| predict.xgb.Booster {xgboost} | R Documentation |
Predicted values based on either xgboost model or model handle object.
## S3 method for class 'xgb.Booster' predict(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE, reshape = FALSE, ...) ## S3 method for class 'xgb.Booster.handle' predict(object, ...)
object |
Object of class |
newdata |
takes |
missing |
Missing is only used when input is dense matrix. Pick a float value that represents missing values in data (e.g., sometimes 0 or some other extreme value is used). |
outputmargin |
whether the prediction should be returned in the for of original untransformed
sum of predictions from boosting iterations' results. E.g., setting |
ntreelimit |
limit the number of model's trees or boosting iterations used in prediction (see Details).
It will use all the trees by default ( |
predleaf |
whether predict leaf index. |
predcontrib |
whether to return feature contributions to individual predictions (see Details). |
approxcontrib |
whether to use a fast approximation for feature contributions (see Details). |
predinteraction |
whether to return contributions of feature interactions to individual predictions (see Details). |
reshape |
whether to reshape the vector of predictions to a matrix form when there are several prediction outputs per case. This option has no effect when either of predleaf, predcontrib, or predinteraction flags is TRUE. |
... |
Parameters passed to |
Note that ntreelimit
is not necessarily equal to the number of boosting iterations
and it is not necessarily equal to the number of trees in a model.
E.g., in a random forest-like model, ntreelimit
would limit the number of trees.
But for multiclass classification, while there are multiple trees per iteration,
ntreelimit
limits the number of boosting iterations.
Also note that ntreelimit
would currently do nothing for predictions from gblinear,
since gblinear doesn't keep its boosting history.
One possible practical applications of the predleaf
option is to use the model
as a generator of new features which capture non-linearity and interactions,
e.g., as implemented in xgb.create.features
.
Setting predcontrib = TRUE
allows to calculate contributions of each feature to
individual predictions. For "gblinear" booster, feature contributions are simply linear terms
(feature_beta * feature_value). For "gbtree" booster, feature contributions are SHAP
values (Lundberg 2017) that sum to the difference between the expected output
of the model and the current prediction (where the hessian weights are used to compute the expectations).
Setting approxcontrib = TRUE
approximates these values following the idea explained
in http://blog.datadive.net/interpreting-random-forests/.
With predinteraction = TRUE
, SHAP values of contributions of interaction of each pair of features
are computed. Note that this operation might be rather expensive in terms of compute and memory.
Since it quadratically depends on the number of features, it is recommended to perform selection
of the most important features first. See below about the format of the returned results.
For regression or binary classification, it returns a vector of length nrows(newdata)
.
For multiclass classification, either a num_class * nrows(newdata)
vector or
a (nrows(newdata), num_class)
dimension matrix is returned, depending on
the reshape
value.
When predleaf = TRUE
, the output is a matrix object with the
number of columns corresponding to the number of trees.
When predcontrib = TRUE
and it is not a multiclass setting, the output is a matrix object with
num_features + 1
columns. The last "+ 1" column in a matrix corresponds to bias.
For a multiclass case, a list of num_class
elements is returned, where each element is
such a matrix. The contribution values are on the scale of untransformed margin
(e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
When predinteraction = TRUE
and it is not a multiclass setting, the output is a 3d array with
dimensions c(nrow, num_features + 1, num_features + 1)
. The off-diagonal (in the last two dimensions)
elements represent different features interaction contributions. The array is symmetric WRT the last
two dimensions. The "+ 1" columns corresponds to bias. Summing this array along the last dimension should
produce practically the same result as predict with predcontrib = TRUE
.
For a multiclass case, a list of num_class
elements is returned, where each element is
such an array.
Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, https://arxiv.org/abs/1705.07874
Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", https://arxiv.org/abs/1706.06060
## binary classification: data(agaricus.train, package='xgboost') data(agaricus.test, package='xgboost') train <- agaricus.train test <- agaricus.test bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 0.5, nthread = 2, nrounds = 5, objective = "binary:logistic") # use all trees by default pred <- predict(bst, test$data) # use only the 1st tree pred1 <- predict(bst, test$data, ntreelimit = 1) # Predicting tree leafs: # the result is an nsamples X ntrees matrix pred_leaf <- predict(bst, test$data, predleaf = TRUE) str(pred_leaf) # Predicting feature contributions to predictions: # the result is an nsamples X (nfeatures + 1) matrix pred_contr <- predict(bst, test$data, predcontrib = TRUE) str(pred_contr) # verify that contributions' sums are equal to log-odds of predictions (up to float precision): summary(rowSums(pred_contr) - qlogis(pred)) # for the 1st record, let's inspect its features that had non-zero contribution to prediction: contr1 <- pred_contr[1,] contr1 <- contr1[-length(contr1)] # drop BIAS contr1 <- contr1[contr1 != 0] # drop non-contributing features contr1 <- contr1[order(abs(contr1))] # order by contribution magnitude old_mar <- par("mar") par(mar = old_mar + c(0,7,0,0)) barplot(contr1, horiz = TRUE, las = 2, xlab = "contribution to prediction in log-odds") par(mar = old_mar) ## multiclass classification in iris dataset: lb <- as.numeric(iris$Species) - 1 num_class <- 3 set.seed(11) bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5, objective = "multi:softprob", num_class = num_class) # predict for softmax returns num_class probability numbers per case: pred <- predict(bst, as.matrix(iris[, -5])) str(pred) # reshape it to a num_class-columns matrix pred <- matrix(pred, ncol=num_class, byrow=TRUE) # convert the probabilities to softmax labels pred_labels <- max.col(pred) - 1 # the following should result in the same error as seen in the last iteration sum(pred_labels != lb)/length(lb) # compare that to the predictions from softmax: set.seed(11) bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5, objective = "multi:softmax", num_class = num_class) pred <- predict(bst, as.matrix(iris[, -5])) str(pred) all.equal(pred, pred_labels) # prediction from using only 5 iterations should result # in the same error as seen in iteration 5: pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5) sum(pred5 != lb)/length(lb) ## random forest-like model of 25 trees for binary classification: set.seed(11) bst <- xgboost(data = train$data, label = train$label, max_depth = 5, nthread = 2, nrounds = 1, objective = "binary:logistic", num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1) # Inspect the prediction error vs number of trees: lb <- test$label dtest <- xgb.DMatrix(test$data, label=lb) err <- sapply(1:25, function(n) { pred <- predict(bst, dtest, ntreelimit=n) sum((pred > 0.5) != lb)/length(lb) }) plot(err, type='l', ylim=c(0,0.1), xlab='#trees')


#include <sysc/datatypes/fx/scfx_ieee.h>
Public Member Functions | |
| scfx_ieee_float () | |
| scfx_ieee_float (float) | |
| scfx_ieee_float (const scfx_ieee_float &) | |
| scfx_ieee_float & | operator= (float) |
| scfx_ieee_float & | operator= (const scfx_ieee_float &) |
| operator float () const | |
| unsigned int | negative () const |
| void | negative (unsigned int) |
| int | exponent () const |
| void | exponent (int) |
| unsigned int | mantissa () const |
| void | mantissa (unsigned int) |
| bool | is_zero () const |
| bool | is_subnormal () const |
| bool | is_normal () const |
| bool | is_inf () const |
| bool | is_nan () const |
| void | set_inf () |
| void | set_nan () |
Definition at line 460 of file scfx_ieee.h.
| sc_dt::scfx_ieee_float::scfx_ieee_float | ( | ) | [inline] |
Definition at line 498 of file scfx_ieee.h.
| sc_dt::scfx_ieee_float::scfx_ieee_float | ( | float | f | ) | [inline] |
Definition at line 504 of file scfx_ieee.h.
| sc_dt::scfx_ieee_float::scfx_ieee_float | ( | const scfx_ieee_float & | a | ) | [inline] |
Definition at line 510 of file scfx_ieee.h.
| void sc_dt::scfx_ieee_float::exponent | ( | int | a | ) | [inline] |
Definition at line 563 of file scfx_ieee.h.
| int sc_dt::scfx_ieee_float::exponent | ( | ) | const [inline] |
Definition at line 556 of file scfx_ieee.h.
| bool sc_dt::scfx_ieee_float::is_inf | ( | ) | const [inline] |
Definition at line 608 of file scfx_ieee.h.
| bool sc_dt::scfx_ieee_float::is_nan | ( | ) | const [inline] |
Definition at line 615 of file scfx_ieee.h.
| bool sc_dt::scfx_ieee_float::is_normal | ( | ) | const [inline] |
Definition at line 600 of file scfx_ieee.h.
| bool sc_dt::scfx_ieee_float::is_subnormal | ( | ) | const [inline] |
Definition at line 593 of file scfx_ieee.h.
| bool sc_dt::scfx_ieee_float::is_zero | ( | ) | const [inline] |
Definition at line 586 of file scfx_ieee.h.
| void sc_dt::scfx_ieee_float::mantissa | ( | unsigned int | a | ) | [inline] |
Definition at line 578 of file scfx_ieee.h.
| unsigned int sc_dt::scfx_ieee_float::mantissa | ( | ) | const [inline] |
Definition at line 571 of file scfx_ieee.h.
| void sc_dt::scfx_ieee_float::negative | ( | unsigned int | a | ) | [inline] |
Definition at line 549 of file scfx_ieee.h.
| unsigned int sc_dt::scfx_ieee_float::negative | ( | ) | const [inline] |
Definition at line 542 of file scfx_ieee.h.
| sc_dt::scfx_ieee_float::operator float | ( | ) | const [inline] |
Definition at line 534 of file scfx_ieee.h.
| scfx_ieee_float & sc_dt::scfx_ieee_float::operator= | ( | const scfx_ieee_float & | a | ) | [inline] |
Definition at line 526 of file scfx_ieee.h.
| scfx_ieee_float & sc_dt::scfx_ieee_float::operator= | ( | float | f | ) | [inline] |
Definition at line 518 of file scfx_ieee.h.
| void sc_dt::scfx_ieee_float::set_inf | ( | ) | [inline] |
Definition at line 623 of file scfx_ieee.h.
| void sc_dt::scfx_ieee_float::set_nan | ( | ) | [inline] |
Definition at line 631 of file scfx_ieee.h.


