|
RandomLib
|
The Random class generates two classes of uniform real results: fixed point numbers (where the spacing between possible results is a constant) and floating point numbers (where the spacing between possible results varies).
The results returned by Fixed(), FixedU(), FixedN(), FixedW(), FixedO(), and FixedC() are "fixed-point reals" with precision p. These are of the form i / 2p where i is an integer. If, for real data type RealType, we restrict p > 0 and p <= std::numeric_limits<RealType>::digits, then all such numbers in [-1,1] are representable. For p = 3, the set of numbers in [0,1] is {0, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8, 1}.
The results returned FixedS() are of "offset fixed-point reals" with precision p. These are of the form (i + 1/2) / 2p where i is an integer. If, for real data type RealType, we restrict p > 0 and p <= std::numeric_limits<RealType>::digits, then all such numbers in (-1/2,1/2) are representable. (This only "works" for radix 2 systems; with larger bases, the results can't be represented exactly.) Note that possibly "exceptional" numbers, -1/2, 0, and 1/2, are not included in this set. For p = 3, the set of numbers in (-1/2,1/2) is {-7/16, -5/16, -3/16, -1/16, 1/16, 3/16, 5/16, 7/16}.
The results returned by Float(), FloatU(), FloatN(), FloatW() are "floating-point reals" with precision p and exponent range e. The possible results for such floating numbers in [-1,1] consist of
For real data type RealType, such numbers are representable if
For e = 0, the numbers become the fixed point numbers with precision p.
Here is an example of the floating point points with p = 3 and e = 2 together with the probabilities yielded by Float(), FloatU(), FloatN().
| X | prob(Float() = X) | prob(FloatU() = X) | prob(FloatN() = X) |
|---|---|---|---|
| 0 | 1/32 | 0 | 0.5/32 |
| 1/32 | 1/32 | 1/32 | 1/32 |
| 2/32 | 1/32 | 1/32 | 1/32 |
| 3/32 | 1/32 | 1/32 | 1/32 |
| 4/32 | 1/32 | 1/32 | 1/32 |
| 5/32 | 1/32 | 1/32 | 1/32 |
| 6/32 | 1/32 | 1/32 | 1/32 |
| 7/32 | 1/32 | 1/32 | 1/32 |
| 4/16 | 1/16 | 1/32 | 1.5/32 |
| 5/16 | 1/16 | 1/16 | 1/16 |
| 6/16 | 1/16 | 1/16 | 1/16 |
| 7/16 | 1/16 | 1/16 | 1/16 |
| 4/8 | 1/8 | 1/16 | 1.5/16 |
| 5/8 | 1/8 | 1/8 | 1/8 |
| 6/8 | 1/8 | 1/8 | 1/8 |
| 7/8 | 1/8 | 1/8 | 1/8 |
| 1 | 0 | 1/8 | 0.5/8 |
The description of floating-point numbers assumes that the underlying hardware supports denormalized numbers. This is the case with most modern computers. The code attempts to deal also with older hardware where there's a gap between 0 and 1/2e + 1, but this has not been tested.
The following table provides a succinct definition of each of the member functions of Random routines that return a real result. Here u is a uniformly distributed random number in (0,1). This is drawn from a continuous distribution; i.e., it may be thought of as consisting of a binary point followed by an infinite sequence of random binary bits. (This is just a useful conceptual framework. None of the implementations of these functions require an explicit realization of u.)
The term "fixed" means a fixed-point real with precision p, and we have h = 1/2p (the smallest positive fixed-point number). The term "float" means a floating-point real with precision p and exponent range e.
| routine | mnemonic | definition |
|---|---|---|
| Fixed() | default (down) | round u down to previous fixed |
| FixedU() | upper | round u up to next fixed |
| FixedN() | nearest | round u to nearest fixed |
| FixedW() | wide | round 2u - 1 to nearest fixed |
| FixedS() | symmetric | round u - 1/2 to nearest offset fixed |
| FixedO() | open | round (1 - h)u up to nearest fixed |
| FixedC() | closed | round (1 + h)u down to nearest fixed |
| Float() | default (down) | round u down to previous float |
| FloatU() | upper | round u up to next float |
| FloatN() | nearest | round u to nearest float |
| FloatW() | wide | round 2u - 1 to nearest float |
The precision and exponent range are determined as follows. Each of the fixed-point routines comes in 3 variants, for example,
Similarly each of the floating-point routines comes in 3 variants, for example,
Typical values of digits and min_exponent are given by
| type | digits | -min_exponent |
|---|---|---|
| float | 24 | 125 |
| double | 53 | 1021 |
| long double | 64 | 16381 |
| long double (Power PC) | 106 | 968 |
| long double (Sun) | 113 | 16494 |
In the following tables, the columns have the following meanings
next(X) is the next representable float following X. prev(X) is the previous representable float preceding X.
| routine | min | max | num | prob |
|---|---|---|---|---|
| Fixed() | 0 | 1-h | 2p | h |
| FixedU() | h | 1 | 2p | h |
| FixedN() | 0 | 1 | 2p + 1 | h (h/2 at endpoints) |
| FixedW() | -1 | 1 | 2p+1 + 1 | h/2 (h/4 at endpoints) |
| FixedS() | -(1-h)/2 | (1-h)/2 | 2p | h |
| FixedO() | h | 1-h | 2p-1 | h/(1-h) |
| FixedC() | 0 | 1 | 2p+1 | h/(1+h) |
| Float() | 0 | 1-h | 2p(1+e/2) | min(1,next(X))-X |
| FloatU() | 1/2p+e | 1 | 2p(1+e/2) | X-max(0,prev(X)) |
| FloatN() | 0 | 1 | 2p(1+e/2)+1 | (min(1,next(X))-max(0,prev(X)))/2 |
| FloatW() | -1 | 1 | 2p+1(1+e/2)+1 | (min(1,next(X))-max(-1,prev(X)))/4 |
From these definitions, we can show that:
We can easily show that certain distributions are equivalent:
| Function | Equivalent |
|---|---|
| FixedU<RealType,p>() | Fixed<RealType,p>() + h |
| FixedU<RealType,p>() | 1 - Fixed<RealType,p>() |
| FixedN<RealType,p>() | Boolean() ? Fixed<RealType,p>() : FixedU<RealType,p>() |
| FixedN<RealType,p>() | 1 - FixedN<RealType,p>() |
| FixedO<RealType,p>() | 1 - FixedO<RealType,p>() |
| FixedC<RealType,p>() | 1 - FixedC<RealType,p>() |
| FixedS<RealType,p>() | Fixed<RealType,p>() - (1-h)/2 |
| FixedS<RealType,p>() | - FixedS<RealType,p>() |
| FixedW<RealType,p>() | (Boolean() ? 1 : -1) FixedN<RealType,p>() |
| FixedW<RealType,p>() | - FixedW<RealType,p>() |
| FixedW<RealType,p-1>() | 2 FixedN<RealType,p>() - 1 |
| FloatN<RealType,p,e>() | Boolean() ? Float<RealType,p,e>() : FloatU<RealType,p,e>() |
| FloatW<RealType,p,e>() | (Boolean() ? 1 : -1) FloatN<RealType,p,e>() |
| FloatW<RealType,p,e>() | - FloatW<RealType,p,e>() |
| Float<RealType,p,0>() | Fixed<RealType,p>() |
| FloatU<RealType,p,0>() | FixedU<RealType,p>() |
| FloatN<RealType,p,0>() | FixedN<RealType,p>() |
| FloatW<RealType,p,0>() | FixedW<RealType,p>() |
A caution about FixedO() and FixedC(). All four of FixedS(), FixedN() - 0.5, FixedO() - 0.5, and FixedC() - 0.5 produce results which are strictly symmetric about 0 and would thus be suitable for an unbiased random walk. The variances of the first two distributions are 1/12 + O(h2), close to the ideal value. On the other hand the variances of the distributions of FixedO() and FixedC() are 1/12 + O(h), significantly further from the ideal value. (In practice, using a strictly symmetric normal distribution is preferable for simulating a random walk.)
Similarly, consider estimating the value of pi by randomly selecting points in a unit square and determining what fraction lie in a circle of diameter 1. Sampling in the square using FixedO() or FixedC() gives poorer results than FixedN(), and FixedS() gives slightly better results.
Because of their definitions in terms of u, any of Fixed(), FixedU(), FixedN(), or FixedS() can be used to cover the periodic unit interval in an unbiased way. Thus to sample an angle uniformly, use 2 * pi * Fixed() or 2 * pi * FixedS(). The latter has the advantage that it is strictly symmetric about zero. In addition, angles which are multiples of pi/2 are avoided (which may obviate the need to check for special cases).
Usually, these real routines would be invoked by specifying the type and allowing the precision to be determined from the type, e.g., Real<double>(). However, in some cases it might be useful to specify a lower precision:


Here is some information about Shooty Game and its developers.
Shooty Game is a simple shooter game made with p5.js!
In 2020, Kang Yiming created the game with p5.js for our school's Computer Studies project. Then, the game gained popularity from many students of the school, and he continued to develop and improve the game.
In 2021, Cen Han built a website for the game, so that Shooty Game can possibly expand beyond the school context. Notably, the website is also built for our school's Computer Elective Programme project.
These are the libraries or services which helped us in making Shooty Game.
Have something to tell us? Just fill in the form.


#include <AcknowledgeReliablePdu.h>
Public Member Functions | |
| AcknowledgeReliablePdu () | |
| virtual | ~AcknowledgeReliablePdu () |
| virtual void | marshal (DataStream &dataStream) const |
| virtual void | unmarshal (DataStream &dataStream) |
| unsigned short | getAcknowledgeFlag () const |
| void | setAcknowledgeFlag (unsigned short pX) |
| unsigned short | getResponseFlag () const |
| void | setResponseFlag (unsigned short pX) |
| unsigned int | getRequestID () const |
| void | setRequestID (unsigned int pX) |
| virtual int | getMarshalledSize () const |
| bool | operator== (const AcknowledgeReliablePdu &rhs) const |
Protected Attributes | |
| unsigned short | _acknowledgeFlag |
| unsigned short | _responseFlag |
| unsigned int | _requestID |
Copyright (c) 2007-2009, MOVES Institute, Naval Postgraduate School. All rights reserved.
| AcknowledgeReliablePdu::AcknowledgeReliablePdu | ( | ) |
| AcknowledgeReliablePdu::~AcknowledgeReliablePdu | ( | ) | [virtual] |
| unsigned short AcknowledgeReliablePdu::getAcknowledgeFlag | ( | ) | const |
| int AcknowledgeReliablePdu::getMarshalledSize | ( | ) | const [virtual] |
Reimplemented from DIS::SimulationManagementWithReliabilityFamilyPdu.
| unsigned int AcknowledgeReliablePdu::getRequestID | ( | ) | const |
| unsigned short AcknowledgeReliablePdu::getResponseFlag | ( | ) | const |
| void AcknowledgeReliablePdu::marshal | ( | DataStream & | dataStream | ) | const [virtual] |
Reimplemented from DIS::SimulationManagementWithReliabilityFamilyPdu.
| bool AcknowledgeReliablePdu::operator== | ( | const AcknowledgeReliablePdu & | rhs | ) | const |
| void AcknowledgeReliablePdu::setAcknowledgeFlag | ( | unsigned short | pX | ) |
| void AcknowledgeReliablePdu::setRequestID | ( | unsigned int | pX | ) |
| void AcknowledgeReliablePdu::setResponseFlag | ( | unsigned short | pX | ) |
| void AcknowledgeReliablePdu::unmarshal | ( | DataStream & | dataStream | ) | [virtual] |
Reimplemented from DIS::SimulationManagementWithReliabilityFamilyPdu.
unsigned short DIS::AcknowledgeReliablePdu::_acknowledgeFlag [protected] |
ack flags
unsigned int DIS::AcknowledgeReliablePdu::_requestID [protected] |
Request ID
unsigned short DIS::AcknowledgeReliablePdu::_responseFlag [protected] |
response flags


