CPPGM Programming Assignment 4 (macro)
Overview
Write a C++ application called macro
that accepts a C++ Source File on standard input and executes phases 1 through 6 and the tokenization part of phase 7.
The input file may contain:
#define
preprocessing directives#undef
preprocessing directives
The input file does NOT include any...
- preprocessing directives other than
#define
or#undef
- pre-defined macro names
- the pragma operator
Output the resulting tokens as per PA2 posttoken
.
Prerequisites
You should complete Programming Assignment 2 before starting this assignment.
Starter Kit
The starter kit can be obtained from:
$ git clone git://git.cppgm.org/pa4.git
It contains a stub implementation of macro
, a compiled reference implementation and a test suite.
You will also want to reuse most of your code from PA2.
Input Format
The C++ Source File shall be read from standard input std::cin
in UTF-8 format as per PA1 pptoken
.
Output Format
The tokens shall be output as per PA2 posttoken
.
Error Reporting
If an error occurs macro
should return EXIT_FAILURE
as per the behaviour of pptoken
in PA1.
Example
For an input of:
#define a 3
#define f(x) x x
f(a)
Produces an output of:
literal 3 int 03000000
literal 3 int 03000000
eof
Restrictions
As per PA1
Features
Once you have executed phase 1-3 as per PA1 pptoken
to produce a sequence of preprocessing-tokens
, you will divide them into preprocessing directives and text-sequences
.
Preprocessing directives can be identified by their prefix, one of:
start-of-file #
start-of-file whitespace-sequence #
new-line #
new-line whitespace-sequence #
Where start-of-file
denotes the beggining of the token sequence.
Any of these patterns must start a preprocessing directive. The preprocessing directive is terminated by the next new-line
.
The only preprocessing directives for PA4 will be macro defines and undefs:
// object-like macros
# define identifier new-line
# define identifier must-whitespace replacement-list new-line
// function-like macros
# define identifier no-whitespace ( ) replacement-list new-line
# define identifier no-whitespace ( identifier-list ) replacement-list new-line
# define identifier no-whitespace ( ... ) replacement-list new-line
# define identifier no-whitespace ( identifier-list , ... ) replacement-list new-line
// undefine macro
# undef identifier new-line
no-whitespace
means that there is no whitespace-sequence
between the two adjacent tokens. must-whitespace
means there must be a whitespace-sequence
between the two adjacent tokens. Otherwise whitespace-sequence
may optionally appear between any tokens.
A replacement-list
is a possibly empty sequence of any preprocessing-tokens
apart from new-line
.
An identifier-list
is a comma-separated sequence of identifiers
.
#define
directives define macros, #undef
directives undefine them.
A text-sequence
is a maximal sequence of tokens that is not part of a preprocessing directive.
Each text-sequence
must be macro replaced using the currently defined set of macros at that point in the file. Once this has been done the macro replaced preprocessing-tokens
should be processed by phase 5-7 as per PA2 posttoken
and output.
The rules for macro replacement are described in section 16.3 Macro Replacement of the standard.
If the name of the macro being replaced is found during this scan of the replacement list (not including the rest of the source file’s preprocessing tokens), it is not replaced. Furthermore, if any nested replacements encounter the name of the macro being replaced, it is not replaced. These nonreplaced macro name preprocessing tokens are no longer available for further replacement even if they are later (re)examined in contexts in which that macro name preprocessing token would otherwise have been replaced.
We shall course-define two invocations X and Y to be nested with respect to each other if the head macro name identifier
token of Y was part of the replaced tokens of the X invocation. This nestedness relationship shall not survive parameter substitution:
#define f(x) 1 g(x)
#define g(x) 2 f(x)
f(f(x))
The trace of the above is:
f(f(x))
f(1 g(x))
f(1 2 f(x))
1 g(1 2 f(x))
1 2 f(1 2 f(x))
However a macro name token that is not replaced due to nesting is never replaced again, even after substitution as:
#define z z[0]
#define f(x) 1 x
f(z)
The trace is:
f(z)
f(z[0])
1 z[0]
z remains unavailable for invocation even after it is substituted in a parameter.
Finally we show:
#define f(x) 1 x
#define g(x) 2 x
g(f)(g)(3)
The trace is:
g(f)(g)(3)
2 f(g)(3) <-- f is nested with respect to g as the token is part of the previous g invocation
2 1 g(3) <-- g is nested with respect to f, therefore it is not replaced (g -> f -> g)
Note that this course-defined interpretation of the nesting semantics is not necessarily the same one used by all other preprocessors (in particular gcc).
Design Notes (optional)
As usual these design notes only describe one possible way to do this assignment, and are entirely optional.
First of all, it is recommended that you read 16.3 a couple of times before you continue reading these notes, and certainly before you start writing code. Some find the logic of macro replacement quite challenging to implement, so be prepared for this.
You can start by making a PreprocessingToken
class that packs up the output of IPPTokenStream
.
Perform a simple top-down predictive parse to get a stream of preprocessing directives and text-sequences
.
Keep a table of currently defined macros.
When you encounter a #define
add the macro to the table. If it currently exists check to see that the identifier-list
and replacement-list
is the same as the currently defined one. It is an error if they do not match.
When you encounter an #undef
remove the macro from the table.
When you encounter a text-sequence
you need to process any macro invocations it contains.
As a first step note that new-lines
are not significant once a text-sequence
has been identified, so you can collapse new-lines
into whitespace-sequence
at this point. After that you will also want to collapse each resulting contiguous sequences of whitespace-sequences
into a singular whitespace-sequence
.
As you find macro invocations you will invoke them and replace the invocation with a new sequence of tokens. These new tokens have to be rescanned.
Consider the following:
#define f(x) 1 x (
#define g(x) 2 x
f(g)b)
The text-sequence
is as follows:
f(g)b)
After the first invocation f(g)
is replaced by 1 g (
resulting in:
1 g (b)
This must be rescanned, and a new invocation is found. That of g (b)
, which is replaced by 2 b
:
1 2 b
Given the nature of that operation, a good data structure to use is a stack, where the top of the stack is the start of the text sequence (ie token order is in reverse). That way when an invocation is replaced, its tokens can be popped off the top of the stack and the replacement list can be pushed onto it, and processing can continue from the top of the stack again.
After you identify a function-like macro invocation and its arguments, there are three possible transformations you may need to perform on each argument before they are used:
- the argument is used as is
- the argument is macro replaced before being used
- the argument is stringized
Which of the three options is applied depends on the usage of the parameter within the replacement-list
of the macro. If it follows a #
, the argument is stringized. If it is next to a ##
the argument is used as is. If neither, than it is macro replaced before being used.
The rules for nesting and recursion are difficult to understand, and even more difficult to implement.
We suggest keeping with each identifier token, a blacklist of nested macro names and a noninvokable flag. When an identifier token is produced as part of a macro invocation its blacklist is assigned to be that of the head token of the macro that was just invoked, and also the name of the macro that was used is added.
Once you have identified a new potential macro invocation, check to see if the heads name is contained in its own blacklist, if it is flag it as noninvokable. If the head token is flagged as noninvokable, abort the invocation.


AWB is a team of singers and musicians from Ahmedabad city, Gujarat India who are desperately spreading the gospel of Jesus through their songs like, “Muskura”, “Yeshu Raja”, “Teri Aur”, “Jitna Khush”, in India and the world over
We travel to different areas of the country (especially in Gujarat) to do Worship Events/Concerts.
Jesus. Yes, Bible Says (Acts 16:31): If you believe that Jesus is the Lord then you and your household will be saved and will get eternal life with him in heaven.
In the mid-night Paul and Silas were praying and singing hymns to God and other prisoners were listening to them. Suddenly there was a violent earthquake and everyone’s chains came loose.
Yes, what we really believe is that we have the power of worship. When we worship in the name of our Lord Jesus our chains of sorrow, pain, grief, loneliness, will be loosed and not only ours but the chains of those who are listening to our worship/singing. We have the power and authority to break the chains of our life through worship in the name of our Lord Jesus “If we believe”.
In the year 2001 in India, a young boy was crying and praying in his room, while he was praying to the Lord Jesus for others who are in pain, suddenly he saw a light in his room and the room was filled with brightness. He opened his eyes to see what is happening and he saw someone was standing in that brightness, and then he heard a voice that “I am coming soon, share this message/testimony to as many people as you can because the time is near”. From that day, the boy was spreading the gospel to people as Jesus said to him to do.
Yes, Jesus is coming soon to bring his believers to heaven, in a moment, in the twinkling of an eye (1 Corinthians 15:52).Every Thursday (Praying / Bible Reading / Worship / Preaching etc.)


Chapter 304E
NURSING SCHOLARS PROGRAM
REPEALED. L 2006, c 75, §16.
Cross References
For present provisions, see §§304A-3301 to 3305.


We are always open to discussing new projects, creative ideas or opportunities.
Fields marked with * are mandatory.


