returned by meshgrid(xnodes,ynodes) 0253 % 0254 % 0255 % Speed considerations: 0256 % Remember that gridfit must solve a LARGE system of linear 0257 % equations. There will be as many unknowns as the total 0258 % number of nodes in the final lattice. While these equations 0259 % may be sparse, solving a system of 10000 equations may take 0260 % a second or so. Very large problems may benefit from the 0261 % iterative solvers or from tiling. 0262 % 0263 % 0264 % Example usage: 0265 % 0266 % x = rand(100,1); 0267 % y = rand(100,1); 0268 % z = exp(x+2*y); 0269 % xnodes = 0:.1:1; 0270 % ynodes = 0:.1:1; 0271 % 0272 % g = gridfit(x,y,z,xnodes,ynodes); 0273 % 0274 % Note: this is equivalent to the following call: 0275 % 0276 % g = gridfit(x,y,z,xnodes,ynodes, ... 0277 % 'smooth',1, ... 0278 % 'interp','triangle', ... 0279 % 'solver','normal', ... 0280 % 'regularizer','gradient', ... 0281 % 'extend','warning', ... 0282 % 'tilesize',inf); 0283 % 0284 % 0285 % Author: John D'Errico 0286 % e-mail address<EMAIL_ADDRESS>0287 % Release: 2.0 0288 % Release date: 5/23/06 0289 0290 % set defaults 0291 params.smoothness = 1; 0292 params.interp = 'triangle'; 0293 params.regularizer = 'gradient'; 0294 params.solver = 'normal'; 0295 params.maxiter = []; 0296 params.extend = 'warning'; 0297 params.tilesize = inf; 0298 params.overlap = 0.20; 0299 params.mask = []; 0300 params.autoscale = 'on'; 0301 params.xscale = 1; 0302 params.yscale = 1; 0303 0304 % was the params struct supplied? 0305 if ~isempty(varargin) 0306 if isstruct(varargin{1}) 0307 % params is only supplied if its a call from tiled_gridfit 0308 params = varargin{1}; 0309 if length(varargin)>1 0310 % check for any overrides 0311 params = parse_pv_pairs(params,varargin{2:end}); 0312 end 0313 else 0314 % check for any overrides of the defaults 0315 params = parse_pv_pairs(params,varargin); 0316 0317 end 0318 end 0319 0320 % check the parameters for acceptability 0321 params = check_params(params); 0322 0323 % ensure all of x,y,z,xnodes,ynodes are column vectors, 0324 % also drop any NaN data 0325 x=x(:); 0326 y=y(:); 0327 z=z(:); 0328 k = isnan(x) | isnan(y) | isnan(z); 0329 if any(k) 0330 x(k)=[]; 0331 y(k)=[]; 0332 z(k)=[]; 0333 end 0334 xmin = min(x); 0335 xmax = max(x); 0336 ymin = min(y); 0337 ymax = max(y); 0338 0339 % did they supply a scalar for the nodes? 0340 if length(xnodes)==1 0341 xnodes = linspace(xmin,xmax,xnodes)'; 0342 xnodes(end) = xmax; % make sure it hits the max 0343 end 0344 if length(ynodes)==1 0345 ynodes = linspace(ymin,ymax,ynodes)'; 0346 ynodes(end) = ymax; % make sure it hits the max 0347 end 0348 0349 xnodes=xnodes(:); 0350 ynodes=ynodes(:); 0351 dx = diff(xnodes); 0352 dy = diff(ynodes); 0353 nx = length(xnodes); 0354 ny = length(ynodes); 0355 ngrid = nx*ny; 0356 0357 % set the scaling if autoscale was on 0358 if strcmpi(params.autoscale,'on') 0359 params.xscale = mean(dx); 0360 params.yscale = mean(dy); 0361 params.autoscale = 'off'; 0362 end 0363 0364 % check to see if any tiling is necessary 0365 if (params.tilesize < max(nx,ny)) 0366 % split it into smaller tiles. compute zgrid and ygrid 0367 % at the very end if requested 0368 zgrid = tiled_gridfit(x,y,z,xnodes,ynodes,params); 0369 else 0370 % its a single tile. 0371 0372 % mask must be either an empty array, or a boolean 0373 % aray of the same size as the final grid. 0374 nmask = size(params.mask); 0375 if ~isempty(params.mask) && ((nmask(2)~=nx) || (nmask(1)~=ny)) 0376 if ((nmask(2)==ny) || (nmask(1)==nx)) 0377 error 'Mask array is probably transposed from proper orientation.' 0378 else 0379 error 'Mask array must be the same size as the final grid.' 0380 end 0381 end 0382 if ~isempty(params.mask) 0383 params.maskflag = 1; 0384 else 0385 params.maskflag = 0; 0386 end 0387 0388 % default for maxiter? 0389 if isempty(params.maxiter) 0390 params.maxiter = min(10000,nx*ny); 0391 end 0392 0393 % check lengths of the data 0394 n = length(x); 0395 if (length(y)~=n) || (length(z)~=n) 0396 error 'Data vectors are incompatible in size.' 0397 end 0398 if n<3 0399 error 'Insufficient data for surface estimation.' 0400 end 0401 0402 % verify the nodes are distinct 0403 if any(diff(xnodes)<=0) || any(diff(ynodes)<=0) 0404 error 'xnodes and ynodes must be monotone increasing' 0405 end 0406 0407 % do we need to tweak the first or last node in x or y? 0408 if xmin<xnodes(1) 0409 switch params.extend 0410 case 'always' 0411 xnodes(1) = xmin; 0412 case 'warning' 0413 warning(['xnodes(1) was decreased by: ',num2str(xnodes(1)-xmin),', new node = ',num2str(xmin)]) 0414 xnodes(1) = xmin; 0415 case 'never' 0416 error(['Some x (',num2str(xmin),') falls below xnodes(1) by: ',num2str(xnodes(1)-xmin)]) 0417 end 0418 end 0419 if xmax>xnodes(end) 0420 switch params.extend 0421 case 'always' 0422 xnodes(end) = xmax; 0423 case 'warning' 0424 warning(['xnodes(end) was increased by: ',num2str(xmax-xnodes(end)),', new node = ',num2str(xmax)]) 0425 xnodes(end) = xmax; 0426 case 'never' 0427 error(['Some x (',num2str(xmax),') falls above xnodes(end) by: ',num2str(xmax-xnodes(end))]) 0428 end 0429 end 0430 if ymin<ynodes(1) 0431 switch params.extend 0432 case 'always' 0433 ynodes(1) = ymin; 0434 case 'warning' 0435 warning(['ynodes(1) was decreased by: ',num2str(ynodes(1)-ymin),', new node = ',num2str(ymin)]) 0436 ynodes(1) = ymin; 0437 case 'never' 0438 error(['Some y (',num2str(ymin),') falls below ynodes(1) by: ',num2str(ynodes(1)-ymin)]) 0439 end 0440 end 0441 if ymax>ynodes(end) 0442 switch params.extend 0443 case 'always' 0444 ynodes(end) = ymax; 0445 case 'warning' 0446 warning(['ynodes(end) was increased by: ',num2str(ymax-ynodes(end)),', new node = ',num2str(ymax)]) 0447 ynodes(end) = ymax; 0448 case 'never' 0449 error(['Some y (',num2str(ymax),') falls above ynodes(end) by: ',num2str(ymax-ynodes(end))]) 0450 end 0451 end 0452 0453 % determine which cell in the array each point lies in 0454 [junk,indx] = histc(x,xnodes); %#ok 0455 [junk,indy] = histc(y,ynodes); %#ok 0456 % any point falling at the last node is taken to be 0457 % inside the last cell in x or y. 0458 k=(indx==nx); 0459 indx(k)=indx(k)-1; 0460 k=(indy==ny); 0461 indy(k)=indy(k)-1; 0462 ind = indy + ny*(indx-1); 0463 0464 % Do we have a mask to apply? 