Less
More
Create an album from these videos to privately share with friends and family.
Upload Videos
Invite friends to privately see this album.
ALL ()
()
Filter ()
Album
() —
by
—
— No Dates
Add videos to this empty album


Tablesorter
TableSorter is a jQuery plugin to sort tabular data in the web UI. It requires jQuery and consists of 3 cornerstones - markup, styles and javascript. While styles and javascript parts are provided out of the box, making the right markup is of plugin users' concern. More on this below.
It can sort table's content as a whole by some (all, if you wish) columns values in ASC or DESC order. Also you can divide all the rows into groups and rows will be sorted within those groups.
How to import?
Use npm i @curveballerpacks/tablesorter
to install the package. To include the plugin in your bundles:
- The Sass preprocessor is used for styles. The source file can be imported into your styles via
@import '@curveballerpacks/tablesorter/src/sass/tablesorter'
and then processed and bundled into your styles. It is worth mentioning that you should set the path for your bundler. For example, forgulp-sass
it might look like:
...
.pipe(sass({
outputStyle: 'compressed',
includePaths: ['node_modules']
}).on('error', sass.logError))
...
or just indicate the full path inside sass @import
statement.
- Tablesorter makes use of Node's modules. It requires its only dependency - jQuery - and can be required itself in your script via
require("@curveballerpacks/tablesorter");
NPM will manage possible duplicates/conflicts of jQuery versions in its dependency graph mostly on its own.
Usage
The table itself must have tablesorter
class. The control row (

with
-s) should have tsTitles
class (customizable via settings) and the data should be placed inside

with tsGroup
class (customizable via settings). In this simple case the 1st and 8st columns of the table will become "sortable" with indicated initial sorting order. In other words, after clicking, for example, on the 1st column's header the table rows will be sorted by the 1st column values in the given order (desc).
$("table.example1").tablesorter({tablesorterColumns: [{col: 0, order: 'desc'}, {col: 7, order: 'asc'}]});
Settings
Besides tablesorterColumns
, it is possible, as mentioned above, to pass into the plugin tablesorterTitlesClass
(defaults to 'tsTitles') and tablesorterGroupsClass
(defaults to 'tsGroup') settings with corresponding changes in the page markup.
More examples
Examples in action and more detailed explanation can be found on codepen or example page inside this package.


Toggle navigation
SimplifAi
Train
Infer
About
Contact
UPLOAD
DEFINE
TRAIN
INFER
You can try out your trained model here.
Enter the model to infer and retrieve it!
See here for API requests.
You can now use your model in Google Spreadsheets.
Please fill in your model's input data.
Here are your outputs


MyShowz
Home
Movies
About
Contact
Contact Us
Leave a Message
If you have a question regarding our services, feel free to contact us using the form below.
First Name
Last Name
Email ID
Mobile Number
Type your message Here...
Submit