SUMMER OF SAM A film review by Mark R. Leeper
Capsule: New York City, the summer 1977, the local Son of Sam killings hang over everybody's lives in the Italian-American community. But this story is really about popular hairdresser Vinny and little his betrayals of his best friend and his wife. The climate of paranoia from the killings will only bring out the worst in Vinny. Spike Lee's portrayal of Italian-Americans is just about like it has been in films likes like DO THE RIGHT THING, in such a big dose it looks a lot like racism. Rating: 5 (0 to 10), high 0 (-4 to +4)
Spike Lee co-produces and co-directs this story of a group of people living in Bedford Stuyvesant section of New York. The film is not really about the Son of Sam murders themselves but about the atmosphere of dread created by the killings. Central to the story is Vinny the hairdresser (played by John Leguizamo), his wife Dionna (Mira Sorvino), and Vinny's boyhood friend Ritchie (Adrien Brody). Vinny is flashy and flamboyant, a local favorite in the club disco scene. Vinny is living the life of easy sex, illegal drugs, loud music, and too much drinking. His wife dotes on him but admits to herself that she somehow failing to satisfy him and is disappointing him. Ritchie has affected a British punk hairstyle and a phony British accent. Most people in the neighborhood cannot understand what Ritchie is doing but Vinny defends his friend.
Meanwhile the paranoia over the killings is getting to the neighborhood. There is a general assumption that the Son of Sam is someone from the neighborhood and someone they all know. With the tacit approval of the police vigilante groups form to try to track down whoever is doing the killing. In a witch hunt atmosphere anyone who seems out of the ordinary, even ball-player Reggie Jackson, can be suspect. All the while we see little bits of the Son of Sam himself living in torment from his personal demons then striking like some Angel of Death. He is just tangential to the story but hangs over all that goes on.
Spike Lee has structured the film a lot like his DO THE RIGHT THING. There is a violent incident toward the end of the film and virtually every scene is buildup to this moment of violence. In DO THE RIGHT THING; however, it was a mystery where the story was going. In SUMMER OF SAM it is very obvious what is going to happen and the viewer is left to wonder only how long it will take and how will it happen. The answer the second question is "longer than you think." Until then you will be putting in a lot of time with some generally unpleasant people. Lee orchestrates all this with the music of the summer of 1997. He uses the time-tested but hardly original technique of creating period feel by just finding out what the popular songs stations were playing and salting them all over the soundtrack. Sex in the film is kept frequent, explicit, but using camera angles so that little explicit is shown to the audience.
What is disturbing is that Spike Lee's view of the Italian Americans in this part of New York is not very deep. If it is beyond stereotype, it is not by much. His portrayal is certainly not very pleasant or understanding. Lee has, in the past, accused other filmmakers of using stereotypes. But it may well instead be a result of just the same sort of rushed and poorly observed writing that is starting to appear in his own work. In fact there have always been unflattering portraits of non-blacks that he has not written well and which do not fall far beyond the stereotypical. During the film he complains about a double standards toward blacks, but he himself would complain if another film had black characters so poorly characterized.
The paranoiac aspects of the film and a good and sympathetic performance by Mira Sorvino make this otherwise wandering and pointlessly over-long film considerably more watchable than it would be otherwise. And as with DO THE RIGHT THING we do see a good, credible build of events that make an unthinkable incident believable. I give SUMMER OF SAM a 5 on the 0 to 10 scale and a high 0 on the -4 to +4 scale. Gee, a film about the summer of 1977 and not one mention of STAR WARS.
Mark R. Leeper<EMAIL_ADDRESS>Copyright 1999 Mark R. Leeper
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Yay!


On a story's video playback page, you can mark that story to become part of "My Clips" by clicking/tapping the "Add to My Clips" button.
If the story is already in the local set of clips, you see a "Remove from My Clips" button instead. Clicking/tapping that button will remove the story from "My Clips." Note that "My Clips" is only a local working area on this browser and device. If you want to use it on another device or at a different time, you need to save it.
One way to move a story in "My Clips" to the very last slot is to go to that story's video page, click/tap the "Remove from My Clips" button and then the "Add to My Clips" button. This removes the story from its old position in the local list of clips and puts it into the last slot.
You have access to your workspace, titled "My Clips" in the main routes for the application, at any time. Selecting "My Clips" will show you the stories you have marked. One main purpose of collecting a set is to then bookmark it for future recollection, or share it so others can make use of this set. From the button at the top of the "My Clips" page, which is named "Share My Clips...", you select it to then be shown a dialog box where you enter the title for your set of clips. On clicking the "copy to clipboard" button in the dialog, that title and the list is now in your clipboard which you can then copy into the browser address bar, into email to colleagues, into your own bookmarks bar, etc., to save and share.
For example, you looked through stories on baseball and kerosene and are intrigued by two, from Ted Radcliffe and Dorothy Jackson. You select the "Add to My Clips" button on each story's page. You navigate to "My Clips" and see these two stories in their story set. You select the "Share My Clips..." button and enter a title "Baseball and Kerosene." The result is a link, example titled set of two stories, which is presented in a text format on your clipboard that allows you to paste and go with that link in browsers. NOTE: the character set for saved titles is limited to letters, numbers, dashes, quotes, periods, and spaces.
Also on the "My Clips" page is a button to "Clear My Clips" which lets you remove everything from your local workspace, i.e., "My Clips". This is useful when you have multiple sets of stories to work on. For example, perhaps you are doing research on 1940s baseball stories and 1960s baseball stories. You set up a number of stories in My Clips from the 1940s. Importantly, you saved this set using the method described above (e.g., after "copy to clipboard" you paste that text link into a text file). You then clear the local workspace, i.e., "My Clips", using "Clear My Clips" and assemble a new list of 1960s stories, and save that set using the copy to clipboard method. Again, copy that link to your text file. If you want to load up either of these sets as your "My Clips", you can do so by using "Clear My Clips", then loading the saved route in your text file for the 1940s or 1960s set, seeing that there is a button "Add X stories into My Clips" where X is the number of stories shown. In this figure, X is 3, i.e., 3 stories in the set.
You click this button to append those X stories into your "My Clips" set. Through use of saving your "My Clips" set, clearing it, loading up saved lists, and adding those saved lists into "My Clips" you have the ability to assemble, title, save, clear and reset groups of stories.