0465 if params.maskflag 0466 % if we do, then we need to ensure that every 0467 % cell with at least one data point also has at 0468 % least all of its corners unmasked. 0469 params.mask(ind) = 1; 0470 params.mask(ind+1) = 1; 0471 params.mask(ind+ny) = 1; 0472 params.mask(ind+ny+1) = 1; 0473 end 0474 0475 % interpolation equations for each point 0476 tx = min(1,max(0,(x - xnodes(indx))./dx(indx))); 0477 ty = min(1,max(0,(y - ynodes(indy))./dy(indy))); 0478 % Future enhancement: add cubic interpolant 0479 switch params.interp 0480 case 'triangle' 0481 % linear interpolation inside each triangle 0482 k = (tx > ty); 0483 L = ones(n,1); 0484 L(k) = ny; 0485 0486 t1 = min(tx,ty); 0487 t2 = max(tx,ty); 0488 A = sparse(repmat((1:n)',1,3),[ind,ind+ny+1,ind+L], ... 0489 [1-t2,t1,t2-t1],n,ngrid); 0490 0491 case 'nearest' 0492 % nearest neighbor interpolation in a cell 0493 k = round(1-ty) + round(1-tx)*ny; 0494 A = sparse((1:n)',ind+k,ones(n,1),n,ngrid); 0495 0496 case 'bilinear' 0497 % bilinear interpolation in a cell 0498 A = sparse(repmat((1:n)',1,4),[ind,ind+1,ind+ny,ind+ny+1], ... 0499 [(1-tx).*(1-ty), (1-tx).*ty, tx.*(1-ty), tx.*ty], ... 0500 n,ngrid); 0501 0502 end 0503 rhs = z; 0504 0505 % Build regularizer. Add del^4 regularizer one day. 0506 switch params.regularizer 0507 case 'springs' 0508 % zero "rest length" springs 0509 [i,j] = meshgrid(1:nx,1:(ny-1)); 0510 ind = j(:) + ny*(i(:)-1); 0511 m = nx*(ny-1); 0512 stiffness = 1./(dy/params.yscale); 0513 Areg = sparse(repmat((1:m)',1,2),[ind,ind+1], ... 0514 stiffness(j(:))*[-1 1],m,ngrid); 0515 0516 [i,j] = meshgrid(1:(nx-1),1:ny); 0517 ind = j(:) + ny*(i(:)-1); 0518 m = (nx-1)*ny; 0519 stiffness = 1./(dx/params.xscale); 0520 Areg = [Areg;sparse(repmat((1:m)',1,2),[ind,ind+ny], ... 0521 stiffness(i(:))*[-1 1],m,ngrid)]; 0522 0523 [i,j] = meshgrid(1:(nx-1),1:(ny-1)); 0524 ind = j(:) + ny*(i(:)-1); 0525 m = (nx-1)*(ny-1); 0526 stiffness = 1./sqrt((dx(i(:))/params.xscale).^2 + ... 0527 (dy(j(:))/params.yscale).^2); 0528 0529 Areg = [Areg;sparse(repmat((1:m)',1,2),[ind,ind+ny+1], ... 0530 stiffness*[-1 1],m,ngrid)]; 0531 0532 Areg = [Areg;sparse(repmat((1:m)',1,2),[ind+1,ind+ny], ... 0533 stiffness*[-1 1],m,ngrid)]; 0534 0535 case {'diffusion' 'laplacian'} 0536 % thermal diffusion using Laplacian (del^2) 0537 [i,j] = meshgrid(1:nx,2:(ny-1)); 0538 ind = j(:) + ny*(i(:)-1); 0539 dy1 = dy(j(:)-1)/params.yscale; 0540 dy2 = dy(j(:))/params.yscale; 0541 0542 Areg = sparse(repmat(ind,1,3),[ind-1,ind,ind+1], ... 0543 [-2./(dy1.*(dy1+dy2)), 2./(dy1.*dy2), ... 0544 -2./(dy2.*(dy1+dy2))],ngrid,ngrid); 0545 0546 [i,j] = meshgrid(2:(nx-1),1:ny); 0547 ind = j(:) + ny*(i(:)-1); 0548 dx1 = dx(i(:)-1)/params.xscale; 0549 dx2 = dx(i(:))/params.xscale; 0550 0551 Areg = Areg + sparse(repmat(ind,1,3),[ind-ny,ind,ind+ny], ... 0552 [-2./(dx1.*(dx1+dx2)), 2./(dx1.*dx2), ... 0553 -2./(dx2.*(dx1+dx2))],ngrid,ngrid); 0554 0555 case 'gradient' 0556 % Subtly different from the Laplacian. A point for future 0557 % enhancement is to do it better for the triangle interpolation 0558 % case. 0559 [i,j] = meshgrid(1:nx,2:(ny-1)); 0560 ind = j(:) + ny*(i(:)-1); 0561 dy1 = dy(j(:)-1)/params.yscale; 0562 dy2 = dy(j(:))/params.yscale; 0563 0564 Areg = sparse(repmat(ind,1,3),[ind-1,ind,ind+1], ... 0565 [-2./(dy1.*(dy1+dy2)), 2./(dy1.*dy2), ... 0566 -2./(dy2.*(dy1+dy2))],ngrid,ngrid); 0567 0568 [i,j] = meshgrid(2:(nx-1),1:ny); 0569 ind = j(:) + ny*(i(:)-1); 0570 dx1 = dx(i(:)-1)/params.xscale; 0571 dx2 = dx(i(:))/params.xscale; 0572 0573 Areg = [Areg;sparse(repmat(ind,1,3),[ind-ny,ind,ind+ny], ... 0574 [-2./(dx1.*(dx1+dx2)), 2./(dx1.*dx2), ... 0575 -2./(dx2.*(dx1+dx2))],ngrid,ngrid)]; 0576 0577 end 0578 nreg = size(Areg,1); 0579 0580 % Append the regularizer to the interpolation equations, 0581 % scaling the problem first. Use the 1-norm for speed. 0582 NA = norm(A,1); 0583 NR = norm(Areg,1); 0584 A = [A;Areg*(params.smoothness*NA/NR)]; 0585 rhs = [rhs;zeros(nreg,1)]; 0586 % do we have a mask to apply? 0587 if params.maskflag 0588 unmasked = find(params.mask); 0589 end 0590 % solve the full system, with regularizer attached 0591 switch params.solver 0592 case {'\' 'backslash'} 0593 if params.maskflag 0594 % there is a mask to use 0595 % permute for minimum fill in for R (in the QR) 0596 p = colamd(A(:,unmasked)); 0597 zgrid=nan(ny,nx); 0598 zgrid(unmasked(p)) = A(:,unmasked(p))\rhs; 0599 else 0600 % permute for minimum fill in for R (in the QR) 0601 p = colamd(A); 0602 zgrid=zeros(ny,nx); 0603 zgrid(p) = A(:,p)\rhs; 0604 end 0605 0606 case 'normal' 0607 % The normal equations, solved with \. Can be fast 0608 % for huge numbers of data points. 0609 if params.maskflag 0610 % there is a mask to use 0611 % Permute for minimum fill-in for \ (in chol) 0612 APA = A(:,unmasked)'*A(:,unmasked); 0613 p = symamd(APA); 0614 zgrid=nan(ny,nx); 0615 zgrid(unmasked(p)) = APA(p,p)\(A(:,unmasked(p))'*rhs); 0616 else 0617 % Permute for minimum fill-in for \ (in chol) 0618 APA = A'*A; 0619 p = symamd(APA); 0620 zgrid=zeros(ny,nx); 0621 zgrid(p) = APA(p,p)\(A(:,p)'*rhs); 0622 end 0623 0624 case 'symmlq' 0625 % iterative solver - symmlq - requires a symmetric matrix, 0626 % so use it to solve the normal equations. No preconditioner. 0627 tol = abs(max(z)-min(z))*1.e-13; 0628 if params.maskflag 0629 % there is a mask to use 0630 zgrid=nan(ny,nx); 0631 [zgrid(unmasked),flag] = symmlq(A(:,unmasked)'*A(:,unmasked), ... 0632 A(:,unmasked)'*rhs,tol,params.maxiter); 0633 else 0634 [zgrid,flag] = symmlq(A'*A,A'*rhs,tol,params.maxiter); 0635 zgrid = reshape(zgrid,ny,nx); 0636 end 0637 % display a warning if convergence problems 0638 switch flag 0639 case 0 0640 % no problems with convergence 0641 case 1 0642 % SYMMLQ iterated MAXIT times but did not converge. 0643 warning(['Symmlq performed ',num2str(params.maxiter), ... 0644 ' iterations but did not converge.']) 