--- title: Where's it tough to shoot your (free throw) shot? author: Josh Brown date: '2020-02-11' slug: wheres-it-tough-to-shot-that-free-throw-shot categories: - basketball - college tags: - analysis banner: '' description: '' images: [] menu: '' ---
Georgia Tech was in a close game at the start of the season against NC State. Down 1 in OT with the ball for the final shot. On the final shot though, James Banks, Georgia Tech’s center drew a shooting foul with under 4 seconds left. Maybe not the most ideal situation with Banks being a career 63.6% FT shooter, but Banks hit both free throws and the Tech basketball team got to celebrate with their new tradition of gas station snacks after an ACC road victory! Watching though, I wondered if NC State was a tough place to shot free throws. Free throws are static, in that, their distance from the basket never change and there is no defender effect on the shooter, unlike normal field goals. What some say does have an effect though, are the fans, their yelling, and the craziness that goes on within the student sections.
Nothing strikes me as NC State being a tough place to shoot free throws, so after having sat on the piece for a little and looking around, I wanted to see if I could quantify if away teams shot worse at certain courts/gyms. Luke Benz has already run some analysis to see if ASU’s Curtain of Distraction had an effect of away teams when shooting at Arizona State. Rather than look at one team though, I wanted to compare free throws of all away teams to see if some places had multiple years of signs of extra misses. Since I’m not looking to show causation, I gathered the box scores from the Kaggle competition for the 2019 NCAA tournament which included the 2002-2003 season through the 2017-2018 season (bummer, but if Kaggle does a 2020 competition and last year’s + this year’s box scores are on there, I’ll do a follow up!). First, I want to look at year over year trends for away free throw percentages.
#load data
seasondetaileddataset <- read_csv("RegularSeasonDetailedResults.csv") %>% as.data.table()
Teams <- read_csv("Teams.csv") %>% subset(select = -c(3:4))
#change columns to Home and Away stats
hometeamwin <- seasondetaileddataset[WLoc == 'H']
awayteamwin <- seasondetaileddataset[WLoc == 'A']
names(hometeamwin) %<>% gsub(pattern="W",replacement="H") %>% gsub(pattern="L",replacement="A") %>% gsub(pattern="HAoc", replacement = "WLoc")
names(awayteamwin) %<>% gsub(pattern="L",replacement="H") %>% gsub(pattern="W",replacement="A") %>% gsub(pattern="AHoc", replacement = "WLoc")
seasondetaileddataset <- rbind(hometeamwin, awayteamwin)
#add new percentages
seasondetaileddataset$`HFT%` <- (seasondetaileddataset$HFTM/seasondetaileddataset$HFTA)*100
seasondetaileddataset$`AFT%` <- (seasondetaileddataset$AFTM/seasondetaileddataset$AFTA)*100
seasonsummarystat <- seasondetaileddataset %>%
filter(AFTA>0) %>%
group_by(Season) %>%
summarise("mediAFT%" = median(`AFT%`),
"meanAFT%" = mean(`AFT%`))
## Warning: Removed 24 rows containing non-finite values (stat_boxplot).
If we take a look at box plots, I take away two things. In more recent years, away teams have started to shoot the ball 1. at a higher percent overall and 2. more frequently hitting more of their free throws as the mean and the lower bounds of the box plots are rising.
mediyearPlot = ggplot(seasonsummarystat, aes(x=Season, y=`mediAFT%`), group=Season) +
geom_line()+
geom_point()
mediyearPlot
meanyearPlot = ggplot(seasonsummarystat, aes(x=Season, y=`meanAFT%`), group=Season) +
geom_line()+
geom_point()
meanyearPlot
Diving a little deeper we’ll also see the mean and median (for those that think the numbers could be skewed) for free throw percentages are rising. Are analytics/shot tracking (arc angle) what’s fueling this?
Big picture though, I wanted to determine which home teams might have the most help from their court vs the normal Free Throw ability at other away gyms. I opted to run a two proportion z-test on the away team’s free throw makes/attempts at particular courts vs the combined free throw makes/attempts for the season. My process/code:
#Add Team Names
seasondetaileddataset <- merge(x = seasondetaileddataset, y = Teams, by.x = "HTeamID", by.y = "TeamID", all.x = T)
seasondetaileddataset <- merge(x = seasondetaileddataset, y = Teams, by.x = "ATeamID", by.y = "TeamID", all.x = T)
colnames(seasondetaileddataset)[37] <- "HomeTeamName"
colnames(seasondetaileddataset)[38] <- "AwayTeamName"
#away team summary stats
awayteamftstats <- seasondetaileddataset[, .("AT Free Throws Made (AID)" = sum(AFTM),
"AT Free Throws Attempted (AID)" = sum(AFTA)),
keyby = .(Season, AwayTeamName)]
#away team free throw stats based on home court
homecourtftstats <- seasondetaileddataset[, .("AT Free Throws Made (HID)" = AFTM,
"AT Free Throws Attempted (HID)" = AFTA,
"AT Free Throw Percentage (HID)" = percent(AFTM/AFTA),
"AwayTeamName" = c(AwayTeamName)),
keyby = .(Season, HomeTeamName)]
#Away Team Season Free Throws with their FT Stats at FT Stats for individual games
ftstats <- homecourtftstats[awayteamftstats, on = c("Season", "AwayTeamName")]
#Free Throw Stats by games at home team gym vs whole season
ftstats <- ftstats[, .("FT Made @ HID" = sum(`AT Free Throws Made (HID)`),
"FT Attempted @ HID" = sum(`AT Free Throws Attempted (HID)`),
#"AT Free Throw Percentage (HID)" = percent(sum(AFTM)/sum(AFTA)),
"FT Made by all AID" = sum(`AT Free Throws Made (AID)`),
"FT Attempted by all AID" = sum(`AT Free Throws Attempted (AID)`)),
keyby = .(Season, HomeTeamName)]
#proportion test
lesstesttable <- ftstats %>% mutate(pval = NA)
for (x in 1:nrow(lesstesttable)) {
testy <- prop.test(x = c(lesstesttable$`FT Made @ HID`[x],lesstesttable$`FT Made by all AID`[x]),
n = c(lesstesttable$`FT Attempted @ HID`[x],lesstesttable$`FT Attempted by all AID`[x]),
alternative = "less",
correct = F)
lesstesttable$pval[x] <- testy$p.value
}
lesstesttable %<>% as.data.table() %>% arrange(pval) %>% filter(pval <= .05)
After filtering for only significant results (those with a p-value < .05) and sorting from most significant to least, we have a winner!
kable(head(lesstesttable,5), align = "c") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 9) %>%
row_spec(1, bold = T, color = ncaa_colors$primary_color[ncaa_colors$espn_name == "Northwestern"], background = "white")
| Season | HomeTeamName | FT Made @ HID | FT Attempted @ HID | FT Made by all AID | FT Attempted by all AID | pval |
|---|---|---|---|---|---|---|
| 2014 | Northwestern | 203 | 340 | 2868 | 4092 | 0.0000334 |
| 2005 | Prairie View | 138 | 265 | 1801 | 2816 | 0.0000645 |
| 2016 | Dayton | 161 | 272 | 2916 | 4186 | 0.0001482 |
| 2010 | San Jose St | 131 | 230 | 2546 | 3726 | 0.0001724 |
| 2015 | Old Dominion | 158 | 279 | 3114 | 4650 | 0.0001926 |
Playing at Welsh-Ryan Arena in the 2013-2014 proved to be the most significant difference. So you probably made the assumption that Northwestern had a really good year! Well, no. They went 14-19 on the season and a mere 8-9 at home. If we look at all of Northwestern’s opponents that played at Welsh-Ryan Arena and their away ft shooting %, it does appear it was tough to shoot free throws at Northwestern.
#Northwestern Section
AwayTeamsNorthwestern2014 <- seasondetaileddataset[HomeTeamName == "Northwestern" & Season == "2014"]
AwayTeamsNorthwestern2014 <- unique(AwayTeamsNorthwestern2014$AwayTeamName)
Northwestern2014 <- seasondetaileddataset[Season == "2014" & AwayTeamName %in% AwayTeamsNorthwestern2014] %>%
select("AFT%", "HomeTeamName", "AwayTeamName")
ggplot(Northwestern2014, aes(x = `AFT%`, y = AwayTeamName)) +
geom_point(aes(color = (HomeTeamName != "Northwestern"))) +
theme_bw() +
theme(legend.position = "bottom",
plot.title = element_text(size = 16, hjust = 0.5),
axis.title = element_text(size = 14)) +
labs(x = "Free Throw Percentage",
y = "Team",
col = "Home Team",
title = "2013-2014 Northwestern Opponent's Free Throw Shooting",
caption = "Code from Luke Benz // twitter: @recspecs730") +
scale_color_manual(values = c("gold",
ncaa_colors$primary_color[ncaa_colors$espn_name == "Northwestern"]),
labels = c("Northwestern Home Game", "Other"))
BIG10 opponents Indiana, Minnesota, Nebraska, and Penn State all had their worst FT shooting performance on the road against Northwestern and only 4 of the 17 teams shot above 70%. Not a good look for the away teams. This was just one season though. When looking at a total for the 15 year period we should look at where’s it been consistently tough to shoot free throws at. Grouping by the home team and tallying the counts of times we see two teams at the top.
lessTeamCount <- lesstesttable %>% count(HomeTeamName, sort = T, name = "Seasons of Significant Difference")
kable(lessTeamCount, align = "c") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10) %>%
scroll_box(width = "100%", height = "500px")
| HomeTeamName | Seasons of Significant Difference |
|---|---|
| Gonzaga | 7 |
| UT Arlington | 7 |
| Louisville | 5 |
| Marshall | 5 |
| Purdue | 5 |
| UNC Wilmington | 5 |
| Ga Southern | 4 |
| Massachusetts | 4 |
| Memphis | 4 |
| Stanford | 4 |
| American Univ | 3 |
| Cleveland St | 3 |
| Colorado | 3 |
| Coppin St | 3 |
| E Michigan | 3 |
| Florida Intl | 3 |
| Georgia Tech | 3 |
| Hampton | 3 |
| Iowa | 3 |
| Liberty | 3 |
| Long Beach St | 3 |
| Michigan St | 3 |
| Mississippi St | 3 |
| Northwestern | 3 |
| Old Dominion | 3 |
| Prairie View | 3 |
| Southern Miss | 3 |
| St Joseph’s PA | 3 |
| Syracuse | 3 |
| Tulsa | 3 |
| UT San Antonio | 3 |
| Utah St | 3 |
| Vanderbilt | 3 |
| Villanova | 3 |
| Virginia | 3 |
| Virginia Tech | 3 |
| Wichita St | 3 |
| Wisconsin | 3 |
| Xavier | 3 |
| Alabama | 2 |
| Alabama A&M | 2 |
| Arizona | 2 |
| Arizona St | 2 |
| Army | 2 |
| Baylor | 2 |
| Bowling Green | 2 |
| Bradley | 2 |
| Bucknell | 2 |
| BYU | 2 |
| Cincinnati | 2 |
| Coastal Car | 2 |
| Col Charleston | 2 |
| Colgate | 2 |
| Davidson | 2 |
| Dayton | 2 |
| Drake | 2 |
| Duke | 2 |
| F Dickinson | 2 |
| FL Gulf Coast | 2 |
| Fresno St | 2 |
| Furman | 2 |
| G Washington | 2 |
| Georgia | 2 |
| Grambling | 2 |
| Illinois | 2 |
| Illinois St | 2 |
| Indiana St | 2 |
| Iona | 2 |
| Louisiana Tech | 2 |
| Loy Marymount | 2 |
| Mercer | 2 |
| Miami FL | 2 |
| Missouri KC | 2 |
| Monmouth NJ | 2 |
| Montana St | 2 |
| Mt St Mary’s | 2 |
| Nebraska | 2 |
| New Hampshire | 2 |
| New Orleans | 2 |
| North Carolina | 2 |
| Northwestern LA | 2 |
| Oklahoma | 2 |
| Pepperdine | 2 |
| Pittsburgh | 2 |
| Princeton | 2 |
| S Dakota St | 2 |
| S Illinois | 2 |
| San Diego St | 2 |
| San Francisco | 2 |
| Savannah St | 2 |
| Seton Hall | 2 |
| SF Austin | 2 |
| Siena | 2 |
| South Florida | 2 |
| St John’s | 2 |
| St Mary’s CA | 2 |
| Tennessee Tech | 2 |
| Texas A&M | 2 |
| UNC Asheville | 2 |
| UNLV | 2 |
| Utah | 2 |
| Utah Valley | 2 |
| VMI | 2 |
| Washington | 2 |
| Abilene Chr | 1 |
| Akron | 1 |
| Albany NY | 1 |
| Alcorn St | 1 |
| Appalachian St | 1 |
| Arkansas | 1 |
| Auburn | 1 |
| Ball St | 1 |
| Binghamton | 1 |
| Boston College | 1 |
| Boston Univ | 1 |
| Brown | 1 |
| Buffalo | 1 |
| Butler | 1 |
| C Michigan | 1 |
| Cent Arkansas | 1 |
| Central Conn | 1 |
| Charleston So | 1 |
| Columbia | 1 |
| Connecticut | 1 |
| Creighton | 1 |
| CS Sacramento | 1 |
| Delaware St | 1 |
| Denver | 1 |
| E Illinois | 1 |
| E Washington | 1 |
| Elon | 1 |
| Evansville | 1 |
| Fairfield | 1 |
| FL Atlantic | 1 |
| Florida | 1 |
| Florida A&M | 1 |
| Florida St | 1 |
| Fordham | 1 |
| Gardner Webb | 1 |
| George Mason | 1 |
| Georgetown | 1 |
| Grand Canyon | 1 |
| Hartford | 1 |
| Hawaii | 1 |
| Hofstra | 1 |
| Holy Cross | 1 |
| Houston | 1 |
| Indiana | 1 |
| IPFW | 1 |
| IUPUI | 1 |
| Kentucky | 1 |
| Lafayette | 1 |
| Lehigh | 1 |
| Lipscomb | 1 |
| Loyola MD | 1 |
| Loyola-Chicago | 1 |
| LSU | 1 |
| Maine | 1 |
| Marquette | 1 |
| Maryland | 1 |
| Michigan | 1 |
| Mississippi | 1 |
| Missouri St | 1 |
| Morgan St | 1 |
| MTSU | 1 |
| Murray St | 1 |
| N Illinois | 1 |
| N Kentucky | 1 |
| Navy | 1 |
| NC A&T | 1 |
| NC Central | 1 |
| New Mexico St | 1 |
| Norfolk St | 1 |
| Notre Dame | 1 |
| Oakland | 1 |
| Ohio | 1 |
| Oklahoma St | 1 |
| Oral Roberts | 1 |
| Pacific | 1 |
| Penn | 1 |
| Penn St | 1 |
| Portland | 1 |
| Presbyterian | 1 |
| Providence | 1 |
| Quinnipiac | 1 |
| Radford | 1 |
| Rhode Island | 1 |
| Rider | 1 |
| Robert Morris | 1 |
| San Jose St | 1 |
| Santa Barbara | 1 |
| SE Louisiana | 1 |
| SMU | 1 |
| South Carolina | 1 |
| Southern Utah | 1 |
| St Francis PA | 1 |
| St Louis | 1 |
| Stony Brook | 1 |
| TCU | 1 |
| Texas | 1 |
| Texas Tech | 1 |
| TN Martin | 1 |
| Toledo | 1 |
| Towson | 1 |
| Tulane | 1 |
| TX Southern | 1 |
| UAB | 1 |
| UC Davis | 1 |
| UC Riverside | 1 |
| UCF | 1 |
| UCLA | 1 |
| ULL | 1 |
| UNC Greensboro | 1 |
| USC | 1 |
| UTEP | 1 |
| VA Commonwealth | 1 |
| Valparaiso | 1 |
| Vermont | 1 |
| W Illinois | 1 |
| W Michigan | 1 |
| W Salem St | 1 |
| Washington St | 1 |
| West Virginia | 1 |
| WI Green Bay | 1 |
| Winthrop | 1 |
| Wyoming | 1 |
| Youngstown St | 1 |
Gonzaga and the University of Texas-Arlington are those two teams where away teams seem to tremble at the free throw line. Again, without looking at causation, I can’t prove why, but feel free to make your own assumption. My opinion for Gonzaga is that many West Coast Conference (WCC) teams roll into McCarthy Athletic Center while Gonzaga is ranked, and the atmosphere is not like any others in the WCC. They’ve created a dynasty up in the corner of Washington that other non-conference opponents are not ready for. For UTA, I don’t have a great guess as to why it is tough. 5 of their 7 seasons of significant difference came between 2012-2017 after College Park Center, their home gym, was built. If anyone wants to explain me what’s going on in Arlington, Texas though, my DMs are open.
Concluding, it’s not always the blue blood schools that have the toughest gyms to shoot FTs in with loud students and fans yelling obnoxious phrases and waving their arms around behind the backboard. I also thought Syracuse might show up because of the depth of shot inside the Carrier Dome. You must remember though: Free Throws are the same distance and shot on the same height goal at every gym (See the Hoosiers clip where they walk into the Championship gym for the first time).
When Kaggle comes out with their 2020 March Madness tournament, I’ll look to post a follow up including the 2018-2019 and 2019-2020 seasons. I’d like to take Luke Benz’s ncaahoopr package and throw out the any free throws shot when Win Percentage is not in the 20-80% range. This would eliminate garbage time free throws that Northwestern might have seen in 2013-2014 or Gonzaga on a constant basis. Oh, and I checked to see how many times NC State showed up on the tally list for significant difference. They didn’t. 😊 Go Jackets!
A 🆆 on Saturday in Chapel Hill = ⛽snacks!
#BuiltDifferent /// #TogetherWeSwarm pic.twitter.com/BOxAabSGLD — Georgia Tech Basketball (@GTMBB) January 6, 2020
The Jackets are back at McCamish Wednesday night vs. Duke.


Building a FHIR IG
This tutorial will provide an overview of the workflow/process used to design/develop a FHIR implementation guide using Trifolia.
The basic steps are as follows, with details below:
- FHIR and IG Publisher Overview
- Create an implementation guide.
- Create profiles for the implementation guide.
- Upload "FHIR resource instances" to the implementation guide.
- Export the implementation guide as a FHIR build package.
FHIR and IG Publisher Overview
See the FHIR current build for more information.
FHIR (Fast Health Interoperability Resources) is designed to enable information exchange to support the provision of healthcare in a wide variety of settings. The specification builds on and adapts modern, widely used RESTful practices to enable the provision of integrated healthcare across a wide range of teams and organizations.
- Implementation guides are a collection of FHIR resources(profiles, value sets, examples and human readable documentation) tied together by the Implementation Guide resource
- Acronym FHIR = F – Fast (to design & to implement) H – Health, I – Interoperable and R – Resources(building blocks)
- Resources are discrete chunks of clinical information, can be assembled into larger constructs and resources are operated on via FHIR’s REST APIs
- FHIR documents are a collection of resources bound together, and are transferred between systems as a Bundle resource
- The root of a FHIR document is a Composition resource that can be signed, authenticated, etc, and has the same basic obligations as a CDA document.
IG Publisher
See the IG Publisher Documentation for more information.
- The FHIR team provides an IG Publishing tool that takes the implementation guide resources and converts them to a set of 3 different types of files (generated resources in xml, json, ttl formats, a set of fragments ready to include in generated html files and Several different zip files: which are used by implementers for various purposes).
- The outcome of the publishing process is a set of HTML files that represent the implementation guide. These files can be posted to a web server.
- Alternatively, you may use the IG publisher to validate and render a set of Profiles, value sets etc without building a formal IG
Create an implementation guide
See Authoring > Implementation Guides for details.
- The "type" of implementation guide is set to a FHIR implementation guide type.
- Create a base identifier for the implementation guide that is a URL (ex: https://trifolia.lantanagroup.com/my_base_id"). This base url will be used by the FHIR IG publisher, and requires that all profiles within the implementation guide have a matching base identifier.
- Provide a description for the implementation guide. This description (if specified in the Implementation Guide Editor) is on the front page of the implementation guide after the IG publisher has been run on the artifacts produced by Trifolia.
- Include additional guidance that will not be inferred by the profiles of the implementation guide in the "Volume 1" tab, by adding sections to "Volume 1". Each of the sections defined in the implementation guide's "Volume 1" tab will be displayed on the "Overview" tab of the implementation guide's front-page.
Create FHIR profiles for the implementation guide
See Authoring > Templates/Profiles > Editor for details.
- Each FHIR profile must have the same base url as the implementation guide in the "long id" field. The identifier for FHIR profiles is defaulted to a combination of the bookmark/name of the FHIR profile and the associated implementation guide's base url.
- Each profile must have a "short id" that matches the outer-most leaf level of the long id (everything to the right of the last / in the long id).
- Define constraints for each profile in the "Constraints" tab of the editor, modifying the cardinality, value set bindings, etc. as appropriate.
Defining and using extensions
In the event that an extension is necessary, you must produce a profile that describes how the extension is intended to be used, and reference that extension as a "Contained template/profile" on the "extension" element of another profile.
Upload "FHIR resource instances" to the implementation guide
See Authoring > ImplementationGuides > Files for details.
In certain cases, (such as "Conformance", "SearchParameter" and "Questionnaire") instances of resources need to be uploaded to Trifolia, then associated with the implementation guide rather than defining profiles. Add these "FHIR resource instances" to the implementation guide's files (as type "FHIR Resource Instance") so that they are included in the FHIR build package when exported from Trifolia.
Export the implementation guide as a FHIR build package
See Exporting > XML/JSON for details.
Export the implementation guide as a FHIR build package. The zip package that will be run against the FHIR ig publisher.
Run the FHIR IG publisher by following these steps:
- Extract the FHIR IG Package to a directory on your machine.
- Download the FHIR IG Publisher jar file from the FHIR's Downloads page or click here to directly download the .jar file. Store the .jar file in the same directory that the FHIR IG Package was extracted to.
- Execute the "RunIGPublisher.bat" batch script.
- If using the GUI interface, select the XXXX.json file exported from the ZIP.
- Press "execute".
Note: The FHIR IG publisher is new functionality, being actively developed. If the latest version of the FHIR IG publisher produces errors, you should seek guidance from http://chat.fhir.org in the #Implementers channel.


