class: center, middle, inverse, title-slide # Statistics ### The R Bootcamp
Twitter:
@therbootcamp
### April 2018 --- ## There are tons of statistical packages in R! .pull-left35[

 | Package| Models| |------:|:----| | `afex`| Anovas| | `rpart`| Decision Trees| | `lme4`| Mixed effects regression| | `BayesFactor`| Bayesian statistics| | `igraph`| Network analysis| | `neuralnet`| Neural networks| ] .pull-right6[  ] --- .pull-left45[ ### We will cover... - Basic structure and arguments of most statistical functions - `formula`, `data` - Simple `htest` objects - Generalized linear model - `glm`, `aov` - Common methods - `print()`, `summary()`, `names()`, `predict()`, `plot()` - Accessing elements from statistical objects with `$` - Working with statistical distributions ] .pull-right5[ ### Examples ```r # T-test comparing weights from Diets 1 and 2 t.test(formula = weight ~ Diet, data = ChickWeight %>% filter(Diet %in% c(1, 2))) # Regression model weight_glm <- glm(formula = weight ~ ., data = ChickWeight) # Summary information summary(weight_glm) weight_glm$coefficients # Acess coefficients weight_glm$coefficients # Access residuals ### Generate random data x1 <- rnorm(n = 100, mean = 10, sd = 5) x2 <- rnorm(n = 100, mean = 5, sd = 1) noise <- rnorm(n= 100, mean = 2, sd = 10) # Create y as a function of x1, x2, and noise y <- x1 + x2 + noise ``` ] --- # Basic structure of statistical functions .pull-left5[ | Test| R Function| |:------|:----| | `formula`|The dependent variable and one or more independent variables| | `data`| The dataframe containing variables| ```r trial_X ``` ``` ## id sex age arm y_primary y_secondary ## 1 1 m 35 1 50 1 ## 2 2 f 42 2 78 1 ## 3 3 f 24 1 46 0 ## 4 4 m 56 2 97 1 ## 5 5 f 49 1 74 1 ``` ] .pull-right45[ ```r TEST(formula = Y ~ X1 + X2 + ... data = DATA ...) ``` ### Examples ```r # T-test # DV = y_1, IV = arm t.test(formula = y_1 ~ arm data = trial_X) # Regression # DV = y_sec, IV = arm, sex, age # Data = trial_X (with filters) glm(formula = y_2 ~ arm + sex + age data = trial_X %>% filter(id > 50 & BMI == "Normal")) ``` ] --- .pull-left45[

 ## Different tests have different arguments - Individual tests may have many optional arguments (look at help menus!) - Each of these have *default* values. If you don't specify them, the function will use the default. - Customize a test by specifying arguments directly. ] .pull-right5[ ### Customised t-test ```rm # Additional arguments to t.test t.test(formula = t_primary ~ arm, data = trial_X, alternative = "greater", # One-sided mu = 10, # H0 conf.level = .90) # Conf level ``` ### Customised regression ```r # Additional arguments to glm # Include -1 to not have an intercept! glm(formula = y_secondary ~ arm + sex + age - 1, data = trial_X, family = "binomial") # Logistic Reg ``` ] --- ## 1 or 2 sample hypothesis tests  --- .pull-left5[

 ## Common 1 or 2 sample hypothesis tests | Test| R Function| |------:|----:| | T-test| `t.test()`| | Correlation Test| `cor.test()`| | Chi-Square Test| `chisq.test()`| ] .pull-right5[ #### Always check help menus! `?t.test`  ] --- ## t-tests with `t.test()` .pull-left3[ ### ChickWeight data ```r ChickWeight ``` ``` ## weight Time Chick Diet ## 1 59 4 30 2 ## 2 93 8 26 2 ## 3 79 6 40 3 ## 4 145 12 28 2 ## 5 48 4 5 1 ## 6 148 18 22 2 ``` ] .pull-right65[ ### Two sample t-test > Is the mean weight of the chicks on Diet 1 different from Diet 2? ```r t.test(formula = weight ~ Diet, # Formula data = ChickWeight %>% # Data in Chickweight filter(Diet %in% c(1, 2))) ``` ``` ## ## Welch Two Sample t-test ## ## data: weight by Diet ## t = -2.6, df = 200, p-value = 0.009 ## alternative hypothesis: true difference in means is not equal to 0 ## 95 percent confidence interval: ## -34.900 -5.042 ## sample estimates: ## mean in group 1 mean in group 2 ## 102.6 122.6 ``` ] --- ## Correlation test with `cor.test()` .pull-left3[ ### ChickWeight data ```r ChickWeight ``` ``` ## weight Time Chick Diet ## 1 59 4 30 2 ## 2 93 8 26 2 ## 3 79 6 40 3 ## 4 145 12 28 2 ## 5 48 4 5 1 ## 6 148 18 22 2 ``` ] .pull-right65[ ### Correlation Test > Is there a correlation between weight and Time? - For `cor.test()`, formula looks like `formula = ~ a + b` ```r cor.test(formula = ~ weight + Time, # Formula data = ChickWeight) # Data in Chickweight ``` ``` ## ## Pearson's product-moment correlation ## ## data: weight and Time ## t = 37, df = 580, p-value <2e-16 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## 0.8109 0.8599 ## sample estimates: ## cor ## 0.8371 ``` ] --- ## Chi-Square test with `chisq.test()` .pull-left3[ ### ChickWeight data ```r ChickWeight ``` ``` ## weight Time Chick Diet ## 1 59 4 30 2 ## 2 93 8 26 2 ## 3 79 6 40 3 ## 4 145 12 28 2 ## 5 48 4 5 1 ## 6 148 18 22 2 ``` ] .pull-right65[ ### Chi-Square test > Are there more observations from one Diet than another? - For `chisq.test()`, main argument should be a table of values created from the `table()` function: ```r table(ChickWeight$Diet) # Look at a table of frequencies ``` ``` ## ## 1 2 3 4 ## 220 120 120 118 ``` ```r chisq.test(x = table(ChickWeight$Diet)) # Run test on table ``` ``` ## ## Chi-squared test for given probabilities ## ## data: table(ChickWeight$Diet) ## X-squared = 53, df = 3, p-value = 2e-11 ``` ] --- .pull-left45[ ## Assigning hypothesis test objects - Hypothesis tests return an object of class `"htest"` - Can assign a hypothesis test to an object, and then extract info with `$`: #### Examples of what's in htest objects | Element| Result| |------:|----:| | `x$statistic`| A test statistic| | `x$parameter`| Degrees of freedom| | `x$p.value`| The p-value| | `x$conf.int`| Confidence interval| ] .pull-right5[ ### What's in an htest object? ```r # One-sample t-test weight_tt <- t.test(x = ChickWeight$weight, mu = 120, alternative = "two.sided") # What's in the weight_tt object? names(weight_tt) ``` ``` ## [1] "statistic" "parameter" "p.value" "conf.int" "estimate" "null.value" "alternative" ## [8] "method" "data.name" ``` ```r # Get the p.value weight_tt$p.value ``` ``` ## [1] 0.5387 ``` ```r # Get the confidence interval weight_tt$conf.int ``` ``` ## [1] 116.0 127.6 ## attr(,"conf.level") ## [1] 0.95 ``` ] --- .pull-left4[ ## Regression example with `glm()` ```r head(ChickWeight) ``` ``` ## weight Time Chick Diet ## 1 42 0 1 1 ## 2 51 2 1 1 ## 3 59 4 1 1 ## 4 64 6 1 1 ## 5 76 8 1 1 ## 6 93 10 1 1 ``` **Goal**: Create a regression model predicting `weight` as a function of Time
 `$$\Large weight = \beta_{0} + \beta_{1} \times Time + \epsilon$$` ] .pull-right55[ - Function: `glm()` - Formula: `formula = weight ~ Time` - Data: `data = ChickWeight` ```r # Create a glm object # formula: weight ~ Time # data: ChickWeight chick_glm <- glm(formula = weight ~ Time, data = ChickWeight) # Print the object chick_glm ``` ``` ## ## Call: glm(formula = weight ~ Time, data = ChickWeight) ## ## Coefficients: ## (Intercept) Time ## 27.5 8.8 ## ## Degrees of Freedom: 577 Total (i.e. Null); 576 Residual ## Null Deviance: 2910000 ## Residual Deviance: 872000 AIC: 5880 ``` ] --- .pull-left7[ ## Using formulas You can keep adding terms with `+` ```r # Include multiple terms with + chick_glm_A <- glm(formula = weight ~ Time + Diet + Chick, data = ChickWeight) ``` To include *all* variables, use the generic notation  `formula = y ~ .` ```r # Use y ~ . to include ALL variables chick_glm_B <- glm(formula = weight ~ ., data = ChickWeight) ``` To include *interaction terms* use `*` ```r # Include an interaction term between Time and Diet chick_glm_C <- glm(formula = weight ~ Time * Diet + Chick, data = ChickWeight) ``` ] --- .pull-left35[

 ## Exploring statistical objects You can apply many *generic* functions to statistical objects such as `print()`, `summary()`, `predict()` and `plot()`. ```r # Create statistical object my_obj <- FUN(formula = ..., data = ...) names(my_obj) # Elements print(my_obj) # Print summary(my_obj) # Summary plot(my_obj) # Plotting predict(my_obj, ..) # Predict ``` ] .pull-right6[

 **Apply the print() function to a statistical object** ```r # Create a glm object chick_glm <- glm(formula = weight ~ Time, data = ChickWeight) # Print the object print(chick_glm) ``` ``` ## ## Call: glm(formula = weight ~ Time, data = ChickWeight) ## ## Coefficients: ## (Intercept) Time ## 27.5 8.8 ## ## Degrees of Freedom: 577 Total (i.e. Null); 576 Residual ## Null Deviance: 2910000 ## Residual Deviance: 872000 AIC: 5880 ``` ] --- .pull-left35[

 ## Exploring statistical objects You can apply many *generic* functions to statistical objects such as `print()`, `summary()`, `predict()` and `plot()`. ```r # Create statistical object my_obj <- FUN(formula = ..., data = ...) names(my_obj) # Elements print(my_obj) # Print summary(my_obj) # Summary plot(my_obj) # Plotting predict(my_obj, ..) # Predict ``` ] .pull-right6[