class: center, middle, inverse, title-slide # Session 2 ## Finding Relationships in Your Data ### Global TIES for Children ---  # Last Time We talked about: - An introduction to R syntax - Loading and viewing your data - R's data types - Basic data wrangling --- # Agenda By the end of this workshop, you should be able to understand this block of code: ```r dat <- read_csv(here("data/dataset.csv")) admin_dat <- read_csv("data/admin.csv") dat %>% left_join( admin_dat %>% select(student_id, registration_date), by = "student_id" ) %>% ggplot(aes(registration_date)) + geom_histogram() + facet_wrap(~ grade) ``` --- # Agenda We'll be covering - R Markdown - Combining datasets (joins and binding) - The wonderful plotting library **ggplot2** For all of this presentation, I will have the Tidyverse attached and our intervention dataset loaded: ```r suppressPackageStartupMessages(library(tidyverse)) intervention <- read_csv( here::here("data/sample_intervention_dataset.csv"), col_types = cols() ) ``` ``` ## Warning: Missing column names filled in: 'X1' [1] ``` The `col_types = cols()` parameter suppresses the **readr** column specification. --- # R Markdown For the rest of these workshops, I'll include a `_notes.Rmd` document in the `inst/slides` folder to supplement the slides. This is an example of an **R Markdown** document, an implementation of **literate programming** (adding plain text alongside code to explain what's going on). An R Markdown document is a Markdown document (basically plain text with some formatting syntax) with "R chunks", blocks of R code that run to produce any kind of output. Example: .font10.rendered[
 # Some header This is some text ```{r} some %>% code() %>% that() %>% runs() ```
 ] This is a powerful way to generate **dynamic** documents (including this slideshow!) from a single, computable source of information. Let's take a look at the `notes` file! --- # R Markdown Output Types R Markdown can generate all sorts of output: .pull-left[ - Web / HTML documents - Word documents - PDF / LaTeX documents - Journal-ready articles via the **rticles** package ] .pull-right[ - Books - Slideshows - Dynamic web dashboards via **Shiny** - Interactive educational applets via the **learnr** package ] ... and much more! Check out [RStudio's intro guide to Rmd](https://rmarkdown.rstudio.com/) and [Yihui Xie (the creator)'s book](https://bookdown.org/yihui/rmarkdown/) on R Markdown for more references. --- class: inverse, middle, center # Combining Data to Make New Data ## 📦 --- # Combination Methods There are two main kinds of combining datasets together: 1. **Binding** datasets, either binding rows or columns together 1. **Joining** (or **merging**) datasets based on shared information **Binding** is useful for combining _similarly structured_ data. **Joining** is the more general case where the datasets need not have similar structure but _must_ have a common variable (or set of variables). --- # Binding The two primary commands relate to the two _dimensions_ of how you can bind data: `bind_rows()` and `bind_cols()` (both from **dplyr**). I'll only be discussing `bind_rows()` since `bind_cols()` is _very rarely_ the right way to add variables from other datasets into a working dataset, _and_ it can be a brittle and potentially dangerous operation. To motivate the use of `bind_rows()`, I will split the intervention dataset into two datasets, each with its own cohort: ```r cohort_a <- intervention %>% filter(Cohort == "A") cohort_b <- intervention %>% filter(Cohort == "B") ``` Row binding is useful when you have _multiple versions_ of the same (or similar) datasets and you'd like to combine them into one. --- # Binding To verify we have different datasets, let's take a peek at them... ```r head(cohort_a, 2) ``` ``` ## # A tibble: 2 x 9 ## X1 Coded_ID Cohort Intervention Female Final_grade Final_score ## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 1 1 A -1 1 3.7 83.1 ## 2 2 2 A 1 0 2.7 70.8 ## # … with 2 more variables: Baseline_math_score <dbl>, ## # Baseline_stereotype_endorsement <dbl> ``` ```r head(cohort_b, 2) ``` ``` ## # A tibble: 2 x 9 ## X1 Coded_ID Cohort Intervention Female Final_grade Final_score ## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 46 4 B 1 1 3.7 85.2 ## 2 47 5 B 1 0 2.7 73.9 ## # … with 2 more variables: Baseline_math_score <dbl>, ## # Baseline_stereotype_endorsement <dbl> ``` --- # Binding ... and now bound together: ```r set.seed(1283) # Always set an RNG seed to get reproducible results! bind_rows(cohort_a, cohort_b) %>% slice_sample(n = 5) ``` ``` ## # A tibble: 5 x 9 ## X1 Coded_ID Cohort Intervention Female Final_grade Final_score ## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 67 44 B 1 0 4 92.7 ## 2 49 8 B 1 1 0.7 38.6 ## 3 41 87 A 1 0 1 40.3 ## 4 76 57 B -1 0 3.7 84.8 ## 5 53 15 B -1 1 0.7 38.9 ## # … with 2 more variables: Baseline_math_score <dbl>, ## # Baseline_stereotype_endorsement <dbl> ``` `slice_sample()` is a function from **dplyr** that picks rows at random, a derivative of `slice()` which filters the dataset to a specific row number range. --- # Binding By default, the row binding operation matches columns based on column order (i.e. match column 1 in `cohort_a` to column 1 in `cohort_b`, 2 to 2, and so on). If the two datasets have **slightly different** columns (like, reordered columns or some missing columns), `bind_rows()` takes care of this situation by **matching by column _name_**. --- # Binding To show this, I'll create a different `cohort_b` with some dropped and reordered columns from `cohort_b`. ```r different_cohort_b <- cohort_b %>% select(Coded_ID, Intervention:Final_score) ``` The columns of `cohort_b` are `X1`, `Coded_ID`, `Cohort`, `Intervention`, `Female`, `Final_grade`, `Final_score`, `Baseline_math_score`, and `Baseline_stereotype_endorsement`... ...whereas the columns of `different_cohort_b` are `Coded_ID`, `Intervention`, `Female`, `Final_grade`, and `Final_score`. --- # Binding Row binding them together: ```r cohort_b %>% head(n = 3) %>% bind_rows(head(different_cohort_b, 3)) ``` ``` ## # A tibble: 6 x 9 ## X1 Coded_ID Cohort Intervention Female Final_grade Final_score ## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 46 4 B 1 1 3.7 85.2 ## 2 47 5 B 1 0 2.7 73.9 ## 3 48 7 B -1 1 2.3 70.7 ## 4 NA 4 <NA> 1 1 3.7 85.2 ## 5 NA 5 <NA> 1 0 2.7 73.9 ## 6 NA 7 <NA> -1 1 2.3 70.7 ## # … with 2 more variables: Baseline_math_score <dbl>, ## # Baseline_stereotype_endorsement <dbl> ``` Note how `X1` and `Cohort` are filled in with **missingness** (shown as NA) since they don't exist in `different_cohort_b`. --- # Joining & Relational Data Unlike stacking similar data into a single dataset with row binding, **joining** or **merging** is **the** way to the bring variables from other datasets together1 based on some shared information called **keys**. There are two major kinds of keys and one minor but important one to remember: 1. **Primary keys**: Columns that uniquely identify each observed entity in your current dataset (major) 1. **Foreign keys**: Columns that uniquely identify entities in _different_ datasets, e.g. a school ID in a child-level dataset (major) 1. **Surrogate keys**: Columns that uniquely identify _each row_ (no duplicates!), e.g. a random serial number for each survey submission (minor but important) Datasets that have these keys that form relations among each other are called **relational data**. .footnote.font10[ (1): _Column_ binding assumes that the row order _and_ meaning is same among column-bound datasets which is _very_ rarely the case. This is why it's better to use joins. ] --- # Joining & Relational Data To demonstrate joins, I'll be using the `nycflights13` dataset like _R for Data Science_. ```r library(nycflights13) ``` It has 5 datasets: - `flights`: A log of all flights _from_ the New York area (JFK, LGA, and EWR) in 2013 - `airlines`: Information on each airline (primary key is `carrier`) - `airports`: Information on each airport in the flight log (primary key is `faa`) - `planes`: Information on each plane itself (primary key is `tailnum`) - `weather`: Hourly readings of the weather at each NY area airport --- # Joining & Relational Data .center[  .font10[ Source: [Grolemund & Wickham](https://r4ds.had.co.nz/relational-data.html#nycflights13-relational) ] ] This image, a _relational model diagram_, shows all of the connections among the datasets. The grey fields represent the primary keys of the datasets. Note that a database _can_ have multiple columns to construct a primary key. This diagram is a great way of communicating the full, detailed structure of your data. It also reinforces the need for **every observed entity** in your study to have its own ID (primary key)! --- # Joining & Relational Data There are four main join operations for datasets `x` and `y`: - `inner_join()`: includes all rows in `x` **and** `y` - `left_join()`: includes all rows in `x` and _matching_ rows in `y` - `right_join()`: includes all _matching_ rows in `x` and all rows in `y` - `full_join()`: includes all rows in `x` **or** `y` `left_`, `right_`, and `full_` are all _outer_ joins: "left outer join," "right outer join," and "full outer join." The following slides will _visually_ demonstrate how the joins work. Sometimes these are shown as Venn Diagrams, but that can be misleading when duplicates are involved. These visualizations are all from [Grolemund & Wickham](https://r4ds.had.co.nz/relational-data.html#nycflights13-relational). --- background-image: url('../images/inner-join.png') --- class: middle In code, this join would be written as: # Creating x and y
# NB: the c() function that combines values into single objects
x <- tibble(key = c(1, 2, 3), val_x = c("x1", "x2", "x3"))
y <- tibble(key = c(1, 2, 4), val_y = c("y1", "y2", "y3"))