@(message: String,message1: String,message2: String,message3: String) @localScripts = { } @main(scripts = localScripts) {


|
NeuralSharp
1.0
NeuralSharp is a FOSS C# library for neural networks.
|
Represents a feed forward neural network. More...
Inherits NeuralSharp.Sequential< float[], IArraysLayer, IArrayError >, and NeuralSharp.IArraysLayer.
Public Member Functions | |
| FeedForwardNN (ICollection< IArraysLayer > layers) | |
| Creates an instance of the More... | |
| override void | Feed (float[] input, float[] output, bool learning=false) |
| Feeds the network forward. More... | |
| void | Feed (float[] inputArray, int inputSkip, float[] outputArray, int outputSkip, bool learning=false) |
| Feeds the network forward. More... | |
| void | BackPropagate (float[] outputErrorArray, int outputErrorSkip, bool learning=true) |
| Backpropagates the given error trough the network. More... | |
| override void | BackPropagate (float[] outputError, bool learning=true) |
| Backpropagates the given error trough the network. More... | |
| void | BackPropagate (float[] outputErrorArray, int outputErrorSkip, float[] inputErrorArray, int inputErrorSkip, bool learning) |
| Backpropagates the given error trough the network. More... | |
| override void | BackPropagate (float[] outputError, float[] inputError, bool learning) |
| Backpropagates the given error trough the network. More... | |
| override void | UpdateWeights (float rate, float momentum=0.0F) |
| Updates the weights of the network. More... | |
| void | SetInputAndOutput (float[] inputArray, int inputSkip, float[] outputArray, int outputSkip) |
| Sets the input array and the output array of the network, connecting its inner layers. More... | |
| float [] | SetInputGetOutput (float[] inputArray, int inputSkip) |
| Sets the input array of the network and creates the output array. More... | |
| float | GetError (float[] outputArray, int outputSkip, float[] expectedArray, int expectedSkip, float[] errorArray, IArrayError errorFunction, int errorSkip) |
| Gets the output error of the network, given its actual and expected output. More... | |
| override float | GetError (float[] output, float[] expectedOutput, float[] error, IArrayError errorFunction) |
| Gets the output error of the network, given its actual and expected output. More... | |
| float | FeedAndGetError (float[] inputArray, int inputSkip, float[] expectedArray, int expectedSkip, float[] errorArray, int errorSkip, IArrayError errorFunction, bool learning) |
| Feeds the network forward and gets its error, given the expected output. More... | |
| override float | FeedAndGetError (float[] input, float[] expectedOutput, float[] error, IArrayError errorFunction, bool learning) |
| Feeds the network forward and gets its error, given the expected output. More... | |
| override void | SetInputAndOutput (float[] input, float[] output) |
| Sets the input array and the output array of the network. More... | |
| override float [] | SetInputGetOutput (float[] input) |
| Sets the input array of the network and creates and sets the output array. More... | |
| override ILayer< float[], float[]> | CreateSiamese () |
| Creates a siamese of the network. More... | |
| override ILayer< float[], float[]> | Clone () |
| Creates a clone of the network. More... | |
| Public Member Functions inherited from NeuralSharp.Sequential< float[], IArraysLayer, IArrayError > | |
| Sequential (Sequential< TData, TLayer, TErrFunc > original, bool siamese) | |
| Either creates a siamese of the given More... | |
| Sequential (ICollection< TLayer > layers) | |
| Creates an instance of the More... | |
| abstract void | SetInputAndOutput (TData input, TData output) |
| Sets the input object and the output object of the network. More... | |
| abstract TData | SetInputGetOutput (TData input) |
| Sets the input object and creates and sets the output object. More... | |
| void | Feed (bool learning=false) |
| Feeds the layer forward. More... | |
| override void | UpdateWeights (float rate, float momentum=0.0F) |
| Updates the weights of the learner. More... | |
| int | CountParameters (List< object > siameseIDs) |
| Counts the amount of parameters of the learner. More... | |
| Public Member Functions inherited from NeuralSharp.ILayer< float[], float[]> | |
| int | CountParameters (List< object > siameseIDs) |
| Counts the amount of parameters of the layer. More... | |
| void | Feed (bool learning=false) |
| Feeds the layer forward. More... | |
| void | UpdateWeights (float rate, float momentum=0.0F) |
| Updates the weights of the layer. More... | |
| void | BackPropagate (TOut outputError, TIn inputError, bool learning) |
| Backpropagates the given error trough the layer. More... | |
| void | SetInputAndOutput (TIn input, TOut output) |
| Sets the input object and the output object of the layer. More... | |
| TOut | SetInputGetOutput (TIn input) |
| Sets the input object of the layer and creates and sets the output object. More... | |
| ILayer< TIn, TOut > | CreateSiamese () |
| Creates a siamese of the layer. More... | |
| ILayer< TIn, TOut > | Clone () |
| Clones the layer. More... | |
Protected Member Functions | |
| FeedForwardNN (FeedForwardNN original, bool siamese) | |
| Either creates a siamese of the given More... | |
| override float [] | NewError () |
| Creates an array which can be used as output error. More... | |
| override void | AddTopLayer (IArraysLayer layer) |
| Adds a top layer to the network. More... | |
| override void | AddBottomLayer (IArraysLayer layer) |
| Adds a bottom layer to the network. More... | |
| Protected Member Functions inherited from NeuralSharp.Sequential< float[], IArraysLayer, IArrayError > | |
| virtual void | AddTopLayer (TLayer layer) |
| Adds a top layer. More... | |
| virtual void | AddBottomLayer (TLayer layer) |
| Adds a layer to the bottom. More... | |
| virtual void | RemoveTopLayer () |
| Removes a top layer. More... | |
| virtual void | RemoveBottomLayer () |
| Removes a bottom layer. More... | |
Properties | |
| int | InputSkip [get] |
| The index of the first used entry of the input array. More... | |
| int | OutputSkip [get] |
| The index of the first used entry of the output array. More... | |
| int | InputSize [get] |
| The lenght of the input of the network. More... | |
| int | OutputSize [get] |
| The length of the output of the network. More... | |
| Properties inherited from NeuralSharp.Sequential< float[], IArraysLayer, IArrayError > | |
| ICollection< TLayer > | Layers [get] |
| The layers of the learner. More... | |
| TLayer | FirstLayer [get] |
| The first layer of the learner. More... | |
| TLayer | LastLayer [get] |
| The last layer of the learner. More... | |
| TData | Input [get] |
| The input object of the learner. More... | |
| TData | Output [get] |
| The output object of the learner. More... | |
| object | SiameseID [get] |
| The siamese identifier of the learner. More... | |
| int | Parameters [get] |
| The amount of parameters. More... | |
| Properties inherited from NeuralSharp.IArraysLayer | |
| int | InputSize [get] |
| The length of the input of the layer. More... | |
| int | InputSkip [get] |
| The index of the first used entry of the input array. More... | |
| int | OutputSize [get] |
| The length of the output of the layer. More... | |
| int | OutputSkip [get] |
| The index of the first used entry of the output array. More... | |
| Properties inherited from NeuralSharp.ILayer< float[], float[]> | |
| TIn | Input [get] |
| The input object of the layer. More... | |
| TOut | Output [get] |
| The output object. More... | |
| int | Parameters [get] |
| The amount of parameters of the layer. More... | |
| object | SiameseID [get] |
| The siamese identifier of the layer. More... | |
Represents a feed forward neural network.
|
protected |
Either creates a siamese of the given
FeedForwardNN
instance or clones it.
| original | The original instance to be created a siamese of or cloned. |
| siamese | true false |
| NeuralSharp.FeedForwardNN.FeedForwardNN | ( | ICollection< IArraysLayer > | layers | ) |
Creates an instance of the
FeedForwardNN
class.
| layers | The layers to be included in the network. |
|
protected |
Adds a bottom layer to the network.
| layer | The layer to be added. |
|
protected |
Adds a top layer to the network.
| layer | The layer to be added. |
| void NeuralSharp.FeedForwardNN.BackPropagate | ( | float [] | outputErrorArray, |
| int | outputErrorSkip, | ||
| bool | learning = true |
||
| ) |
Backpropagates the given error trough the network.
| outputErrorArray | The output error to be backpropagated. |
| outputErrorSkip | The index of the first entry of the output error array to be used. |
| learning | Whether the network is being used in a training session. |
| override void NeuralSharp.FeedForwardNN.BackPropagate | ( | float [] | outputError, |
| bool | learning = true |
||
| ) |
Backpropagates the given error trough the network.
| outputError | The output error to be backpropagated. |
| learning | Whether the network is being used in a training session. |
| void NeuralSharp.FeedForwardNN.BackPropagate | ( | float [] | outputErrorArray, |
| int | outputErrorSkip, | ||
| float [] | inputErrorArray, | ||
| int | inputErrorSkip, | ||
| bool | learning | ||
| ) |
Backpropagates the given error trough the network.
| outputErrorArray | The output error to be backpropagated. |
| outputErrorSkip | The index of the first entry of the output error to be used. |
| inputErrorArray | The array to be written the input error into. |
| inputErrorSkip | The index of the first entry of the output error array to be used. |
| learning | Whether the network is being used in a training session. |
Implements NeuralSharp.IArraysLayer.
| override void NeuralSharp.FeedForwardNN.BackPropagate | ( | float [] | outputError, |
| float [] | inputError, | ||
| bool | learning | ||
| ) |
Backpropagates the given error trough the network.
| outputError | The output error to be backpropagated. |
| inputError | The array to be written the input error into. |
| learning | Whether the network is being used in a training session. |
|
virtual |
Creates a clone of the network.
Implements NeuralSharp.Sequential< float[], IArraysLayer, IArrayError >.
|
virtual |
Creates a siamese of the network.
Implements NeuralSharp.Sequential< float[], IArraysLayer, IArrayError >.
| override void NeuralSharp.FeedForwardNN.Feed | ( | float [] | input, |
| float [] | output, | ||
| bool | learning = false |
||
| ) |
Feeds the network forward.
| input | The array to be copied the input from. |
| output | The array to be copied the output into. |
| learning | Whether the network is being used in a training session. |
| void NeuralSharp.FeedForwardNN.Feed | ( | float [] | inputArray, |
| int | inputSkip, | ||
| float [] | outputArray, | ||
| int | outputSkip, | ||
| bool | learning = false |
||
| ) |
Feeds the network forward.
| inputArray | The array to be copied the input from. |
| inputSkip | The index of the first entry of the given input array to be used. |
| outputArray | The array to be copied the output into. |
| outputSkip | The index of the first entry of the given output array to be used. |
| learning | Whether the network is being used in a training session. |
| float NeuralSharp.FeedForwardNN.FeedAndGetError | ( | float [] | inputArray, |
| int | inputSkip, | ||
| float [] | expectedArray, | ||
| int | expectedSkip, | ||
| float [] | errorArray, | ||
| int | errorSkip, | ||
| IArrayError | errorFunction, | ||
| bool | learning | ||
| ) |
Feeds the network forward and gets its error, given the expected output.
| inputArray | The array to be copied the input from. |
| inputSkip | The index of the first entry of the given input array to be used. |
| expectedArray | The expected output of the network. |
| expectedSkip | The index of the first entry of the expected array to be used. |
| errorArray | The array to be written the output error into. |
| errorSkip | The index of the first entry of the output array to be used. |
| errorFunction | The error function to be used. |
| learning | Whether the network is being used in a training session. |
| override float NeuralSharp.FeedForwardNN.FeedAndGetError | ( | float [] | input, |
| float [] | expectedOutput, | ||
| float [] | error, | ||
| IArrayError | errorFunction, | ||
| bool | learning | ||
| ) |
Feeds the network forward and gets its error, given the expected output.
| input | The array to be copied the input from. |
| expectedOutput | The expected output of the network. |
| error | The array to be written the output error into. |
| errorFunction | The error function to be used. |
| learning | Whether the network is being used in a training session. |
| float NeuralSharp.FeedForwardNN.GetError | ( | float [] | outputArray, |
| int | outputSkip, | ||
| float [] | expectedArray, | ||
| int | expectedSkip, | ||
| float [] | errorArray, | ||
| IArrayError | errorFunction, | ||
| int | errorSkip | ||
| ) |
Gets the output error of the network, given its actual and expected output.
| outputArray | The actual output of the network. |
| outputSkip | The index of the first entry of the given output array to be used. |
| expectedArray | The expected output of the network. |
| expectedSkip | The index of the first entry of the given output array to be used. |
| errorArray | The array to be written the output error into. |
| errorFunction | The error function to be used. |
| errorSkip | The index of the first entry of the error array to be used. |
| override float NeuralSharp.FeedForwardNN.GetError | ( | float [] | output, |
| float [] | expectedOutput, | ||
| float [] | error, | ||
| IArrayError | errorFunction | ||
| ) |
Gets the output error of the network, given its actual and expected output.
| output | The actual output of the network. |
| expectedOutput | The expected output of the network. |
| error | The array to be written the output error into. |
| errorFunction | The error function to be used. |
|
protected |
Creates an array which can be used as output error.
| void NeuralSharp.FeedForwardNN.SetInputAndOutput | ( | float [] | inputArray, |
| int | inputSkip, | ||
| float [] | outputArray, | ||
| int | outputSkip | ||
| ) |
Sets the input array and the output array of the network, connecting its inner layers.
| inputArray | The input array to be set. |
| inputSkip | The index of the first entry of the input array to be used. |
| outputArray | The output array to be set. |
| outputSkip | The index of the first entry of the output array to be used. |
Implements NeuralSharp.IArraysLayer.
| override void NeuralSharp.FeedForwardNN.SetInputAndOutput | ( | float [] | input, |
| float [] | output | ||
| ) |
Sets the input array and the output array of the network.
| input | The input array to be set. |
| output | The output array to be set. |
| float [] NeuralSharp.FeedForwardNN.SetInputGetOutput | ( | float [] | inputArray, |
| int | inputSkip | ||
| ) |
Sets the input array of the network and creates the output array.
| inputArray | The input array to be set. |
| inputSkip | The index of the first entry of the output array to be used. |
Implements NeuralSharp.IArraysLayer.
| override float [] NeuralSharp.FeedForwardNN.SetInputGetOutput | ( | float [] | input | ) |
Sets the input array of the network and creates and sets the output array.
| input | The input array to be set. |
| override void NeuralSharp.FeedForwardNN.UpdateWeights | ( | float | rate, |
| float | momentum = 0.0F |
||
| ) |
Updates the weights of the network.
| rate | The learning rate to be used. |
| momentum | The momentum to be used. |
|
get |
The lenght of the input of the network.
|
get |
The index of the first used entry of the input array.
|
get |
The length of the output of the network.
|
get |
The index of the first used entry of the output array.