 **Apply the names() function to a statistical object** ```r # Create a glm object chick_glm <- glm(formula = weight ~ Time, data = ChickWeight) # Print the object names(chick_glm) ``` ``` ## [1] "coefficients" "residuals" "fitted.values" "effects" "R" ## [6] "rank" "qr" "family" "linear.predictors" "deviance" ## [11] "aic" "null.deviance" "iter" "weights" "prior.weights" ## [16] "df.residual" "df.null" "y" "converged" "boundary" ## [21] "model" "call" "formula" "terms" "data" ## [26] "offset" "control" "method" "contrasts" "xlevels" ``` ] --- .pull-left35[

 ## Exploring statistical objects You can apply many *generic* functions to statistical objects such as `print()`, `summary()`, `predict()` and `plot()`. ```r # Create statistical object my_obj <- FUN(formula = ..., data = ...) names(my_obj) # Elements print(my_obj) # Print summary(my_obj) # Summary plot(my_obj) # Plotting predict(my_obj, ..) # Predict ``` ] .pull-right6[

 **Apply the predict() function to a statistical object** ```r # Create a glm object chick_glm <- glm(formula = weight ~ Time, data = ChickWeight) # Create a dataframe of new data New_Data <- tibble(Time = c(10, 15, 20, 45, 5)) # Predict weights of New_Data using chick_glm predict(chick_glm, newdata = New_Data) ``` ``` ## 1 2 3 4 5 ## 115.50 159.51 203.53 423.60 71.48 ``` ] --- .pull-left45[ ## Using Statistical Distributions - R gives you a host of tools for sampling data from common statistical distributions | Distribution| R Function| |------:|----:| | Normal| `rnorm()`| | Uniform|`runif()`| | Beta|`rbeta()`| | Binomial|`rbinom()`| - Use these to create simulations and play around with models - You can use `sample()` to draw random samples from a vector of values ] .pull-right5[ ?distributions  ] --- .pull-left45[ ## Using Statistical Distributions - R gives you a host of tools for sampling data from common statistical distributions | Distribution| R Function| |------:|----:| | Normal| `rnorm()`| | Uniform|`runif()`| | Beta|`rbeta()`| | Binomial|`rbinom()`| - Use these to create simulations and play around with models - You can use `sample()` to draw random samples from a vector of values ] .pull-right5[ ```r # Simulate 10 flips of a fair coin sample(x = c("H", "T"), size = 10, replace = TRUE) ``` ``` ## [1] "H" "H" "H" "T" "T" "T" "H" "T" "H" "H" ``` ```r # 5 values from a normal distribution rand_samp <- rnorm(n = 5, mean = 0, sd = 1) rand_samp # Print the sample ``` ``` ## [1] 1.80077 0.05261 0.92688 1.00398 -0.95148 ``` ```r t.test(x = rand_samp) # Perform 1 sample t-test ``` ``` ## ## One Sample t-test ## ## data: rand_samp ## t = 1.2, df = 4, p-value = 0.3 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## -0.7376 1.8707 ## sample estimates: ## mean of x ## 0.5666 ``` ] --- .pull-left45[ ## Summary - There are tons of statistical packages in R - Most require a formula, data, and other optional arguments - Use help menus to understand arguments and syntax! - Once you've created a statistical object, use generic methods (`print()`, `names()`, `summary()`), to explore it - Use random sampling functions to run simulations ] .pull-right5[ `?t.test`  ] --- ## Live Demo & Pratical
Link to Statistics practical --- --- .pull-left45[ ### Generalised linear model ] .pull-right5[ ] --- # Examples with ChickWeight Data .pull-left4[ ```r ChickWeight ``` ``` ## weight Time Chick Diet ## 1 59 4 30 2 ## 2 93 8 26 2 ## 3 79 6 40 3 ## 4 145 12 28 2 ## 5 48 4 5 1 ## 6 148 18 22 2 ``` ] .pull-right55[ ![Source: http://awallpapersimages.com/wp-content/uploads/2016/07/Chicken-cute-baby-image.jpg](https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/cutechicken.jpg) ] --- # Final notes .pull-left7[ - When using a hypothesis test, always ask: > What are the arguments? > What format or class should the arguments be? - When in doubt, always look at the help files and examples at the end. - Save hypothesis tests as new objects, then apply `names()` to see what elements it contains, then extract what you want with `$` ```r # Run test and save as test_A test_A <- t.test(formula = weight ~ Diet, data = ChickWeight %>% filter(Diet %in% c(1, 2))) names(test_A) # What is in the object? test_A$statistic # Ah ok! Show me the test statistic ``` ] .pull-right25[ `?t.test`  #### Questions? ] --- # Two types of statistics: Descriptive and Inferential .pull-left4[ ### Inferential - Used to make inferences about a larger population. Typically done in tandem with a *hypothesis test* #### Examples | Hypothesis Test| R Function| |------:|----:| | T-test| `t.test()`| | Correlation Test| `cor.test()`| | Chi-Square Test| `chisq.test()`| | ANOVA, Post-hoc| `aov(), TukeyHSD()`| - Hypothesis tests typically return lists of outputs (e.g.; p-value, test statistic) ] .pull-right55[ #### R implimentation ```r t.test(x = c(4, 3, 6, 5, 3, 2), mu = 0, alternative = "two.sided") ``` ``` ## ## One Sample t-test ## ## data: c(4, 3, 6, 5, 3, 2) ## t = 6.4, df = 5, p-value = 0.001 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## 2.289 5.378 ## sample estimates: ## mean of x ## 3.833 ``` ] --- # ANOVA with `aov()` .pull-left3[ ### ChickWeight data ```r ChickWeight ``` ``` ## weight Time Chick Diet ## 1 59 4 30 2 ## 2 93 8 26 2 ## 3 79 6 40 3 ## 4 145 12 28 2 ## 5 48 4 5 1 ## 6 148 18 22 2 ``` ] .pull-right6[ ### ANOVA > Is there difference in weights based on Diet? - Applying `summary()` to an `aov` object prints a nice table. ```r E <- aov(formula = weight ~ Diet, # Formula data = ChickWeight) # Data in Chickweight summary(E) # Sow a summary of the results ``` ``` ## Df Sum Sq Mean Sq F value Pr(>F) ## Diet 3<PHONE_NUMBER>4 10.8 6.4e-07 *** ## Residuals<PHONE_NUMBER> 4806 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` ] --- # Post-hoc tests with `TukeyHSD()` .pull-left4[ > Which specific pairs of Diets differed?


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New REI to open in Pigeon Forge
PIGEON FORGE, Tenn. (WVLT) — REI Co-op said they are planning to open a new store in Pigeon Forge.
The new store will be located at 2655 Teaster Lane and is expected to open in Spring 2020.
“When exploring the Great Smoky Mountains, one of the best places to start is Pigeon Forge,” said Gail Kirkland, REI regional director for the south. “From hiking to backpacking to mountain biking, our goal is to connect our members to the activities they love and the ones they are eager to try.”
The store offers a variety of outdoor gear and apparel.


DIRECTIONS
-
Preheat Oven:
Preheat oven to 350 degrees F(175 degrees C).
-
Make Lemon Filling:
In a medium saucepan ...
- Whisk together 1 cup sugar, flour, cornstarch, and salt.
- Stir in water, lemon juice and lemon zest.
- Cook over medium-high heat, stirring frequently, until mixture comes to a boil.
- Stir in butter.
- Place egg yolks in a small bowl and gradually whisk in 1/2 cup of hot sugar mixture.
- Whisk egg yolk mixture back into remaining sugar mixture.
- Bring to a boil and continue to cook while stirring constantly until thick.
- Remove from heat.
- Pour filling into baked pastry shell.
-
Make Meringue:
In a large glass or metal bowl ...
- Whip egg whites until foamy.
- Add sugar gradually, and continue to whip until stiff peaks form.
- Spread meringue over pie, sealing the edges at the crust.
-
Bake:
Bake in preheated oven for 10 minutes, or until meringue is golden brown.
This is our favorite recipte here at Granny's Pies. It has been enjoyed by pie fans for many years.
It's Granny's favorite! We hope you'll find that this recipe is delicious and also easy to follow;
it's a piece of cake pie!
USER COMMENTS
This is a very fun recipe to follow, because Grandma makes it sweet and simple. This pie is thickened with cornstarch and flour in addition to egg yolks, and contains no milk.
- Emilie S.
Q: What do you call an ape who loves pie?
A: A meringue-utan.
- Vickie K
This site can really engage my interest. Follow the recipe above and make it so! This pie is number one.
- Jean-Luc Piecard


Equivalent to pressing ESC in Microsoft Excel for Windows or ESC or COMMAND+PERIOD in Microsoft Excel for the Macintosh to cancel the marquee after you copy or cut a selection.
Syntax
CANCEL.COPY(render_logical)
Render_logical is a logical value that, if TRUE, places the contents of the Microsoft Excel Clipboard on the Clipboard or, if FALSE or omitted, does not place them on the Clipboard. Render_logical is available only in Microsoft Excel for the Macintosh.
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From wikimedia
Hypertext Markup Language (HTML) is the standard markup language
for creating web pages and web applications
With Cascading Style
Sheets (CSS) and JavaScript, it forms a triad of cornerstone
technologies for the World Wide Web.[4]
Web browsers receive HTML documents from a web server or from
local storage and render the documents into multimedia web pages.
HTML describes the structure of a web page semantically and
originally included cues for the appearance of the document.
Link: visit wiki


|
OpenM++ compiler (omc)
|
Declares the Literal class and sub-classes. More...
Go to the source code of this file.
Classes | |
| class | Literal |
| Literal encapsulates a C++ literal or an openM++ literal. More... | |
| class | IntegerLiteral |
| A C++ integer literal. More... | |
| class | FloatingPointLiteral |
| A C++ floating point literal. More... | |
| class | CharacterLiteral |
| A C++ character literal. More... | |
| class | StringLiteral |
| A C++ string literal. More... | |
| class | BooleanLiteral |
| A C++ boolean literal. More... | |
| class | PointerLiteral |
| A C++ pointer literal. More... | |
| class | TimeLiteral |
| An openM++ time literal. More... | |
Declares the Literal class and sub-classes.