| Cambodia(https://www.cdc.gov/malaria/images/map/Cambodia_3.14.16.jpg) |
Present throughout the country including Siem Reap city. None in the city of Phnom Penh and the temple complex at Angkor Wat. |
Low |
Chloroquine
Mefloquine |
P. falciparum 86%
P. vivax 12%
P. malariae 2%,
P. knowlesi rare |
In the provinces of Banteay Meanchey, Battambang, Kampot, Koh Kong, Odder Meanchey, Pailin, Preah Vihear, Pursat, and Siem Reap bordering Thailand: Atovaquone-proguanil or doxycycline
All other areas with malaria: Atovaquone-proguanil, doxycycline, or mefloquine |
1) City(ies) of travel
2) Province(s) of travel
Map of provinces of Cambodia
To determine if a city is within a certain province |
| Cameroon(https://www.cdc.gov/malaria/images/map/Cameroon_3.14.16.jpg) |
All |
High |
Chloroquine |
P. falciparum >85%
P. ovale 5-10%
P. vivax rare |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Canada |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Canary Islands (Spain) |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Cape Verde(https://www.cdc.gov/malaria/images/map/CapeVerde_3.14.16.jpg) |
Limited cases on Sao Tiago (also known as Santiago) Island. |
Very Low |
Chloroquine |
PrimarilyP. falciparum |
Mosquito avoidance only |
Island of Travel Map of Cape Verde |
| Cayman Islands (U.K.) |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Central African Republic(https://www.cdc.gov/malaria/images/map/CentralAfricanRep_3.14.16.jpg) |
All |
High |
Chloroquine |
P. falciparum 85%
P. malariae, P. ovale, and P. vivax 15% combined |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Chad(https://www.cdc.gov/malaria/images/map/Chad_3.14.16.jpg) |
All |
High |
Chloroquine |
P. falciparum 85%
P. malariae, P. ovale, and P. vivax 15% combined |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Chile |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| China(https://www.cdc.gov/malaria/images/map/China_3.24_updatedrec.jpg) |
Present in the counties along the China-Burma (Myanmar) border in Yunnan Province. Limited transmission in Motuo county in Tibet. No malaria in areas where most major river cruises pass. |
Very Low |
Chloroquine
Mefloquine |
Primarily P. vivax
P. falciparum in Yunnan Province only. |
Along China-Burma (Myanmar) border in the western part of Yunnan province: Atovaquone-proguanil or doxycycline. Motuo county in Tibet: Mosquito avoidance only. |
1) Month(s) of travel
2) City(ies) of travel
3) Province(s) of travel
4) Altitude of city(ies) of travel
Altitude information and to determine if a city is within a certain province |
| Christmas Island (Australia) |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Cocos (Keeling) Islands (Australia) |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Colombia(https://www.cdc.gov/malaria/images/map/Colombia_3.14.16.jpg) |
All areas at altitudes below 1,700 m (5,577 ft). None in Bogota, Cartagena, and Medellin. |
Low |
Chloroquine |
P. falciparum 50%
P. vivax 50% |
Atovaquone-proguanil, doxycycline, or mefloquine |
1) City(ies) of travel
2) Altitude of city(ies) of travel
Altitude information for Colombia |
| Comoros(https://www.cdc.gov/malaria/images/map/Comoros_3.14.16.jpg) |
All |
No data |
Chloroquine |
Primarily P. falciparum |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Congo, Republic of the(https://www.cdc.gov/malaria/images/map/RepoftheCongo_3.14.16.jpg) (Congo-Brazzaville) |
All |
High |
Chloroquine |
P. falciparum 90%
P. ovale 5-10%
P. vivax rare |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Cook Islands (New Zealand) |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Costa Rica |
Last outbreak reported March through December 2017 in Matina Canton in Limon Province, Sarapiqui Canton in Heredia Province, and San Carlos Canton in Alajuela Province. [Report of malaria in traveler to Costa Rica posted on May 10, 2018 has been rescinded. The traveler did not have malaria. More information here(https://www.cdc.gov/malaria/new_info/2018/Costa_Rica_5_22_2018.html).] |
Very Low |
None |
Primarily P. vivax historically |
Mosquito avoidance only |
|
| Côte d’Ivoire(https://www.cdc.gov/malaria/images/map/CotedIvoire_3.14.16.jpg) |
All |
High |
Chloroquine |
P. falciparum 85%
P. ovale 5-10%
P. vivax rare |
Atovaquone-proguanil, doxycycline, or mefloquine |
|
| Croatia |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Cuba |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Cyprus |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|
| Czech Republic |
None |
None |
Not Applicable |
Not Applicable |
Not Applicable |
|


Table of Styles
Here is a list of each class in Tachyons along with corresponding styles.
| Selectors | Declarations | Module |
|---|
%= head %>
<%= siteHeader %>Here is a list of each class in Tachyons along with corresponding styles.
| Selectors | Declarations | Module |
|---|


JOHN
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---
title: Programming Systems Seminar
ref: ps2014
description: PhD seminar on advances in programming languages, co-taught with Prof. Peter Müller.
semester: Spring 2014
number: 264-5811-00L
lecturer: Martin Vechev, Peter Müller
ta:
assistants:
edoz:
session-time-place: Tue 13-15, CAB H53
lecture-time-place:
exercise-time-place:
credits: 2
image: assets/images/orator.jpg
---
Overview
This graduate seminar provides doctoral students in computer science a chance to read and discuss current research papers. Enrollment requires permission of the instructors. Credit units are granted only to active participants.
The main goals of this seminar are learning about current research results in the area of programming languages, static program analysis, program verification, and related areas, as well as practice of scientific presentations and discussions.
Presentation Schedule
| Date | Title | Presenter(s) |
| Jan 14 |
Almost-Correct Specifications: A Modular Semantic Framework for Assigning Confidence to Warnings, PLDI'13 |
Maria, Mitko A. |
| Jan 21 | Verifying Eventual Consistency of Optimistic Replication Systems, POPL'14 |
Yannis, Malte |
| Jan 28 | How tools in IDEs shape developers' navigation behavior, CHI'13 |
Mitko A., Andrei |
| Feb 4 | Precise Reasoning for Programs Using Containers, POPL 2011 |
Milos, Alex |
| Feb 11 |
Understanding IC3, SAT'12 |
Mitko D., Uri |
| Feb 21 |
Inductive data flow graphs, POPL'13 |
Yannis, Andrei |
| Feb 28 |
Replicated data types: specification, verification, optimality, POPL'14 |
Vesko, Alex |
| Mar 14 |
Compositional shape analysis by means of bi-abduction, POPL'09 |
Malte, Valentin |
| March 28 |
Ironclad C++: A Library-Augmented Type-Safe Subset of C++, OOPSLA'13 |
Yannis, Mitko D. |
| April 11 |
Automated error diagnosis using abductive inference, PLDI'12 |
Valentin, Maria |
| April 25 | Show no weakness: sequentially consistent specifications of TSO libraries, DISC'12 |
Andrei, Uri |
| May 9 | Discussion, Retreat |
Peter, Martin |
| May 16 | Automating Separation Logic Using SMT |
Alex, Milos |
| May 30 | MrCrypt: Static Analysis for Secure Cloud Computations, OOPSLA'13 |
Vesko, Lucas |


About this vacancy
Period
Location
Slots
Notes
June 1, 2020 - June 25, 2020
Location
Malibu, California
Slots
2
Notes
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam fermentum enim neque.
Acme Corporation
Posted 5 days ago
| Name | Order | Status | Remark | |
|---|---|---|---|---|
| Document Selection | 1 | Active | Question videos | Edit Delete |
|
1st Interview
|
2 | Active |
Has interview Question videos |
Edit Delete |
| 2nd Interview | 3 | Archived |
Has interview |
Edit Delete |
| Photo | Name | Stage | Status | |
|---|---|---|---|---|
|
Zhane Ballard
Applied 01.01.2019 |
Stage 1 | Vacancy applied | View Process Shortlist | |
|
Kerys Driscoll
Applied 01.01.2019 |
Stage 1 | Interview invited | View Process Shortlist | |
|
Bentley Enriquez
Applied 01.01.2019 |
Stage 2 | Interview scheduled | View Process Shortlist |
10 Feb. 2014
12:05
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Take me to your leader! Switzerland is small and neutral! We are more like Germany, ambitious and misunderstood!
3 Jan. 2014


{% load i18n %}
{% trans 'We sent this message because you have requested to reset your password. Please, follow this link:' %}
https://{{ domain }}{% url 'password_reset_confirm' uidb64=uid token=token %}
{% trans 'Your registered email address is:' %} {{ user.email }}