About
DFS - LAX
DFS, or Daily Fantasy Sports, is a term used to describe fantasy sports websites that operate on a day to day basis
FanDuel and DraftKings are among the most popular at this time
While many sports are available to be played on these sites, Lacrosse was not an option
I made this site to offer lacrosse fans a DFS alternative similar to what they would find on other DFS sites
The concept is simple, pick a team before the game starts. When the game starts, your team is locked.
Sit back and Enjoy the game. Then check back later to see how you did
Contact me at<EMAIL_ADDRESS>if you have any feedback! I would love to hear from you.


Hi there, nice meeting you. I'm a software engineer and a tech enthusiast.
Drop me an email or a LinkedIn message to get my resume in pdf!
Work in global compliance platform, implement backend solutions to keep PayPal complaint with financial regulatory requirements across countries and regions.
Deliver business project, implement customer experience optimisation initiative and develop backend platform capabilities. Drive the analysis, design, implementation, code review, integration, testing and deployment phase.
Worked in Xerox DocuShare team, developing an enterprise content management platform. Developed new features and refactored legacy code.
GPA: 4.69/ 5.0
GPA: 3.87/ 4.0


Watch globs and execute a function upon change, with intelligent defaults for debouncing and queueing.
var watch = require('glob-watcher');
watch(['./*.js', '!./something.js'], function(done){
// This function will be called each time a globbed file is changed
// but is debounced with a 200ms delay (default) and queues subsequent calls
// Make sure to signal async completion with the callback
// or by returning a stream, promise, observable or child process
done();
// if you need access to the `path` or `stat` object, listen
// for the `change` event (see below)
// if you need to listen to specific events, use the returned
// watcher instance (see below)
});
// Raw chokidar instance
var watcher = watch(['./*.js', '!./something.js']);
// Listen for the 'change' event to get `path`/`stat`
// No async completion available because this is the raw chokidar instance
watcher.on('change', function(path, stat) {
// `path` is the path of the changed file
// `stat` is an `fs.Stat` object (not always available)
});
// Listen for other events
// No async completion available because this is the raw chokidar instance
watcher.on('add', function(path, stat) {
// `path` is the path of the changed file
// `stat` is an `fs.Stat` object (not always available)
});
watch(globs[, options][, fn])
Takes a path string, an array of path strings, a glob string or an array of glob strings as globs
to watch on the filesystem. Also optionally takes options
to configure the watcher and a fn
to execute when a file changes.
Note: As of 5.0.0, globs must use /
as the separator character because \\
is reserved for escape sequences (as per the Bash 4.3 & Micromatch specs). This means you can't use path.join()
or __dirname
in Windows environments. If you need to use path.join()
, you can use normalize-path against your paths afterwards. If you need to use __dirname
, you can set it as the cwd
option that gets passed directly to chokidar. The micromatch docs contain more information about backslashes.
Returns an instance of chokidar.
fn([callback])
If the fn
is passed, it will be called when the watcher emits a change
, add
or unlink
event. It is automatically debounced with a default delay of 200 milliseconds and subsequent calls will be queued and called upon completion. These defaults can be changed using the options
.
The fn
is passed a single argument, callback
, which is a function that must be called when work in the fn
is complete. Instead of calling the callback
function, async completion can be signalled by:
Stream
or EventEmitter
Child Process
Promise
Observable
Once async completion is signalled, if another run is queued, it will be executed.
options
options.ignoreInitial
If set to false
the fn
is called during chokidar instantiation as it discovers the file paths. Useful if it is desirable to trigger the fn
during startup.
Passed through to chokidar, but defaulted to true
instead of false
.
Type: Boolean
Default: true
options.delay
The delay to wait before triggering the fn
. Useful for waiting on many changes before doing the work on changed files, e.g. find-and-replace on many files.
Type: Number
Default: 200
(milliseconds)
options.queue
Whether or not a file change should queue the fn
execution if the fn
is already running. Useful for a long running fn
.
Type: Boolean
Default: true
options.events
An event name or array of event names to listen for. Useful if you only need to watch specific events.
Type: String | Array<String>
Default: [ 'add', 'change', 'unlink' ]
Options are passed directly to chokidar.
MIT


INTRODUCTIONThe ladders we climb are the Clues we find , spanning the campus is the gamble of dice
This event require participation in teams . It is a theme based event which involves reaching various checkpoints with the help of clues they collect and complete the task if allotted. The rush stops after completion of circuit.
Rules:
1.After Registration, Every team will be provided with a copy of Snakes and ladders and a clue.
2.The Clue will guide them to the next hotspot.
3. There they will throw a dice which will guide their position on the game of Snakes and ladders.
4.In the game of Snakes and Ladders, if they encounter a snake they should perform a special fun task as penalty .
5.If the team encounter a ladder , a bonus will be awarded which will help in reducing the total time of team in static rush.
6.They will have to complete the task (if they encounter a snake ) correctly at each hotspot to move further.
7.At the final hotspot they will be given a Simple Electronic Circuit which they have to build on the spot and Submit.
8.The team that completes static rush in the minimum time, will be the winner of Static Rush 2017.
9.The task at each hotspot along with the next clue will be given randomly keeping in mind that the clue of the hotspot visited earlier is not given again.
10.Also their last position in the game of Snakes and Ladders will be written on the backside of the page so that the person on the next hotspot can determine the position.
11. Total number of members in a team should not exceed 3.
Venue and Timing:
Day and Date :Time :Venue :
Important Declaration:
1. Please keep checking the Celesta app, website and Celesta, IIT Patna facebook page for any future updates.
2. The rules can change without prior notification.
3. In case of any dispute, the decisions made by the organising committee and judges are final and binding.
Organisers:


Varenna-Bellagio-Lierna by ferry
by Monika Suchoszek
One lazy day designated for sightseeing between two days of demanding hiking was the perfect idea! Ok … this day ultimately was not so lazy with 16km of walking ;-) Varenna was our first destination that day, one of the top rated towns on the shores of Lake Como. Immediately after arriving we noticed a really long queue in front of the ferry. The first thing we did was buying tickets and actually getting some information about the ferry schedule which was very difficult to find online (scan of the ferry schedule in practical info section on the bottom of the post).
We didn’t know exactly what we should see in this town. I just noticed on MAPS.ME (our favorite app with offline maps) a castle above the town and thought there was going to be a nice view from there. I was right ! Just look at the pictures below. You have to buy a ticket to enter the castle which is in ruins now but nicely maintained with “ghost” sculptures scattered around the place and few birds of prey sitting in the small olive garden where you can have a rest in the refreshing shadow. The best spot for a great view is the tower of the castle but unfortunately there were some very annoying insects attacking people so we could really barely enjoy the view and take photos.
Colorful houses, narrow streets, lots of flowers, a breathtaking view from the castle…the beauty of this town charmed us! :) However there was still a lot of sightseeing waiting for us in the next village. Before lunch we took a ferry to Bellagio, a must to visit while travelling in this region of Italy. The ferry took us there in around 15 min and it was really charming to see Varenna disappearing in the distance. The first thing you’ll see while getting close to the shore are Bellagio’s nice, big hotels.
This small city is always really crowded, full of expensive shops and art galleries, shopping lovers will definitely feel satisfied there! ;) We had a really italian lunch, carbonara and of course mandatory ice cream :) Next, we just walked through the park and along the shore of Como lake where we stumbled upon an exhibition with old motorcycles.
We got quite tired in the afternoon and decided to head back to Lierna where the plan was to lay dawn on the beach and chill until sunset and then take a train back to Lecco. The ferry left from Bellagio on time, however, it was really tricky to find a right spot to wait. There are a lot of people waiting and the screens show only the most popular connections, which left many people confused (our connection was served by a boat, not a ferry). The beach in Lierna was not very nice, really rocky and narrow. A few days later we discovered much better one in another village, Collico.
Practical info:
-
Castello di Vezio - entrance fee 4 euro per person, check their website for opening hours.
-
Ferry ticket from Varenna to Lierna with a change in Bellagio - 4,60 euro per person
-
Train tickets:
-
Lecco - Varenna 2,90 euro per person. We made a mistake here and bought tickets on the train which cost us extra 4 euro per person! Note that this fee doesn’t apply when statring from a station without a ticket machine, for example Lierna
-
Lierna - Lecco 2,50 euro per person
-
Subscribe via RSS