MAURICE A film review by Thuan N. P. Nguyen Copyright 1987 Thuan N. P. Nguyen
SUMMARY: MAURICE tells the story of a platonic love affair between Maurice Hall and his Cambridge classmate Clive Durham which took place when homosexuality was still a criminal offense in England. Under societal pressures to conform, Clive tries to suppress his love for Maurice and marries a [dull] upper-class woman. Maurice also tries to suppress his attraction for men but eventually comes to term with his feelings and casts aside the homophobic and class-divisive values of his society.
Cinematography: +4 Acting: +2 on the -4 to +4 scale
MAURICE is quite a bit more exciting than the above summary suggests.  Maurice Hall is outwardly an ordinary man, an upstanding member of wealthy English society, as is Clive Durham. But they share a secret. Each loves the other. Maurice has been attracted to men as long as he can remember, but he was never able to discuss these feelings with anyone because the teachers & clergymen constantly tell their wards about the "unspeakable sin of the Greeks" and watched them constantly. It is by accident that Maurice meets Clive at Cambridge. Their friendship blossoms and the two fall in love but for a long time, neither is capable of acknowledging his emotion for the other.
The movie succeeds beautifully in capturing the moods of romantic love, the wistful longing glances, the words almost-but-not-quite spoken. The interior settings are rich with period details and everything radiates warmth. The outdoor settings are equally romantic. The fields and woods always appear green, lush and misty. Clive's country estate possessed a dignified, timeless charm as filmed by director James Ivory. The costuming was equally flawless, as with A ROOM WITH A VIEW, the previous film by the Merchant-Ivory producer- director team.
The most interesting part of the movie's storyline, for me, involves the two paths taken by Clive and Maurice in response to the trial of their mutual friend, Lord Risley. Entrapped by a policeman, Risley was sent to hard labor for committing a homosexual "offense." Clive becomes ill with worry and fear and eventually breaks off his affair with Maurice to marry a woman and enter a career in politics. These changes, however, are nothing more than monumental acts of self-deception for Clive. Maurice also tries to conform. He goes to a hypnotist who tries to "cure" him of his "sickness."
James Wilby turns in a convincing performance as the torn Maurice. This role was originally to be played by Julian Sands, who played George in A ROOM WITH A VIEW. I think James Wilby's appearance is more suited for the role of the "ordinary" man which Maurice is. Julian Sands would have been too "pretty." Hugh Grant plays dashing Clive Durham and manages to effectively communicate the pathetic changes that occur in Clive (keep track of who has the mustache :-) ). The supporting cast is generally amusing. Ben Kingsley puts in a appearance as the American hypnotist.
 The author, E. M. Forster, wrote it with a happy ending in mind, with the idea that "two men should fall in love and remain in it for the ever and ever that fiction allows" and said that he "shouldn't have bothered to write otherwise." The story's main weakness is the contrivedness of some of the meetings. (re: Maurice & Alec) I enjoyed the movie a great deal and found these faults to be minor. As a matter of fact, I'll probably go back to see it *again* soon, which is my way of saying "this is one of the best movies I've seen this year."
MAURICE Starring: James Wilby, Hugh Grant, Rupert Holmes Also starring: Denholm Elliott, Ben Kingsley, (and many others) Director: James Ivory Producer: Ismail Merchant Adapted from the novel MAURICE, by E. M. Forster.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


If you are new to Arm development, it is recommended to follow this short tutorial and learn how to build a simple test executable program that will blink a LED. You do not need any physical hardware for this, the test uses an emulated STM32F4DISCOVERY board.
Prerequisites
Before creating a new project, please be sure you checked all prerequisites described in the Prerequisites page, including the need to install the build tools if you use Windows as your development platform.
It is also recommended to set the workspace preferences and to install the desired CMSIS packs.
Create a new C++ project
To create a new project, go to Eclipse menu, File → New (or the button in the upper left corner), and select the C++ Project:
In the New C/C++ Project window, select C/C++ Managed Build and click the Next > button.
In the C++ Project window:
- in the Project name: field enter the name of the new project, for example blinky
- in the Project type: section expand the Executable type and select STM32F4xx C/C++ Project
- in the Toolchains: section select Arm Cross GCC
- click the Next > button
In the Target processor settings window be sure to:
- select the STM32F407xx chip family
- set the Flash size (kB) field to 1024 kB
- set the Clock (Hz): field to 8000000
- for a more complete example, change the Use system calls: fields to Semihosting (POSIX system calls via host)
- click the Next > button.
In the Folders page leave the suggested folders unchanged and click the Next > button.
In the Select Configurations page leave the suggested Debug/Release configurations checked and click the Next > button.
In the Cross GNU Arm Toolchain window:
- select the Toolchain name: xPack GNU Arm Embedded GCC (mandatory)
- if the xPack was installed via xpm, the path is already filled in; otherwise
browse for the
bin
folder within the toolchain folder; don’t be afraid to use the absolute path, it is not stored in the project - click the Finish button
/bin
folder
where the programs with long, prefixed names are stored, and not the inner
/bin
folder where the short name programs may be available.The result of the wizard is a simple project, with a main()
function printing a greeting on the standard output.
Build the project
To start the build:
- select the new project in the Project Explorer section
- click the hammer icon
The build process should leave in the Console window a listing like this:
The files created by the build process are left in a folder named by the name of the configuration, for example Debug or Release.
As seen above, the Debug folder is populated with:
- all make files required for the build (makefile and various *.mk files, generated by CDT),
- the object files and dependency files generated during the build (main.o and main.d) and
- the final executable file, with all debug symbols, useful for a debug session (blinky.elf)
- the binary executable file, ready to be programmed in flash (blinky.hex)
- a program map, with all symbols and their associated addresses (blinky.map)
Assign a board and a device to the project
Although optional, it is highly recommended to assign a board and a device to the project, using the packs definitions. If you did not do it before, install the STM32F4xx_DFP package, as explained in the Packs Manager page.
To assign the board and device:
- select the project
- either with right click or via Project menu, enter Properties
- expand the C/C++ Build entry
- select the Settings entry
- select the Devices tab
- in the Boards section, expand STMicroelectronics
- expand STM32F4-Discovery
- select STM32F407VG
- click OK
Debug the test using QEMU Arm
The most convenient way to run a debug session with this test is to use the QEMU Debugging plug-in; you don’t need any physical hardware.
As for any debugging plug-in,
- select the blinky.elf file
- either with right click Debug As… or in the Debug menu, select Debug Configurations
- doble click the GDB QEMU Debugging
- select the Debugger tab
- check if the Board name: and Device name: are correctly filled-in; without assigning the board and device to the project, these fields must be filled in manually;
- enable Extra verbose
When done, click the Debug button.
If you did not disable the graphic windows, an animated image of the board is shown, with the 4 LEDs blinking.
The Eclipse debugger is active, with a breakpoint in main()
. From here you
can either step over several instructions, or click Resume
(the green right pointing triangle) and watch the program run.
The QEMU process will display some information in its console:
Terminate
The test will run for about 20 seconds, and terminate by itself.
To cancel it earlier, click the Terminate button (the top red square).
Semihosting
Contrary to usual POSIX environments, like GNU/Linux, embedded systems
usually do not provide standard input/output devices. In the code generated
by the template, the output of the printf()
calls, including
trace_printf()
,
is redirected to a special debugging channel implemented by most debuggers,
using the semihosting protocol.
Next step
Once you checked the development environment to be functional, proceed with creating real projects using the STM32Fx templates, Freescale KLxx templates, or, if your target processor is not yet supported, using the Generic Cortex-M template.
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Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
| Version: | 1.1 |
| Published: | 2018-09-22 |
| Author: | David Cooley [aut, cre], Milo Yip [ctb] (Author of c++ rapidjson library, provided through THL A29 Limited, a Tencent company), Alexander Chemeris [ctb] (Author of c++ msinttypes library) |
| Maintainer: | David Cooley <dcooley at symbolix.com.au> |
| License: | MIT + file LICENSE |
| Copyright: | see file COPYRIGHTS |
| NeedsCompilation: | no |
| SystemRequirements: | GNU make |
| Materials: | README |
| CRAN checks: | rapidjsonr results |
| Reference manual: | rapidjsonr.pdf |
| Package source: | rapidjsonr_1.1.tar.gz |
| Windows binaries: | r-devel: rapidjsonr_1.1.zip, r-release: rapidjsonr_1.1.zip, r-oldrel: rapidjsonr_1.1.zip |
| OS X binaries: | r-release: rapidjsonr_1.1.tgz, r-oldrel: rapidjsonr_1.1.tgz |
| Old sources: | rapidjsonr archive |
| Reverse linking to: | bigrquery, geojsonsf, jsonify |
Please use the canonical form https://CRAN.R-project.org/package=rapidjsonr to link to this page.