HALLOWEEN H20 A film review by Matt Williams
RATING: 1/2* out of * * * *
Michael Myers is back (yawn), and, oooh, aren't we scared. This seventh entry into the Halloween series has eschewed its traditional numeral in favor of the more cryptic H20 (it's been 20 years since the original Halloween...get it?) However, don't let the moniker change fool you, Halloween H20 is every bit as moldy and dull as the seventh film in a series is likely to be.
For those unfamiliar with the series, Michael Myers is the bogeyman personified.   His target of choice was babysitter Laurie Strode (Jamie Lee Curtis), who had unknown ties to Michael.
Twenty years after that rampage (and a little additional mayhem from Halloween 2), Laurie Strode has gone into hiding. She is now known as Keri Tate, a teacher at a private school in Northern California. She has tried to put her trauma behind her, even raising a son, John (Josh Hartnett), and striking up a relationship with fellow teacher Will Brennan (Adam Arkin). But every year on October 31st, she gets chills.
Little does she suspect that Michael is still hunting her after all these years. Where has Michael been, and what has he been doing over all this time? Who knows? The film blatantly forgets the events of the past four sequels, but never accounts for Michael's doings over that time. The film just doesn't bother with trivialities like that. Simply take discomfort from the knowledge that he's back in the saddle again, twenty years later.
The film makes a big hoopla over the return of Jamie Lee Curtis, but it still provides several teen victims. Along with John, there's his girlfriend Molly (Michelle Williams), his best friend Charlie (Adam Hann-Byrd), and Charlie's main squeeze Sarah (Jodi Lyn O'Keefe). Good luck in finding anything out about these characters. The most complex of them has a character description of "generic teen victim". It's even a chore to discover their names (which were probably tacked on as a mere afterthought).
Halloween H20 suffers from an overly prolonged setup. Aside from a brief pre-credits teaser, nothing at all happens for the first hour of the film. With an 85-minute running time, that's saying quite a lot. As a placeholder, the film packs in the false alarms. They occur with such frequency that they quickly lose their shock value. Maybe the first five are even slightly heart-quickening but when the number reaches ten, and twenty, and beyond, you're more likely to be checking your watch.
And throughout all this, very little is actually accomplished. The setup is simple: an isolated group of people are stalked by a killer. Countless of other horror films (even countless other Halloween sequels) have used the same premise, but established it with much more efficiency. When the final conflict occurs, it's a mere flash in the pan, bright, quick and substanceless.
The entire experience of Halloween H20 leaves you feeling cheated. However, anyone going into the seventh entry of a horror series shouldn't expect much. Don't be deceived.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of over 500 reviews, and information on over 600 upcoming releases
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


File Transfer Protocol (FTP). Zou et al [130] suggested two covert channels into the FTP. The first one
encodes covert bits directly into the FTP commands, so if there are N commands, every command will represents
log2N bits. The second one varies the number of FTP NOOP commands send during idle periods. The number
of send NOOP commands is equal to the integer value of the covert data. Before sending a new value, an ABOR
command needs to be sent. For FTP, it is normal to send NOOP or ABOR continually to prevent the control
connections from entering idle status.


Updated XsdClassesGen for VS.NET 2003
Here. I've updated XsdClassesGen for VS.NET 2003. For those of you unfamiliar with it (it's never been as popular as CollectionGen for some reason), XsdClassesGen is a VS.NET Custom Tool that generates C# or VB.NET wrapper class source code from an XSD. It's just like running xsd.exe from the command line, but integrated into VS.NET (which is where all tools belong, afaic).


A-Star with collision avoidance. Walkers calculate their paths by including future positions of other walkers in their considerations, effectively avoiding collisions upon path search. A collision is defined by two or more dots being on the same cell on the grid at the same time.
Random seed:


|
Groovy 2.1.7 | |||||||
| PREV NEXT | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| Command | Provides the interface required for command extensions. |
| Class Summary | |
|---|---|
| AnsiDetector | |
| BufferManager | Manages the shells buffers. |
| CommandAlias | Provides simple command aliasing. |
| CommandRegistry | A registry of shell Command instances which may be executed. |
| CommandSupport | Support for Command instances. |
| CommandsMultiCompletor | Completor for interactive shells. |
| ComplexCommandSupport | Support for more complex commands. |
| Groovysh | An interactive shell for evaluating Groovy code from the command-line (aka. groovysh). |
| IO | Container for input/output handles. |
| IO.Verbosity | |
| InteractiveShellRunner | Support for running a Shell interactively using the JLine library. |
| Interpreter | Helper to interpret a source buffer. |
| Main | Main CLI entry-point for groovysh. |
| ParseCode | Container for the parse code. |
| ParseStatus | Container for parse status details. |
| Parser | Provides a facade over the parser to recognize valid Groovy syntax. |
| ReflectionCompletor | Implements the Completor interface to provide competions for GroovyShell by using reflection on global variables. |
| RelaxedParser | A relaxed parser, which tends to allow more, but won't really catch valid syntax errors. |
| RigidParser | A more rigid parser which catches more syntax errors, but also tends to barf on stuff that is really valid from time to time. |
| Shell | A simple shell for invoking commands from a command-line. |
| ShellRunner | Support for running a Shell. |
| Exception Summary | |
|---|---|
| CommandException | Thrown to indicate a problem with command execution. |
| Error Summary | |
|---|---|
| ExitNotification | Notification to signal the shell to exit. |
Copyright © 2003-2013 The Codehaus. All rights reserved.


MRS. WINTERBOURNE A film review by James Berardinelli Copyright 1996 James Berardinelli
RATING (0 TO 10): 4.5 Alternative Scale: ** out of ****
United States, 1996 U.S. Release Date: 4/19/96 (wide) Running Length: 1:45 MPAA Classification: PG-13 (Profanity, Mature themes) Theatrical Aspect Ratio: 1.85:1
Cast: Shirley MacLaine, Ricki Lake, Brendan Fraser, Miguel Sandoval Director: Richard Benjamin Producers: Dale Pollock, Ross Canter, Oren Koules Screenplay: Lisa-Marie Radano and Phoef Sutton based on the novel I MARRIED A DEAD MAN by Cornell Woolrich Music: Patrick Doyle U.S. Distributor: TriStar Pictures
It's said that nothing new comes out of Hollywood these days, and that's certainly the case with MRS. WINTERBOURNE, a romantic comedy based on Cornell Woolrich's I MARRIED A DEAD MAN. This film creates a sense of deja vu that may have viewers flashing back to last year's WHILE YOU WERE SLEEPING, a remarkably similar story. However, while SLEEPING boasted the talents of a charismatic Sandra Bullock and affable Bill Pullman, MRS. WINTERBOURNE has an irritating Ricki Lake and bland Brendan Fraser. It's not hard to guess which is the better movie.
Stripped to its bare bones, MRS. WINTERBOURNE is about a lonely woman finding a family and romance when she pretends to be the wife of a dead man. Connie Doyle (Ricki Lake) is abandoned on the streets of New York City when she refuses to abort her boyfriend's child. Nine months pregnant and homeless, she stows away on a train to Boston, where she meets Hugh Winterbourne (Brendan Fraser) and his expecting wife, Patricia (Susan Haskell). The train is involved in a wreck, and Hugh and Patricia are killed. As a result of certain artificial circumstances, Connie is believed to be Patricia, and her baby, who is born just after the crash, is accepted as the late Hugh's son. The pair are invited to the Winterbournes' Boston mansion to meet Hugh's mother, Grace (Shirley MacLaine), and his twin brother, Bill (also Fraser). While Grace accepts Connie's story without question, Bill is suspicious, especially when she signs a check "Connie Doyle" instead of "Patricia Winterbourne".
Perhaps because it bears such a marked resemblance to WHILE YOU WERE SLEEPING, MRS. WINTERBOURNE comes across as stale and recycled. The plot is littered with the kinds of contrivances and coincidences common to all mistaken identity stories, but, instead of dealing with such issues in a fresh and intelligent manner, the script treats them with a workmanlike, sit-com approach (not surprising, considering that co-screenwriter Phoef Sutton worked on CHEERS). And, although MRS. WINTERBOURNE offers a few humorous moments, the comedy isn't consistent.
As I've often said in the past, chemistry is critical to a romantic comedy's success. Here, there's precious little between leads Ricki Lake and Brendan Fraser. Fraser, who hasn't given a good performance since SCHOOL TIES, makes Bill ineffectual and uninteresting. Lake, a TV talk show host and veteran of John Waters movies, does a credible Rosie O'Donnell imitation -- that's to say she's equally aggravating. At least Shirley MacLaine is her usual solid self, but, although her presence saves a few scenes, she can't rescue MRS. WINTERBOURNE.
For director Richard Benjamin, who debuted behind the camera with 1982's MY FAVORITE YEAR, MRS. WINTERBOURNE isn't likely to be a stand- out on his resume. This film mistakes action for energy, ridiculous circumstances for comedy, and a mismatched male/female pairing for romance. Those in search of a lighthearted love story would do better checking out THE TRUTH ABOUT CATS AND DOGS, or, for something really similar, renting WHILE YOU WERE SLEEPING. Unlike MRS. WINTERBOURNE, neither of those films is mired in mediocrity.
- James Berardinelli e-mail<EMAIL_ADDRESS>web: http://www.cybernex.net/~berardin
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The Timeline has functions to add multiple custom time bars which can be dragged by the user.
timechange event, index: , time:
timechange
timechanged event, index: , time:
timechanged


Troubleshooting Guide
Use this guide to troubleshoot any issues with your BrainPad. Please follow the steps on this page before going to BrainPad Discussion Forum for help.
Identify which BrainPad you have
A small number of early adopters received older versions of the BrainPad. The current production BrainPad has "BP2 Rev B" printed in the lower left corner. If your BrainPad doesn't look exactly like the image below, check the Older BrainPad page for more details.
Tip
Check the reset button. If it is smaller than the directional buttons you have an Older BrainPad.
Bootloader
Push and hold the reset button for about 3 seconds. Does the LightBulb turn Green? If so you have a working bootloader and can skip the rest of this section.
If you did not get a green light in the step above, try updating the bootloader. To update the bootloader, download the latest bootloader DFU file from the Downloads page and then go to the DFU Files page for instructions on loading DFU files.
When the LightBulb turns Green, the BrainPad will present itself as a virtual storage device. Your computer should see it like any other storage device. The first time this happens, the operating system will need a few seconds to load the needed drivers.
This virtual drive opens a window containing a text file that contains information such as the boot loader version. You can update the loader to the newest version to get the latest and greatest BrainPad features.
Sample Demo
When the BrainPad virtual drive window is open (see Bootloader section), download this demo file and drag it into the virtual drive window.
TinyCLR OS
We recommend starting with Microsoft MakeCode to verify the hardware is functional first. Then, load TinyCLR OS as detailed in the System Setup. Once loaded, your PC will detect a TinyCLR OS device as shown below.
Visual Studio will also see the device:
It is important that the TinyCLR OS version on the PC matches what is on the BrainPad. The same goes for the NuGet packages.
Our experts are ready to help on our TinyCLR OS Forum.
You are on the documentation website for the BrainPad. The main website is found at www.brainpad.com