0645 case 3 0646 % SYMMLQ stagnated, successive iterates were the same 0647 warning 'Symmlq stagnated without apparent convergence.' 0648 otherwise 0649 warning(['One of the scalar quantities calculated in',... 0650 ' symmlq was too small or too large to continue computing.']) 0651 end 0652 0653 case 'lsqr' 0654 % iterative solver - lsqr. No preconditioner here. 0655 tol = abs(max(z)-min(z))*1.e-13; 0656 if params.maskflag 0657 % there is a mask to use 0658 zgrid=nan(ny,nx); 0659 [zgrid(unmasked),flag] = lsqr(A(:,unmasked),rhs,tol,params.maxiter); 0660 else 0661 [zgrid,flag] = lsqr(A,rhs,tol,params.maxiter); 0662 zgrid = reshape(zgrid,ny,nx); 0663 end 0664 0665 % display a warning if convergence problems 0666 switch flag 0667 case 0 0668 % no problems with convergence 0669 case 1 0670 % lsqr iterated MAXIT times but did not converge. 0671 warning(['Lsqr performed ',num2str(params.maxiter), ... 0672 ' iterations but did not converge.']) 0673 case 3 0674 % lsqr stagnated, successive iterates were the same 0675 warning 'Lsqr stagnated without apparent convergence.' 0676 case 4 0677 warning(['One of the scalar quantities calculated in',... 0678 ' LSQR was too small or too large to continue computing.']) 0679 end 0680 0681 end 0682 0683 end % if params.tilesize... 0684 0685 % only generate xgrid and ygrid if requested. 0686 if nargout>1 0687 [xgrid,ygrid]=meshgrid(xnodes,ynodes); 0688 end 0689 0690 % ============================================ 0691 % End of main function - gridfit 0692 % ============================================ 0693 0694 % ============================================ 0695 % subfunction - parse_pv_pairs 0696 % ============================================ 0697 function params=parse_pv_pairs(params,pv_pairs) 0698 % parse_pv_pairs: parses sets of property value pairs, allows defaults 0699 % usage: params=parse_pv_pairs(default_params,pv_pairs) 0700 % 0701 % arguments: (input) 0702 % default_params - structure, with one field for every potential 0703 % property/value pair. Each field will contain the default 0704 % value for that property. If no default is supplied for a 0705 % given property, then that field must be empty. 0706 % 0707 % pv_array - cell array of property/value pairs. 0708 % Case is ignored when comparing properties to the list 0709 % of field names. Also, any unambiguous shortening of a 0710 % field/property name is allowed. 0711 % 0712 % arguments: (output) 0713 % params - parameter struct that reflects any updated property/value 0714 % pairs in the pv_array. 0715 % 0716 % Example usage: 0717 % First, set default values for the parameters. Assume we 0718 % have four parameters that we wish to use optionally in 0719 % the function examplefun. 0720 % 0721 % - 'viscosity', which will have a default value of 1 0722 % - 'volume', which will default to 1 0723 % - 'pie' - which will have default value 3.141592653589793 0724 % - 'description' - a text field, left empty by default 0725 % 0726 % The first argument to examplefun is one which will always be 0727 % supplied. 0728 % 0729 % function examplefun(dummyarg1,varargin) 0730 % params.Viscosity = 1; 0731 % params.Volume = 1; 0732 % params.Pie = 3.141592653589793 0733 % 0734 % params.Description = ''; 0735 % params=parse_pv_pairs(params,varargin); 0736 % params 0737 % 0738 % Use examplefun, overriding the defaults for 'pie', 'viscosity' 0739 % and 'description'. The 'volume' parameter is left at its default. 0740 % 0741 % examplefun(rand(10),'vis',10,'pie',3,'Description','Hello world') 0742 % 0743 % params = 0744 % Viscosity: 10 0745 % Volume: 1 0746 % Pie: 3 0747 % Description: 'Hello world' 0748 % 0749 % Note that capitalization was ignored, and the property 'viscosity' 0750 % was truncated as supplied. Also note that the order the pairs were 0751 % supplied was arbitrary. 0752 0753 npv = length(pv_pairs); 0754 n = npv/2; 0755 0756 if n~=floor(n) 0757 error 'Property/value pairs must come in PAIRS.' 0758 end 0759 if n<=0 0760 % just return the defaults 0761 return 0762 end 0763 0764 if ~isstruct(params) 0765 error 'No structure for defaults was supplied' 0766 end 0767 0768 % there was at least one pv pair. process any supplied 0769 propnames = fieldnames(params); 0770 lpropnames = lower(propnames); 0771 for i=1:n 0772 p_i = lower(pv_pairs{2*i-1}); 0773 v_i = pv_pairs{2*i}; 0774 0775 ind = strmatch(p_i,lpropnames,'exact'); 0776 if isempty(ind) 0777 ind = find(strncmp(p_i,lpropnames,length(p_i))); 0778 if isempty(ind) 0779 error(['No matching property found for: ',pv_pairs{2*i-1}]) 0780 elseif length(ind)>1 0781 error(['Ambiguous property name: ',pv_pairs{2*i-1}]) 0782 end 0783 end 0784 p_i = propnames{ind}; 0785 0786 % override the corresponding default in params 0787 params = setfield(params,p_i,v_i); %#ok 0788 0789 end 0790 0791 0792 % ============================================ 0793 % subfunction - check_params 0794 % ============================================ 0795 function params = check_params(params) 0796 0797 % check the parameters for acceptability 0798 % smoothness == 1 by default 0799 if isempty(params.smoothness) 0800 params.smoothness = 1; 0801 else 0802 if (length(params.smoothness)>1) || (params.smoothness<=0) 0803 error 'Smoothness must be scalar, real, finite, and positive.' 0804 end 0805 end 0806 0807 % regularizer - must be one of 4 options - the second and 0808 % third are actually synonyms. 0809 valid = {'springs', 'diffusion', 'laplacian', 'gradient'}; 0810 if isempty(params.regularizer) 0811 params.regularizer = 'diffusion'; 0812 end 0813 ind = find(strncmpi(params.regularizer,valid,length(params.regularizer))); 0814 if (length(ind)==1) 0815 params.regularizer = valid{ind}; 0816 else 0817 error(['Invalid regularization method: ',params.regularizer]) 0818 end 0819 0820 % interp must be one of: 0821 % 'bilinear', 'nearest', or 'triangle' 0822 % but accept any shortening thereof. 0823 valid = {'bilinear', 'nearest', 'triangle'}; 0824 if isempty(params.interp) 0825 params.interp = 'triangle'; 0826 end 0827 ind = find(strncmpi(params.interp,valid,length(params.interp))); 0828 if (length(ind)==1) 0829 params.interp = valid{ind}; 0830 else 0831 error(['Invalid interpolation method: ',params.interp]) 0832 end 0833 0834 % solver must be one of: 0835 % 'backslash', '\', 'symmlq', 'lsqr', or 'normal' 0836 % but accept any shortening thereof. 