This chapter discusses a forward chaining rule based system and its expert system applications. It shows how the forward chaining system works, how to use it, and how to implement it quickly and easily using Prolog.
A large number of expert systems require the use of forward chaining, or data driven inference. The most famous of these is Digital Equipment Corporation's XCON system. It configures computers. It starts with the data about the customer order and works forward toward a configuration based on that data. The XCON system was written in the OPS5 (forward chaining rule based) language.
Data driven expert systems are different from the goal driven, or backward chaining systems seen in the previous chapters.
The goal driven approach is practical when there are a reasonable number of possible final answers, as in the case of a diagnostic or identification system. The system methodically tries to prove or disprove each possible answer, gathering the needed information as it goes.
The data driven approach is practical when combinatorial explosion creates a seemingly infinite number of possible right answers, such as possible configurations of a machine.
Forward chaining systems are often called "production" systems. Each of the rules is actually a miniature procedure called a production. When the patterns in the left hand side match working storage elements, then the actions on the right hand side are taken. This chapter concentrates on building a production system called Oops.
Production systems are composed of three components. These are:
• the rule set;
• a working storage area which contains the current state of the system;
• an inference engine which knows how to apply the rules.
The rules are of the form:
left hand side (LHS) ==> right hand side (RHS).
The LHS is a collection of conditions which must be matched in working storage for the rule to be executed. The RHS contains the actions to be taken if the LHS conditions are met.
The execution cycle is:
1. Select a rule whose left hand side conditions match the current state as stored in the working storage.
2. Execute the right hand side of that rule, thus somehow changing the current state.
3. Repeat until there are no rules which apply.
Production systems differ in the sophistication of the algorithm used to select a rule (step 1). The first version of Oops will use the simplest algorithm of just selecting the first rule whose conditions match working storage.
The knowledge engineer programs in Oops by creating rules for the particular application. The syntax of the rules is:
rule <rule
id>:
[<N>: <condition>, .......]
==>
[<action>, ....].
where:
rule id - a unique identifier for the rule;
N - optional identification for the condition;
condition - a pattern to match against working storage;
action - an action to take.
Each condition is a legal Prolog data structure, including variables. Variables are identified, as in Prolog, by an initial upper case letter, or underscore. In general, the condition pattern is matched against those stored in working storage. The comparison operators (>, =<, etc.) are also defined for comparing variables which are bound during the pattern matching.
Note that the data representation of Oops is richer than that used in Clam. In Clam there were only attribute - value pairs, or object - attribute - value triples. In Oops the data can be represented by any legal Prolog term including variables.
The following RHS actions are supported:
assert(X) - adds the term X to working storage;
retract(all) - retracts all of the working storage terms which were matched in the LHS of the rule being executed;
retract(N) - retracts LHS condition number N from working storage;
X = <arithmetic expression> - sets X to the value of the expression;
X # Y - unifies the structures X and Y;
write(X) - writes the term X to the terminal;
nl - writes a new line at the terminal;
read(X) - reads a term and unifies it to X;
prompt(X, Y) - writes X and reads Y;
In the Winston & Horn LISP book there is an example of a forward chaining animal identification system. Some of those rules would be expressed in Oops like this:
rule id6:
[1: has(X, pointed_teeth),
2: has(X, claws),
3: has(X, forward_eyes)]
==>
[retract(all),
assert(isa(X, carnivore))].
This rule would fire if working storage contained the Prolog terms:
has(robie, pointed_teeth)
has(robie, claws)
has(robie, forward_eyes)
When the rule fired, these three terms would be removed by the retract(all) action on the RHS, and would be replaced with the term:
isa(robie, carnivore)
Since the working storage elements which matched the conditions were removed, this rule would not fire again.
Rules about relationships are also easily coded. Again from Winston & Horn's example the rule which says children are the same type of animal as their parents is expressed as follows:
rule id16:
[1: isa(Animal, Type),
2: parent(Animal, Child)]
==>
[retract(2),
assert(isa(Child, Type))].
This would transform working storage terms like:
isa(robie, tiger)
parent(robie, suzie)
to:
isa(robie, tiger)
isa(suzie, tiger)
The working storage is initialized with a statement of the form:
initial_data([<term>, .......]).
Each term is a legal Prolog term which is asserted in working storage.
For example:
initial_data([gives(robie,
milk),
eats_meat(robie),
has(robie, tawny_color),
has(robie, dark_spots),
parent(robie, suzie)].
It would be better if we could set up the system to ask the user for the initial terms. In a conventional programming language we might set up a loop which repeatedly asked for data until the user typed in "end".
To do the same thing in a production system requires a bit of trickery, which goes against the grain of rule based systems. Theoretically, the rules are independent and don't communicate with each other, but by setting flags in working storage the programmer can force a specific order of rule firings.
Here is how to code the input loop in Oops. It violates another tenet of production systems by making use of the known rule selection strategy. In the case of Oops, it is known that rule 1 will be tried before rule 2.
initial_data([read_facts]).
rule 1:
% This is the end condition of
[1: end, % the loop. If "end" and
2: read_facts] % "read_facts" are both
% in working storage,
==>
[retract(all)]. % then remove them both.
rule 2:
% This is the body of the loop.
[1: read_facts] % If "read_facts" is in WS,
==> % then prompt for an attribute
[prompt('Attribute? ', X), % and assert it. If X
assert(X)]. % is "end", rule 1 will fire next.
The animal identification problem is one that can be solved either in a data driven (forward chaining) approach as illustrated here and in Winston & Horn, or in a goal driven (backward chaining) approach. In fact, where the list of possible animals is known the backward chaining approach is probably a more natural one for this problem.
A more suitable problem for a forward chaining system is configuration. The Oops sample program in the appendix shows such a system. It lays out furniture in a living room.
The program includes a number of rules for laying out furniture. For example:
• The couch goes on the longer wall of the room, and is not on the same side as the door.
• The television goes opposite the couch.
• If there is a lamp or television on a wall without a plug, buy an extension cord.
Here are those rules in Oops. They are more complex due to the need to work with the amount of wall space available.
% f1 - the couch goes opposite the door
rule f1:
[1: furniture(couch, LenC), % an unplaced couch
position(door, DoorWall), % find the wall with the door
opposite(DoorWall, OW), % the wall opposite the door
right(DoorWall, RW), % the wall right of the door
2: wall(OW, LenOW), % available space opposite
wall(RW, LenRW), % available space to the right
LenOW >= LenRW, % if opposite bigger than right
LenC =< LenOW] % length of couch less than
% wall space
==>
[retract(1), % remove the furniture term
assert(position(couch, OW)), % assert new position
retract(2), % remove the old wall, length
NewSpace = LenOW - LenC, % calculate the space left
assert(wall(OW, NewSpace))]. % assert new space
% f3 - the tv should be opposite the couch
rule f3:
[1: furniture(tv, LenTV),
2: position(couch, CW),
3: opposite(CW, W),
4: wall(W, LenW),
LenW >= LenTV]
==>
[retract(1),
assert(position(tv, W)),
retract(4),
NewSpace = LenW - LenTV,
assert(wall(W, NewSpace))].
% get extension cords if needed
rule f12:
[1: position(tv, W),
2: not(position(plug, W))]
==>
[assert(buy(extension_cord, W)),
assert(position(plug, W))].
rule f13:
[1: position(table_lamp, W),
2: not(position(plug, W))]
==>
[assert(buy(extension_cord, W)),
assert(position(plug, W))].
The program also uses rules to control a dialog with the user to gather initial data. It needs to know about room dimensions, furniture to be placed, plug locations, etc. It does this by setting various data gathering goals. For example the initial goal of the system is to place_furniture. It gives directions to the user and sets up the goal read_furniture. Once read_furniture is done (signified by the user entering end : end), it sets up the next goal of read_walls. Here is the beginning code.
rule 1:
[1: goal(place_furniture), % The initial goal causes a
2: legal_furniture(LF)] % rule to fire with
% introductory info.
==> %
It will set a new goal.
[retract(1),
cls, nl,
write('Enter a single item of furniture at each prompt.'), nl,
write('Include the width (in feet) of each item.'), nl,
write('The format is Item:Length.'), nl, nl,
write('The legal values are:'), nl,
write(LF), nl, nl,
write('When there is no more furniture, enter "end:end".'), nl,
assert(goal(read_furniture))].
rule 2:
[1: furniture(end, end), % When the furniture is read
2: goal(read_furniture)] % set the new goal of reading
==> %
wall sizes
[retract(all),
assert(goal(read_walls))].
rule 3:
[1: goal(read_furniture), % Loop to read furniture.
2: legal_furniture(LF)]
==>
[prompt('furniture> ', F:L),
member(F, LF),
assert(furniture(F, L))].
rule 4: %
If rule 3 matched and failed
[1: goal(read_furniture), % the action, then member
2: legal_furniture(LF)] % must have failed.
==>
[write('Unknown piece of furniture, must be one of:'), nl,
write(LF), nl].
The room configurer illustrates both the strengths and weaknesses of a pure production system. The rules for laying out the furniture are very clear. New rules can be added, and old ones deleted without affecting the system. It is much easier to work with this program structure than it would be to understand procedural code which attempted to do the same thing.
On the other hand, the rules which interact with the user to collect data are difficult to read and have interdependencies which make them hard to maintain. The flow of control is obscured. This code would be better written procedurally, but it is done using Oops to illustrate how these kinds of problems can be solved with a production architecture.
An ideal architecture would integrate the two approaches. It would be very simple to let Oops drop back down to Prolog for those cases where Oops is inappropriate. This architecture is covered in chapter 7.
The implementation of Oops is both compact and readable due to the following features of Prolog:
• Each rule is represented as a single Prolog term (a relatively complex structure).
• The functors of the rule structure are defined as operators to allow the easy-to-read syntax of the rule.
• Prolog's built-in backtracking search makes rule selection easy.
• Prolog's built-in pattern matching (unification) makes comparison with working storage easy.
• Since each rule is a single term, unification causes variables to be automatically bound between LHS conditions and RHS actions.
• The Prolog database provides an easy representation of working storage.
Each rule is a single Prolog term, composed primarily of two lists: the right hand side (RHS), and the left hand side (LHS). These are stored using Prolog's normal data structures, with rule being the predicate and the various arguments being lists.
In Clam, DCG was used to allow a friendly, flexible rule format. In Oops, Prolog operators are used. The operators allow for a syntax which is formal, but readable. The operator syntax can also be used directly in the code.
Without operator definitions, the rules would look like normal hierarchical Prolog data structures:
rule(==>(:(id4, [:(1, flies(X)), :(2, lays_eggs(X))], [retract(all), assert(isa(X, bird))])).
The following operator definitions allow for the more readable format of the rules:
op(230, xfx, ==>).
op(32, xfy, :).
op(250, fx, rule).
Working storage is stored in the database under the key fact. So to add a term to working storage:
asserta( fact(isa(robie, carnivore)) ),
and to look for a term in working storage:
fact( isa(X, carnivore) ).
Figure 5.1 shows the major predicates in the Oops inference engine. The inference cycle of recognize - act is coded in the predicate go. It searches for the first rule which matches working storage, and executes it. Then it repeats the process. If no rules match, then the second clause of go is executed and the inference ends. Then the second clause prints out the current state showing what was determined during the run. (Note that LHS and RHS are bound to lists.)
Figure 5.1. Major predicates in Oops inference engine
go:-
call(rule ID: LHS ==> RHS),
try(LHS, RHS),
write('Rule fired '), write(ID), nl,
!, go.
go:-
nl, write(done), nl,
print_state.
This code illustrates the tremendous expressiveness of Prolog. The code is very tight due to the built-in pattern matching and backtracking search. Especially note that since the entire rule is a single Prolog term, the unification between variables in the conditions and actions happens automatically. This leads to a use of variables which is, in some senses, cleaner and more powerful than that found in OPS5.
The try/2 predicate is very simple. If match/2 fails, it forces go to backtrack and find the next rule. The LHS is passed to process so retract statements can find the facts to retract.
try(LHS, RHS):-
match(LHS),
process(RHS, LHS).
The match/2 predicate recursively goes through the list of conditions, locating them in working storage. If the condition is a comparison test, then the test is tried, rather than searched for in the database.
match([]) :- !.
match([N:Prem|Rest])
:-
!,
(fact(Prem);
test(Prem)), % a comparison test rather than a fact
match(Rest).
match([Prem|Rest])
:-
(fact(Prem); % condition number not specified
test(Prem)),
match(Rest).
The test/1 predicate can be expanded to include any kind of test. Oops uses most of the basic tests provided with Prolog, plus a few. For example:
test(X >= Y):- X >= Y, !.
test(X = Y):- X is Y, !. % use = for arithmetic
test(X # Y):- X = Y, !. % use # for unification
test(member(X, Y)):- member(X, Y), !.
test(not(X)):-
fact(X),
!, fail.
If match/2 succeeds, then process/2 is called. It executes the RHS list of actions. It is equally straightforward.
process([], _) :- !.
process([Action|Rest],
LHS) :-
take(Action, LHS),
process(Rest, LHS).
Only the action retract needs the LHS. The take/2 predicate does a retract if that is what's called for, or else passes control to take/1 which enumerates the simpler actions. Some examples are given here.
take(retract(N),
LHS) :-
(N == all; integer(N)),
retr(N, LHS), !.
take(A, _) :-take(A), !.
take(retract(X)) :- retract(fact(X)), !.
take(assert(X)) :- asserta(fact(X)), write(adding-X), nl, !.
take(X # Y) :- X=Y, !.
take(X = Y) :- X is Y, !.
take(write(X)) :- write(X), !.
take(nl) :- nl, !.
take(read(X)) :- read(X), !.
The retr predicate searches for LHS conditions with the same identification (N) and retracts them. If all was indicated, then it retracts all of the LHS conditions.
retr(all, LHS) :-retrall(LHS), !.
retr(N, []) :-write('retract error, no '-N), nl, !.
retr(N, [N:Prem|_]) :- retract(fact(Prem)), !.
retr(N, [_|Rest]) :- !, retr(N, Rest).
retrall([]).
retrall([N:Prem|Rest])
:-
retract(fact(Prem)),
!, retrall(Rest).
retrall([Prem|Rest])
:-
retract(fact(Prem)),
!, retrall(Rest).
retrall([_|Rest])
:- % must have been a test
retrall(Rest).
Oops can be made to look like the other shells by the addition of a command loop predicate with commands similar to those in Clam and Native. Figure 5.2 shows the architecture of Oops.
Explanations for forward chaining systems are more difficult to implement. This is because each rule modifies working storage, thus covering its tracks. The most useful information in debugging a forward chaining system is a trace facility. That is, you want to know each rule that is fired and the effects it has on working storage.
Each fact can have associated with it the rule which posted it, and this would give the immediate explanation of a fact. However, the facts which supported the rules which led up to that fact might have been erased from working memory. To give a full explanation, the system would have to keep time stamped copies of old versions of facts.
Figure 5.2. Major predicates of the Oops shell
The trace option is added in Oops in a similar fashion to which it was added in Clam. The inference engine informs the user of the rules which are firing as they fire.
Oops in its current state is a simple forward chaining system. More advanced forward chaining systems differ in two main aspects.
• more sophisticated rule selection when many rules match the current working storage;
• performance.
The current rule selection strategy of Oops is simply to pick the first rule which matches. If many rules match, there might be other optimal choosing strategies. For example, we could pick the rule that matched the most recently asserted facts, or the rule which had the most specific match. Either of these would change the inference pattern of the system to give effects which might be more natural.
Oops is also inefficient in its pattern matching, since at each cycle of the system it tries all of the rules against working memory. There are various indexing schemes which can be used to allow for much faster picking of rules which match working memory. These will be discussed in the chapter 8.
OPS5, which is probably the most well known example of a forward chaining, or production, system offers two different means of selecting rules. One is called LEX and the other is MEA. Both make use of time stamped data to determine the best rule to fire next. They differ slightly in the way in which they use the data. Both of these strategies can be added to Oops as options.
For both, the first step is to collect all of the rules whose LHS match working memory at a given cycle. This set of rules is called the conflict set. It is not actually the rules, but rather instantiations of the rules. This means that the same rule might have multiple instantiations if there are multiple facts which match a LHS premise. This will often happen when there are variables in the rules which are bound differently for different instantiations.
For example, an expert system to identify animals might have the following condition on the LHS:
rule 12:
[...
eats(X, meat),
...]
==> ...
In working memory there might be the following two facts:
...
eats(robie, meat).
eats(suzie, meat).
...
Assuming the other conditions on the LHS matched, this would lead to two different instantiations of the same rule. One for robie and one for suzie.
The simplest way to get the conflict set is to use findall or its equivalent. (If your system does not have a findall, a description of how to write your own can be found in Clocksin and Mellish section 7.8, Assert and Retract: Random, Gensym, Findall.) It collects all of the instantiations of a term in a list. The three arguments to findall are:
• a term which is used as a pattern to collect instantiations of variables;
• a list of goals used as a query;
• an output list whose elements match the pattern of the first argument, and for which there is one element for each successful execution of the query in the second argument.
The instantiations of the conflict set will be stored in a structure, r/4. The last three arguments of r/4 will be the ID, LHS, and RHS of the rule which will be used later.
The first argument of r/4 is the LHS with the variables instantiated with the working storage elements that were matched. Each match of a LHS premise and working storage element is also accompanied by a time stamp indicating when the working storage element was last updated.
The query to be executed repeatedly by findall will be similar to the one currently used to find just the first matching rule:
?- rule ID : LHS ==> RHS, match(LHS, Inst)
Note that match now has a second argument to store the instantiation of the rule which will be the first argument to r/4.
The following predicate puts the above pieces together and uses findall to build a list (CS) of r/4s representing all of the rules which currently match working storage.
conflict_set(CS)
:-
findall(r(Inst, ID, LHS, RHS),
[rule ID: LHS ==> RHS, match(LHS, Inst)],
CS).
The match predicate just needs to be changed to capture the instantiation of the conditions. Notice there is an additional argument, Time, in the fact predicate. This is a time stamp that will be used in the selection process.
match([], []) :- !.
match([N:Prem|Rest],
[Prem/Time|IRest]) :-
!,
(fact(Prem, Time);
test(Prem), Time = 0), %comparison, not a fact
match(Rest, IRest).
match([Prem|Rest],
[Prem/Time|IRest]) :-
(fact(Prem, Time); % no condition number
test(Prem), Time = 0),
match(Rest, IRest).
The timestamp is just a chronological counter that numbers the facts in working memory as they are added. All assertions of facts are now handled by the assert_ws predicate as follows:
assert_ws(fact(X,
T)) :-
getchron(T),
asserta(fact(X, T)).
The getchron predicate simply keeps adding to a counter.
getchron(N)
:-
retract( chron(N) ),
NN is N + 1,
asserta( chron(NN) ), !.
Now that we have a list of possible rules and instantiations in the conflict set, it is necessary to select one. First we will look at the OPS5 LEX method of rule selection. It uses three criteria to select a rule.
The first is refraction. This discards any instantiations which have already been fired. Two instantiations are the same if the variable bindings and the time stamps are the same. This prevents the same rule from firing over and over, unless the programmer has caused working memory to be repeatedly updated with the same fact.
The second is recency. This choses the rules which use the most recent elements in working memory. The conflict set rules are ordered with those rules with the highest time stamps first. This is useful to give the system a sense of focus as it works on a problem. Facts which are just asserted will most likely be used next in a rule.
The third is specificity. If there are still multiple rules in contention, the most specific is used. The more conditions there are that apply in the LHS of the rule, the more specific it is. For example, a rule with 4 conditions is more specific than one with 3 conditions. This criteria ensures that general case rules will fire after more specific case rules.
If after these tests there are still multiple rules which apply, then one is chosen at random.
The LEX strategy changes the way in which Oops rules are programmed. In the first version of Oops, the knowledge engineer had to make sure that the working storage elements which caused the rule to fire are changed. It was the knowledge engineer's responsibility to ensure that a rule did not repeatedly fire.
The opposite is also true. Where looping is required, the facts matching the LHS must be continually reasserted.
In the original version of Oops the knowledge engineer knew the order in which rules would fire, and could use that information to control the inference. Using LEX he/she can still control the inference, but it requires more work. For example, if it is desirable to have the couch placed first by the system, then that rule must be structured to fire first. This can be done by adding a goal to place the couch first and asserting it after the data is gathered. For example:
rule gather_data
...
==>
[...
assert( goal(couch_first) ) ].
rule couch
[ goal(couch_first),
...
The gather_data rule will assert the couch_first goal after all other assertions. This means it is the most recent addition to working storage. The Lex recency criteria will then ensure that the couch rule is fired next.
The rule which is supposed to fire last in the system also needs to be handled specially. The easiest way to ensure a rule will fire last is to give it an empty list for the LHS. The specificity check will keep it from firing until all others have fired.
To implement the LEX strategy, we modify the go predicate to first get the conflict set and then pass it to the predicate select_rule which picks the rule to execute. After processing the rule, the instantiation associated with the rule is saved to be used as a check that it is not reexecuted.
go :-
conflict_set(CS),
select_rule(CS, r(Inst, ID, LHS, RHS)),
process(RHS, LHS),
asserta( instantiation(Inst) ),
write('Rule fired '), write(ID), nl,
!, go.
go.
The select_rule predicate applies the three criteria above to select the appropriate rule. The refract predicate applies refraction, and lex_sort applies both recency and specificity through a sorting mechanism.
select_rule(CS,
R) :-
refract(CS, CS1),
lex_sort(CSR, [R|_]).
First let's look at refract, which removes those rules which duplicate existing instantiations. It relies on the fact that after each successful rule firing, the instantiation is saved in the database.
refract([], []).
refract([r(Inst,
_, _, _)|T], TR) :-
instantiation(Inst),
!, refract(T, TR).
refract([H|T],
[H|TR]) :-
refract(T, TR).
Once refract is done processing the list, only those rules with new instantiations are left on the list.
The implementation of lex_sort doesn't filter the remaining rules, but sorts them so that the first rule on the list is the desired rule. This is done by creating a key for each rule which is used to sort the rules. The key is designed to sort by recency and specificity. The sorting is done with a common built-in predicate, keysort, which sorts a list by keys where the elements are in the form: key - term. (If your Prolog does not have a keysort, see Clocksin and Mellish section 7.7, Sorting.)
lex_sort(L,
R) :-
build_keys(L, LK),
keysort(LK, X),
reverse(X, Y),
strip_keys(Y, R).
The build_keys predicate adds the keys to each term. The keyed list is then sorted by keysort. It comes out backwards, so it is reversed, and finally the keys are stripped from the list. In order for this to work, the right key needs to be chosen.
The key which gives the desired results is itself a list. The elements are the time stamps of the various matched conditions in the instantiation of the rule. The key (a list) is sorted so that the most recent (highest number) time stamps are at the head of the list. These complex keys can themselves be sorted to give the correct ordering of the rules. For example, consider the following two rules, and working storage:
rule t1:
[flies(X),
lays_eggs(X)]
==>
[assert(bird(X))].
rule t2:
[mammal(X),
long_ears(X),
eats_carrots(X)]
==>
[assert(animal(X, rabbit))].
fact( flies(lara), 9).
fact( flies(zach), 6).
fact( lays_eggs(lara), 7).
fact( lays_eggs(zach), 8).
fact( mammal(bonbon), 3).
fact( long_ears(bonbon), 4).
fact( eats_carrots(bonbon), 5).
There would be two instantiations of the first rule, one each for lara and zach, and one instantiation of the second rule for bonbon. The highest numbers are the most recent time stamps. The keys (in order) for these three instantiations would be:
[9, 7]
[8, 6]
[5, 4, 3]
In order to get the desired sort, lists must be compared element by element starting from the head of the list. This gives the recency sort. The sort must also distinguish between two lists of different lengths with the same common elements. This gives the specificity sort. For AAIS prolog the sort works as desired with recency being more important than specificity. It should be checked for other Prologs.
Here is the code to build the keys:
build_keys([], []).
build_keys([r(Inst,
A, B, C)|T], [Key-r(Inst, A, B, C)|TR]) :-
build_chlist(Inst, ChL),
sort(ChL, X),
reverse(X, Key),
build_keys(T, TR).
build_chlist([], []).
build_chlist([_/Chron|T],
[Chron|TC]) :-
build_chlist(T, TC).
The build_keys predicate uses build_chlist to create a list of the time stamps associated with the LHS instantiation. It then sorts those, and reverses the result, so that the most recent time stamps are first in the list.
The final predicate, strip_keys, simply removes the keys from the resulting list.
strip_keys([], []).
strip_keys([Key-X|Y],
[X|Z]) :-
strip_keys(Y, Z).
The other strategy offered with OPS5 is MEA. This is identical to LEX with one additional filter added. After refraction, it finds the time stamp associated with the first condition of the rule and picks the rule with the highest time stamp on the first condition. If there is more than one, then the normal LEX algorithm is used to pick which of them to use.
At first this might seem like an arbitrary decision; however, it was designed to make goal directed programming easier in OPS5. The flow of control of a forward chaining system is often controlled by setting goal facts in working storage. Rules might have goals in the conditions thus ensuring the rule will only fire when that goal is being pursued.
By making the goal condition the first condition on the LHS of each rule, and by using MEA, the programmer can force the system to pursue goals in a specified manor. In fact, using this technique it is possible to build backward chaining systems using a forward chaining tool.
The test for MEA is simply added to the system. First, the filter is added to the select_rule predicate. It will simply return the same conflict set if the current strategy is not MEA.
select_rule(R,
CS) :-
refract(CS, CS1),
mea_filter(0, CS1, [], CSR),
lex_sort(CSR, [R|_]).
The actual filter predicates build the new list in an accumulator variable, Temp. If the first time stamp for a given rule is less than the current maximum, it is not included. If it equals the current maximum, it is added to the list of rules. If it is greater than the maximum, that timestamp becomes the new maximum, and the list is reinitialized to have just that single rule.
mea_filter(_, X, _, X) :- not strategy(mea), !.
mea_filter(_, [], X, X).
mea_filter(Max,
[r([A/T|Z], B, C, D)|X], Temp, ML) :-
T < Max,
!, mea_filter(Max, X, Temp, ML).
mea_filter(Max,
[r([A/T|Z], B, C, D)|X], Temp, ML) :-
T = Max,
!, mea_filter(Max, X, [r([A/T|Z], B, C, D)|Temp], ML).
mea_filter(Max,
[r([A/T|Z], B, C, D)|X], Temp, ML) :-
T > Max,
!, mea_filter(T, X, [r([A/T|Z], B, C, D)], ML).
These examples illustrate some of the difficulties with expert systems in general. The OPS5 programmer must be intimately familiar with the nature of the inferencing in order to get the performance desired from the system. He is only free to use the tools available to him.
On the other hand, if the programmer has access to the selection strategy code, and knows the type of inferencing that will be required, the appropriate strategy can be built into the system. Given the accessibility of the above code, it is easy to experiment with different selection strategies.
5.1 - Add full rule tracing to OOPS.
5.2 - Add a command loop which turns on and off tracing, MEA/LEX strategies, loads rule files, consults the rules, lists working storage, etc.
5.3 - Add a feature that allows for the saving of test case data which can then be run against the system. The test data and the results are used to debug the system as it undergoes change.
5.4 - Allow each rule to optionally have a priority associated with it. Use the user defined rule priorities as the first criteria for selecting rule instantiations from the conflict set.
5.5 - Add features on the LHS and RHS that allow rules to be written which can access the conflict set and dynamically change the rule priorities. Figure out an application for this.
5.6 - Add new syntax to the knowledge base that allows rules to be clustered into rule sets. Maintain separate conflict sets for each rule set and have the inference engine process each rule set to completion. Have higher level rules which can be used to decide which rule sets to execute. Alternatively, each rule set can have an enabling pattern associated with it that allows it to fire just as individual rules are fired.
5.7 - Each fact in working storage can be thought of as being dependent on other facts. The other facts are those which instantiated the LHS of a rule which udpated the fact. By keeping track of these dependencies, a form of truth maintenance can be added to the system. When a fact is then removed from working storage, the system can find other facts which were dependent on it and remove them as well.
Copyright ©1989,2000 Amzi! inc. All Rights Reserved.