x %>%
 inner_join(y, by = "key") ``` ## # A tibble: 2 x 3 ## key val_x val_y ## <dbl> <chr> <chr> ## 1 1 x1 y1 ## 2 2 x2 y2 ``` --- background-image: url('../images/outer-joins.png') ## Outer Joins --- background-image: url('../images/join-duplicates.png') ## Duplicates in Key Columns .footnote[ Reflection moment: What would happen if there were `\(m\)` rows of missingness in `x`'s `key` and `\(n\)` rows of missingness in `y`'s `key`? What would that mean for data integrity? ] --- # Joining & Relational Data .font12[ You can also use **multiple columns as a key**, necessary for nested or long-form (e.g. temporal) data. Suppose we wanted to examine how, on average, adverse weather affected JetBlue's flights -- a necessary calculation for the airline's dispatch team. We'd need to join JFK's `weather` reports to the `flights` log: ] .font10[ ```r flights %>% filter(origin == "JFK" & carrier == "B6") %>% inner_join( weather, by = c("year", "month", "day", "hour", "origin") ) %>% head(n = 4) ``` ``` ## # A tibble: 4 x 29 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 544 545 -1 1004 1022 ## 2 2013 1 1 557 600 -3 838 846 ## 3 2013 1 1 558 600 -2 849 851 ## 4 2013 1 1 558 600 -2 853 856 ## # … with 21 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, ## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, ## # hour <dbl>, minute <dbl>, time_hour.x <dttm>, temp <dbl>, dewp <dbl>, ## # humid <dbl>, wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>, ## # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour.y <dttm> ``` ] --- ## Couple Things to Note .font10[ ```r flights %>% filter(origin == "JFK" & carrier == "B6") %>% inner_join( weather, by = c("year", "month", "day", "hour", "origin") ) ``` ] **1)** I only filtered for `origin` because of what the help page for `flights` says! Since datasets can be shared in R packages as well, you can access their data dictionaries with the `?` operator (e.g. `?flights`). **2)** "B6" is from the `airlines` dataset: .font10[ ```r airlines %>% filter(carrier == "B6") ``` ``` ## # A tibble: 1 x 2 ## carrier name ## <chr> <chr> ## 1 B6 JetBlue Airways ``` ] Reflection moment: Why did I use an inner join? Why not use a left outer join? --- # Recap .pull-left[ Suppose I have a dataset `school_db` that has a primary key column `school_id` and some information about each school, and suppose I have a survey of schoolchildren across multiple schools `survey` with primary key column `child_id` and foreign key column `school_id`. How would I combine the school data into the survey? ] .pull-right.font10[ ```r # 1. survey %>% inner_join(school_db, by = "school_id") # 2. survey %>% bind_rows(school_db) # 3. school_db %>% left_join(survey, by = "child_id") # 4. survey %>% left_join(school_db, by = "school_id") ``` ] --- # Recap .pull-left[ Suppose I have a dataset `school_db` that has a primary key column `school_id` and some information about each school, and suppose I have a survey of schoolchildren across multiple schools `survey` with primary key column `child_id` and foreign key column `school_id`. How would I combine the school data into the survey? ] .pull-right.font10[ ```r # 1. survey %>% inner_join(school_db, by = "school_id") # 2. survey %>% bind_rows(school_db) # 3. school_db %>% left_join(survey, by = "child_id") # 4. *survey %>% * left_join(school_db, by = "school_id") ``` ] --- class: middle, center, inverse # Visually Exploring Your Data ## 📊📉 --- # Putting It All Together Data wrangling and exploratory data analysis would be incomplete without actually **looking** at your data. ### At this stage, we'll be putting together all we know so far, basic data wrangling and merging to ask all sorts of questions, with plotting to visualize our data. I'll continue to try to answer "how, on average, adverse weather affected JetBlue's flights" visually to introduce... --- .center[ # `ggplot2` ] **ggplot2** is a **huge** package dedicated to implementing the Grammar or Graphics in R. The concept is the idea that **everything in a plot is a layer**, from the input data to geometries to statistics to coordinate transformations. This allows users to **compose** together plot features, similar to how the pipe `%>%` composes together functions. **ggplot2** is already attached since it's provided with the **tidyverse** package. --- # Intro to ggplot2 Let's first look at the `precip` column in `weather`. `precip` is the amount of accumulated precipitation in the last hour of measurement. What does its distribution look like? ```r jfk_weather <- weather %>% filter(origin == "JFK") ``` To define the input data, we use the `ggplot()` function to start a plot and the `aes()` function to specify the input variables. ```r jfk_weather %>% ggplot(aes(x = precip)) ``` --- # Intro to ggplot2 `aes(x = )` specifies that this is a 1 variable plot. `aes(x = , y = )` specifies a 2 variable plot. .pull-left[ .font10[ ```r jfk_weather %>% * ggplot(aes(x = precip)) ``` ] ] .pull-right[  An empty plot! Let's add a histogram... ] --- # Intro to ggplot2 Histograms (and kinds of plots in general) are a type of **geometry**. We can access geometries with the `geom_*()` functions. Here, I'll use `geom_histogram()`: .font10[ jfk_weather %>%
 ggplot(aes(x = precip)) + # Note the plus!! In ggplot, you "add" layers like a stack.
 geom_histogram()  ] --- # Intro to ggplot2 It's pretty clear that most hours of the year had no or very little precipitation. But what about the days where there _was_ rain? Let's look at `precip` against `dep_delay` (in seconds) in `flights`. We'll need to join the `weather` data to `flights`! .font10[ jfk_flights_weather <-
 flights %>%
 filter(origin == "JFK") %>%
 inner_join(
 weather %>%
 select(-time_hour), # Deselect columns with `-`
 by = c("year", "month", "day", "hour", "origin")
 ) ] --- # Intro to ggplot2 Let's double-check that we have `dep_delay` and `precip`... .font10[ ```r jfk_flights_weather %>% filter(precip > 0) %>% select(dep_delay, precip) ``` ``` ## # A tibble: 7,266 x 2 ## dep_delay precip ## <dbl> <dbl> ## 1 12 0.02 ## 2 20 0.02 ## 3 -8 0.03 ## 4 -3 0.03 ## 5 -2 0.03 ## 6 0 0.03 ## 7 -4 0.03 ## 8 3 0.03 ## 9 4 0.03 ## 10 9 0.03 ## # … with 7,256 more rows ``` ] --- # Intro to ggplot2 ... And visually let's create a scatter plot with `geom_point()`: .pull-left[ .font10[ ```r jfk_flights_weather %>% filter(precip > 0) %>% select(dep_delay, precip) %>% ggplot(aes(x = precip, y = dep_delay)) + geom_point() ```  ] ] .pull-right[ Very dense scatter plot! Let's add a linear fit using the `stat_smooth()` statistic layer for a trendline and add some transparency on the dots to prevent over-plotting. ] --- # Intro to ggplot2 .pull-left[ ```r jfk_flights_weather %>% filter(precip > 0) %>% select(dep_delay, precip) %>% ggplot(aes(x = precip, y = dep_delay)) + geom_point(alpha = 0.4) + * stat_smooth(method = "lm") ``` `method = "lm"` refers to using the base R linear model `lm()` function that Michael will discuss later! ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-27-1.png) ] --- # Intro to ggplot2 .pull-left[ ```r jfk_flights_weather %>% filter(precip > 0) %>% select(dep_delay, precip) %>% ggplot(aes(x = precip, y = dep_delay)) + geom_jitter(alpha = 0.4) + * stat_smooth(method = "lm") ``` We can also use a jitter plot to reduce the regularity in the dots and imply that the measurements are not supposed to be on a grid. ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-29-1.png) ] --- # Intro to ggplot2 We can show categorical information in several ways. A couple popular methods are with bar plots and different colors. Let's look at average departure delay each month by airline: .pull-left[ .font10[ ```r jfk_flights_weather %>% * mutate(carrier = as.factor(carrier)) %>% group_by(carrier, month) %>% summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) %>% ggplot(aes( x = month, y = avg_dep_delay, color = carrier )) + geom_point() ``` The `mutate()` statement which converts `carrier` to a factor is necessary since **ggplot2** uses factors for categorical data and does not automatically convert columns passed to the `color` and `fill` parameters. ] ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-31-1.png) ] --- # Intro to ggplot2 .pull-left[ .font10[ ```r current_plot <- jfk_flights_weather %>% left_join(airlines, by = "carrier") %>% mutate(name = as.factor(name)) %>% group_by(name, month) %>% summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) %>% ggplot(aes( x = month, y = avg_dep_delay, color = name )) + geom_line() current_plot ``` By assigning the plot to a symbol, we can save our work and build upon it in later stages! ] To make this publication-ready, we need a few things: a title and good axis labels, some color scale tweaking, and a decent background. I'll adjust those with `labs()` and some `scale\_\*()` and `theme\_\*()` functions from the fun package **ggthemes**. ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-33-1.png) ] --- # Intro to ggplot2 .pull-left[ .font10[ ```r current_plot + labs( x = "Month", y = "Average departure delay (minutes)", title = "Monthly Trend of Departure Delays from JFK", color = "Airline" ) + ggthemes::theme_clean() + ggthemes::scale_color_calc() ``` ] For me, there are too many airlines on this plot -- it looks like spaghetti. Instead, let's split geometries into different plots using `facet_wrap()`... ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-35-1.png) ] --- # Intro to ggplot2 .pull-left[ .font10[ ```r jfk_flights_weather %>% left_join(airlines, by = "carrier") %>% mutate(name = as.factor(name)) %>% group_by(name, month) %>% summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) %>% ggplot(aes( x = month, y = avg_dep_delay )) + geom_line() + * facet_wrap(~ name) + labs( x = "Month", y = "Average departure delay (minutes)", title = "Monthly Trend of Departure Delays from JFK" ) + ggthemes::theme_clean() ``` `facet_wrap()` partitions the geometries based on the variable specified after the `~` symbol (in R, this denotes a _formula_. Michael will discuss formulae later). ] ] .pull-right[ ![](02-plotting-joins_files/figure-html/unnamed-chunk-37-1.png) ] --- # `esquisse` If you just want to play around with _possible_ visualizations, I recommend using the **esquisse** package. .center[ ## _Demo_ ] Type ```r esquisse::esquisser() ``` into the console! --- # All in all... **ggplot2** has an _enormous_ amount of power, too much to cover here. Moreover, the topic of data visualization is beyond the scope of this series of workshops. Nevertheless, these are recommended resources for using **ggplot2** and data visualization in R overall: .pull-left[ ### [Data Visualization RStudio Cheatsheet](https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf) _Extremely_ handy cheatsheet that covers _most_ of what you need to know to work with **ggplot2**. I refer to this almost every time I create plots because I tend to forget which function does what! ] .pull-right[ ### [From Data to Viz](https://www.data-to-viz.com/) _Beautiful_ website that covers data visualization overall, including which situations would require certain kinds of plots and discussions of caveats and traps that people can fall into. The site provides R code snippets for each plot on the site.1 ] .footnote.font10[ (1): It's written using R Markdown! ] --- # Exercise Suppose you examine the final score by intervention group. Consult [From Data to Viz](https://www.data-to-viz.com/), and then construct a plot that looks at this relationship. Include any other variables that you deem necessary / interesting!


{% extends "main_app/common.html" %} {% load static %} {% block content %}
Note : Your mobile number must include your country code such as +91, and must not contain spaces.


{% include head.html %}
{% include header.html %}
Standalone test spies, stubs and mocks for JavaScript.
Works with any unit testing framework.


Doors at residential units set on fire in 2 loan shark harassment cases, 30-year-old man arrested
SINGAPORE: A 30-year-old man has been arrested for his suspected involvement in loan-sharking activities, the police said in a news release on Thursday (Nov 7).
The police said they were alerted on Nov 5 to two separate cases of loan shark harassment at residential units along West Coast Road and Ang Mo Kio Avenue 5.
In both cases, the doors of the units were set on fire.
Following investigations, officers from the Criminal Investigation Department, Clementi Police Division and Ang Mo Kio Police Division identified the man and arrested him the following day.
READ: 3 arrested for loan shark activities after tomato sauce, paint splashed on gate and vehicle
The man will be charged in court on Nov 8 under the Moneylenders Act.
If found guilty of loan shark harassment, first time offenders face a maximum fine of S$50,000, with mandatory imprisonment of up to five years, and mandatory caning of up to six strokes.
In the case of a second or subsequent conviction, offenders shall be liable to a fine of not less than S$6,000, and not more than S$60,000, with mandatory imprisonment of between two to nine years, and mandatory caning of up to 12 strokes.
The police said that they have zero tolerance against loan shark harassment activities.
“Those who cause hurt, endanger lives, deliberately vandalise properties, or cause annoyance and disruptions to public peace and security will be arrested and dealt with severely in accordance with the law,” they added.
READ: 129 arrested for suspected involvement in loan-sharking activities
Members of the public are advised to stay away from loan sharks and not to work with or assist them in any way.
The public can call the police or the X-Ah Long hotline at 1800-924-5664 if they suspect or know of anyone who could be involved in illegal loansharking activities.


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These pages contain the documentation for current and previous spray releases.
Please select a release in the side-bar!


A Reexamination of Schema
Implementing Materialized Views to Reduce Load Times
The Reddit crawler that I wrote runs every 15 minutes and stores every word used more than once during that period of time. As you may imagine, the database grew quite quickly. Roughly one year after the projects inception, the database now contains more than 12 million rows. Performance gradually decreased and the database design that I initally created needed to be refactored.
Before diving in to the specific changes that I made, let's set some benchmarks.
| API Call | Time Elapsed |
|---|---|
| /topwords?interval=day | ~12-15s |
| /topwords?interval=month | ~15-20s |
| /topwords?interval=year | ~25-30s |
| /topwords?interval=custom | ~30s |
| /search | ~30s |
The database schema looked this:
Notice that the last column is a datetime. The data that I really needed, however, was a date. This resulted in a lot of operations casting this column to a date (e.g. date_trunc(day, 'date')). I indexed the 'day' column, but these operations did not take advantage of the index, resulting in very slow queries.
To fix this issue, I renamed the 'day' column to 'full_date' and then created a true 'day' column with a corresponding index.
The database schema now looks like this:
This change improved performance significantly, reducing load times by about 30%, to between 9-20s. While a step in the right direction, the application was not nearly snappy enough.
All of my queries involved summation of word counts over multiple days and distinct crawler runs (e.g. find the 10 most common words over the last day, month, year or get sum for everyday the word is present in the database). Additionally, since I was interested in word usage rates, I also needed total word counts for each time period. Enter the materialized view.
Here is the definition for a few of the materialized views I use in this application.
What makes materialized views so perfect for this application is the ability to dynamically refresh the view. In this case, I just refresh the materialized views at the end of each crawler run, preprocessing the data that needs to be returned for each API call. Here are a few examples of what these ultimately look like.
The creation of these materialized views (and the corresponding API manager refactor) resulted in enormous performance gains. The crawler, in effect, now takes on the work of compiling the expensive summation queries. This happens behind the scenes, instead of at request time. I encourage you to take a look at the SQL queries I now use to fetch the data for these pages.
In conclusion, let's take a look at the current benchmarks for the API calls against the Reddit databases. All in all, I am really pleased with this effort. I learned a ton about database design, materialized views, and indexes.
| API Call | Time Elapsed |
|---|---|
| /topwords?interval=day | <.5s |
| /topwords?interval=month | <.5s |
| /topwords?interval=year | <.5s |
| /topwords?interval=custom | ~3s |
| /search | ~3s |