Employees
Name
UID
Department
Seniority
John DOE
ABCD123456
Law compliance
60
John O'CONNORS
ABCD123456
Law compliance
80
John SMITH
ABCD123456
Shuttle compliance
60
John WAT
ABCD123456
Good inspection
60
Employee
John O'CONNORS
Creation :
02/01/2016 09:45 by John STEVENSON
Last update :
03/01/2016 09:45 by John STEVENSON
UID
STEVJ002
Profile
ADMIN_ALL
ADMIN_READER
AGR_VISA_APPLIER
RULE_WRITER
HR_READER
HR_WRITER
Department
Law compliance
Shuttle compliance
Good inspection
Journey supervisor
Seniority
Cancel
Save


the expected behaviour is that each time you submit the form below, a
notification containing the entered message and sporting the selected style
should appear on remote clients that have loaded this page.
- A max of 5 notifications should be shown at any one time.
- Notifications should automatically disappear after 5 seconds.
- Notifications should appear at the top-center of the screen.
- Notifications should appear on the local client only if Display to Self is checked.


This page is where you can pick up where you left off, or start with a new text and/or strategy. To add a new text, head to Text Manager.
Poem with 2 pre and post context
Poem with 2 pre context
Poem with 2 post context
Vocab List
Begin new memorization!
Poem with 2 pre context
Poem with 2 post context
Vocab List


NDRF, VIT university to develop hydrogen-powered two-wheeler
The project, when realised, will replace petrol
by Special CorrespondentThe National Design and Research Forum would extend its support to the project on hydrogen-powered two wheeler along with VIT University, said NDRF chairman Mylswamy Annadurai on Tuesday.
Addressing the gathering at the valedictory function of ‘The Future Scientist- State Science Festival’, he said that NDRF and VIT would help the team to take the invention of hydrogen powered vehicle to the next level. Such inventions may bring a break-through and provide alternative to petrol.
Lithium-Ion technology
The impact of electric vehicles operated on lithium ion are being discussed in the science forums.
There are concerns over lithium ion cell phone batteries that may have ill-effects when not used properly, he said. Such inventions may have a major impact and change the world, he added.
G.Viswanathan, VIT chancellor, K.Asokan, editor, Hindu- Tamil Thisai and S.T. Balakrishnan, State secretary, Tamil Nadu Science Forum also spoke.
The State Science Festival was jointly organised by VIT, Hindu- Tamil Thisai, NDRF and TNSF.


Macros | |
| #define | HIDKeypad_A 4 |
| Key code for 'a' and 'A'. More... | |
| #define | HIDKeypad_B 5 |
| Key code for 'b' and 'B'. More... | |
| #define | HIDKeypad_C 6 |
| Key code for 'c' and 'C'. More... | |
| #define | HIDKeypad_D 7 |
| Key code for 'd' and 'D'. More... | |
| #define | HIDKeypad_E 8 |
| Key code for 'e' and 'E'. More... | |
| #define | HIDKeypad_F 9 |
| Key code for 'f' and 'F'. More... | |
| #define | HIDKeypad_G 10 |
| Key code for 'g' and 'G'. More... | |
| #define | HIDKeypad_H 11 |
| Key code for 'h' and 'H'. More... | |
| #define | HIDKeypad_I 12 |
| Key code for 'i' and 'I'. More... | |
| #define | HIDKeypad_J 13 |
| Key code for 'j' and 'J'. More... | |
| #define | HIDKeypad_K 14 |
| Key code for 'k' and 'K'. More... | |
| #define | HIDKeypad_L 15 |
| Key code for 'l' and 'L'. More... | |
| #define | HIDKeypad_M 16 |
| Key code for 'm' and 'M'. More... | |
| #define | HIDKeypad_N 17 |
| Key code for 'n' and 'N'. More... | |
| #define | HIDKeypad_O 18 |
| Key code for 'o' and 'O'. More... | |
| #define | HIDKeypad_P 19 |
| Key code for 'p' and 'P'. More... | |
| #define | HIDKeypad_Q 20 |
| Key code for 'q' and 'Q'. More... | |
| #define | HIDKeypad_R 21 |
| Key code for 'r' and 'R'. More... | |
| #define | HIDKeypad_S 22 |
| Key code for 's' and 'S'. More... | |
| #define | HIDKeypad_T 23 |
| Key code for 't' and 'T'. More... | |
| #define | HIDKeypad_U 24 |
| Key code for 'u' and 'U'. More... | |
| #define | HIDKeypad_V 25 |
| Key code for 'v' and 'V'. More... | |
| #define | HIDKeypad_W 26 |
| Key code for 'w' and 'W'. More... | |
| #define | HIDKeypad_X 27 |
| Key code for 'x' and 'X'. More... | |
| #define | HIDKeypad_Y 28 |
| Key code for 'y' and 'Y'. More... | |
| #define | HIDKeypad_Z 29 |
| Key code for 'z' and 'Z'. More... | |
| #define HIDKeypad_A 4 |
Key code for 'a' and 'A'.
| #define HIDKeypad_B 5 |
Key code for 'b' and 'B'.
| #define HIDKeypad_C 6 |
Key code for 'c' and 'C'.
| #define HIDKeypad_D 7 |
Key code for 'd' and 'D'.
| #define HIDKeypad_E 8 |
Key code for 'e' and 'E'.
| #define HIDKeypad_F 9 |
Key code for 'f' and 'F'.
| #define HIDKeypad_G 10 |
Key code for 'g' and 'G'.
| #define HIDKeypad_H 11 |
Key code for 'h' and 'H'.
| #define HIDKeypad_I 12 |
Key code for 'i' and 'I'.
| #define HIDKeypad_J 13 |
Key code for 'j' and 'J'.
| #define HIDKeypad_K 14 |
Key code for 'k' and 'K'.
| #define HIDKeypad_L 15 |
Key code for 'l' and 'L'.
| #define HIDKeypad_M 16 |
Key code for 'm' and 'M'.
| #define HIDKeypad_N 17 |
Key code for 'n' and 'N'.
| #define HIDKeypad_O 18 |
Key code for 'o' and 'O'.
| #define HIDKeypad_P 19 |
Key code for 'p' and 'P'.
| #define HIDKeypad_Q 20 |
Key code for 'q' and 'Q'.
| #define HIDKeypad_R 21 |
Key code for 'r' and 'R'.
| #define HIDKeypad_S 22 |
Key code for 's' and 'S'.
| #define HIDKeypad_T 23 |
Key code for 't' and 'T'.
| #define HIDKeypad_U 24 |
Key code for 'u' and 'U'.
| #define HIDKeypad_V 25 |
Key code for 'v' and 'V'.
| #define HIDKeypad_W 26 |
Key code for 'w' and 'W'.
| #define HIDKeypad_X 27 |
Key code for 'x' and 'X'.
| #define HIDKeypad_Y 28 |
Key code for 'y' and 'Y'.
| #define HIDKeypad_Z 29 |
Key code for 'z' and 'Z'.


[§206E-19] Annual report. The authority shall submit to the governor and the legislature, at least twenty days prior to the start of any regular session, a complete and detailed report of its activities. [L 1976, c 153, pt of §1]


Computer
Science & Engineering →CSO
Lab →List Of
Experiments
Representation of Integers and their Arithmetic
The objective of this experiment is to learn about representation of signed and unsigned integers. Specifically you will learn the advantages of using 2's complement representation of numbers for performing arithmetic over other representations..