Debugging During Testing and Moving Coins with Collisions¶
Our player moves, but our coins don’t. Let’s have the coins move down, and when it reaches the bottom, it “respawns” near the top. Also, when a player collides with a coin, the coin disappears.
Let’s also use this point to show a hidden feature of debugging: use during testing and test-driven development. As you are writing a test, you are in discovery mode and, since your code is still emerging, things are going to break. This is a good place to use debugging, as part of test writing.
Steps¶
Terminate the game, if it is running, and make sure you have no breakpoints set.
Edit
game.py
to have the following:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
from random import randrange import arcade SCREEN_WIDTH = 600 SCREEN_HEIGHT = 600 MOVEMENT_SPEED = 5 GRAVITY = 0.3 BOUNCINESS = 0.5 class Coin(arcade.Sprite): def reset_pos(self): self.center_y = randrange(SCREEN_HEIGHT + 20, SCREEN_HEIGHT + 100) self.center_x = randrange(SCREEN_WIDTH) def update(self): self.center_y -= 1 if self.top < 0: self.reset_pos() class Player(arcade.Sprite): def update(self): self.change_y -= GRAVITY self.center_x += self.change_x self.center_y += self.change_y if self.left < 0: self.left = 0 elif self.right > SCREEN_WIDTH - 1: self.right = SCREEN_WIDTH - 1 if self.bottom < 0: self.bottom = 0 if self.change_y < 0: self.change_y *= -BOUNCINESS elif self.top > SCREEN_HEIGHT - 1: self.top = SCREEN_HEIGHT - 1 class MyGame(arcade.Window): def __init__(self, width, height, title): super().__init__(width, height, title) arcade.set_background_color(arcade.color.WHEAT) self.player = Player('player.png', 0.5) self.player.center_y = 20 self.all_sprites_list = arcade.SpriteList() self.all_sprites_list.append(self.player) self.coins = arcade.SpriteList() for i in range(20): coin = Coin('coin.png', 0.2) coin.center_x = randrange(SCREEN_WIDTH) coin.center_y = randrange(SCREEN_HEIGHT) self.coins.append(coin) self.all_sprites_list.append(coin) def on_draw(self): arcade.start_render() self.all_sprites_list.draw() def update(self, delta_time): self.all_sprites_list.update() hit_list = arcade.check_for_collision_with_list(self.player, self.coins) for coin in hit_list: coin.kill() def on_key_press(self, key, modifiers): if key == arcade.key.UP: self.player.change_y = MOVEMENT_SPEED elif key == arcade.key.LEFT: self.player.change_x = -MOVEMENT_SPEED elif key == arcade.key.RIGHT: self.player.change_x = MOVEMENT_SPEED def on_key_release(self, key, modifiers): if key == arcade.key.UP: pass elif key == arcade.key.LEFT or key == arcade.key.RIGHT: self.player.change_x = 0 def main(): game = MyGame(SCREEN_WIDTH, SCREEN_HEIGHT, 'Coin Game') arcade.run() return game if __name__ == '__main__': main()
- Lines 17-20. The regular sprite
update
function, called whenever the game’son_draw
says to update (line 63). Here we move the coin down a pixel. Then, if it reaches the bottom, “respawn” by calling itsreset_pos
method. - Lines 13-15. Move this coin somewhere near the top and randomly positioned left/right.
- Line 52. As we see next, we also want to keep a sprite list of just for coins.
- Line 57. Add this coin to the coin list.
- Lines 67-68. Arcade makes it easy to find which sprites overlap with
a certain sprite.
hit_list
is thus, which coins collided with the player. - Lines 69-70. Kill each of the colliding coins.
- Lines 17-20. The regular sprite
Run under the debugger. Just to see what we’ve done with the coins, click the debug button to run the game, without any breakpoints. You see the coins moving down, and when the player hits one, it “respawns” higher up.
Generate a test. Let’s test the Coin’s
reset_pos
method. PyCharm can help you generate the test code. Click on line 13 (def reset_pos
) and chooseNavigate | Test
.Create new test. There isn’t a test to navigate to, so PyCharm asks if you want to create one. Choose
Create New Test...
Select method. Click the checkbox to generate a test
test_reset_pos
and clickOK
.Run the test. Right-click in the
test_coin.py
editor and chooseRun 'Unittests in test_co...
to run the file under the project-default test runner (Python’s standardunittest
.)
PyCharm opens a Run tool window with the output of the test run. This run tool window is different: it is a run configuration of type Python Unittests and has a lot of UI specific to test running.
Our first test fails. That’s normal: we told it to fail.
Enter test. Let’s fill in a simple test:
1 2 3 4 5 6 7 8 9
from unittest import TestCase class TestCoin(TestCase): def test_reset_pos(self): from game import Coin c = Coin() self.assertEquals(c.center_x, 0) c.update()
Rerun test. Click the green play button in the test window to rerun the test. The test now passes.
We’d like to know more than “doesn’t equal”. The coin should be near the top. Let’s use the debugger to investigate.
- Set breakpoint. Click in the gutter to set a breakpoint
on line 9 in the test (
c.update()
). - Run test under debugger. Right-click in the editor and choose
Debug 'Unittests in test_co...
to start debugging the test. Execution stops on that line. - Step into. Click the step into button to go into the coin’s
update
method. Click the step into my code button 3 times until execution is on line 14 ofgame.py
(self.center_y =
). - Use Evaluate Expression. Click the evaluate expression button
and get the minimum value of
SCREEN_HEIGHT + 20
, which is 620. We now know what to put in our test. Do the same forSCREEN_HEIGHT + 100
which gives 700. - Add test assertion. Let’s assert that the value is between the
correct range:
self.assertTrue(620 < c.center_y < 700)
. Add this line afterc.update()
. - Remove breakpoint. Click the red circle to remove the breakpoint in the test file.
- Re-run. Click the debug button in the debug window to re-run the tests under the debugger, which now passes.
What’s Going On¶
We wrote a simple Python unittest, which executes under the test runner
configured for the run configuration. By default, PyCharm assigns the test
runner under Settings | Tools | Python Integrated Tools
. If you don’t
nose
or pytest
, this defaults to Python’s standard unittest
test
runner from the standard library.
The Debug tool window changes to have all the capabilities of a visual test
runner. But the two-tab interface – Debugger
and Console
– are
still available.
Regarding the game, we use Arcade’s built-in collision detection code. You pass it an Arcade sprite and a second sprite list, and Arcade returns each of the sprites in the sprite list which overlap with the passed-in sprite.
Our coins move down the screen and, when they reach the bottom, respawn themselves somewhere close to the top of the screen.


"This is my 2 website, which I completed as part of the Code Nation Develop course"
Welcome to my personal portfolio. feel free to look around my website.
about.html contact.html Code Nation linkedin githubCode Nation Joined a programming bootcamp at Code nation where I was challenged to develop a personal page with html, css and flexbox. I successfully completed the website on time. I also gained great competencies on github, trello and slack.


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Present contact information using <address>. Preserve formatting by ending all lines with
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<address>


--- layout: post title: Double check locking Extension Method for ASP.NET Cache date: 2010-06-22 13:26:47.000000000 +02:00 categories: [] tags: - ASP.NET - C# - Caching - SharePoint status: publish type: post published: true meta: {} author: login: admin email<EMAIL_ADDRESS>display_name: Philippe first_name: Philippe last_name: Vlérick excerpt: !ruby/object:Hpricot::Doc options: {} ---
On my current project, we need to cache some of the results retreived when querying into SharePoint. As the retrieving operation is quite expensive (as always with SharePoint) and that performances are very important, we must make sure that it is only executed once.
So, the way to do this is to use the double check locking mechanism. The idea is to check if the cache contains the item sought, if it does not contain it, lock then check again, in case another thread added it while we were acquiring the lock.
I wrote a generic extension method to do this, to which the lock handle to lock on is given as a parameter, as well as the function used to retrieve the item if it is not in the cache. In this case, the retrieved item will be stored in the cache (and of course returned to the called).
Code speaks louder than words:
public static class CacheExtension { public static T SetAndGetItemFromCache<T>(this Cache cache, String cacheKey, Object lockHandle, Func<Cache, T> addItemToCache) { Object item = cache.Get(cacheKey); if (item == null) { lock (lockHandle) { item = cache.Get(cacheKey); if (item == null) { item = addItemToCache(cache); } else { //The sought item has been added meanwhile in another thread/process } } } else { //Cache contains the key, return it } if (item is T) { return (T)item; } else { //The Object in the cache is not of type T, some other component might be using that key throw new ApplicationException(String.Format("Object retreived from the cache is not of the expected {0} type. Another component might be using the same key to store in the cache.", typeof(T).FullName)); } } }
This can be used on any System.Web.Caching.Cache object.