|
M480 BSP V3.05.005
The Board Support Package for M480 Series
|
Modules | |
| SDH Exported Type Defines | |
Macros | |
| #define | SDH_ERR_ID |
| #define | SDH_TIMEOUT |
| #define | SDH_NO_MEMORY |
| #define | Successful |
| #define | Fail |
| #define | SDH_TYPE_UNKNOWN |
| #define | SDH_TYPE_SD_HIGH |
| #define | SDH_TYPE_SD_LOW |
| #define | SDH_TYPE_MMC |
| #define | SDH_TYPE_EMMC |
| #define | SDH_NO_SD_CARD |
| #define | SDH_ERR_DEVICE |
| #define | SDH_INIT_TIMEOUT |
| #define | SDH_SELECT_ERROR |
| #define | SDH_WRITE_PROTECT |
| #define | SDH_INIT_ERROR |
| #define | SDH_CRC7_ERROR |
| #define | SDH_CRC16_ERROR |
| #define | SDH_CRC_ERROR |
| #define | SDH_CMD8_ERROR |
| #define | MMC_FREQ |
| #define | SD_FREQ |
| #define | SDHC_FREQ |
| #define | SD_PORT0 |
| #define | SD_PORT1 |
| #define | CardDetect_From_GPIO |
| #define | CardDetect_From_DAT3 |