Vim documentation: usr_12
main help file*usr_12.txt* For Vim version 7.2. Last change: 2007 May 11 VIM USER MANUAL - by Bram Moolenaar Clever tricks By combining several commands you can make Vim do nearly everything. In this chapter a number of useful combinations will be presented. This uses the commands introduced in the previous chapters and a few more. |12.1| Replace a word |12.2| Change "Last, First" to "First Last" |12.3| Sort a list |12.4| Reverse line order |12.5| Count words |12.6| Find a man page |12.7| Trim blanks |12.8| Find where a word is used Next chapter: |usr_20.txt| Typing command-line commands quickly Previous chapter: |usr_11.txt| Recovering from a crash Table of contents: |usr_toc.txt|
*12.1* Replace a word The substitute command can be used to replace all occurrences of a word with another word: :%s/four/4/g The "%" range means to replace in all lines. The "g" flag at the end causes all words in a line to be replaced. This will not do the right thing if your file also contains "thirtyfour". It would be replaced with "thirty4". To avoid this, use the "\<" item to match the start of a word: :%s/\<four/4/g Obviously, this still goes wrong on "fourteen". Use "\>" to match the end of a word: :%s/\<four\>/4/g If you are programming, you might want to replace "four" in comments, but not in the code. Since this is difficult to specify, add the "c" flag to have the substitute command prompt you for each replacement: :%s/\<four\>/4/gc REPLACING IN SEVERAL FILES Suppose you want to replace a word in more than one file. You could edit each file and type the command manually. It's a lot faster to use record and playback. Let's assume you have a directory with C++ files, all ending in ".cpp". There is a function called "GetResp" that you want to rename to "GetAnswer". vim *.cpp Start Vim, defining the argument list to contain all the C++ files. You are now in the first file. qq Start recording into the q register :%s/\<GetResp\>/GetAnswer/g Do the replacements in the first file. :wnext Write this file and move to the next one. q Stop recording. @q Execute the q register. This will replay the substitution and ":wnext". You can verify that this doesn't produce an error message. 999@q Execute the q register on the remaining files. At the last file you will get an error message, because ":wnext" cannot move to the next file. This stops the execution, and everything is done. Note: When playing back a recorded sequence, an error stops the execution. Therefore, make sure you don't get an error message when recording. There is one catch: If one of the .cpp files does not contain the word "GetResp", you will get an error and replacing will stop. To avoid this, add the "e" flag to the substitute command: :%s/\<GetResp\>/GetAnswer/ge The "e" flag tells ":substitute" that not finding a match is not an error.
*12.2* Change "Last, First" to "First Last" You have a list of names in this form: Doe, John Smith, Peter You want to change that to: John Doe Peter Smith This can be done with just one command: :%s/\([^,]*\), \(.*\)/\2 \1/ Let's break this down in parts. Obviously it starts with a substitute command. The "%" is the line range, which stands for the whole file. Thus the substitution is done in every line in the file. The arguments for the substitute command are "/from/to/". The slashes separate the "from" pattern and the "to" string. This is what the "from" pattern contains: \([^,]*\), \(.*\) The first part between \( \) matches "Last" \( \) match anything but a comma [^,] any number of times * matches ", " literally , The second part between \( \) matches "First" \( \) any character . any number of times * In the "to" part we have "\2" and "\1". These are called backreferences. They refer to the text matched by the "\( \)" parts in the pattern. "\2" refers to the text matched by the second "\( \)", which is the "First" name. "\1" refers to the first "\( \)", which is the "Last" name. You can use up to nine backreferences in the "to" part of a substitute command. "\0" stands for the whole matched pattern. There are a few more special items in a substitute command, see |sub-replace-special|.
*12.3* Sort a list In a Makefile you often have a list of files. For example: OBJS = \ version.o \ pch.o \ getopt.o \ util.o \ getopt1.o \ inp.o \ patch.o \ backup.o To sort this list, filter the text through the external sort command: /^OBJS j :.,/^$/-1!sort This goes to the first line, where "OBJS" is the first thing in the line. Then it goes one line down and filters the lines until the next empty line. You could also select the lines in Visual mode and then use "!sort". That's easier to type, but more work when there are many lines. The result is this: OBJS = \ backup.o getopt.o \ getopt1.o \ inp.o \ patch.o \ pch.o \ util.o \ version.o \ Notice that a backslash at the end of each line is used to indicate the line continues. After sorting, this is wrong! The "backup.o" line that was at the end didn't have a backslash. Now that it sorts to another place, it must have a backslash. The simplest solution is to add the backslash with "A \<Esc>". You can keep the backslash in the last line, if you make sure an empty line comes after it. That way you don't have this problem again.
*12.4* Reverse line order The |:global| command can be combined with the |:move| command to move all the lines before the first line, resulting in a reversed file. The command is: :global/^/m 0 Abbreviated: :g/^/m 0 The "^" regular expression matches the beginning of the line (even if the line is blank). The |:move| command moves the matching line to after the mythical zeroth line, so the current matching line becomes the first line of the file. As the |:global| command is not confused by the changing line numbering, |:global| proceeds to match all remaining lines of the file and puts each as the first. This also works on a range of lines. First move to above the first line and mark it with "mt". Then move the cursor to the last line in the range and type: :'t+1,.g/^/m 't
*12.5* Count words Sometimes you have to write a text with a maximum number of words. Vim can count the words for you. When the whole file is what you want to count the words in, use this command: g CTRL-G Do not type a space after the g, this is just used here to make the command easy to read. The output looks like this: Col 1 of 0; Line 141 of 157; Word 748 of 774; Byte 4489 of 4976 You can see on which word you are (748), and the total number of words in the file (774). When the text is only part of a file, you could move to the start of the text, type "g CTRL-G", move to the end of the text, type "g CTRL-G" again, and then use your brain to compute the difference in the word position. That's a good exercise, but there is an easier way. With Visual mode, select the text you want to count words in. Then type g CTRL-G. The result: Selected 5 of 293 Lines; 70 of 1884 Words; 359 of 10928 Bytes For other ways to count words, lines and other items, see |count-items|.
*12.6* Find a man page *find-manpage* While editing a shell script or C program, you are using a command or function that you want to find the man page for (this is on Unix). Let's first use a simple way: Move the cursor to the word you want to find help on and press K Vim will run the external "man" program on the word. If the man page is found, it is displayed. This uses the normal pager to scroll through the text (mostly the "more" program). When you get to the end pressing <Enter> will get you back into Vim. A disadvantage is that you can't see the man page and the text you are working on at the same time. There is a trick to make the man page appear in a Vim window. First, load the man filetype plugin: :runtime! ftplugin/man.vim Put this command in your vimrc file if you intend to do this often. Now you can use the ":Man" command to open a window on a man page: :Man csh You can scroll around and the text is highlighted. This allows you to find the help you were looking for. Use CTRL-W w to jump to the window with the text you were working on. To find a man page in a specific section, put the section number first. For example, to look in section 3 for "echo": :Man 3 echo To jump to another man page, which is in the text with the typical form "word(1)", press CTRL-] on it. Further ":Man" commands will use the same window. To display a man page for the word under the cursor, use this: \K (If you redefined the <Leader>, use it instead of the backslash). For example, you want to know the return value of "strstr()" while editing this line: if ( strstr (input, "aap") == ) Move the cursor to somewhere on "strstr" and type "\K". A window will open to display the man page for strstr().
*12.7* Trim blanks Some people find spaces and tabs at the end of a line useless, wasteful, and ugly. To remove whitespace at the end of every line, execute the following command: :%s/\s\+$// The line range "%" is used, thus this works on the whole file. The pattern that the ":substitute" command matches with is "\s\+$". This finds white space characters (\s), 1 or more of them (\+), before the end-of-line ($).  The "to" part of the substitute command is empty: "//". Thus it replaces with nothing, effectively deleting the matched white space. Another wasteful use of spaces is placing them before a tab. Often these can be deleted without changing the amount of white space. But not always! Therefore, you can best do this manually. Use this search command: / You cannot see it, but there is a space before a tab in this command. Thus it's "/<Space><Tab>". Now use "x" to delete the space and check that the amount of white space doesn't change. You might have to insert a tab if it does change. Type "n" to find the next match. Repeat this until no more matches can be found.
*12.8* Find where a word is used If you are a UNIX user, you can use a combination of Vim and the grep command to edit all the files that contain a given word. This is extremely useful if you are working on a program and want to view or edit all the files that contain a specific variable. For example, suppose you want to edit all the C program files that contain the word "frame_counter". To do this you use the command: vim `grep -l frame_counter *.c` Let's look at this command in detail. The grep command searches through a set of files for a given word. Because the -l argument is specified, the command will only list the files containing the word and not print the matching lines. The word it is searching for is "frame_counter". Actually, this can be any regular expression. (Note: What grep uses for regular expressions is not exactly the same as what Vim uses.) The entire command is enclosed in backticks (`). This tells the UNIX shell to run this command and pretend that the results were typed on the command line. So what happens is that the grep command is run and produces a list of files, these files are put on the Vim command line. This results in Vim editing the file list that is the output of grep. You can then use commands like ":next" and ":first" to browse through the files. FINDING EACH LINE The above command only finds the files in which the word is found. You still have to find the word within the files. Vim has a built-in command that you can use to search a set of files for a given string. If you want to find all occurrences of "error_string" in all C program files, for example, enter the following command: :grep error_string *.c This causes Vim to search for the string "error_string" in all the specified files (*.c). The editor will now open the first file where a match is found and position the cursor on the first matching line. To go to the next matching line (no matter in what file it is), use the ":cnext" command. To go to the previous match, use the ":cprev" command. Use ":clist" to see all the matches and where they are. The ":grep" command uses the external commands grep (on Unix) or findstr (on Windows). You can change this by setting the option 'grepprg'.
Next chapter: |usr_20.txt| Typing command-line commands quickly Copyright: see |manual-copyright| vim:tw=78:ts=8:ft=help:norl: top - main help file


Logotipo da Udacity
Marshal Mori
Front-End Ninja
Logo
Featured Work
Appify
https://github.com/udacity/Appify/
Sunflower
https://github.com/udacity/Sunflower/
Bokeh
https://github.com/udacity/bokeh/


Checkbox group created from passed checkboxes and value is required
Value is required.
Favorites: {{ form1.controls.fruitsEx.value }}
Checkbox group created from list of values and value is required
Value is required.
Favorites: {{ form2.controls.fruits1.value }}
Checkboxes with different context
Qualification form
Graduated
Engineering completed
5 year of work experience
Value is required
Value is invalid
Graduation is mandatory
Engineering is mandatory
Minimum 5 years of work experience required
Checkbox group created from list of SelectItem Objects and value is required
Value is required.
Languages Known: {{ form3.controls.language.value }}
Checkbox with same context
Apple
Banana
Grapes
Value is required.
Apple is mandatory
Banana is mandatory
Grape is mandatory
Favorites: {{ form4.getRawValue() | json }}


Supported Features:
- Leaderboards
- In App Purchases
- Analytics
- Rewarded Ads
- Interstitial Ads
- Banner Ads
- Android Export
Integration:
To integrate this plugin into your Unity Project, download a release from Releases and import the .unitypackage
into your project. You can do so by double clicking the .unitypackage
release file on your file explorer while having your Unity Project Open, or from Unity you can head to the Assets > Import Package > Custom Package menu and then choose the release file.
Once you've imported the package you'll find a new Menu option
Which will provide you with a Settings panel through so that you can configure your project's properties.
This package provides tools for mocking behavior in Editor, so you can configure if you wish for your In Editor Platform to support different features, and as well configure placements for Ads, In App Purchases, Leaderboards and Analytic Events.
Included with this package is a Demo Application that you can load and explore, to see how the different features supported through Kiln's API can be used
API Examples
Initialize
Before making use of anything else, it is necessary to initialize the SDK
try
{
await Kiln.API.Init();
Logger.Log("Initialized");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Load and Display Rewarded Ad
try
{
await Kiln.API.LoadRewardedAd(placementId);
Logger.Log("Rewarded Ad Loaded");
IRewardedAdResponse response = await Kiln.API.ShowRewardedAd(placementId);
Logger.Log($"Rewarded Ad Displayed. With reward: {response.GetWithReward()}");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Load and Display Interstitial Ad
try
{
await Kiln.API.LoadInterstitialAd(placementId);
Logger.Log("Interstitial Ad Loaded");
await Kiln.API.ShowInterstitialAd(placementId);
Logger.Log("Interstitial Ad Displayed");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Load, Display, Hide and Destroy Banner Ads
try
{
// Banner Alignment and Size
BannerPosition bannerPosition = BannerPosition.TopCenter;
BannerSize bannerSize = BannerSize.Width300Height50;
await Kiln.API.LoadBannerAd(placementId, bannerPosition, bannerSize);
Logger.Log("Banner Ad Loaded");
await Kiln.API.ShowBannerAd(placementId);
Logger.Log("Banner Ad Displayed");
await Kiln.API.HideBannerAd(placementId);
Logger.Log("Banner Ad Hidden");
await Kiln.API.DestroyBannerAd(placementId);
Logger.Log("Banner Ad Destroyed");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
In App Purchases: Get Available Products
try
{
List<IProduct> products = await Kiln.API.GetAvailableProducts();
foreach (IProduct p in products)
{
Debug.Log(p.ToString());
}
}
catch (Kiln.Exception ex)
{
Debug.Log(ex);
}
catch (System.Exception ex)
{
Debug.Log(ex.ToString());
}
In App Purchases: Get Purchases
This returns the currently active purchases, which means purchases of non consumables and of currently unconsumed consumables.
try
{
List<IPurchase> purchases = await Kiln.API.GetPurchases();
foreach (IPurchase p in purchases)
{
Logger.Log(p.ToString());
}
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
In App Purchases: Purchase Product
try
{
IPurchase purchase = await Kiln.API.PurchaseProduct(productID, "DEVELOPER PAYLOAD TEST");
Logger.Log($"Product {purchase.GetProductID()} ready for consumption");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
In App Purchases: Consume Product
try
{
await Kiln.API.ConsumePurchasedProduct(purchaseToken);
Logger.Log($"Product with token {purchaseToken} consumed.");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Leaderboards: Set User Score
try
{
await Kiln.API.SetUserScore(leaderboardID, scoreToSubmit);
Logger.Log("User score submitted successfully");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Leaderboards: Get User Score
try
{
ILeaderboardEntry entry = await Kiln.API.GetUserScore(leaderboardID);
Logger.Log(entry.ToString());
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Leaderboards: Get Leaderboard Scores
try
{
List<ILeaderboardEntry> entries = await Kiln.API.GetScores(leaderboardID, amount, offset);
foreach (ILeaderboardEntry entry in entries)
{
Logger.Log(entry.ToString());
}
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Leaderboards: Get Platform Leaderboard
If the platform supports a native Leaderboards UI, it'll display it.
try
{
await Kiln.API.ShowPlatformLeaderboardUI();
Logger.Log("Platform Leaderboards closed");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}
Analytics: Send Analytics Event
try
{
Kiln.API.SubmitAnalyticsEvent(eventID);
Logger.Log($"Analytics Event Fired.");
}
catch (Kiln.Exception ex)
{
Logger.Log(ex);
}
catch (System.Exception ex)
{
Logger.Log(ex.ToString());
}


Amber Electric - Live Prices
PostCode - XXXX
It's cheap to use energy now.
00
:
00
:
00
--
12
01
02
03
04
05
06
07
08
09
10
11
88c
88c


Holy Mountain Brewing started with Mike Murphy and Colin Lenfesty's shared interest in beer, specifically oak-inspired beers. After some planning and development, they began their operation in 2014 and continue to this day. While oak-inspired beers may be their specialty, they do provide a wide range of ales and lagers for all beer fanatics.
Not satisfied with your current choice?