0837 valid = {'backslash', '\', 'symmlq', 'lsqr', 'normal'}; 0838 if isempty(params.solver) 0839 params.solver = '\'; 0840 end 0841 ind = find(strncmpi(params.solver,valid,length(params.solver))); 0842 if (length(ind)==1) 0843 params.solver = valid{ind}; 0844 else 0845 error(['Invalid solver option: ',params.solver]) 0846 end 0847 0848 % extend must be one of: 0849 % 'never', 'warning', 'always' 0850 % but accept any shortening thereof. 0851 valid = {'never', 'warning', 'always'}; 0852 if isempty(params.extend) 0853 params.extend = 'warning'; 0854 end 0855 ind = find(strncmpi(params.extend,valid,length(params.extend))); 0856 if (length(ind)==1) 0857 params.extend = valid{ind}; 0858 else 0859 error(['Invalid extend option: ',params.extend]) 0860 end 0861 0862 % tilesize == inf by default 0863 if isempty(params.tilesize) 0864 params.tilesize = inf; 0865 elseif (length(params.tilesize)>1) || (params.tilesize<3) 0866 error 'Tilesize must be scalar and > 0.' 0867 end 0868 0869 % overlap == 0.20 by default 0870 if isempty(params.overlap) 0871 params.overlap = 0.20; 0872 elseif (length(params.overlap)>1) || (params.overlap<0) || (params.overlap>0.5) 0873 error 'Overlap must be scalar and 0 < overlap < 1.' 0874 end 0875 0876 % ============================================ 0877 % subfunction - tiled_gridfit 0878 % ============================================ 0879 function zgrid=tiled_gridfit(x,y,z,xnodes,ynodes,params) 0880 % tiled_gridfit: a tiled version of gridfit, continuous across tile boundaries 0881 % usage: [zgrid,xgrid,ygrid]=tiled_gridfit(x,y,z,xnodes,ynodes,params) 0882 % 0883 % Tiled_gridfit is used when the total grid is far too large 0884 % to model using a single call to gridfit. While gridfit may take 0885 % only a second or so to build a 100x100 grid, a 2000x2000 grid 0886 % will probably not run at all due to memory problems. 0887 % 0888 % Tiles in the grid with insufficient data (<4 points) will be 0889 % filled with NaNs. Avoid use of too small tiles, especially 0890 % if your data has holes in it that may encompass an entire tile. 0891 % 0892 % A mask may also be applied, in which case tiled_gridfit will 0893 % subdivide the mask into tiles. Note that any boolean mask 0894 % provided is assumed to be the size of the complete grid. 0895 % 0896 % Tiled_gridfit may not be fast on huge grids, but it should run 0897 % as long as you use a reasonable tilesize. 8-) 0898 0899 % Note that we have already verified all parameters in check_params 0900 0901 % Matrix elements in a square tile 0902 tilesize = params.tilesize; 0903 % Size of overlap in terms of matrix elements. Overlaps 0904 % of purely zero cause problems, so force at least two 0905 % elements to overlap. 0906 overlap = max(2,floor(tilesize*params.overlap)); 0907 0908 % reset the tilesize for each particular tile to be inf, so 0909 % we will never see a recursive call to tiled_gridfit 0910 Tparams = params; 0911 Tparams.tilesize = inf; 0912 0913 nx = length(xnodes); 0914 ny = length(ynodes); 0915 zgrid = zeros(ny,nx); 0916 0917 % linear ramp for the bilinear interpolation 0918 rampfun = inline('(t-t(1))/(t(end)-t(1))','t'); 0919 0920 % loop over each tile in the grid 0921 h = waitbar(0,'Relax and have a cup of JAVA. Its my treat.'); 0922 warncount = 0; 0923 xtind = 1:min(nx,tilesize); 0924 while ~isempty(xtind) && (xtind(1)<=nx) 0925 0926 xinterp = ones(1,length(xtind)); 0927 if (xtind(1) ~= 1) 0928 xinterp(1:overlap) = rampfun(xnodes(xtind(1:overlap))); 0929 end 0930 if (xtind(end) ~= nx) 0931 xinterp((end-overlap+1):end) = 1-rampfun(xnodes(xtind((end-overlap+1):end))); 0932 end 0933 0934 ytind = 1:min(ny,tilesize); 0935 while ~isempty(ytind) && (ytind(1)<=ny) 0936 % update the waitbar 0937 waitbar((xtind(end)-tilesize)/nx + tilesize*ytind(end)/ny/nx) 0938 0939 yinterp = ones(length(ytind),1); 0940 if (ytind(1) ~= 1) 0941 yinterp(1:overlap) = rampfun(ynodes(ytind(1:overlap))); 0942 end 0943 if (ytind(end) ~= ny) 0944 yinterp((end-overlap+1):end) = 1-rampfun(ynodes(ytind((end-overlap+1):end))); 0945 end 0946 0947 % was a mask supplied? 0948 if ~isempty(params.mask) 0949 submask = params.mask(ytind,xtind); 0950 Tparams.mask = submask; 0951 end 0952 0953 % extract data that lies in this grid tile 0954 k = (x>=xnodes(xtind(1))) & (x<=xnodes(xtind(end))) & ... 0955 (y>=ynodes(ytind(1))) & (y<=ynodes(ytind(end))); 0956 k = find(k); 0957 0958 if length(k)<4 0959 if warncount == 0 0960 warning 'A tile was too underpopulated to model. Filled with NaNs.' 0961 end 0962 warncount = warncount + 1; 0963 0964 % fill this part of the grid with NaNs 0965 zgrid(ytind,xtind) = NaN; 0966 0967 else 0968 % build this tile 0969 zgtile = gridfit(x(k),y(k),z(k),xnodes(xtind),ynodes(ytind),Tparams); 0970 0971 % bilinear interpolation (using an outer product) 0972 interp_coef = yinterp*xinterp; 0973 0974 % accumulate the tile into the complete grid 0975 zgrid(ytind,xtind) = zgrid(ytind,xtind) + zgtile.*interp_coef; 0976 0977 end 0978 0979 % step to the next tile in y 0980 if ytind(end)<ny 0981 ytind = ytind + tilesize - overlap; 0982 % are we within overlap elements of the edge of the grid? 0983 if (ytind(end)+max(3,overlap))>=ny 0984 % extend this tile to the edge 0985 ytind = ytind(1):ny; 0986 end 0987 else 0988 ytind = ny+1; 0989 end 0990 0991 end % while loop over y 0992 0993 % step to the next tile in x 0994 if xtind(end)<nx 0995 xtind = xtind + tilesize - overlap; 0996 % are we within overlap elements of the edge of the grid? 0997 if (xtind(end)+max(3,overlap))>=nx 0998 % extend this tile to the edge 0999 xtind = xtind(1):nx; 1000 end 1001 else 1002 xtind = nx+1; 1003 end 1004 1005 end % while loop over x 1006 1007 % close down the waitbar 1008 close(h) 1009 1010 if warncount>0 1011 warning([num2str(warncount),' tiles were underpopulated & filled with NaNs']) 1012 end 1013 1014 1015 1016