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{{team.members[i].role}}
{{team.members[i+ 1].role}}
{{team.members[i + 2].role}}
{{team.members[i+ 3].role}}


{% extends 'base.html' %} {% load staticfiles %} {% load state_fb_search %} {% block main %}
{% with num_friends=profile.friends.count %} {% if not num_friends %} Sadly, *none* of your friends {% else %} Only {{ num_friends }} of your friends {% endif %} have joined VoteSwap. {% endwith %} VoteSwap can only connect you to your Facebook friends and friends-of-friends, so the more friends you get to join the better chance we have of defeating Trump.
Since you live in a {{ profile_context.state_type }}, your best strategy is to share VoteSwap with your friends in {{ profile_context.paired_state_type }} states. Sharing VoteSwap on Facebook will reach the most people, but sending direct messages to your friends in {{ profile_context.paired_state_type }} states is probably more effective. Here are some suggested messages.
To your friends in swing states:
You live in an important swing state, where third parties might split the vote and make Trump win (remember Al Gore?). If you want a third party candidate to get votes while making sure Trump loses, join https://voteswap.us VoteSwap pairs up swing-state and safe-state voters so they can swap votes. Clinton will win the swing states, third parties get more votes (and more federal funding), and Trump loses. Even if you're already voting for Clinton, the more people that join the better our chances are of pairing up voters and saving our country from Trump.
Share with your frends in swing states: {% for state in swing_states %}{% if not forloop.first %}, {% if forloop.last %} or {% endif %}{% endif %}{{ state }}{% endfor %}.
To your friends in safe states: {% if profile_context.is_3p_in_swing %}
I want to vote for a third party in {{ profile.state }}, but I also don't want Trump to win. You should join https://voteswap.us and swap votes with me. I'll vote Clinton in {{ profile.state }} if you vote {{ profile.preferred_candidate }} in your state.{% else %}
Do you want to prevent Trump from winning but also think third parties should be a viable choice? Join https://voteswap.us and swap your Clinton vote in your safe state with a third-party voter in a swing state. Third parties will get more votes, and Clinton will win the important swing states so Trump will lose.{% endif %}
Share with your friends in safe states like {% for state in safe_states %}{% if not forloop.first %}, {% if forloop.last %} or {% endif %}{% endif %}{{ state }}{% endfor %}.


{% include head.html %} {% for css in site.data.egjs.css %} {% endfor %} {% for dist in site.data.egjs.dist %} {% endfor %}
{% include facebook.html %} {% include header.html %} {% include promo.html %} {% include nav.html %}InfiniteGrid offers five layouts. You can place items with the layout.
{{layout.name}}


--- layout: post esbma_id: 1115 title: SourceGear announces Project "Allerton" date: 2005-01-12 19:45:55 keywords: sourcegear ---
I'm writing this blog entry to share an exciting bit of news from SourceGear: We have decided to announce the fact that we are developing a new product which will be released later this year. The code-name for this new product is "Allerton". In a nutshell, Allerton is a Linux/Solaris/MacOS client for Team Foundation.
A bit of background: With Visual Studio 2005, Microsoft will be introducing a suite of new tools called Visual Studio Team System targeted especially at enterprise customers. Team Foundation is the server component which offers source control and work item tracking as part of Visual Studio Team System.
The primary client for Team Foundation is Visual Studio itself, but that obviously runs only on Windows. If you have even one person who needs to access source control or work item tracking from a non-Windows platform, this is a big problem. In large enterprise environments, this is not only a big problem, it is a common one.
To address this need, we are building tools which allow users on Linux/Solaris/MacOS to access the source control and work item tracking features of Team Foundation. The Allerton product will include a rich GUI client, a command-line client, and support for integration with Eclipse.
Pricing for this product has not yet been announced. Don't ask me about it. I'm not saying anything until we make an official announcement.
We plan to release Allerton later this year in the same time frame as Visual Studio 2005. We will demo the product at our booth at Tech-Ed in June.
No. SourceGear has two main products, SourceOffSite and Vault. These two products are very different. One is a SourceSafe add-on, and the other is a SourceSafe replacement. Vault and SourceOffSite are very distinct. They are developed by two different teams.
It may seem goofy for the company who makes Vault to develop an add-on product for somebody else's source control tool. But Allerton is the next logical step for the SourceOffSite side of our company. Just as SourceOffSite is an add-on for SourceSafe, Allerton is an add-on for Team Foundation.
Heck no. The Vault team is busy working on the next release. Vault's momentum is strong and growing. December 2004 was a record month for SourceGear revenue, and it was the first month that Vault sales beat SourceOffSite sales.
The positioning of Team Foundation and Vault will be so different that the sense of competition between them will be a lot less than you think. Compared to Vault, Team Foundation will be much larger and much more expensive. Team Foundation will be most popular in large enterprise scenarios with hundreds of developers. Vault is very popular in the "professional developer" market segment where teams typically have less than 100 developers.
I believe Team Foundation will succeed. I believe Vault will continue to succeed. I also believe Perforce, AccuRev, and Subversion will continue to succeed. The source control market is very fragmented, with lots of profitable and popular products. That won't change anytime soon.
For the money! As a private company, we don't publicly disclose our revenue numbers, but trust me on this -- SourceOffSite makes a lot of money. This product has made me a believer in the strategy of building add-ons for popular products.
It may take a long time, but eventually Team Foundation is going to become a popular product. Some ISV is going to make a boatload of money selling Unix clients for it. It seems silly to think about that money going to somebody else. Our experience with SourceOffSite makes SourceGear the natural company for the job.
If you think Allerton is a change of focus for us, that is only because you have no idea how much time I've spent thinking about the opportunities in software for woodworkers. :-)
Allerton Park is a really cool place, not too far from SourceGear's office. If you hate this code-name, don't worry -- it's just a placeholder to be used until we figure out the real product name.


--- layout: post title: A Journey to Programming date: 2014-09-05 09:57:58.000000000 -07:00 categories: - Blog tags: [] status: draft type: post published: false meta: _wpas_skip_google_plus: '1' _wpas_skip_linkedin: '1' _wpas_skip_tumblr: '1' _wpas_skip_path: '1' _wpas_skip_facebook: '1' author: ---
-## A Journey to Programming
-
-Every programmer has a different motivation and path for heading towards the craft. When thinking about my own journey, I realized how powerful it might be to hear counter-narratives to the geek-guy turns brilliant engineer/business mind and heads to Silicon Valley that we [hear](http://www.theguardian.com/technology/2007/jul/25/media.newmedia) [so](http://www.forbes.com/sites/jjcolao/2014/01/06/the-inside-story-of-snapchat-the-worlds-hottest-app-or-a-3-billion-disappearing-act/) [often](http://www.dailymail.co.uk/sciencetech/article-2128518/Instagram-The-nerds-billion-551-days-camera-app.html). Drive-by - I came from a single-parent, mixed-race household dependent on welfare to being a developer in an industry that celebrates what feels like the very opposite of my background.
-
-
-### How I Began
-
-
-As mentioned, my journey started very humbly. I am the child of a single parent who had unexpectedly became pregnant at 41 and unexpectedly had triplets and no recourse to raise them but to turn to the welfare system. My mother plugged it into our heads that we needed to get a good education - that this was the way out of poverty. My current politics don’t leave me necessarily convinced that this American-Dream-like narrative is a cure-all - but back in the 90s, when I was growing up, this basic formula still worked fairly well for those with enough privilege (my own being: white-passing, able-bodied, liberal-location dwelling, among others). That being said, it was no walk-in-the-park to get that education. My mother was adamant about her children remaining in a town with a good public education system, despite consequently being limited to the few subsidized apartments controlled by lazy landlords.
-
-### A Different World
-
-A lot of my personal story has to do with education. But, today, education - at least in Computer Science - is different and still shifting.
-