P2IRC cloud
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At the beginning of each period, participants are free to submit buy/sell orders to trade shares. Participants submit orders simultaneously; neither sees the order from the other before submitting his/her own.
To submit a buy order, participants should specify the maximum buying price per share (BP) and the number of shares (BN) they are willing to buy. To submit a sell order, participants should specify the minimum selling price per share (SP) and the number of shares (SN) they are willing to sell.
Please note that you might only sell at most the number of shares you hold but not more; you might not buy more than you could afford with your cash. If you have no intention to buy or sell, you might stay put and submit no order at all. You might submit either buy/sell order but not both.
A transaction is only possible when one participant wants to buy and the other to sell. Moreover, the submitted maximum buying price (BP) has to be higher than the minimum selling price (SP). When this is the case, transaction takes place at the average of these two prices. That is transaction price (P) = 0.5*(BP+SP). The number of shares traded (N) equals the smaller of the two numbers, i.e. N is the minimum of BN and SN.
If transaction takes place, N shares are removed from the selling party to the buying party; accordingly, cash equal to the transaction price (P) is added to the selling party and subtracted from the buying party.
Otherwise, no transaction takes place and the orders submitted, if any, are not revealed to participants.
At the end of each period, dividends will be randomly drawn, with equal probabilities of being $1/share or $2/share. Dividend per share will be the same for all participants and all shares. Each participant is informed of the draw at the end of each period and distributed according to the shares he/she holds at the time.


{% include head.html %}
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New links in your inbox every
Sunday
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Yes, please!
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Long Island based artist, business professional, and burdgeoning web developer.
Combining an extensive background in various professional fields with my artistic endeavors in order to scale e-commerce business.
Currently enrolled in Columbia University's School of Engineering Coding Bootcamp.
Interested in building out e-commerce websites for other Small Business Owners
Long Island, New York


Salmon Cookies
New Store Location
Min Number Customer Per Hour
Max Number Customer Per Hour
Average Cookie Number


The IWebStorage interface of the Web Storage API provides access to the session storage or local storage for a particular document on the loaded web page, allowing you to for example add, modify or delete stored data items.
More...
|
| string | Key (long index) |
| | Returns the name of the key in the storage. The order of keys is user-agent defined, so you should not rely on it. More...
|
| |
| bool | Set (string key, string value) |
| | Adds the key and value to the storage, or updates key's value if it already exists. More...
|
| |
| bool | Remove (string key) |
| | Removes the element with the specified key from the storage. More...
|
| |
| bool | Clear () |
| | Removes all key/value pairs in the storage. More...
|
| |
| bool | Contains (string key) |
| | Returns true if storage contains specified key or false otherwise. More...
|
| |
|
| long | Count [get] |
| | Gets number of key/value pairs in the storage. Returns 0 if the storage hasn't been initialized yet or has no key/value pairs. More...
|
| |
| string | this[string key] [get] |
| | Gets the value associated with the given key or null when the key doesn't exist. More...
|
| |
The IWebStorage interface of the Web Storage API provides access to the session storage or local storage for a particular document on the loaded web page, allowing you to for example add, modify or delete stored data items.
| bool DotNetBrowser.IWebStorage.Clear |
( |
| ) |
|
Removes all key/value pairs in the storage.
- Returns
- true if storage was successfully cleared. Returns false in case of exceptions or storage was not initialized.
| bool DotNetBrowser.IWebStorage.Contains |
( |
string |
key | ) |
|
Returns true if storage contains specified key or false otherwise.
- Parameters
-
| key | the name of the key you want to check. Cannot be null. |
- Returns
- true if storage contains specified key or false otherwise.
- Exceptions
-
| ArgumentNullException | when the key is null. |
| string DotNetBrowser.IWebStorage.Key |
( |
long |
index | ) |
|
Returns the name of the key in the storage. The order of keys is user-agent defined, so you should not rely on it.
- Parameters
-
| index | an integer representing the number of the key you want to get the name of. This is a zero-based index. Must be in the range greater than 0 and less than the Count. |
- Returns
- a string containing the name of the key or null.
- Exceptions
-
| ArgumentOutOfRangeException | when the index isn't in the range greater than 0 and less than the Count. |
| bool DotNetBrowser.IWebStorage.Remove |
( |
string |
key | ) |
|
Removes the element with the specified key from the storage.
- Parameters
-
| key | key the name of the key you want to remove. Cannot be null. |
- Returns
- true if key/value pair was successfully removed, or key doesn't exist. Returns false in case of exceptions.
- Exceptions
-
| ArgumentNullException | when the key is null. |
| bool DotNetBrowser.IWebStorage.Set |
( |
string |
key, |
|
|
string |
value |
|
) |
| |
Adds the key and value to the storage, or updates key's value if it already exists.
- Parameters
-
| key | the name of the key you want to create. Cannot be null. |
| value | the value you want to give the key you are creating. Cannot be null. |
- Returns
- true if key/value pair was successfully added or updated. Returns false in case of exceptions.
- Exceptions
-
| ArgumentNullException | when the key or value is null. |
| long DotNetBrowser.IWebStorage.Count |
|
get |
Gets number of key/value pairs in the storage. Returns 0 if the storage hasn't been initialized yet or has no key/value pairs.
| string DotNetBrowser.IWebStorage.this[string key] |
|
get |
Gets the value associated with the given key or null when the key doesn't exist.
- Parameters
-
| key | the name of the key you want to retrieve the value of. Cannot be null. |
- Returns
- The element with the specified key.
- Exceptions
-
| ArgumentNullException | when the key is null. |


A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
| Version: | 2.2 |
| Published: | 2017-12-15 |
| Author: | Joshua Millstein |
| Maintainer: | Joshua Millstein <joshua.millstein at usc.edu> |
| License: | Artistic-2.0 |
| NeedsCompilation: | yes |
| SystemRequirements: | gsl (with development libraries) |
| Materials: | README |
| CRAN checks: | cit results |
| Reference manual: | cit.pdf |
| Package source: | cit_2.2.tar.gz |
| Windows binaries: | r-devel: cit_2.2.zip, r-release: cit_2.2.zip, r-oldrel: cit_2.2.zip |
| OS X binaries: | r-release: cit_2.2.tgz, r-oldrel: cit_2.2.tgz |
| Old sources: | cit archive |
Please use the canonical form https://CRAN.R-project.org/package=cit to link to this page.


- bbd - package bbd
-
- BBDAbstractFactory - Class in bbd
-
- BBDAbstractFactory() - Constructor for class bbd.BBDAbstractFactory
-
- BBDBeanArrayList<E> - Class in bbd
-
This is an array list of Java beans.
- BBDBeanArrayList() - Constructor for class bbd.BBDBeanArrayList
-
Creates a new instance of BBDBeanList
- BBDBeanArrayList(ResultSet, IBBDBeanAPI) - Constructor for class bbd.BBDBeanArrayList
-
Constructor from JDBC result set.
- BBDBeanJTable<B extends java.util.List,L extends BBDBeanArrayList<B>> - Class in bbdSwing
-
- BBDBeanJTable(IBBDBeanBroker<B, L>) - Constructor for class bbdSwing.BBDBeanJTable
-
Create a table using the select method from the broker and the field
names for headings from the meta data.
- BBDBeanJTable(L) - Constructor for class bbdSwing.BBDBeanJTable
-
Create a table using a list of BBDBeanArrayList that could be
custom created from memory content.
- BBDBeanJTable.BBDDefaultTableModel - Class in bbdSwing
-
Custom table model to hand special features related to cell
editors and renders based on the class of the objects present in
a column.
- BBDCrudPanel - Class in bbdSwing
-
This Swing panel displays a BBDJTable and provides full
change, replace, update (CRUD) capablity with no additional
programming.
- BBDCrudPanel(BBDJTable) - Constructor for class bbdSwing.BBDCrudPanel
-
Creates new form BBDCrudPanel
- BBDDefaultBroker<R extends IBBDRow<IBBDField>,L extends BBDRowArrayList<R>> - Class in bbd
-
Broker for BBD database table
This contains the methods for accessing
the database BBD database table.
- BBDDefaultBroker() - Constructor for class bbd.BBDDefaultBroker
-
Default constructor
- BBDDefaultField - Class in bbd
-
Default implementation class that encapsulated one field from one row from
the database.
- BBDDefaultField() - Constructor for class bbd.BBDDefaultField
-
Creates a new instance of BBDDefaultField
- BBDDefaultField(Object) - Constructor for class bbd.BBDDefaultField
-
Constructor to populate the object.
- BBDDefaultRow<E extends IBBDField> - Class in bbd
-
Default implementation of one row of data returned by the API.
- BBDDefaultRow() - Constructor for class bbd.BBDDefaultRow
-
Creates a new instance of BBDDefaultRow
- BBDJTable<R extends IBBDRow<IBBDField>,L extends BBDRowArrayList<R>> - Class in bbdSwing
-
- BBDJTable(IBBDBeanBroker<R, L>) - Constructor for class bbdSwing.BBDJTable
-
Create a table using the select method from the broker and the field
names for headings from the meta data.
- BBDRegEx - Class in bbd
-
- BBDRegEx(Pattern, String) - Constructor for class bbd.BBDRegEx
-
Construct reg exp class with a pattern and an error message.
- BBDRowArrayList<R extends IBBDRow<IBBDField>> - Class in bbd
-
Rowsets returned from the database are converted to an arrayList of rows.
- BBDRowArrayList() - Constructor for class bbd.BBDRowArrayList
-
Creates a new instance of SQLRowArrayList
- BBDRowArrayList(ResultSet, IBBDAPI<R>) - Constructor for class bbd.BBDRowArrayList
-
Constructor from JDBC result set.
- bbdSwing - package bbdSwing
-
- BBDTableColumnEditLocker - Class in bbdSwing
-
- BBDTableColumnEditLocker(BBDBeanJTable, int[]) - Constructor for class bbdSwing.BBDTableColumnEditLocker
-
Constructor
- BBDTableColumnHider<T extends javax.swing.JTable> - Class in bbdSwing
-
- BBDTableColumnHider(T) - Constructor for class bbdSwing.BBDTableColumnHider
-
- BBDUnitTestCase<R extends IBBDRow<IBBDField>,L extends BBDRowArrayList<R>> - Class in bbd
-
Abstrate test case used with JUnit 4.0 annotations.
- BBDUnitTestCase() - Constructor for class bbd.BBDUnitTestCase
-
- BBDUnitTestRow - Class in bbd
-
- BBDUnitTestRow() - Constructor for class bbd.BBDUnitTestRow
-
- BBDValidator<R extends IBBDRow<IBBDField>,L extends BBDRowArrayList<R>> - Class in bbd
-
Most applications written to integrate with databases use the datbase to
validate information.
- BBDValidator() - Constructor for class bbd.BBDValidator
-
- broker - Variable in class bbdSwing.BBDBeanJTable
-
Broker for database access.