FoodSafe
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The project code implements two popular clustering algorithms; kmeans (and kmeans++ variant) and dbscan.


{{site.user_description}}
Design is an essential element in any business, since it is responsible for creating the online public face of a brand. It is something you have to take care of in order to get the best results and to enjoy all the benefits it offers you.
Knowledge in development languages such as Java, Python, JavaScript. Knowledge in database and caching such as PostgreSQL, MySQL among others. Problem solving, creative, communication, teamwork, leadership, organization, effective communication.
Application development continues to be one of the most sought-after careers in the technology world. For this reason, knowledge of various technologies and tools is required, which can be difficult to master at the beginning.


Card demo
HAX, card demo
This is the card GENERIC
This is the card HEADER
This is the card CONTENT
This is the card SUBHEADER


The world of sport is continually changing over the years, and the use of technology is just one of those areas that has made an impact on many sports in the modern day. See the annual sports technology awards for the latest technology ideas in the world of sport.
Making changes to a sport, with the inclusion of new technologies, has often caused controversy. One criticism of the use of technology is that it can slow down the speed of the game, but on the other hand for many people it makes watching it more enjoyable to see the correct decisions being made. If a new technology is available, and shown to be accurate, then it should be used so that the correct decisions can be made. It is only fair to the players and teams that the right decisions are made.


--- layout: default nav: attributes > ic-append-from ---
ic-append-from
- The Append From AttributeThe ic-append-from
attribute binds a given HTML element's children to a URL. It does not
cause any requests to happen by itself, but it can respond to events caused by other attributes.
When Intercooler issues a request for an element with an ic-append-from
, it will issue a
GET
and will append the resulting HTML fragment as children of the current element. If you wish to limit the total
number of children on of an element, you can use the ic-limit-children
attribute.
Note that with these attributes, you will often want to take advantage of the ic-last-refresh
parameter
that Intercooler includes in its requests. See Requests & Responses for more information.
The value of the attribute should be a valid relative path (e.g. ic-append-from="/foo/bar"
).
ic-append-from
implies a dependency on its path, and Intercooler will issue requests for elements
whenever it detects an action that the path depends on. See Dependencies for
more information.
Here is a simple example, using a poll interval to update:


Topic Tool
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Instructions
Overview
I use a private, secure, and encrypted video therapy platform
Step 0: Do Google Chrome
You will need to use


UD for Nheengatu
Tokenization and Word Segmentation
-
Words are generally delimited by whitespace or punctuation.
-
According to the orthographic conventions adopted, a hyphen separates the syntactic words of most multiword tokens. For example, these are cases of incorporation of an auxiliary verb into the main verb (e.g., putari ‘to want’ in ayuíri-putari ‘I want to return’). However, the component words of a group of multiword tokens are not hyphen-separated, e.g., paraname, which derives from the cliticization of the postposition upé ‘on’ to paranã ‘river’.
Morphology
Tags
-
The Nheengatu treebank presently employs 15 of the 17 universal POS tags, namely, ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, and VERB. X and SYM have not come to use yet, since they didn’t occur in the sentences hitherto annotated.
-
There is just one copula, ikú, which also functions as an auxiliary expressing progressive aspect.
-
Nheengatu has a rich system of particles, which convey a variety of grammatical notions, e.g., negation, tense, and aspect.
-
There are two classes of personal pronouns in Nheengatu, both tagged PRON. So-called first-class pronouns are free morphemes, while second-class pronouns are subject-agreement prefixes that attach to stative verbs, functioning as number-person agreement markers, or realizing the internal argument of nouns (genitive complement) or postpositions. According to the adopted orthographical conventions, these prefixes are written as independent tokens. Presently, these forms constitute independent words in our treebank. In the future, we may treat these prefixes as part of the verb, despite orthography.
-
Determiners and (non-personal) pronouns seem to only differ in syntactic behavior, i.e., whether they modify a noun or function as the head of the NP.
-
The base form, i.e., the non-inflected, infinitive form of certain verbs (e.g., kitika ‘to grate’) can function as ADJ (and possibly also as ADV or NOUN), but this was not attested in the sentences hitherto annotated.
Features
-
Verbs inflect for the six logically possible combinations of the values 1, 2, and 3 for the person feature and the values singular and plural for the number feature. In conservative or historical varieties of Nheengatu, the number distinction is neutralized in Person 3 of the active series. In innovative varieties, the opposition between singular and plural seems to be emerging due to the grammaticalization of the person 3 plural pronoun as an inflectional prefix, see Avila (2021) and Cruz (2011, 2015).
-
Verbs fall into one of two inflectional paradigms. Person-number of transitives and non-stative intransitives is marked with prefixes of the active series. Stative intransitives receive prefixes of the inactive series, also called second-class pronouns.
-
Mood, tense, and aspect is expressed through auxiliaries or particles.
-
Gender does not play any morpho-syntactic role in Nheengatu.
-
Nouns and pronouns only inflect for number. There is no number agreement proper in the NP domain. The plural is marked with the suffix itá, which attaches either to the determiner or the noun (it is uncertain to us whether a double marking is possible). Nouns under the scope of a semantically plural quantifier (e.g., musapiri ‘three’) need not receive the plural suffix.
-
The treebank employs the layered features
Number[psor]=Sing
,Person[psor]=3
, andPerson[grnd]
. The last one encodes the person of the internal argument of a postpostion, i.e., the reference object, relatum or ground (also landmark in cognitive grammar), which is realized in Nheengatu in certain circumstances by the non-contiguity prefix s-. The first two layered features encode the number and person of the complement of a noun when it is realized by the non-adjacency relational prefix (e.g., suka ‘her/his/its house’). -
Some noun classes signal syntactic adjacency or non-adjacency between the head noun and its dependent employing relational prefixes. Relational prefixes also occur with subclasses of postpositions and inactive verbs.
Syntax
-
The basic word order is SVO.
-
Nheengatu has postpositions. Analogously, native subordinating conjunctions and relative pronouns are left adjacent to the verb, i.e., postverbal. By contrast, prepositions or conjunctions borrowed from Portuguese are preverbal. Some native conjunctions show a similar behavior.
-
In the genitive construction, the possessor precedes the head (possessum) noun, which receives the relational adjacency prefix if it belongs to certain noun paradigms.
-
There are two auxiliaries for the ingressive and the progressive aspect, namely sú and ikú, respectively. The former precedes, while the latter follows the main verb. Both auxiliaries inflect for person and number, as the main verb does.
-
The base (non-finite) form of auxiliaries expressing modality, e.g., putari ‘to want’, left-adjoins the main verb, which inflects for person and number, e.g.ayuíri-putari ‘I want to return’.
-
Individual-level predicates head copulaless sentences, while stage-level predicates require the copula. In both situations, an adjective or a postpositional phrase, inter alia, conveys the sentence predication.
-
Subjects trigger agreement with the verb. Objects are either noun phrases (or pronouns) or postpositional phrases that realize a core argument. Ditransitive verbs govern a dative complement realized by a postpositional phrase.
-
Subtype relations used so far: acl:relcl, acl:advcl, and nmod:poss.
Treebanks
There is one Nheengatu UD treebank:
* [UD_Nheengatu-CompLin](../treebanks/yrl_complin/index.html)