Particle Renderers
Quad Renderer
This renderer visualizes each particle as a quad. There are several modes to choose from how this quad is oriented. There are also different modes how to blend the particle with the background. This renderer is very versatile.
Orientation: This mode defines how the quad is oriented and around which axis it may rotate. In the 'rotating' modes the quad geometry constantly rotates around some axis that is decided when the particle is spawned. In the 'fixed' modes the quads themselves will not rotate, but have a fixed plane decided when they spawn. If they have a non-zero rotation speed, their texture will rotate around the quads center. In the 'axis' modes, the quads have one fixed axis and one that orients itself into the direction of the camera.
Billboard - This is the most common mode. Billboards always face the camera. If the camera moves around the effect, the billboards keep orienting towards it. When billboards should rotate, they always rotate in screen-space, meaning around the current forward axis of the camera.
Rotating: Ortho Emitter Dir - In this mode the quads rotate around the orthogonal axis between the direction of the emitter and the direction the particle moves into. This mode is particularly useful for simulating debris of concrete for bullet impacts and such. When the particles have a high rotation speed, they will fly off in a spectacular fashion. For this type of effect it is also best to use alpha-masked textures representing debris, and to use the 'Opaque' render mode.
Rotating: Emitter Dir - Similar to the mode above, but uses the direction of the emitter as its rotation axis. This can be used for muzzle flashes (the part that shows along the direction of the barrel) or impact effects.
Fixed: Emitter Dir - In this mode the quad always uses the emitter direction as its plane normal (with some optional Deviation). This mode is useful for creating shockwave effects at a point of impact, or things like ripples in water. It can also be used for muzzle flashes (the part sideways out of a barrel).
Fixed: World Up - Similar to Fixed: Emitter Dir but the axis used is always the world up direction. This can be useful for effects where the emitter may have an arbitrary direction, but the particles should always face upwards. For example for the ripples of water impact effects.
Fixed: Random Dir - In this mode each quad gets a random axis assigned when it is spawned. This can be useful for creating shockwave effects in explosions.
Axis: Emitter Dir - In this mode the quads fixed axis is the direction of the emitter. The quad will rotate around this axis to face the camera as much as possible. Additionally, the quad will not scale around its center, but around one of its edges. Therefore, when scaling up or down, that edge will stay in a fixed position. This can be used to create muzzle flashes and other impact effects which should generally move into the direction of the emitter, but also face the camera as much as possible, to be well visible. This mode won't look too convincing if the particles' movement deviates strongly from the emitter direction. For most common use cases, the particles may not move at all, but only change their size.
Axis: Particle Dir - In this mode the quads fixed axis is its own fly direction. The quad will rotate around this axis to face the camera as much as possible. This is useful for creating sparks or laser blasts which shall stretch a little while moving into some direction.
Render Mode: This mode specifies how the color from the particle will be combined with the scene background.
Opaque - The particle will use the alpha channel of the texture as mask. Pixels are either fully transparent or fully opaque. This is mostly useful for debris.
Additive - The particle's color will be added to the background. The alpha-channel is not used. This is used for everything that should glow (magic spells, sparks, ...)
Blended - The alpha channel of the texture is used to interpolate the particle's color with the background. This is used for everything that should be transparent, but not glowing (smoke and such). It is also the most tricky mode to make look good, as it depends the most on a good texture with a proper alpha channel. Particles rendered with this mode must be sorted by distance by the renderer, which incurs an additional performance cost.
Blended Foreground/Background - Same as Blended but when there are multiple particle systems using Blended mode, this allows you to influence in which order the systems are rendered. This is used to fix rendering issues.
Distortion - This is used to create a heat haze effect, which distorts the scene behind it. The alpha-channel of Texture is used to determine the shape of the distortion effect. The Distortion Texture and Distortion Strength are used to decide for each pixels how much to distort the background. Any texture can be used as the distortion texture, but the effect works best using a normal map.
Texture: The texture used for rendering. May be a texture atlas or contain flipbook animations.
Texture Atlas: Specifies how to interpret the content in Texture:
None - The texture contains only a single image.
Random Variations - The texture contains NumSpritesX x NumSpritesY images in a regular grid. Each image is independent and each particle uses a random one.
Flipbook Animation - The texture contains an animation starting at the top left, going to the right and down. Every particle starts with the first image, and over its lifetime will traverse through the frames to play the animation.
Random Row, Animated Column - The texture contains NumSpritesY animations, each with NumSpritesX frames. Each particle plays one random animation over its lifetime.
TintColorParam: An optional effect parameter name. If set, the parameter is used to tint the final color of the particle.
Particle Stretch: Only available in the 'axis' render modes. Allows to stretch the particles along their fixed axis. Useful to create sparks.
Mesh Renderer
This renderer visualizes each particle using a mesh.
Mesh: The mesh to use for rendering.
Material: The material to use on the mesh.
TintColorParam: An optional effect parameter name. If set, the parameter is used to tint the color of the meshes.
Light Renderer
The light renderer treats each particle as a light source and thus illuminates the scene around it. Usually one would add this renderer as a second renderer to a particle system, such that one can easily reuse the behavior and color of for example billboards. Since light sources have a very high performance overhead, and adding as many lights into a scene as billboards would often be way too much, this renderer may visualize only a fraction of all particles in the system.
SizeFactor: A factor to scale the particle's size with to determine the light influence radius. If the particles are also visualized as, for instance, billboards, the light source around the billboard should often be three to five times bigger.
Intensity: The light intensity.
Percentage: How many of the particles in the system should also be used as light sources. Typically you should only use 10% or so of the particles. Fewer lights is not only better for performance, it often also looks better, as there will be more contrast and flickering. Too many lights will result in constant brightness, making the effect less interesting.
TintColorParam: An optional effect parameter name. If set, the parameter is used to tint the color of the lights.
IntensityScaleParam: An optional effect parameter name. If set, the parameter is used to scale the intensity of the lights.
SizeScaleParam: An optional effect parameter name. If set, the parameter is used to scale the light influence radius.
Trail Renderer
This renderer visualizes particles as long lines that draw the path that the particle took. Trail particles are made up of a fixed number of segments. The more segments the renderer uses, the longer the trails will be. Also the faster a particle moves, the longer the trail will stretch. More segments cost more performance to update and render. For very short sparks that should just stretch a little, it is better to use billboards with Orientation set the Axis: Particle Dir and StretchFactor set to some value between 2 and 5.
Render Mode, Texture, Texture Atlas, TintColorParam: These options are identical to the quad renderer.
Segments: The number of segments to use for each particle. More segments cost more performance but also result in longer and more detailed trails.
Effect Renderer
This renderer visualizes each particle using another particle effect. The referenced effect is spawned at the position of each particle and then moved along as the particle moves. This allows you to create effects like fireworks, where each 'rocket' is represented by a single particle flying into the sky, but visualizes as a complete burning particle effect.
Effect: The particle effect to spawn and move along for each particle in this system. Usually the other particle system would use at least one continuous emitter, such that the effect will be active for the whole lifetime of the particle that references it.
RandomSeeed: An optional random seed to pass into the spawned effect.


I moved to Portland, Oregon in September of 2016 after 7 years in Michigan. I'm a PNW native and am thrilled to be back next to the ocean.
Having a deep love of reading, learning, and helping others, I started my professional career training in librarianship. I shifted quickly to archival training, then to data analysis once I left graduate school and began working, to project management, and now to fledgling developer. All of my career paths have focused on facilitating access to information for others, whether directly in libraries and archives or indirectly through building tools or munging data.
My current hobbies include reading (currently a lot of sci-fi), running, climbing, or practicing aerial arts. I'm always looking for more climbing/hiking partners!
I know how to quit Vim.


Your channel: {{ if and .Channel .Channel.Snippet }} {{ .Channel.Snippet.Title }} {{ else }}
Snippet hasn't loaded
{{ end }}
Destroy Session
Your session data on this website (not YouTube or Google) will be deleted.
Select your destination playlist
{{ if .UserPlaylists }}
{{ range .UserPlaylists }} {{ if .Snippet }}
{{ .Snippet.Title }}
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{{ end }} {{ end }}
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You do not have any playlists on your channel. Create and refresh this page.
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Source playlists
All entered links to playlists will be checked. You'll see their names and the number of videos in the table.
{{ if .SourcePlaylists }}
Title
Videos Count
{{ range $index, $playlist := .SourcePlaylists }}
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Copy all videos from the playlists table into your selected playlist.
Delete Selected
Delete selected playlists in the table.
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You can remove countries that do not match your criteria by clicking on the country name in the box. Clicking the links below the chart will open a window to the country's wiki page in a new tab.
Wiki Links
{% for key, value in links.items() %}
{{key}}
{% endfor %}


Architecture Overview
This platform is a web application. Below are some bullet points on its most important features:
- The essential technology stack used is Javascript (JQuery & Bootstrap) / Spring / Hibernate / MySQL.
- It is assumed that each organization running the system will have their own installation of it running on a cloud server.
- Plugins are used add functionality to online roleplays.
Developer Documentation
Granularity: if something is changeable on the level of roleplayset, roleplay or roleplay in motion.
Maven is used at the build tool, so looking at the POM is a good place to start.
@since 1.0
@version 1.0


Feathers
Get started with Feathers, the open source library of user interface components for the hardware-accelerated Starling Framework. A robust skinning system enables your creativity, fluid layouts make it easy to deploy to a variety of screens, and support for both touch and mouse interaction puts your apps on mobile and desktop. With Feathers and Starling, you can build fully-standalone native apps for iOS, Android, Windows, and Mac OS X, and you can even deploy to desktop web browsers with Adobe Flash Player. See an overview of features.
Installation & Getting Started
Set up Starling and Feathers in your preferred development environment:
Follow the instructions in Getting Started with Feathers to create your first "Hello World" app using Feathers.
Help & Support
There are a number of places where you can get help with Feathers issues or to ask questions.
Bookmark the Feathers API Reference for easy access to all of the classes, properties, methods, and events that Feathers components offer.
Study the Frequently Asked Questions (FAQ). Feathers makes some default choices that aren't necessarily intuitive because they improve performance significantly. For beginners, the FAQ will answer a number of common questions.
Ask for help at Feathers in the Starling Forum. Many of the community's experts (including Josh Tynjala, the main author behind Feathers) visit frequently to answer questions. Be sure to try a couple of searches to see if someone else has had the same issue -- maybe your questions have been answered already! If not, then feel free to start a new thread.
Submit bug reports and feature requests on Github. If you're not quite sure if you've found a bug or not, or if you simply have a question, then please post in the forums first. We can escalate to a bug report later, if needed.
Looking for quick recipes that show you how to do common tasks using Feathers? Check out the Feathers Cookbook.
Upgrades
Core Concepts
Feathers Components
A more detailed look at each of the components that Feathers provides.
A bit further down, you can find number of articles that go into detail about the Feathers component architecture and how to create custom components.
Text Renderers
Text Editors
Media Player Framework
Feathers includes a number of components for building media players.
Layouts
A detailed look at each of the layout algorithms that Feathers provides out-of-the-box.
Custom Layouts
If the built-in layouts don't provide what you need, Feathers containers support custom layouts too.
Skinning and Themes
Some tutorials on how to skin components and how to use Feathers themes.
More skinning resources.
Custom Components
Often, applications and games need components that don't come standard in a user interface library. The following articles will help you get started developing custom components on top of Feathers.
Custom Item Renderers
Item renderers are custom components used by lists to display items from the data provider. They have a few extra considerations to keep in mind.
Managing UI interactions
Animation
Miscellaneous
Feathers Community
Contributing to Feathers
Would you like to contribute a bug fix or new feature to the next version of Feathers? Please open a Pull Request on the Feathers Github project.
Extensions and Components
The following community projects are built on Feathers. Feel free to share your components and extensions in the Feathers forum.
Platform UI Guidelines
Please take a look at the official documentation linked below for the platforms that Feathers supports. Become familiar with the guidelines for each platform, and try to tailor your application's experience to the specific platform that your code is running on at any given moment. Cross-platform user interface libraries like Feathers can be convenient and powerful, but do not forget that you are responsible for meeting the expectations of your users on each platform. One size rarely fits all.