The actual receiver status is mapped into various synchronization states generally used by receivers. The STREAMS module is configured to interpret the time codes of DCF C51, PZF535, PZF509, GPS166, Trimble SV6 GPS, ELV DCF7000, Schmid, Wharton 400A and low cost receivers (see list below).
The reference clock support in ntp contains the necessary configuration tables for those receivers. In addition to supporting several different clock types and 4 devices, the generation a a PPS signal is also provided as an configuration option. The PPS configuration option uses the receiver generated time stamps for feeding the PPS loopfilter control for much finer clock synchronization.
CAUTION: The PPS configuration option is different from the hardware PPS signal, which is also supported (see below), as it controls the way ntpd is synchronized to the reference clock, while the hardware PPS signal controls the way time offsets are determined.
The use of the PPS option requires receivers with an accuracy of better than 1ms.
Fudge factors
Only two fudge factors are utilized. The time1 fudge factor defines the phase offset of the synchronization character to the actual time. On the availability of PPS information the time2 fudge factor defines the skew between the PPS time stamp and the receiver timestamp of the PPS signal. This parameter is usually zero, as usually the PPS signal is believed in time and OS delays should be corrected in the machine specific section of the kernel driver. time2 needs only be set when the actual PPS signal is delayed for some reason. The flag1 enables input filtering. This a median filter with continuous sampling. The flag2 selects averaging of the samples remaining after the filtering. Leap second-handling is controlled with the flag3. When set a leap second will be deleted on receipt of a leap second indication from the receiver. Otherwise the leap second will be added, (which is the default). flag3 should never be set. PPS handling is enabled by adding 128 to the mode parameter in the server/peer command.
ntpq (8)
timecode variable
The ntpq program can read clock variables command list several variables. These hold the following information: refclock_time is the local time with the offset to UTC (format HHMM). The currently active receiver flags are listed in refclock_status. Additional feature flags of the receiver are optionally listed in parentheses. The actual time code is listed in timecode. A qualification of the decoded time code format is following in refclock_format. The last piece of information is the overall running time and the accumulated times for the clock event states in refclock_states. When PPS information is present additional variable are available. refclock_ppstime lists then the PPS timestamp and refclock_ppsskew lists the difference between RS232 derived timestamp and the PPS timestamp.
Currently, fourteen clock types (devices /dev/refclock-0 -
/dev/refclock-3) are supported by the PARSE driver.
A note on the implementations:
Verified implementations are:
These variants are tested for the decoding with my own homegrown receivers. Interfacing with specific commercial products may involve some fiddeling with cables. Especially commericial RAWDCF receivers have a seemingly unlimited number of ways to draw power from the RS232 port and to encode the DCF77 datastream. You are mainly on your own here unless i have a sample of the receiver.
These implementations are verified by the Meinberg people themselves and i have access to one of these clocks.
Meinberg PZF535/PZF509 receiver (FM
demodulation/TCXO / 50us)
Meinberg PZF535/PZF509
receiver (FM demodulation/OCXO / 50us)
Meinberg DCF U/A
31/DCF C51 receiver
(AM demodulation / 4ms)
ELV DCF7000 (sloppy AM
demodulation
/ 50ms)
Walter Schmid DCF receiver Kit (AM demodulation /
1ms)
RAW DCF77 100/200ms pulses (Conrad DCF77 receiver module /
5ms)
RAW DCF77 100/200ms pulses (TimeBrick DCF77 receiver module
/ 5ms)
Meinberg GPS166/GPS167
receiver (GPS / <<1us)
Trimble SVeeSix
GPS receiverTAIP protocol (GPS / <<1us)
Trimble SVeeSix
GPS receiver TSIP protocol (GPS / <<1us) (no kernel support
yet)
Radiocode Clocks Ltd RCC 8000 Intelligent Off-Air Master
Clock
support
Diem's Computime Radio Clock
RAWDCF receiver (DTR=high/RTS=low)
WHARTON 400A Series Clocks with a 404.2 Serial Interface
Actual data formats and set-up requirements of the various clocks can be found in NTP PARSE clock data formats.
The reference clock support carefully monitors the state transitions of the receiver. All state changes and exceptional events such as loss of time code transmission are logged via the syslog facility. Every hour a summary of the accumulated times for the clock states is listed via syslog.
PPS support is only available when the receiver is completely synchronized. The receiver is believed to deliver correct time for an additional period of time after losing synchronizations, unless a disruption in time code transmission is detected (possible power loss). The trust period is dependent on the receiver oscillator and thus a function of clock type. This is one of the parameters in the clockinfo field of the reference clock implementation. This parameter cannot be configured by ntpdc.
In addition to the PPS loopfilter control a true PPS hardware signal can be applied on Sun Sparc stations via the CPU serial ports on the CD pin. This signal is automatically detected and will be used for offset calculation. The input signal must be the time mark for the following time code. (The edge sensitivity can be selected - look into the appropriate kernel/parsestreams.c for details). Meinberg receivers can be connected by feeding the PPS pulse of the receiver via a 1488 level converter to Pin 8 (CD) of a Sun serial zs-port. To select PPS support the STREAMS driver for PARSE must be loaded and the mode parameter ist the mode value of above plus 128. If 128 is not added to the mode value PPS will be detected to be available but it will not be used. For PPS to be used you MUST add 128 to the mode parameter.
For the Meinberg GPS166/GPS167 receiver is also a special firmware release available (Uni-Erlangen). This release should be used for proper operation.
The raw DCF77 pulses can be fed via a level converter directly into Pin 3 (Rx) of the Sun. The telegrams will be decoded an used for synchronization. AM DCF77 receivers are running as low as $25. The accuracy is dependent on the receiver and is somewhere between 2ms (expensive) to 10ms (cheap). Upon bad signal reception of DCF77 synchronizations will cease as no backup oscillator is available as usually found in other reference clock receivers. So it is important to have a good place for the DCF77 antenna. For transmitter shutdowns you are out of luck unless you have other NTP servers with alternate time sources available.
Additional Information


Galvanize Delivers
Order Delivery
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$8.99 >>> ORDER
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$11.99 >>> ORDER
BBQ
$14.99 >>> ORDER
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$7.99 >>> ORDER
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Item
Item Price
Subtotal
Tax
Total
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phone
Telephone
home
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PLACE AN ORDER


Flight embarrassment motivates Derbyshire woman to lose 10 stone
She was determined to slim down
by Ben LyonsAn awkward incident on a plane prompted a determined Derbyshire woman to lose weight and transform her health and her life.
Katrina Brown, from Heanor, was verging on 24st when she found the motivation to start losing weight in October, last year.
With a dress size of 30, Katrina struggled to walk around and her weight caused her leg and back injuries.
Furthermore, on a flight to Dublin, her size meant that other passengers had to move seats so that she could fly.
Incredibly, just 13 months on, the 30-year-old has slimmed down to 13st 8lbs thanks to a big shift in her diet.
As a result of her weight loss, Katrina's energy levels have increased, her confidence has been boosted and she now swims and goes for long walks.
Katrina, a soft play manager from Heanor, said: "I was always big growing up and I just got bigger and bigger.
"I wasn't fit enough to do any exercise, didn't do much cooking at all and had takeaways or went out for food most nights.
"At my heaviest, I was struggling to even walk around because I kept hurting my leg and back.
"I was off work for a while with the injuries too, so I decided it was time to do something about it.