Audio samples for CHiVE
Audio samples for CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network, V. Wan, C.-a. Chan, T. Kenter, J. Vit, and R. A. Clark, Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML 2019), 2019.
Samples that go with the main experiment
These samples go with the side-by-side experiments (i.e., Table 1 in the paper).
Samples of further evaluations
These samples go with the MOS test (i.e., Section 4.2, Table 2 in the paper).
These samples are of randomly sampled pitch contours (i.e., Section 4.2, Figure 4 in the paper).
Samples of prosody transfer
These samples go with the prosody transfer illustrations (i.e., Section 4.3, Figures 5 and 6 in the paper, and some additional ones).


Passion for animals and travel. These are usually mutually exclusive so I take my talents to the road. I will travel anywhere within South Carolina (continental US, minus Alaska, pending) to provide care for your animals. My most extensive experience is with dogs. I have worked as a groomer for almost fifteen years and as a trainer for more than a decade. I also have experience with cats, rabbits, goats, chickens, rats, lizards, fish, parrots and guinea pigs.
This is some text that I added to add some bulk. If you like it, please like us on our social media and be sure to tell your friends and family!
This is some text that I added to add some bulk. If you like it, please like us on our social media and be sure to tell your friends and family!
Phone number:<PHONE_NUMBER>
Email address: c.peacepaws@gmail.com