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Flow of the questionnaire design process. |
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Figure 2
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Combined responses of respondents’ views on characteristics ... |
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Figure 3
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Combined responses on perceived barriers to urban community ... |
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Figure 4
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Similarities and differences in stakeholders’ views of the t ... |
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Figure 5
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Similarities and differences in stakeholders’ views of the t ... |
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Figure 6
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People-centered resilience building: Multi-dimensional appro ... |


Police search for missing 27-year-old Ilisapeci Sili
by Naveel KrishantPolice are searching for 27-year-old Ilisapeci Sili who has been reported missing at the Totogo Police Station.
Sili was last seen attending a family function in Matata Settlement in Lami on the 25th of October.
Several attempts have been made by her family to locate her which has turned out negative.
Please call Crime Stoppers on 919 if you have information on Sili's whereabouts.


7 Important Things you need know about MISS UNIVERSE ANAMBRA
7 Important Things you need know about MISS UNIVERSE ANAMBRA
.
.
.
1. Registration is totally free: No form purchase, no entry fee payment whatsoever. 2. No Camp fee payment: Meals, Resort/Hotel plus all Logistics for the camping exercise would be fully sponsored and paid for, no fee is required of you. 3. Open to every lady of Igbo origin (not just Anambra Origins), but who must be at least 18 and under 28 years of age before January 1st in the year she wishes to contest. 4. No need for a break-the-bank budget or Sponsor(s) to contest or win, just abide by all pageant rules, follow instructions to the letter and you're good to win. 5. Influencial persons, team members, a judge or even all judges can't help you win because the crowd don't lie, only your performance can, don't be beguiled. 6. Car, cash and other prizes belong permanently to Winner, and on no condition would it be handed over alongside Crown. 7. To enter contest: Visit www.missuniverseanambra.com, click the 'Register Now' button, fill in details, submit and if successfully shortlisted, you get a confirmation SMS.
GOODLUCK! Register to Participate1 Million Naira (=N= 1,000,000) A Personal Kia Rio 1 Year wardrobe Allowance A Slot to Miss Nigeria 2019 An All-Expense paid Vacation
As a registered pageant company, the Miss Universe Anambra Organisation aspires to stand out uniquely and be one of the best pageant brands in the country and, in the nearest future, the world at large...
Inspiring Women: One major hurdle for women to surmount is a lack of self-confidence. With this stereotype in mind, we strive to break it in our contestants by inspiring them to be very confident...
Registration for Miss Universe Anambra 2019 would commence on the 10th of February, 2019. Potential contestants ...
#ROADTOMISSUNIVERSEANAMBRA2019 is the official social media hash for the Miss Universe Anambra 2019...
As a registered pageant company, the Miss Universe Anambra Organisation aspires to stand out uniquely and be one of the best pageant brands...


Jose Mourinho cost Manchester United £20million with transfer blunder
by Mark BrusManchester United could reportedly have signed Harry Maguire for as much as £20million less than they eventually paid for him.
However, former manager Jose Mourinho was adamant he did not want to pay a penny over £50m to Leicester City for the England defender, despite a £60m deal being in place, according to the Daily Mirror.
MORE: Manchester United could complete January swap transfer in bid to rescue their season
Maguire ended up joining United a year later and has looked a solid signing for the club, and it certainly seems like they could have done with him coming in earlier.
The Red Devils were not convincing at the back last season as they relied on the likes of Chris Smalling and Eric Bailly, with Maguire proving a clear upgrade on both.
It’s fair to say Mourinho did not endear himself to United fans during his time in charge at Old Trafford, and reports like this won’t do much to boost his reputation at the club.
The Portuguese tactician has often spent vast sums of money on top players throughout his career, and it’s unclear why he was so unconvinced by this deal.
Maguire may not be the best centre-back in the world, but in this market a £60m fee for him looks like pretty reasonable business.


{% extends "beam/base.html" %} {% load crispy_forms_tags %} {% load i18n %} {% block title %}{% trans "Log in" %} | {{ block.super }}{% endblock %} {% block content %}
{% trans "Your account doesn't have access to this page. To proceed, please log in with an account that has access." %}
{% else %}{% trans "Please log in to see this page." %}
{% endif %} {% endif %} {# Assumes you setup the password_reset view in your URLconf #} {% url 'password_reset' as reset_url %} {% if reset_url %} {% endif %} {% endblock %}


The USA Computing Olympiad fosters pre-college computing through training, contests, and international competitions. A handful of coaches organizes these features and also chaperone students who represent the USA in international venues. Find a wealth of details and other information at the USACO Web Site.
Specific goals of the USACO include:
This website includes training for students who typically have a year or two of programming experience and also have interest in 'algorithmic programming'. The techniques taught and drilled here are difficult. Expect to spend anywhere from a few minutes to several hours per problem if you are an excellent programmer. Beginners will not enjoy these problems, as they require extensive practice and experience. Someday we hope to have an additional lower-level training site for those just beginning in programming.
This web site is designed to accomplish the goals above.
The USACO will send you certificates as you progress through this site. Furthermore, outstanding participation will enhance your chances of being chosen for USACO training camp in June.
Small awards will also be given at the end of the season to those who have the fastest solutions for some of the more complicated problems. Watch the web site for the standings as they emerge.
Don't be fooled by the first few problems. Subsequent problems are ever more challenging.
Different people will take different spans of time to move through this material, especially the 100+ programming challenges. Some students already have years and years of experience, perhaps even at the international level. Others are just beginning to compete. Any rate much lower than one problem per two weeks is probably not high enough to achieve a rewarding training effect. It is anticipated some of the best students will average one problem or more per day for the first two sections (slower after that).
To get the most out of this web site, you should solve the problems in order, understand each paragraph of the instructional texts, and digest the analyses to see how they apply to your solutions. Feel free to improve your solution after you read the analysis. Furthermore, feel free to send us new or better analyses if you have them.
A few items to ease your use of the site:


Match Play. Red trails 0-1 to 5. How should Red play 43?
pips Red=106 White=58
(CorrectAnswer= 0/0, ErrorScore= 0)
With a 42-pip deficit in the race Red simply cannot afford to play safe and therefore he must hit with 8/5*.
If he is hit on the 5-pt he is probably lost anyway so as the saying goes, “in for a penny, in for a pound”. Therefore, he should maximise covers and his attacking force with 13/9. Any play other than 13/9, 8/5* is a blunder.
With a 42-pip deficit in the race Red simply cannot afford to play safe and therefore he must hit with 8/5*.
If he is hit on the 5-pt he is probably lost anyway so as the saying goes, “in for a penny, in for a pound”. Therefore, he should maximise covers and his attacking force with 13/9. Any play other than 13/9, 8/5* is a blunder.
If he is hit on the 5-pt he is probably lost anyway so as the saying goes, “in for a penny, in for a pound”. Therefore, he should maximise covers and his attacking force with 13/9. Any play other than 13/9, 8/5* is a blunder.
With a 42-pip deficit in the race Red simply cannot afford to play safe and therefore he must hit with 8/5*.
If he is hit on the 5-pt he is probably lost anyway so as the saying goes, “in for a penny, in for a pound”. Therefore, he should maximise covers and his attacking force with 13/9. Any play other than 13/9, 8/5* is a blunder.
1. Cubeful 2-ply 13/9 8/5* Eq.: -0.228 0.433 0.021 0.001 - 0.567 0.305 0.008 2. Cubeful 2-ply 16/12 8/5* Eq.: -0.273 (-0.045) 0.400 0.019 0.000 - 0.600 0.241 0.001 3. Cubeful 2-ply 8/5* 6/2 Eq.: -0.361 (-0.133) 0.385 0.017 0.000 - 0.615 0.252 0.001 4. Cubeful 0-ply 8/5*/1 Eq.: -0.352 (-0.124) 0.358 0.019 0.000 - 0.642 0.216 0.001 5. Cubeful 0-ply 7/4 7/3 Eq.: -0.497 (-0.269) 0.228 0.009 0.000 - 0.772 0.062 0.000


--- layout: post title: Venetian Fussball date: '2016-11-21T00:04:15+01:00' categories: - Illustration picture_url: https://film-Storyboards.tumblr.com/post/153462601301 ---


mameosx-util
is a command line program that prints information about the games for front-end authors. The binary is included inside the standard application bundle, under the MacOS directory. For example, if you installed MAME OS X in /Applications
, then the binary is at:
/Applications/MAME OS X.app/Contents/MacOS/mameosx-util
This command has the following usage:
mameosx-util [OPTIONS] [<gamename|wildcard>]
The options are summarized in this table:
| Option | Description |
|---|---|
| --listxml | List comprehensive details for all of the supported games. The output is quite long, so it is usually better to redirect this into a file. The output is in XML format. By default all games are listed; however, you can limit this list by specifying a driver name or wildcard after the --listxml command. |
| --listfull | Displays a list of game driver names and descriptions. By default all games are listed; however, you can limit this list by specifying a driver name or wildcard after the --listfull command |
| --listsource | Displays a list of drivers and the names of the source files their game drivers live in. Useful for finding which driver a game runs on in order to fix bugs. By default all games are listed; however, you can limit this list by specifying a driver name or wildcard after the --listsource command. |
| --listclones | Displays a list of clones. By default all clones are listed; however, you can limit this list by specifying a driver name or wildcard after the -listclones command. |
| --listcrc | Displays a full list of CRCs of all ROM images referenced by all drivers within MAME. |


$logger is a simple log mechanism to provide easy logging features to your code.
There's still a lot to be implemented in the given area but for the moment it works only on the two known levels of "info" and "error" without any actual logging actions.
(function () { angular.module("jsnbt") .controller('AppController', function ($scope, $logger) { var logger = $logger.create('AppControllerLogger'); try { logger.info('action succeeded'); } catch (ex) { logger.error(ex); throw ex; } }); })();