"I went to Dublin and ordered an extra leg room seat, but I couldn't even sit on it because I didn't have a seat belt extender.
"That was really embarrassing because some of the people on the plane had to move for me.
"I didn't want to have to go through that again."
Katrina joined the Ilkeston Slimming World group in October, last year.
Supported by five of her friends and family, she lost 6.5lbs in her first week and soon made big changes to her daily diet.
Katrina said: "We go to Slimming World together. My mum, sister, sister-in-law, and two friends all go to the same group.
"I've always done Slimming World in the past, but I never stayed to a group for long enough.
How Katrina's daily diet has changed
Average daily diet before weight loss
Breakfast: No breakfast
Lunch: Cheese and chip cob
Dinner: Pizza takeaway
Snacks: Chocolate, crisps and cakes
Drinks: Lots of Coca-Cola
Average daily diet now after weight loss
Breakfast: Fruit and frozen Müller Light yogurt
Lunch: Homemade soup
Dinner: Meatball pasta with salad
Snacks: Healthy bars and fruit
Drinks: Coke Zero and squash
"But, I stuck to it this time. I cook food from scratch most of the time, planning my daily diet by writing it all down.
"I used to drink a lot of Coca-Cola, but I've swapped it for Coke Zero and squash. I do still have hot chocolate quite often though.
"For snacks, I used to have chocolate, cake or whatever I could get hold of.
"But, now I have fruit, low-fat yogurts, and do portion control by weighing my food before I eat it."
Now a size 14, Katrina is delighted that her weight loss has allowed her to get into swimming and enjoy walking again.
She added: "The weight loss means I can now go swimming twice a week and dog walking most days. I would never have dreamed of going swimming before my weight loss.
"I never do less than 12,000 steps a day, and some days I do 20,000 when we take the dogs out on longer walks.
"I used to read weight loss stories in the paper and think 'how have they done that?', and I never thought it would be me who's lost the 10st.
"The weight loss has given me loads more energy at work and my confidence has grown so much.
"Now, I'd like to lose another stone and see how I feel then."
Those who would like to find out more about Slimming World groups in Derbyshire can visit the website here and Facebook page here.


Writing your beerProduction Query and Resolvers
Step 1 - casl code
- Copy wines.casl and call it beer.casl.
- Change the data and main code appropriately
- Make sure you use the resultsToDict function to create the output casResults
data1 = newTable('data1', {'year', 'newcastle', 'courage', 'boltmaker' , 'boddingtonbitter', 'blacksheep'},
{'double', 'double' , 'double', 'double', 'double', 'double'},
{2000,10,20,30,40,50},
{2001,5,10,15,5,0},
{2002,6,7,11,12,13},
{2003,5,8,0,0, 50},
{2004,11,5,7,8,100},
{2005,1,1,0,0,1000},
{2006,0,0,0,0,3000});
saveresult data1 casout='wineList' caslib = 'casuser' replace;
where = 'year GE ' || _args_.from || ' AND year LE ' || _args_.to ;
action table.fetch r = result /
table = { caslib = 'casuser' name = 'wineList'
vars = _appEnv_._selections_
where=where },
;
/* extract the data and return it as a dictionary */
casResults = resultsToDict(result.Fetch);
send_response({casResults=casResults});
Step 2 - Graphql Schema changes
- Add a BeerProduction type in schema.graphql. Use WineProductionCas as a template
type BeerProduction {
"""
An array containing beer production
"""
beers: [BeerList]
}
Add a BeerList type that mirrors WineList but with some British Beer names
type BeerList {
newcastle : Int
courage : Int
boltmaker : Int
boddington: Int
blakcsheep: Int
}
Update type Query with the following line
beerProduction(from: Int, to: Int): BeerProduction
Step 3 - Resolver
- create beerProduction.js that is a copy of wineProductionCas.js
'use strict';
let caslBase = require('../lib/caslBase');
let getSelections = require('../lib/getSelections');
module.exports = async function beerProduction (_, args, context, info) {
let {store} = context;
/* get the selection list from the AST and extened args with that information */
let selections = getSelections(info, 'beers', true, args);
/* execute setup and execute sccasl action on CAS */
let result = await caslBase(store,['argsToTable.casl', 'resultsToDict.casl', 'beer.casl'], selections.args,selections._selections_);
return result;
}
Step 4: Modify index.js in the resolver directory
let beerProduction = require('./beerProduction');
Update Query with the root resolver
const Query = {
...,
beerProduction: (parent, args,context,info) => beerProduction(parent, args, context, info)
}
Add beers resolver
Note we are reusing the casResults.js resolver.
const BeerProduction = {
beers: (parent,args, context, info) => casResults (parent, args, context, info)
// Note: In the original blog casWines.js was used as the resolver.
}
Export BeerProduction
Add BeerProduction to the exported values in index.js
module.exports = {Query, ReportView, SASResults, WineProduction, WineProductionCas, BeerProduction}
Testing the query
- restart the graphql-server
- start graphql-app
- Use the playground link to test the query.


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Running Room Canada Inc
Legal Name:
Running Room Canada Inc
Operating Name:
Running Room
Alternate Name:
RUNNING ROOM CANADA
Mailing Address:
9750 47 Ave NWEDMONTON, Alberta
T6E 5P3
Location Address:
9750 47 Ave NWEDMONTON, Alberta
T6E 5P3
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
The Running Room is proudly a family-owned company. It originated in 1984 out of founder John Stanton's wish to purchase quality running shoes from someone knowledgeable about the sport. New to the sport of running, John had a thirst for knowledge as well as a desire to buy the right product. This entrepreneurial retailer decided to fill a niche in the marketplace by opening a small one-room store in the renovated living room of an old house in Edmonton, hence the name, "Running Room." The concept was highly successful, and the stores have since expanded to over 114 locations across Canada and the United States.
Running Room's reputation has been built upon product innovation, quality and knowledge of the sport of running. Running Room's unique private label assortment was developed to provide customers with the best in style, functionality and fabric innovations at a reasonable price.
The Running Room is truly a store for runners by runners. All team members are runners whose philosophy is that if you're out there running on the same roads as the customers, you can better relate to them.
In addition to the best selection of top brand-name running shoes and apparel at the Running Room, you will find an incredible number of customer services. It offers training programs such as Walking, Learn to Run, 5K Training, Marathon and Half Marathon training, 10K Training and For Women Only Running. With over 800,000 training program graduates to date, the success of these programs is evident. The Running Room training programs are committed to educating customers on a lifestyle of fitness. These various programs meet the needs of a wide variety of customers, ranging from those who are just getting into a fitness routine to those contemplating a marathon. Running Room's free Run Club meets twice weekly on Wednesday evenings and Sunday mornings where runners of all levels run in a social, supportive group. This social component brought to running by the Running Room builds a true sense of community in each location.
Communicating with its customers via its in-store team, its website, Running Room Magazine, forums, Twitter and Facebook keeps the company agile and innovative.
The opening of the Walking Room expanded the goal of the Running Room to be as inclusive as possible while delivering its active living message. The Running Room carries national brands like Adidas, Asics, Nike, New Balance and Saucony, as well as the Running Room's private label line of apparel. Running Room Fit-Wear® and Walk Fit-Wear® are designed and tested to perform under the extreme weather conditions faced by North Americans.
John's sons were the catalysts who inspired John to run and found the Running Room. The Running Room has been here for 30 years. John Jr., and Jason have been partners for seven years.
John Stanton Jr. is one of the managing partners at the Running Room with responsibilities in Retail Operations, Human Resources and National Events. John started with the Running Room teaching the original learn to run clinic while running track for the University of Alberta and eventually winning the provincial triathlon championships. While John's competitive days in track and triathlon are long behind him, a great day still starts with a 5 km run. John enjoys travelling throughout North America staying in touch with our passionate customers and over 1200 team members. John is married to Dr. Nancy McNeilly and is a proud father of two young children who keep him very active.
What started as a simple step of Jason Stanton following his big brother on a school morning running club, 30 years later the Running Room is North America's largest chain of running stores. Jason started his career early in the Running Room, as a 12 year old on opening day he was sweeping the back room. Since then he has gone on to manage various areas of the business from the retail stores to logistics, purchasing and finally a managing partner. Jason has been active in his community sitting on various committees that include Kids with Cancer, Run for the Cure and the Retail Council of Canada. In 2003 Jason was proud to be on the local organizing committee to host the IAAF World Road Race championships. Jason has continued to be active in running, competing in many road races, triathlons and over 20 marathons. He also competed in 3 Ironman events including the Hawaii Ironman. Jason has been happily married for 15 years to Melissa and is the proud father of 2 beautiful girls.
"We believe that the Running Room philosophy and our in-store environment are unlike any other retail business in North America. While we offer clothing, shoes, products and accessories for walkers and runners, we also help people to change their lives through fitness activities," says the Stanton family.
John adds: "Through the Running Room, people can gain a tremendous sense of belonging that comes from walking or running alongside others who share similar goals. I think the Running Room is very important in the eyes of its customers, because we think about their well-being."
The family-owned company is now North America's largest chain of specialty running stores.
The Running Room brand will always be driven by its emphasis on continuous improvement, customer service and community involvement.
The success of the Running Room is reliant on the fact that it doesn't just sell shoes and equipment; it sells a commitment to active living.
"True success is never knowing if you're working or playing," says John Stanton.
Full profile
Full profile
Contact information
Company description
451110 - Sporting Goods Stores
Product / Service / Licensing
The Running Room is expanding throughout Canada and the United States.
Market profile
Alliances:
- Technology
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
451110 - Sporting Goods Stores
Products, services and licensing
Product / Service / Licensing
The Running Room is expanding throughout Canada and the United States.
Market
Market profile
Alliances:
- Technology


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The compositional percentage we recommend for this particular nutritional build
is:
Protein: 40%; Carbohydrates: 40%; Fats: 20%.
This build is ideal for those who are light to moderately active.
The measurement procedures will be in standard quality.
Estimated Cost: Lowest
The compositional percentage we recommend for this particular nutritional build
is:
Protein: 60%; Carbohydrates: 40%; Fats: 10%.
This build is ideal for those who are highly moderately active to sports participation.
Also, all supplementation will be measured in "millis" (milliLiters, milliGrams, etc.)
Estimated Cost: High
This is not, necessary, for nutritional ratios but, rather, your choice of the ratio
via the above options. However, the grade of the formula will be significantly higher
and measured in "nanos" (nanoLiters, nanoGrams, etc.)
Estimated Cost: Highest