HYNITRON CST816S¶
this driver does not exist, so it has been created. Still work in progress ….
there is a sample in this repository which can be copied to the zephyr samples directory
west build -p -b pinetime samples/sensor/cst816s
Overview¶
the Hynitron cst816s is a touchscreen. In zephyr is no touchscreen driver yet. In order to investigate, the touchscreen driver has been created as a sensor. In fact it senses you finger ;)
Requirements¶
for this sensor does not exist any driver, so here’s what I did to create one under zephyr
adapt CMakeLists.txt¶
~/zephyrproject-2/zephyr/drivers/sensor add_subdirectory_ifdef(CONFIG_CST816S cst816s)
adapt Kconfig¶
~/zephyrproject-2/zephyr/drivers/sensor
add yaml file¶
~/zephyrproject-2/zephyr/dts/bindings/sensor add hynitron,cst816s.yaml
edit KConfig¶
source “drivers/sensor/cst816s/Kconfig”
create driver¶
see under drivers/sensor/cst816s
complement the pinetime.dts file with the following (under boards/arm/pinetime)
&i2c1 {
cst816s@15 {
compatible = "hynitron,cst816s";
reg = <0x15>;
label = "CST816S";
};
};


Before Change
total_training_cycle = (flags_obj.train_epochs //
flags_obj.epochs_between_evals)
for cycle_index in range(total_training_cycle):
tf.logging.info("Starting a training cycle: %d/%d",
cycle_index, total_training_cycle)
After Change
// Train for 10 epochs and then evaluate.
// Train for another 10 epochs and then evaluate.
// Train for a final 5 epochs (to reach 25 epochs) and then evaluate.
n_loops = math.ceil(flags_obj.train_epochs / flags_obj.epochs_between_evals)
schedule = [flags_obj.epochs_between_evals for _ in range(int(n_loops))]
schedule[-1] = flags_obj.train_epochs - sum(schedule[:-1]) // over counting.
for cycle_index, num_train_epochs in enumerate(schedule):


1. Download the client
You can download Fallout Online Reloaded's client in this link: https://forum.fonline-reloaded.net/index.php?topic=1.0
Full client is recommended and I'm going to use it in this tutorial.
Tutorial for everyone who wants to start playing FOnline Reloaded. Step by step instructions.
You can download Fallout Online Reloaded's client in this link: https://forum.fonline-reloaded.net/index.php?topic=1.0
Full client is recommended and I'm going to use it in this tutorial.
After downloading the FOnline Reloaded's client you should configure it. First of all, run Updater.exe and make sure that host and port are set properly.
Host: europe.fonline.org
Port: 4040
Click Check button and get updates. It might look like this:
Begin checking...
Receive PublicNameColorizing.txt 2470 bytes.
Done, for 0 seconds.
Receive data\reloaded002.zip 74610 bytes.
Done, for 0 seconds.
Update complete.
Checking end.
Now, close Updater.exe and prepare for setting config file. You can change settings in 2 ways:
Let's open FOConfig.exe and check all tabs. You can see my config and do the same way as I usually do.
You can change game's resolution to any resolution as you want to but keep in mind that the game couldn't work properly in higher resolutions. I have tested some of them and you can play in Full HD (1920x1080) without any problems. To change the resolution you have to click input field and write your number (width or height). Remember that you can change resolution (and any other settings via FOnline.cfg too.
After configuring the client, you can run FOnline.exe and register your character.
Now you can create your first character and start playing FOnline Reloaded.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Ipsam fugit animi libero nihil magnam quis beatae accusantium sed numquam ea quasi, atque, impedit, quidem error accusamus veniam alias iusto assumenda blanditiis doloremque soluta? Explicabo assumenda consectetur dolor.


Kelvin George Keya
My background well aligns the skills needed to a greatness and am positive it will make a valuable addition somewhere
s
So far i have been able to create some few projects and have used mostly html and css tags here are the links to the projects
- https://kev127.github.io/aboutme/
- https://github.com/kev127/nairobi
- https://github.com/kev127/kenya
With my educational background while in college i have strategically focused my coursework in the following areas
- A+(Hardware)
- (Networking)
- Microsoft Suite
- Social Media
- SOHO(Small Office Home Office)
- The internet
and this are my areas of specialization.
- Technical Support Specialist
- Field Service Technican
- IT Support Technical
- IT Support Administrator
- IT Support Specialist
- Network Support Specialist
- IT Technican
- Desktop Support Engineer
- Windows System Engineer
- Microsoft Office Associate
below is a list of my intrests and hobbies
- Being involved with charities
- Joining social, environmental groups
- Travelling
- Cooking and baking
- solving puzzle games
My continued willingness to learn new skills and a perfect chance to improve my skills


NaOne - Email<EMAIL_ADDRESS>/ Phone|Line<PHONE_NUMBER>(Thailand) {% endblock %}


See ActiveRecord::Transactions::ClassMethods for documentation.
Methods
Classes and Modules
Module ActiveRecord::Transactions::ClassMethodsPublic Class methods
[ show source ]
# File vendor/rails/activerecord/lib/active_record/transactions.rb, line 9 9: def self.included(base) 10: base.extend(ClassMethods) 11: 12: base.class_eval do 13: [:destroy, :save, :save!].each do |method| 14: alias_method_chain method, :transactions 15: end 16: end 17: end
Public Instance methods
Reset id and @new_record if the transaction rolls back.
[ show source ]
# File vendor/rails/activerecord/lib/active_record/transactions.rb, line 204 204: def rollback_active_record_state! 205: id_present = has_attribute?(self.class.primary_key) 206: previous_id = id 207: previous_new_record = new_record? 208: yield 209: rescue Exception 210: @new_record = previous_new_record 211: if id_present 212: self.id = previous_id 213: else 214: @attributes.delete(self.class.primary_key) 215: @attributes_cache.delete(self.class.primary_key) 216: end 217: raise 218: end
See ActiveRecord::Transactions::ClassMethods for detailed documentation.
[ show source ]
# File vendor/rails/activerecord/lib/active_record/transactions.rb, line 187 187: def transaction(&block) 188: self.class.transaction(&block) 189: end
Executes method within a transaction and captures its return value as a status flag. If the status is true the transaction is committed, otherwise a ROLLBACK is issued. In any case the status flag is returned.
This method is available within the context of an ActiveRecord::Base instance.
[ show source ]
# File vendor/rails/activerecord/lib/active_record/transactions.rb, line 226 226: def with_transaction_returning_status(method, *args) 227: status = nil 228: self.class.transaction do 229: status = send(method, *args) 230: raise ActiveRecord::Rollback unless status 231: end 232: status 233: end