Core components of recommender systems
The way that recsys is usually taught by starting from its taxonomy (collaborative filtering, content based, hybrid based) is confusing to me at first. In this post I am going to take another path to explain recsys by discussing the core components first and then draw the connection to the recsys taxonomy. Essentially, the majority of recsys objective is to map items and users on the same vector space through a learning objectives so that we can determine items to give to users for a recommendation or which items to show in a similar items page. My post is mostly based on this wonderful talk by James Kirk from Spotify so I highly recommend you to watch the video to gain further understanding.
Main components of a recommendation system:
- Interactions Matrix
- User and Item Features
- User and Item Representation Function
- Learning Objective
- Prediction Function
Let’s go through it one by one!
Interaction Matrix
Interaction Matrix is the main components of a recsys, usually of \(M \times N\) size, where \(M\) is the number of users and \(N\) is the number of items. Note that user-item definition in a recsys is not restrictive to an actual user or item. The user is the receiving and acting entity on the recommendation, whereas the item is the passive entity that is being recommended to the user.
The matrix contains the interaction value between a user and an item. The interaction value can be:
- positive (likes, 5-star review, purchases, views, etc) or negative (downvotes, 1-star review, skips, etc).
- binary or continuous, depending on the data and the problem.
- explicit or implicit. explicit interactions are usually an exact number given by the user (upvotes/downvotes, ratings, etd). implicit interactions are actions that are suggested but not stated clearly (views, clicks, counts, etc)
Source: James Kirk’s slides
Interaction value should reflect what is the intended effect that we want to happen in our recommender system. For example, if we want users to purchase more given our recommender system, then we want our interaction value to be related to purchase. If we want users to engage more in our platform, then our interaction value should be related to engagement.
We might also want to handle and preprocess our interaction matrix before using it. In some settings, users can give negative explicit interaction. Negative interactions can be a rich signals for the recsys to learn from but we need to be careful in handling them. Some loss functions such as learning-to-rank cannot accomodate negative signals. Interaction matrix is also usually sparse, so we need to handle missing value. In explicit interactions, we can’t just replace them with zeroes as it indicates that the user do not like the item. Hence we just go about modelling with sparse data. In implicit interactions, it is much safer to replace with zeroes as it indicates no action from the user (no views, no plays, etc). Missing values are usually what we want to predict with our recsys: giving item recommendation to a particular user that hasn’t been interacted by the user.
User/Item Features
There are two types of features in a recsys:
- Indicator features: which represents user and item individually. The feature is unique to every user/item and encoded as one-hot vector. Since indicator is unique to every user/item, this feature does not scale to a lot of users. Indicator feature alone also cannot be used to solve cold-start problem (new users who does not have any interaction data).
- Metadata features: any information that we know about user/item that can be incorporated to the recsys. Examples: age, gender, number of child, location, word embeddings, image representation, etc. In some cases, it might be beneficial to preprocess metadata features before using it in our recsys model. For example: converting any string/categorical metadata feature to a numerical type.
In a lot of libraries out there, if we do not specify any metadata features for item and user and use interaction matrix solely in our recsys, then by default only the indicator feature is used. The model is only going to learn representation that is unique for every user/item.
Source: James Kirk’s slides
- Pure collaborative filtering is an approach where a recsys uses only indicator features.
- Pure content-based filtering is an approach where a recsys uses only metadata features for the item and only indicator features for the user. Pure content-based does not share metadata features among users.
- When both indicator and metadata features are used they’re known as a hybrid recsys.
User/Item Representation Function
In recsys we want to find a good representation for the user and item. A representation is typically a low-dimensional vector that encodes information regarding the user and item. Getting the representation involves transforming the user/item features via a representation function. Some examples of representation function:
- Linear Kernels
- Neural Networks (Fully Connected, Transformers, Word2vec, Autoencoder)
- Passthrough (None)
Depending on the design, there are many ways to choose our representation function. Linear kernels are effective with both indicator and metadata features. Linear kernels are also a natural choice for matrix factorization problem. If we have an unstructured data, e.g., text/image that we want to utilize, we can use separate architecture like word2vec or autoencoder to get the text/image representation and then feed it into our recsys representation function. We can also use different representation function for different features, for example, linear kernels for indicator and metadata and passthrough for text/image (directly using representation output from the word2vec or autoencoder).
Prediction Function
A function that outputs the estimated items’ relevance to a particular user. This function converts user/item representation into a prediction. The prediction itself can vary depending on the design: it can be a score that tells us how relevant an item to a user but it can also be a relevance rank on several items for a particular user. Some examples of prediction function:
- Dot product
- Cosine similarity
- Euclidian distance
- Neural network
Learning Objective
This is essentially our loss function that let us uncover the best representation for the user/item. The learning objective converts both the user/item representation and prediction into a loss to learn the best parameter for the recsys model. Learning objective can have a huge impact on the output of the recommendation system. Changes in learning objective can dramatically affect how our recsys feel to the user. Examples of loss function characteristics:
- Some loss functions learn to approximate the interaction value of a user-item and some loss functions learn to uprank positive interaction and downrank negative interactions for a particular user. The former predicts the interaction value and the latter predicts the ranking of items (learning-to-rank).
- Some loss function accomodate negative interactions.
- Some loss function are sensitive to interaction magnitude.
- Some loss function only account for pairs with interactions (sparse), some loss can be made to compare every interaction pairs (dense), and some loss can learn by comparing pairs with interactions by sampling (sampled).
Due to these differences in nature, we must choose learning objective that best fit our goal. Some examples of loss function:
- Root-mean-square error
- KL Divergence
- Alternating least square
- Bayesian personalized ranking (learning-to-rank)
- Weighted approximately ranked pairwise (learning-to-rank)
Combining it all together
Common architecture of a recommender system. Source: James Kirk’s slides
There we have it: the architecture of recommender systems. Let’s try to design a recommender system based on some cases:
For you page. The example case is to recommend item on a homepage personalized for every user:
- Interaction matrix: user-item (binary or continuous)
- User features: indicator, metadata
- Item features: indicator, metadata
- User representation: linear
- Item representation: linear
- Learning objective: RMSE, binary cross entropy, WARP, BPE
- Prediction function: dot product, cosine similarity
Similar items. The example case is to recommend item given another item:
- Interaction matrix: item-item (binary or continuous)
- User features: indicator, metadata
- Item features: same as user features
- User representation: linear
- Item representation: linear
- Learning objective: RMSE, binary cross entropy, WARP, BPE
- Prediction function: dot product, cosine similarity
With this architecture, we can also incorporate more complex feature representation into the system. For example, if we want to utilize the social graph between user-user, then we can introduce graph neural network that takes an input of user graph (user features) and outputs the representation to be further processed (user representation).
For you page utilizing social graph structure:
- Interaction matrix: user-item (binary or continuous)
- User features: user-user graph
- Item features: indicator, metadata, etc
- User representation: graph neural network
- Item representation: linear
- Learning objective: custom RMSE
- Prediction function: feed forward neural network
Simpler recsys such as user-based and item-based that does not require training also fits in this architecture. Let’s take a look:
User-based recsys:
- Interaction matrix: user-item
- User Features: Indicator but we use the row in the interaction matrix
- Item Features: -
- User representation: -
- Item representation: -
- Learning objective: -
- Prediction function: dot product, cosine similarity. to predict the rating of an item \(y\) that user \(x\) hasn’t seen, we query top \(k\) users that are similar to user \(x\) (have rated item \(y\)) and calculate the weighted average of the ratings.
Item-based recsys:
- Interaction matrix: user-item
- User Features: -
- Item Features: Indicator but we use the column in the interaction matrix
- User representation: -
- Item representation: -
- Learning objective: -
- Prediction function: dot product, cosine similarity. to predict the rating of an item \(y\) that user \(x\) hasn’t seen, we query top \(k\) users that are similar to item \(y\) (\(y\) has been rated by user \(x\)) and calculate the weighted average of the ratings.
Is there a recsys model that does not fit into this architecture?
Yes! In some cases we can use the interaction matrix to design the training data and not use it as our label. You can find some of the examples here where the author tries to create a pseudo-sentences of products to then be fed into a word2vec training. This setting does not use the interaction matrix as the label.
Thanks for reading! Contact me on twitter if you spot any mistake.


The future of biotechnology is in realizing fully understood, lineage agnostic organisms, beginning with single cells.
Building cells is hard. But, if we could better work together, then we should be able to build many cells for many purposes. Build-A-Cell is an open community supporting people working together to build a diversity of synthetic cells.
What organism do you wish to build?
We believe in open source science and engineering, and collaborative development. Visit our engineering project for technical information, data, and analysis. If there's anything you'd like to know, ask us, and if there's anything you'd like to add, send a pull request.
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Make the main content with XHTML, CSS, and images.
Relevant specifications: OPS, XHTML, CSS.
Use XHTML 1.1, but without the following modules:
(XHTML 1.1 difference from XHTML 1 strict:
lang
attribute not allowed (use xml:lang
instead)name
attribute on a
and map
elements not allowed (use id
instead)Include XML declaration and XHTML doctype, at the top:
and xmlns
attribute in html
:
Any unicode character, in UTF-8 or UTF-16, is allowed. But readers may have limited rendering capabilities.
(ADE 1.0 doesn't support:            ‾ ⌈ ⌉ ⌊ ⌋)
A subset of CSS 2.1 is supported. A brief summary is awkward to make. For details, see the CSS part of the OPS specification.
Be simple, and use CSS 1 without the following properties:
background-image
background-repeat
background-attachment
background-position
background
word-spacing
letter-spacing
text-transform
list-style-image
(There are also a few other minor details unsupported.) And don't use absolute positioning.
The CSS can be linked from the XHTML head
, or put in style
in head
.
(ADE 1.0 doesn't support:
text-align: justify;
font-variant: small-caps;
display: oeb-page-head;
display: oeb-page-foot;
oeb-column-number: [integer];)
The XHTML can have images of the following types:
image/jpeg
image/png
image/gif
image/svg+xml
Use OpenType fonts. Reference them in the CSS with @font-face
, eg.:
Other descriptors allowed are: font-variant
, font-weight
, font-size
.