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I am a 24 year old student at BCIT in CIT.  I used to do a lot of raiding and competitive gaming.
I've quit know to focus on school, but occasionaly if I am up to schoolwork I will stream on the weekends
Some of the courses I am taking this term are


Spoilers: Ben and Callum enjoy some roleplay in EastEnders
Oooft, talk about getting masterful and dominating Callum, our brows are sweating. No, we kid, believe it or not, this isn’t the Walford couple experimenting but actually Ben Mitchell (Max Bowden) showing his support for Callum Highway (Tony Clay) by helping him train for his police assessments in EastEnders.
Callum has made his ambition to join the police force clear and viewers recently breathed a sigh of relief when Ben decided to go legit and support the man he loved in his chosen career. Last night saw Ben and Callumembrace as Ben pledged to turn his back on his life of crime and now he is set to do what he can to help Callum join the beat.
Ahead of Callum’s first tests to join the force, Ben play acts as someone Callum needs to apprehend – and comes at him with a banana (behave) which is meant to represent a gun. It’s Callum’s job to tackle Ben and disarm him.
While we never thought we’d see Ben fighting for the right side of the law after his recent actions, but he steps in to help Callum practise his much needed moves to pass the first stages of the assessment.
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As the couple get a bit rough and ready, Callum seems to appreciate Ben’s support and dedication to helping him reach his goal. But judging by the faces on the lads here, it looks like they might have been caught in the act and have some explaining to do.
We must say, Ben makes a very believable perp – it’s almost like he’s been arrested before.
Ben’s decision to turn his back on crime is surely a relief to Martin Fowler (James Bye) too but any joy will be short lived as tonight, Martin gets the shock of his life when bad news reaches him about Stacey (Lacey Turner).
Ben and Callum’s training session airs on Thursday 5 December at 7:30pm on BBC One.


Log into your D&D Beyond Account. Move your mouse over the 'Characters' tab at the top of the window and select 'My Characters'. Find the character you want to convert and click 'View'. Take a look at the Address bar, it should look something like: https://www.dndbeyond.com/profile/YourUserName/characters/8675309 Find the Character ID. In the above example it's 8675309. Paste this ID in the 'Character ID' text box and click 'Get Character Data'. It will take a short while in order for the data to populate. Click the 'Download XML' button to save the character to your computer.Character ID:


IRI: http://www.cgg.sm/ontology/council-of-12#CouncilOfTwelves
IRI: http://www.cgg.sm/ontology/council-of-12#composedByCouncilors
IRI: http://www.cgg.sm/ontology/council-of-12#functionsOfCouncilOfTwelves


--- layout: post title: select * from priorityList order by priorityValue categories: - other tags: [] status: publish type: post published: true meta: _edit_last: '1' ---


Table of Contents
1. Introduction
Transition-based dependency parsers are a type of popular tool for solving structured prediction problems or joint prediction problems. There are many constructions for transition-based dependency parsers such as transition systems, feature engineering, neural-network predictors and the importance of training against a “dynamic oracle.” In short, those methods rely on the heuristic learning strategies.
In contrast to previous approaches, in this paper, they focus on building dependency parsers which have a compiler can automatically translate a simple specification of dependency parsing and labeled data into machine learning updates. In this way, dependency parsers can use machine learning technology to get more accuracy in complex prediction problems.
The credit assignment is an issue in complex prediction problems. There are two common ways:
- The system may ignore the possibility that a previous prediction may have been wrong. Alternatively, ignore that different errors may have different costs (consequences). Alternatively, that train-time prediction may differ from the test-time prediction. These and other issues lead to statistical inconsistency: when features are not rich enough for perfect prediction the machine learning may converge suboptimally.
- The system may use handcrafted credit assignment heuristics to cope with errors the underlying algorithm makes and the long-term outcomes of decisions.
For some applications, for example, the dependency parsing, it can be much more complicated if applying two strategies.
In the paper, they show a learning to search compiler (or credit assignment compiler) can handle credit assignment when applied to dependency parsing. Moreover, the advantages are as follows:
- The system automatically employs a cost sensitive learning algorithm instead of a multiclass learning algorithm, ensuring the model learns to avoid compounding errors.
- The system automatically “rolls in” with the learned policy and “rolls out” the dynamic oracle insuring competition with the oracle.
- Advanced machine learning techniques or optimization strategies are enabled with command-line flags with no additional implementation overhead, such as neural networks or “fancy” online learning.
- The implementation is future-friendly: future compilers may yield a better parser.
- Train/test asynchrony bugs are removed. Essentially, you only write the test-time “decoder” and the oracle.
- The implementation is simple: This one is about 300 lines of C++ code.
To sum up, the author explains the system in four parts: introducing solve structured prediction tasks using learning to search, implementing learning to search to solve dependency parsing, experimental result, and related work.
2. Learning to search
Learning to search is a family of approaches for solving structured prediction problems or joint prediction problems. Learning to search includes many algorithms such as the incremental structured perceptron, SEARN, DAGGER, AGGREVATE, and others. On the whole, these algorithms have the similar framework, but their difference are in the rollin/rollout policy and the base learner.
In this paper, they build an efficient framework using imperative learning to search, in figure 1. In the framework, it includes:
- a “decoder” for the target structured prediction task (e.g., dependency parsing),
- an annotation in the decoder that computes losses on the training data,
- a reference policy on the training data that returns at any prediction point a “suggestion” as to an excellent action to take in that state.
To fully understand this proposed framework, we need back to the concept of the learning to search.
Learning to search approaches solve structured prediction problems by (1) decomposing the production of the structured output regarding a specific search space (states, actions); and (2) learning hypotheses that control policy that takes actions in this search space. Figure 2 provides an example to illustrate the learning to search approach. In this example, it executes the program three times and able to explore three different trajectories and compute their losses. The rollin policy determines the initial trajectory, the state R is the position of one-step deviations and the rollout policy is to complete the trajectory after a deviation. In brief, the learning to search first follows the learned policy to finds different predictions from the base learner, yields low Hamming loss and gets the reference policy.
By varying the rollin/rollout policy and base learner, it can come up different algorithms. In this paper, their rollin=mixture of the learned and the reference (dynamic oracle) policies and rollout=reference (dynamic oracle) policy, and their base learner has varied options. In contrast, DAGGER uses the rollin=learned policy and the rollout=reference policy, and SEARN uses the rollin=rollout=stochastic mixture of learned and reference policies. Next, we will explore the implementation of the learning to search to solve dependency parsing.
3. Dependency Parsing by Learning to Search
A transition-based dependency parser takes a sequence of actions and parses a sentence from left to right by maintaining a stack S, a buffer B, and a set of dependency arcs. An example in figure 3 whos a processing. To classify transition-based dependency parsers, we can learn from three aspects which are the transition system, the base learner, and the reference policy. There are three primary transition systems: arc-standard, arc-eager, arc-hybrid. For the base learner, it can be diverse such as perceptron, natural network, and SGD. As for the reference policy, there are greedy, beam search, and dynamic oracles.
In this paper, they use arc-hybrid for the transition system, the natural network for the base learner (also try more powerful base learners), and dynamic oracles for the reference policy. The arc-hybrid includes three actions: SHIFT, REDUCE, and REDUCE-RIGHT. For others transition system, those have more actions than arc-hybrid. The fundamental principle of dynamic oracles is that for some transition systems there is more than one path that could lead to the gold tree. In contrast, for greedy parsing, we are only considering one of them, which is the one determined by the deterministic oracle. In short, all frameworks are similar as discussed in section 2, but the implementation can be much more comfortable.
In section 2, to implement a parser using the learning to search framework, we need to provide a decoder, a loss function, and reference policy. Therefore, the author provides six functions:
- GETVALIDACTION returns a set of valid actions that can be taken based on the current configuration.
- GETFEAT extracts features based on the current configuration.
- GETGOLDACTION implements the dynamic Oracle
- PREDICT is a library call implemented in the learning to search system.
- TRANS function implements the hybrid-arc transition system.
- LOSS function is used to measure the distance between the predicted output and the gold annotation.
To notice that, there are two LOSS functions, one for unlabeled dependency parser and other for labeled dependency parser.
Compare with other implementation, the learning to search compiler have two advantages for implementation: First, in the learning to search framework, there is no need to implement a learning algorithm. Second, learning to search provides a unified framework, which allows the library to serve common functions for ease of implementation.
4. Experimental Results
Experiments on standard English Penn Treebank and nine other languages from CoNLL-X show that the compiled parser is competitive with recently published results (see figure 4 and figure 5).
5. Related Works
Some works use the learning to search approach to solve various other structured prediction problems, but these works are a unique setting under author’s unified learning framework. In conclusion, the learning to search compiler is the first work that develops a general programming interface for dependency parsing, or more broadly, for structured prediction.
Reference
- Kai-Wei Chang, He He, Hal Daume III, and John Langford. 2015a. Learning to Search for Dependencies. arXiv:1503.05615.
- Ballesteros, Miguel. “Transition-based Dependency Parsing.” 2015. PowerPoint presentation


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Dropdown
Dropdowns are toggleable, contextual overlays for displaying lists of links and contents.
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Lightbox is a script that displays images and videos by filling the screen, and dimming out the rest of the web page.
Count To
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Interval
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Carousel
The carousel is a slideshow for cycling through a series of content, built with CSS 3D transforms and a bit of JavaScript. It works with a series of images, text, or custom markup. It also includes support for previous/next controls and indicators.
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Small library to animate elements on your page as you scroll.
AOS allows you to animate elements as you scroll down, and up. If you scroll back to top, elements will animate to it's previous state and are ready to animate again if you scroll down.
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To see the list of available animations, visit the oficial documentation.
It's recommended to add class .overflow-hidden
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Modal
Modals are streamlined, but flexible dialog prompts powered by JavaScript.
TheSaaS is based on Bootstrap 4. It means you can use any code from Bootstrap documentation to design your pages. We styled most of the components to look better in your pages.
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[1]
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[2]
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