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{% extends 'expt_layout.html' %} {% block expt_title %}Insertion Sort{% endblock expt_title %} {% block toc %}
{% endblock toc %} {% block Theory %}Introduction
Insertion sort is one of the sorting algorithms.
To sort the elements in array in ascending or
descending Insertion sort can be used.
Principle of Insertion Sort
The array is divided in two parts (not actually) left part is sorted and right part is unsorted. By Iterating from left to right and moving our line which divides to right we make whole array sorted.
Algorithm Steps
1. Iterate through array from index 1 to n-1 i.e. iterate whole array as 0th element is already sorted.
(0 base indexing)
2. At each iteration check if current element is greater than previous element If it is, then current element is at
correct position and we continue.
3. And If not, then we have to put it at correct position via comparing all elements behind and moving them 1 step ahead until we find correct position for that current element.
4. By the end of all iterations we get whole array sorted .
Example
1. Take example of arr = [6, 2, 3, 5, 9, 8, 1] of n=7 elements. First step is to iterate through arr from
index 1.
2. So at index i=1 check consider [6,2] so 2 is not in its correct position i.e. compare 2 with previous elements .
and move them to right until we find a smaller element than 2. so here 6 will be moved to index 1 and then assign 2
to its correct position so now array till index 1 is sorted. [2, 6]
3. Similarly as step 2 for each i do same things and at finally whole array will be sorted. See the image below for
better understanding.
Implementation
C++ implementation
#include <iostream>
using namespace std;
// Insertion sort function
void Insertion_Sort(int arr[], int n)
{
int i, j, cur;
// Iterate thorugh array
for (i = 1; i < n; i++)
{
// Store current element in cur
cur = arr[i];
// Iterating variable for finding
// correct position of current element
j = i - 1;
// Iterate on behind the current element
// Until we reach start of array or arr[j]>cur
while (j >= 0 && arr[j] > cur)
{
// Each step assign arr[j] to arr[j+1]
// i.e. move element ahead
arr[j + 1] = arr[j];
// Decrement j
j--;
}
// We found correct position for cur
// i.e j+1 so assign that to cur
arr[j + 1] = cur;
}
}
// Display array function
void Display_Array(int arr[], int n)
{
cout<<"Displaying Array... : ";
for (int i = 0; i < n; i++)
cout << arr[i] << " ";
cout << endl;
}
// Main Function
int main()
{
// Sample array we can take input from user also
int arr[] = {6, 2, 3, 5, 9, 8, 1};
// Number of elements
int n = sizeof(arr) / sizeof(arr[0]);
// Displaying array before sorting
Display_Array(arr, n);
// Calling function insertion sort
Insertion_Sort(arr, n);
// Displaying array after sorting
Display_Array(arr, n);
return 0;
}
Python implementation
# Insertion sort function
def Insertion_Sort(arr,n):
for i in range(1,n):
cur = arr[i]
j = i - 1
while (j >= 0 and arr[j] > cur):
arr[j + 1] = arr[j]
j-=1
arr[j + 1] = cur
# Display array function
def Display_Array(arr,n):
print("Displaying Array... : ",end="")
for i in range(n):
print(arr[i],end=" ")
print()
# Main program
arr = [6, 2, 3, 5, 9, 8, 1]
n = len(arr)
# Displaying array before sorting
Display_Array(arr, n)
# Calling function insertion sort
Insertion_Sort(arr, n)
# Displaying array after sorting
Display_Array(arr, n)
{% endblock Theory %}
{% block Analysis %}
We will analyze and compare different sorting algorithms on the basis of different parameters like Time Complexity, In-place, Stability, etc.The above graph is for the comparison of insertion sort, Bubble sort, Quick sort and selection sort on the basis on time.
In a comparison based sorting algorithms, we compare elements of an array with each other to determines which of two elements should occur first in the final sorted list.
All comparison-based sorting algorithms have a complexity lower bound of nlogn (Think!)
Here is the comparison of time and space complexities of some popular comparison based sorting algorithms:
Input
Output
Program


{% extends "base.html" %} {% load static i18n %} {% block title %}Merge Job Submitted{% endblock %} {% block content %}
Samples have been queued for merging and {{ merged_sample }} will be available for usage soon. The sample will also be added to the assembly queue once the merging is completed.


Before Change
key = model.lower()
url = "/".join(FrequencyResponse._split_path(rel_path))
url = "https://github.com/jaakkopasanen/AutoEq/tree/master/results/{}".format(url)
url = urllib.parse.quote(url, safe="%/:=&?~//+!$,;"@()*[]")
urls[key] = "- [{model}]({url})".format(model=model, url=url)
return urls
After Change
for model, rel_paths in skipped.items():
// Add skipped models with only one item, these have no averaged results
if len(rel_paths) == 1:
urls[model.lower()] = "- [{model}]({url})".format(model=model, url=form_url(rel_paths[0]))
return urls
def main():


--- layout: post title: 'Learning in the Open: I – Starting with ActiveRecord' date: '2010-01-17T23:33:00.001Z' author: solrevdev tags: modified_time: '2010-01-17T23:33:45.443Z' blogger_id: tag:blogger.com,1999:blog-31446756.post-8993586581468834730 blogger_orig_url: http://www.solrevdev.com/2010/01/learning-in-open-i-starting-with.html ---


There are two parts to the client management. They are RFC 7951, which relates to creating a new client dynamically and RFC 7592 which extends that to allow for managing clients with get, update or deletion.
There is also a native facility for oa4mp that can be used. This is kept mostly for legacy purposes and can be ignored. This came in to existence well before the RFC's were written and rather very similar. Generally unless you have clients that are using it, ignore it.
All of the api elements below live in the clientManagement element of the server configuration. Note that if you omit that element, the defaults are used and the endpoint is active.
| Attribute | Required? | Default | Description |
| anonymousOK | N | false | (RFC 7591 protocol only). Allow posts to create a new client. These will not be approved automatically. |
| autoApprove | N | false | (RFC 7591 protocol only.) Whether or not to automatically approve anonymous requests. This may be required in highly automated systems with severely restricted access. Generally though setting it true is a terrible idea and a security risk. |
| autoApproverName | N | auto-approved | (RFC 7591 protocol only.) If auto approve is set to true, this will be the name of the approver in the client approval store. |
| enabled | N | true | whether or not to enable this api. |
| endpoint | N | oidc-cm | The endpoint in this service. If your has address https://www.foo.bar/oauth2, then not specifying this means that the endpoint would be constructed as https://foo.bar/oauth2/oidc-cm |
| protocol | Y | - | The protocol for this endpoint, one of rfc7951, rfc7952 or oa4mp. |
| template | N | - | (RFC 7591 protocol only) The id of a configured client which is to be used as a template. This means that create requests will create an copy of this client and use any arguments as overrides. This allows you to have a uniform registration for all clients (e.g. all of your clients use the device flow, are auto-approved, and need identical configurations). This only applies to the rfc 7951 api. You would need to create this client (tip: never approve it) in the CLI before setting this. |
| url | N | - | If you want to completely specify an endpoint not, e.g., on this server, you must give the complete url. For instance, if you wanted a to have https://clients.foo.bar instead. |
<clientManagement> <api protocol="rfc7951" enabled="true" url="https://clients.foo.bar" anonymousOK="true" template="template:/client/generic" /> <api protocol="rfc7952" enabled="true" endpoint="oidc-cm" /> <api protocol="oa4mp" enabled="false" /> </clientManagement>
Note that omitting a protocol means that the component will be enabled with the defaults. Therefore you must explicitly disable things you do not want.
If you don't want any sort of dynamic client registration, simply disable everything as follows:
<clientManagement enabled="false">
Any other attributes will be ignored, so you can just turn it on or off.


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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
James Martin shows you how to make your own sweet chilli sauce to add a kick to this crab stir fry.
For the garlic and chilli sauce, place all the ingredients in a small saucepan with two tablespoons water. Bring to the boil, then reduce the heat and simmer for a few minutes until the sugar is dissolved. Set aside to cool.
For the steamed rice, add 500ml/18fl oz water to a pan then add the rice and salt. Cook on a low heat until all the water has disappeared. Place a lid on top and set aside for 20 minutes.
For the chilli crab, heat a wok until hot and add the vegetable oil, ginger, garlic and chillies and stir fry for two minutes.
Meanwhile, whisk the tomato sauce, garlic and chilli sauce, hoisin sauce and fish sauce together in a bowl. Add the sugar and 75ml/2½fl oz water and mix to combine. Add to the wok and bring to the boil.
Add the crab then reduce the heat and simmer for 3-4 minutes until the sauce has thickened slightly and the crab is heated through.
To serve, place a softened banana leaf on each serving plate and spoon on the crab and the rice next to it. Garnish with chopped coriander.


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A zone may be selected in ol3 or Cesium map using maj+drag and trigger an event.
Then application code may react to the event by zooming, selecting features, ...


29 February 1996
Disease Outbreak Reported
As of 27 February, 24 cases of which 17 were fatal had been reported. Control measures instituted in Gabon by the Ministry of Health and an international medical team appear to be containing the further spread. However, sporadic secondary cases can still be expected. Teams sent to investigate rumours of cases in Djoum on the border between Gabon and Cameroon did not find any case of suspect Ebola haemorrhagic fever. Another team was sent from Brazzaville on 26 February to villages in northern part of Congo for a similar inquiry.


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I ran into some trouble with getting a good native postgresql driver installed. Here are some links and resources I found to be useful. I also wrote a checklist for bootstrapping a new Rails app with postgres as the adapter.
There are several choices for postgresql adapters. The most active and up-to-date one appears to be ruby-pg, previously known as ruby-postgres. ruby-pr was the one I was using originally, but this one is non-native and unmaintained.
A good starting point was Robby's 'Installing Ruby on Rails and Postgresql on OS X 2nd edition. ShiftEleven also had detailed instructions for installing postgresql with macports.
The exact steps I took that worked for me were:
# start postgres on computer start.
sudo launchctl load -w /Library/LaunchDaemons/org.macports.postgresql83-server.plist
# the ARCHFLAGS is because of Leopard weirdness.
sudo env ARCHFLAGS="-arch i386" gem install pg


The show inspired by a Reddit comment and based loosely on true historical events.
Vienna, 1917. five men who were to change the world forever lived within blocks of each other: Adolf Hitler, Josef Stalin, Leon Trotsky, Sigmund Freud, and Josip Broz Tito. This is their story.