'Fragile' - Forest boss reveals what rest of season may hold for Man City loanee Aro Muric
There are concerns goalkeeper's parent club could question his lack of game time at the City Ground and reassess his stay
by Sarah ClapsonNottingham Forest boss Sabri Lamouchi has praised goalkeeper Arijanet Muric’s “professional” attitude while he has been out of the team.
But the head coach has hinted the youngster will find it tough to get back into the starting line-up in the near future.
Muric started the campaign as the Reds’ No.1 after joining on a season-long loan from Manchester City, before some error-strewn performances saw him lose his place to the more experienced Brice Samba.
It has left the 21-year-old as back-up shot-stopper on the bench, and some suggestions that his parent club might ask questions about his lack of game time come January.
Asked if Muric could still have a part to play this term, Lamouchi said: “I am just focused on the situation now, and the situation now must stay like this because I think we have found the right way.
“But we know it is so fragile, so you never know.
“But he is doing his job. He is very positive in training sessions and is a nice guy.”
Muric has been on international duty during the two-week respite from Championship action, being between the posts for Kosovo in defeats to Czech Republic and England.
And even though he may not be getting on the pitch for Forest, Lamouchi says he has not let it affect him in training.
“He’s been good,” said the Frenchman. “He is a young goalkeeper, just 21 years old.
“He played two games with his national team and has come back and trained with us.
“He is a professional.”
Nottingham Forest latest
This is your round up of all things Forest; the one-stop shop that will keep you updated on the latest goings on at the City Ground and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Reds throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- Lamouchi's verdict on Hefele
- Injury update ahead of Bristol City
- Could Reds 'target' be on the move?
- The challenges of delivering Marinakis' vision
For all your latest NFFC news, opinion, analysis and transfer gossip, click here
You can follow our Reds reporter Sarah Clapson on Twitter, and her email address is<EMAIL_ADDRESS>Sign up for our free Forest WhatsApp service for latest news
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How to join us
Official Chia Instructions.
Make sure you are running official Chia client 1.2.0 or later.
Connection
https://pool.openchia.io
Chia UI
- Go to "Plots" menu in the left menu.
- If you already have a Plot NFT select "Change Pool" then enter https://pool.openchia.io.
- If you don't have a Plot NFT, add one, select "Connect to pool" and then enter https://pool.openchia.io.
Chia CLI
- Create Plot NFT:
- chia plotnft create -s pool -u https://pool.openchia.io
- Verify plotnft and copy contract address:
- chia plotnft show
- Create plots using that plotnft:
- chia plots create -c <contract address from above>


Advanced Algorithms
Advanced algorithms is an advanced-level graduate course for students who specialize in algorithms in the department of computer science of TU Kaiserslautern. I gave that course in the summer term of 2017.
The overarching theme of the course is how to tackle NP-hard problems; in the course I surveyed
- pseudopolynomial algorithms
- fixed-parameter tractability
- randomized algorithms, and
- approximations.
For all these topics, we discussed algorithmic design patterns and exemplary algorithms where they are successfully applied. Moreover, we also discussed the corresponding impossibility results and lower bounding techniques:
- strong NP-hardness
- para-NP hardness, W[1] hardness, ETH-based lower bounds
- randomized complexity classes, and
- gap reductions and PCP theorems
I gave this course in the summer term 2017 at TU Kaiserslautern, see the course catalog and the course website of my group there. It was a very small course (~10), but with a very active group of students, and overall a very enjoyable experience. According to the evaluation, this was felt in both ways.
My first own video lectures
I took the opportunity to try out some new technical gadgets for the course. To make a long story short, I now have nice HD videos of all my lectures:
What I would like to argue here is that there is little reason not to do such videos. It is neither technologically challenging, nor expensive in terms of money and time.
Extra effort for professors and lecturers is the number one reason for blocking innovations in teaching in my experience. I would like to share my experience with advanced algorithms here, to show that very little extra effort suffices to create lecture videos that offer a significant benefit for students over a traditional course.
Technical Details
The course was organized as traditional lecture with live sessions, but I used my ThinkPad X201 Tablet to take it to the digital age.
I prepared LaTeX slides for definitions, code, theorems, etc., used these as background in Xournal and wrote proofs and informal discussions on top of them by hand. I used SimpleScreenRecorder for the screencasts, and an affordable external mic for good audio quality.
I put the videos on youtube for several reasons
- youtube supports adjusting the playback speed. (Actually an HTML5 feature, but unfortunately not included in a convenient GUI by many video platforms.) It cannot be stressed too much how helpful this is when watching lectures (speaking from my own experience).
- youtube has by far the best support for mobile devices.
- youtube digests almost anything that remotely looks like a video; it simply works and is fast.
- Students are already watching videos on youtube …
It might be seen as an issue that youtube is completely public. There is no (convenient) option, e.g., to require a password before one can watch the videos. (Videos can be made private so that they are only visible to invited users, but that requires all students to log into youtube. This is inconvenient …) Personally, I do not see a problem having the videos publicly available; see also my approach to deal with mistakes. Of course, sometimes legal issues make that impossible.
PDF lecture notes
Xournal allows to save the result (slides plus handwritten notes) as one pdf. This serves as written lecture notes for convenient lookup of details. (Searching a pdf is much faster than finding the right part in the video).
You find the lecture notes on the course website at TU KL.
As a graphical learner, I find it personally extremely helpful to have exactly the same screen content in the pdfs that I also had in class. This is something (typed) scribes will not give you.
I also used these pdfs to add clarifications, see below.
How it worked
The didactic idea behind the switch between prepared slides and notes written by hand during class was to emphasize and slow down for parts that require careful thought and attention. Unlike a traditional blackboard lecture (which also serves this purpose), however, no time is lost in the transition, since I use the same pen as pointing device in the “slide mode”.
For lectures that traditionally would have been taught on the blackboard, this combination is a great alternative. There are a few advantages of blackboards—its sheer size, more physical “action” with the speaker walking around—but overall, I see the benefits of mixing in slides and interactive material and having the videos outweighing that by far.
The benefit of adjustable playback speed cannot be emphasized enough.
Errata
Every lecturer makes mistakes. The public videos make it possible for everyone to hunt for and point out my mistakes.
I would like to see this as an opportunity, rather than a daunting prospect. Correcting the videos is a lot of effort, and I did not try to do that. Instead, I added corrections and clarifications to the pdfs created in class (in blue, to make them easy to spot).
These pdfs are what students mostly work with after having seen the video or the live session once, so it is the right place to have the corrections. (And much more convenient than any separate list of errata.) Moreover, adding the annotations in Xournal is as little effort as it can be.
Did I feel embarrassed about some of my mistakes? Absolutely. In a few occasions, what I said was plain wrong. Sure, this should not happen, and I feel bad about it; with better preparation, I could have avoided it. But did it make the course and the videos less effective for the students? I do not think so. Most errors were found quickly (by my students or myself) and the correction is available for everyone (not only that motivated student who bothered to ask after class).
In summary, I think keeping the videos as they are and correcting the pdf notes is an effective and inexpensive way to deal with the mistakes all of us make.
Evaluation
This is a screenshot of the results of the official course evaluation for Advanced Algorithms. The original website is only accessible from within the university network of TU Kaiserslautern.


{% extends "base.html" %} {% block header %}
Have Google accounts that forward email, but you never log into?
You might be missing Google Chat or Hangouts messages.
Sign up to create automatic responses so people know how to get in touch.
It's {{ view.price }} charged monthly, with a {{ view.trial_length }} no-questions-asked free trial, cancellable any time.