Generals propose a time to attack.
Generals must communicate by sending messengers.
Generals can send many messengers.
Generals must come to a consensus on the time to attack.
"I will attack on Day 4 at 0600, will you join me?"
Attach a sealed response to the original message.
Send out copies of the newly combined message to the other generals and ask them to do the same.
The objective is to have them all agreeing to the same time by adding all of their seals onto the same message chain.
Many messengers in transit at any one time.
Each general could receive multiple messengers each with different attack times.
Some of the generals will agree to more than one attack time.
It quickly degenerates into a completely untrustworthy tangle of conflicting messages and attack times.
How do you establish truth in a distributed network?
Public ledger containing all transactions (blockchain).
A transaction: send x amount from A->B.
Transactions are processed by a peer-to-peer network.
Transactions can arrive at different times at different nodes.
Timestamps can be faked.
Bad actors can (attempt to) double spend.
How do you agree on the order of the transactions?
Maintaining the blockchain is called mining.
Miners gather a set of valid transactions into a block and add to the blockchain.
Apply a cryptographic hash function that (eventually) results in a hash of a certain difficulty.
Publish block hash to the network.
This is known as "Proof-of-Work".
sha256("But here's my number so call me maybe")
c7331b414067c5101f66657909eb56eb5d50e4b5883c35ca5f08bfb2d3798fd6
Notice how the hash drastically changes due to a one character change:
sha256("But here's my number so call me maybe.")
dc15744ef69e4a81e7dc0f04921b471101389609e729624438c3b15afb4d09a4
if sha256(sha256(prevHash + transactionsHash + random number)) < target
Bitcoin has a target hash that looks something like this:
00000000000000001e8d6829a8a21adc5d38d0a473b144b6765798e61f98bd1d
Only if this is true can a miner publish to the network.
Guessing the right random number takes time.
Miners receive one block soultion at a time (usually).
Simple to validate and agree on the transaction order.
Proof-of-Work adds a cost to the sending of a messenger.
Slows down the sending of messengers.
Essentially, only one message is being transmitted.
Effort is put into propagating the TRUTH.


404
We couldn’t find
this page.
Maybe it’s out there, somewhere...
You can always find insightful stories on our
homepage
.


Speech
The Secretary of State for Education speaks to the British Chambers of Commerce about vocational education, literacy and numeracy.
We’re implementing a long-term plan for schools - rooted in evidence - driven by moral purpose.
We want every child to have a chance to flourish. We inherited an education system which was one of the most stratified and segregated in the developed world. Thousands of children - overwhelmingly from poorer backgrounds - were receiving an inadequate education.
That had to change - and that’s why we have been implementing reforms to help all children succeed; targeting support on the most disadvantaged; executing a long-term educational plan, rooted in evidence of what works.
The most successful schools in this country - and the most successful education jurisdictions in the world - share particular qualities. Greater autonomy for those at the frontline; sharper, more intelligent accountability; and doing everything possible to drive up the standard of teaching.
Our plan for schools is focused on each of these objectives. And it’s working.
Since May 2010, the number of pupils taught in underperforming secondary schools has fallen by almost a quarter of a million.
And we’ve raised the bar on what counts as success - setting a tough new minimum floor standard, which no school should fall below.
In 2010, 407 secondary schools would have fallen below the floor. This year, it was just 154.
Still 154 too many - but a significant improvement.
These improvements in state education will help deliver the sort of skills and knowledge that help young people to get the jobs of the future and, of course, to create the jobs of the future.
But there’s still more to do.
That’s why - as part of our long term plan for education - we are reforming qualifications. Every qualification that young people study for, academic or vocational, must be demanding, rigorous and a route to employment.
That simply wasn’t the case in the past.
There was a flight from quality. The number of pupils studying core academic subjects at GCSE - like maths, sciences, languages, English, history and geography - fell from 50% to 22%.
We’ve reversed that trend.
In four years, the number of pupils studying physics is up by a third; biology, almost a third; chemistry, up by a third; languages, by almost a fifth.
And there was a devaluation of vocational study. Allegedly ‘equivalent’ in size to two, three or more GCSEs, many vocational qualifications were not rigorous in content, accreditation or assessment.
Professor Alison Wolf, Britain’s leading expert on vocational education, investigated this scandal for this government.
As she recorded in 2011:
The staple offer for between a quarter and a third of the post-16 cohort is a diet of low-level vocational qualifications, most of which have little to no labour market value.
Among 16 to 19 year olds, the review estimates that at least 350,000 get little to no benefit from the post-16 education system.
She quoted the Principal of Central Bedfordshire College, Ali Hadawi, saying:
The changes wrought over recent years have seen a systematic de-skilling of the quality of provision with the emphasis on achievement of a qualification being primary and the dumbing down of the content, quality and rigour.
As Alison said:
All [qualifications] which are used, vocational or academic, should make serious demands of students, develop and accredit distinctive skills and attainments, facilitate progression post-16 and incorporate clearly established, and properly monitored, national standards. They must, therefore, have a strong element of external assessment. We know that, without regular external referencing, assessment standards in any subject invariably diverge across institutions and assessors.
Because vocational education lacked the rigour students need and the responsiveness employers demand, we asked Alison to review vocational qualifications and entrepreneur Doug Richard to look into apprenticeships.
Thanks to their hard work, we’re now delivering the most important reforms to vocational education since 1944.
Poor quality vocational qualifications - those not valued by employers or universities - are being removed from league tables altogether.
From this year, the only courses which will count in 14 to 16 league tables are those that are high quality, rigorous and proven to support progression.
New qualifications meeting these higher standards are being developed by awarding organisations in particularly sought-after sectors like engineering and construction.
And from September this year, new Tech Levels will provide a high-quality vocational alternative to A levels - each endorsed by professional associations or employers.
Along with a high-level maths qualification and an extended project, Tech Levels will count towards the Technical Baccalaureate, or TechBacc - an ambitious new benchmark for the most talented students.
We are also fundamentally reforming apprenticeships.
Your own figures show that the 53 accredited chambers of commerce delivered 3,476 apprenticeships and brokered a further 868.
That’s fantastic. But it doesn’t begin to meet the demand for high-quality apprenticeships.
I can understand why - in the past - bureaucracy put some of you off.
So we have tackled the bureaucracy. We’ve made it clear that employing an apprentice brings with it no additional requirements in terms of health and safety or wages than employing any other employee.
We have extended the apprenticeship grant for employers to support small firms making the commitment to recruit and train young apprentices - providing £85m in 2014 to 2015 and 2015 to 2016.
And we continue to improve the National Apprenticeship Service’s online vacancy system and small business support team - all designed to help cut bureaucracy and make the system easier for business.
I can also appreciate that in the past, apprenticeship frameworks were designed to fit the needs of politicians and bureaucrats not employers.
So we are now ensuring that every new apprenticeship reflects the needs of business.
So-called ‘short duration’ apprenticeships have been stripped out, and poor-quality training has been removed; all apprenticeships are now real jobs, with more stretching qualifications in English and maths.
Old-style apprenticeship frameworks - long, complicated masterpieces of Sir-Humphrey-speak, scattered with ‘pathways’ and ‘personal learning and thinking skills’, listing every possible task and qualification apprentices could and should cover - are being replaced by new standards designed by employers, our trailblazers.
These trailblazers will publish new apprenticeship standards conceived, designed, created, tested and approved by employers.
Four hundred employers are now involved as trailblazers, including BAE, the National Grid, Cisco, Jaguar Land Rover, Nestlé, GlaxoSmithKline, Balfour Beatty and Santander - as well as, just as importantly, many of the small companies in their supply chains.
These trailblazers will publish new apprenticeship standards - short and simple, setting out exactly what it means to be fully competent, and how competency should be assessed - in a way that works for business, as well as for young people.
That means there is now no excuse for employers not to offer apprenticeships.
It’s our shared responsibility to ensure this country is both socially just and economically efficient - and it’s the best way I know of making sure you have the skilled workers you need to win in the global race.
The reforms we’ve introduced - to schools and apprenticeships - are raising standards and extending opportunity.
But there is still further to go.
Particularly in the most important vocational skills of all - English and maths.
So today I’d like to outline one simple national ambition we should set ourselves to ensure educational opportunity is genuinely extended to all.
We need to ensure we eliminate illiteracy and innumeracy in Britain.
In the same way as developing nations know they need to secure clean drinking water and eliminate malaria if their children are to flourish.
And in the same way as our forefathers more than 100 years ago knew they had to eradicate polio and TB if children were to flourish.
So we must ensure no child grows up in modern Britain with their futures irredeemably blighted by illiteracy and innumeracy.
That is why I am introducing policies to make sure children leave primary schools literate and numerate.
So by the end of year 1, every child is now checked to make sure they are decoding words fluently - helping teachers to make sure pupils are making progress in reading, and to identify any child who might need extra help.
Teachers will also be expected to assess pupils at the end of key stage 1 - when children are 6 or 7 - to ensure they are making appropriate progress in literacy and numeracy. A new test in spelling, punctuation and grammar will inform teachers’ assessment of writing and help keep children on course.
At the end of primary, we’re reforming tests in reading, spelling, punctuation and grammar and mathematics to make sure all children are literate and numerate. The basic standards all schools - and all children - must reach will be made more rigorous, with more demanding multiplication and division and more stress on fluent comprehension.
We want at least 85% of primary school pupils to reach the level of literacy and numeracy that means they’re on course to get good grades at GCSE.
Failure to secure a good maths or English GCSE renders any student effectively unemployable.
Yet in 2012, 44% of students failed to secure a GCSE pass in maths and English by the age of 16 and almost half never studied these subjects again afterwards.
So now, students who fail to achieve a GCSE pass in maths and English at 16 will continue to study those subjects afterwards; and those who achieve a pass are encouraged to take higher-level qualifications.
To help teachers spread best practice, we’re creating 30 new regional maths hubs - each led by an outstanding school, each offering support to all the schools in its local area in all aspects of maths education.
And they will take the lead in our new Chinese maths teacher exchange programme, inviting Chinese teachers to England and sending our leading maths teachers to China, to learn from each other - and to help make sure that our pupils get a maths education every bit as good as that in the most successful jurisdiction in the world.
The ambition I’m outlining today is demanding.
And if we’re to meet it we depend on the support of you and your members.
Whether by offering apprenticeships in your business, or shaping the next generation of apprenticeships as a trailblazer.
By offering work experience or careers guidance to local schools and colleges and offering traineeships to young people not yet ready for jobs or apprenticeship.
By volunteering as a school governor, demanding higher standards from your local schools and insisting government does not waver in this ambition.
And most of all - by committing yourself to support our national mission to eliminate illiteracy and innumeracy and make opportunity more equal for all.
It is in all our interests that we ensure the next generation secure the quality of education they need to flourish fully as individuals and contribute fully to our society.
That is my mission - and I know it is yours.
Thank you.
Published: 1 April 2014