Robb Elementary School permanently closed in the summer of 2022 after a May shooting where 19 children and two adults were killed. The data below is for the 2021-2022 school year.
{{ campus.name }} is {% if campus.school_type == 'E' or campus.school_type == 'B' %}an{% else %}a{% endif %} {% if campus.charter %}charter {% endif %}{{ campus.get_school_type_display|lower }} in {{ campus.location|default:"Texas" }}, in the {{ campus.district.name }} school district. As of the {{ stat.year.name }} school year, it had {{ stat.all_students_count|intcomma }} students. {{ stat.at_risk_percent|floatformat }}% of students were considered at risk of dropping out of school. {{ stat.bilingual_esl_percent|floatformat }}% of students were enrolled in bilingual and English language learning programs.
The school received an accountability rating of {{ stat.smart_accountability_rating_display|capfirst }} for the {{ stat.smart_accountability_rating_header_display }} school year.
{% if stat.four_year_graduate_all_students_percent and stat.four_year_graduate_all_students_percent >= 0 %} In the Class of {{ stat.year.start_year }}, {{ stat.four_year_graduate_all_students_percent|floatformat }}% of students received their high school diplomas on time or earlier. {% endif %} {% if stat.dropout_all_students_percent and stat.dropout_all_students_percent >= 0 %} The dropout rate for students in grades 9-12 was {{ stat.dropout_all_students_percent|floatformat }}% during the {{ stat.year.previous_year }} school year. {% endif %}
{% if campus.is_secondary_school %} {% if stat.avg_sat_score_all_students >= 0 %} The average SAT score at {{ campus.name }} was {{ stat.avg_sat_score_all_students }} for {{ stat.year.previous_year }} graduates. {% endif %} {% if stat.avg_act_score_all_students >= 0 %} The average ACT score was {{ stat.avg_act_score_all_students }}. {% endif %} {% endif %}
{% if stat.teacher_avg_base_salary %} As of the {{ stat.year.name }} school year, an average teacher's salary was ${{ stat.teacher_avg_base_salary|floatformat:"0"|intcomma }}, which is ${{ stat.teacher_avg_base_salary|subtract:state.teacher_avg_base_salary|absolute|floatformat:"0"|intcomma }} {% if stat.teacher_avg_base_salary|subtract:state.teacher_avg_base_salary < 0 %}less{% else %}more{% endif %} than the state average. {% endif %} {% if stat.teacher_avg_experience %} On average, teachers had {{ stat.teacher_avg_experience|floatformat }} years of experience. {% endif %}
{{ campus.website|urlize }}
{% endif %} {% if campus.street and campus.location and campus.zip_code %}Students in {{ campus.name }} are part of the Texas Education Agency’s {{ campus.district.region.region_name }}. Students who started eighth grade in {{ latest_county_cohort.year.start_year }} in this region had a college graduation rate of {{ latest_region_cohort.percent_completed_higher_education|percentify }} percent. View a more comprehensive breakdown of the higher education outcomes in {{ campus.district.region.region_name }}.


--- type: formControls title: Chosen multi select usage: Controls using chosen library with a custom style to search result in a list inside a select. stylesheet: '/scss/controls/chosen.scss' --- United States United Kingdom Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina


{% extends 'base.html' %} {% block content %} Here are your uploads {{request.user}} {% for photo in uploads %}


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Hi Ron,
Congrats on achieving statistical significance (stat. sig.) on 3 of your 6 total teacher and student questionnaires (as of December 6). Great job you're almost there!
Please see the image below on how to find the most up-to-date information for your schools through your clarity3.brightbytes.net login. Please keep in mind that the system updates every night at midnight, so you won't see your responses until the next day.
Please let us know if you have any questions. For support issues and immediate assistance, please contact us at<EMAIL_ADDRESS>or at<PHONE_NUMBER>.
Best,
Aglae
Aglae Velasco Shaw
Project Manager
<EMAIL_ADDRESS>| www.BrightBytes.net
(415) 652-3952 | www.calendly.com/aglae
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Mauricio Pochettino rejected Daniel Levy request before Tottenham sacking
Mauricio Pochettino rejected a request from Tottenham to resign before he was sacked on Tuesday evening, according to reports.
The 47-year-old has been dismissed after five-and-a-half years at Spurs and less than six months after he guided the team to the Champions League final.
Jose Mourinho has now been appointed as Pochettino’s replacement after a deal was finalised shortly after the Argentine’s departure was confirmed.
Pochettino had reportedly become an increasingly distant figure at Spurs this season and even suggested that he would have left the club if his side beat Liverpool in Madrid on June 1.
According to The Times, Pochettino had been in extensive talks with Spurs over the past week to resolve his future.
And the report claims that Pochettino refused to accept Spurs’ request to step down, which would have seen him lose out on £12 million in compensation.
Advertisement Advertisement
After the Argentine refused that request, Levy was subsequently left no choice but to remove Pochettino and his coaching staff from their positions.
‘It is not a decision the board have taken lightly, nor in haste,’ said Levy.
‘Regrettably domestic results at the end of last season and the beginning of this season have been extremely disappointing.
‘It falls on the board to make the difficult decisions – this one made more so given the many memorable moments we have had with Mauricio and his coaching staff.’


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Cayley
Pseudo-Random Bits from Finite Groups
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The Cayley PRNG over \(S_{32}\) with fixed generators. More...
#include <Cayley32.h>
Public Member Functions | |
| Cayley32 () | |
| Constructor. More... | |
| void | srand (uintx_t &seed) |
| Seed the generator. More... | |
| Public Member Functions inherited from Cayley32e | |
| Cayley32e () | |
| Constructor. | |
| uint64_t | rand () |
| Generate 64 pseudo-random bits. More... | |
| Public Member Functions inherited from CCayley | |
| CCayley (uint32_t n) | |
| Constructor. More... | |
| ~CCayley () | |
| Destructor. More... | |
| virtual void | srand (uint64_t(*rnd)(void)) |
| Seed the generator. More... | |
| CPerm | GetGenerator (int i) const |
| Get generator. More... | |
| const CPerm & | GetPerm () const |
| Get current permutation. More... | |
| const uint32_t | GetSize () const |
| Get permutation size. More... | |
Private Member Functions | |
| void | ChooseGenerators () |
| Choose generators. More... | |
Additional Inherited Members | |
| Protected Member Functions inherited from CCayley | |
| void | ChooseGenerators (uint64_t(*rnd)(void)) |
| Choose generators. More... | |
| void | NextPerm () |
| Compute next permutation. More... | |
| Protected Attributes inherited from CCayley | |
| uint32_t | m_nSize = 0 |
| Size of permutations. | |
| uint32_t | m_nOrder = 0 |
| Order of generators. | |
| CPowerTable | m_nPower [2] |
| Power tables for a pair of generators. | |
| CPerm * | m_pCurPerm = nullptr |
| Current permutation. | |
| uint64_t | m_nDelayLine [m_nDelay] |
| Delay line. More... | |
| int | m_nTail = 0 |
| Index of last element in delay line. | |
| Static Protected Attributes inherited from CCayley | |
| static const int | m_nDelay = 32 |
| Delay size. | |
A 64-bit Cayley PRNG with permutation size 32 and fixed generators.
Definition at line 26 of file Cayley32.h.
| Cayley32::Cayley32 | ( | ) |
The default constructor declares Cayley32 to be a 64-bit instance of CCayley with a permutation size of 32.
Definition at line 54 of file Cayley32.cpp.
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private |
Choose the generators and initialize the power tables. A pair of fixed generators is used here, but they should be replaced and not be made public to protect against reverse engineering. CCayley::ChooseGenerators() will find generators that have a high probability of being strong.
Definition at line 63 of file Cayley32.cpp.
| void Cayley32::srand | ( | uintx_t & | seed | ) |
Initialize the pseudorandom number generator by choosing the generators and choosing a pseudorandom initial permutation.
| seed | Seed value. |
Definition at line 78 of file Cayley32.cpp.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 1
Preheat oven to 200C/400F/Gas 6. Place three cocktail sticks into a bowl of water to soak for ten minutes.
Place the pork loin between two pieces of cling film. Flatten out the pork with a meat mallet or the end of a rolling pin, until 1cm/¼in thick.
Place the prunes along the centre of the meat, season with salt and freshly ground black pepper and roll up like a cigar. Secure the pork with the soaked cocktail sticks.
Heat the oil in a large ovenproof frying pan. Add the tomato halves, sweet potato and the stuffed pork.
Cook the pork on all sides until golden-brown all over, then transfer the pan with the pork and vegetables to the oven to roast for 8-10 minutes, or until the pork is completely cooked through. When the pork is cooked, remove from the pan and rest for five minutes in a warm place.
Meanwhile, boil the beans in a pot of boiling salted water for 3-4 minutes, until just cooked, then drain.
To serve, slice the pork and arrange the slices on a serving plate with the roasted vegetables and beans alongside.