What our art means
1986
Gilbert and George
We want Our Art to speak across the barriers of knowledge directly to People about their Life and not about their knowledge of art. The twentieth century has been cursed with an art that cannot be understood. The decadent artists stand for themselves and their chosen few, laughing at and dismissing the normal outsider. We say that puzzling, obscure and form-obsessed art is decadent and a cruel denial of the Life of People. There is also an intent to change people, but "The art-material must be subservient to the meaning and purpose of the picture." It states:
We want to learn to respect and honour "the whole". The content of mankind is our subject and our inspiration. We stand each day for good traditions and necessary changes. We want to find and accept all the good and bad in ourselves. The conclusion is an affirmation of "our life-search for new meanings and purpose to give to life."
These sprites are the shadow heartless from the Kingdom Hearts series.  These are just different ways that they move, attack and spawn.


communication provider used to transport push messages to a push Device.
This models the Java enum com.mwaysolutions.gofer2.push.domain.ProviderType
.
receives incoming push notification messages once registered using listenPushNotification()
.
For each incoming push notification message the callback is executed performing some app-specific action. The caller will wait until the optionally returned promise gets resolved, before signalling the operating system the message got processed completely.
Notice, the next push notification message may be passed to the callback even before the returned promise completed. This is, the optional promise returned just controls when the end of processing gets signalled to the operating system, but not when the app is ready.
Also, care must be taken as processing of a push notification message must not exceed more than 30s according to push plugin documentation at https://github.com/phonegap/phonegap-plugin-push/blob/master/docs/PAYLOAD.md. For that reason it makes no sense to display some blocking user interface here!
If during any of these steps some error is detected, the callback is executed. This holds even when during processing of a push notification message the callback itself signalled an error. In this case, both parameters are supplied.
an incoming push notification message.
transmission status of a push Job.
This models the Java enum com.mwaysolutions.gofer2.push.domain.State
.
authorizes current Relution server logged onto to send push notifications by transmitting the registration token.
installs a callback for receiving push notification messages, and registers the device with the 3rd party push service provider.
Usually push configuration is provided to init()
and a call to this method is chained passing
the sink callback. Application using an explicit login then call configurePushDevice()
as part
of the LogonCallback
while anonymous applications call the latter directly.
In general it is not wise to unregister from push messages. However, this functionality is
available by passing null
as callback.
to install, or explicitly null to unregister.
promise of registration, for informal purposes.
posts push notification(s).
Usually the server sends push notifications on its own behalf. However, this method may be used by the client app itself to send push notifications either to other clients, or to itself which can be used to test connectivity.
The implementation relies on backend code generated by CLI which forwards the body JSON to the server-side implementation of this method.
to deliver.
promise of async execution resolving to UUIDs of jobs in asynchronous delivery, empty when no apps or devices got selected by the message criteria or null when no target apps or devices exist at all.
registers a target device with the current Relution server.
The implementation relies on backend code generated by CLI. That code attempts fetching an existing device using the metadata information send as request body data. If it finds one, that device is updated. Otherwise a new device is created and stored in the database. The updated device registration record then is sent as response body data.
to register.
promise of registered device.
Generated using TypeDoc


--- layout: default title: Home --- ISMRM Iberian Chapter "Affiliates" is a Jekyll theme designed & developed by WowThemesNet, best suitable for affiliate marketing. Meetings 2021 Featured {% for post in site.posts %} {% if post.featured == true %} {% include featuredbox.html %} {% endif %} {% endfor %} {% include sidebar.html %} Our sponsors Sponsors image should go here.
Sponsors image should go here.


gnome-keyring-daemon(1) gnome-keyring-daemon(1) NAME gnome-keyring-daemon - keep password and other secrets for users DESCRIPTION gnome-keyring-daemon is a daemon in the session, similar to ssh-agent, and other applications can use it to store passwords and other sensitive information. AUTHORS gnome-keyring-daemon was written by Alexander Larsson<EMAIL_ADDRESS>and others. This manual page was written by Sven Arvidsson<EMAIL_ADDRESS>for the Debian project (but may be used by others). GNOME 2007-05-05 gnome-keyring-daemon(1)


Add a match
Match number:
Team number:
Alliance
Red
Blue
Color Wheel
Not utilized
Rotated to a specific color
Rotated a certain number of times
Power cells scored in auto:
Power cells scored in teleop:
Bots hanging from the shield generator:
Bots on the floor under the shield generator:
Is the Shield Generator level?


Udacity
Lesson 1
HTML
HTML-Hyper Text Markup Language
HTM is
made up of
-text content
-markup
-references to other documents
-links to other pages
Examples of HTML Markup, Tag Elements and Attributes
Tags
< opening tag> contents< /closing tag>
b-bold
em-emphasis
HTML Attributes
< tag attr="value">contents< /tag>
"value" is usually the url for the tag
you want to show on the webpage
< a href="url">link text< /a>
a- anchor or link
< img src="url"alt="text">
img- image
White Space
br- break and it is a void tag
p- paragraph
There are two classes of tags Div & Span


JSDoc MemberOf Namespace
JSDoc plugin to automatically generate memberof namespace tags
This plugin creates a @memberof
tag, if missing, from the @namespace
tag defined in index.js
(index scope) or the file itself (module scope). This enables for a complex project with many modules or packages to be documented easily without needing to manually add @memberof
tags to every comment to keep documentation organised.
Sample documentation for test module is available on docs page.
Installation and Configuration
JSDoc should be installed and configured.
Install
jsdoc-memberof-namespace
npm install jsdoc-memberof-namespace --save-dev
Add
node_modules/jsdoc-memberof-namespace
toplugins
in JSDoc configuration fileSample
conf.json
{ "source": { "include": "src" }, "plugins": [ "plugins/markdown", "node_modules/jsdoc-memberof-namespace" ], "opts": { "recurse": true } }
Usage
// index.js
/**
* Acme Corporation
* @namespace acme
*/
// Product.js
/**
* Base product
* Documented as acme.Product
*/
class Product {
/**
* Add product to basket
* Documented as acme.Product#buy
*/
buy() { }
}
Multiple namespaces can be defined in multiple places and the closest applicable namespace will be used:
- In the current file (last namespace defined if multiple)
- In the
index.js
in the same directory as the file - In the
index.js
in any parent directory closest to the file
Index Scope
Namespace can be defined in index.js
located in same directory as the file or any of the parent directories. It is the easiest way to document your project by simply adding @namespace
once alongside your re-exports.
Module Scope
Namespace can be defined in the file itself. This namespace will only apply to other documentation comments in the file and only those defined after the namespace. The behaviour is similar to @module
tag.
Notes
- Only classes, constants, and functions are processed, for other kinds to be included please submit an issue with sample code
- Prefix references with namespaces as you normally would even if your IDE highlights them as (yet) unresolved
/** * @type {acme.Foo} * @param {acme.Bar} bar */
- Enable
useLongnameInNav
default template configuration option to use fully qualified names for navigation"templates": { "default": { "includeDate": false, "useLongnameInNav": true } }
- Add
--debug
command-line option to increase JSDoc logging level and look for messages with[JMN]
prefix


decisiorama.sensitivity package¶
Submodules¶
decisiorama.sensitivity.oat module¶
One-at-a-time sensitivity analysis
This module contains functions to carry out a one-at-a-time sensitivity analysis.
-
decisiorama.sensitivity.oat.
local_sensitivity
(func, pars, vicinity=0.05, n_pert=10)¶ Create a local sensitivity analysis around a fractional value of the centered parameter values (vicinity).
- Parameters
func (function) – Function that calculate the sensitivity objectives
pars (1D array) – Parameters around which the OAT sensitivity will be calculated. This vector contains all of the arguments to run the function
vicinity (float) – Fraction of the parameter that will be used in generating the local sensitivity
n_pert (int) – Number of points for each variable in the calculation of the local sensitivity
- Returns
lsi – Local sensitivity index as a normalised value
- Return type
1D array
-
decisiorama.sensitivity.oat.
oat_sensitivity
(func, pars, dx)¶ one at the time sensitivity analysis
- Parameters
func (function) – Function that calculate the sensitivity objectives
pars (1D array) – Parameters around which the OAT sensitivity will be calculated. This vector contains all of the arguments to run the function
dx (2D array) – Matrix containing the perturbance for each of the variables.
- Returns
perturbed_res (3D array) – Matrix with the results of the model runs for each perturbation with dimensions of parameter, perturbation and objetive function
delta (3D array)


This website displays how cooperation and altruism can and do exist in our "survival of the fittest" world.  If the best mentality is to take for yourself then why do we work together? Through methods pioneered by Robert Axelrod in the 1980s we can see how cooperation is evolutionarily advantageous.
In 1980, Robert Axelrod, professor of political science at the University of Michigan, held a tournament of various strategies for the prisoner's dilemma. He invited a group of well-known game theorists to submit strategies to compete. A computer simulation ran these strategies against each other . Each strategy specified whether to cooperate or defect based on the previous moves of its opponent.
The result was the publication of The Evolution of Cooperation. In this book Axelrod expounds on discoveries found in his tournaments. The primary finding being that the best strategies were "nice", meaning they were never the first to defect. The most effective of these nice strategies was called "Tit For Tat." Tit For Tat was so successful because of it's simplicity. It only had two rules; first, be nice; second, respond appropriately. If Tit For Tat's opponent was "mean", it would be mean back. If its opponent was nice, it would also be nice. This simple strategy was surprisingly effective.
What does Tit for Tat teach us?
The prisoner's dilemma is a game that shows why two individuals might not cooperate, even if it appears that it is in their best interests to do so
Two members of a criminal gang are arrested and imprisoned. Each prisoner is in solitary confinement with no means of speaking to or exchanging messages with the other. The police admit they don't have enough evidence to convict the pair on the principal charge. They plan to sentence both to a year in prison on a lesser charge. Simultaneously, the police offer each prisoner a Faustian bargain. If he testifies against his partner, he will go free while the partner will get three years in prison on the main charge. Oh, yes, there is a catch ... If both prisoners testify against each other, both will be sentenced to two years in jail.
A strategy is only as good as its competition.
This version of Axelrod's tournament is written in JavaScript. If you would like to contribute a strategy you may do so through github.


{% extends 'ui/admin/form.html' %} {% block success %} Success win {{ win_id }}, has been deleted. {% endblock %} {% block breadcrumb-name %}Delete a win{% endblock %} {% block admin-title %}Delete a win{% endblock %} {% block form %}
The win will be removed, but it can be recovered if necessary.


Simply download the CSS file from the swatch of your choice and replace the one in Bootstrap. No messing around with hex values.
We've all been there with the black bar and blue buttons. See how a splash of color and typography can transform the feel of your site.
Swatches are built for and tested with the latest version of Bootstrap. Previous versions are also available for download.
Licensed under Apache 2.0 and maintained by the community on GitHub.
Changes are contained in just two LESS files, enabling modification and ensuring forward compatibility.


---
layout: splash
author_profile: false
description: "My name is Anna Zharkova. Welcome to my personal blog!. Place where we talk about mobile development"
title: "Anna Zharkova"
og_title: "Anna Zharkova"
header:
overlay_image: "/assets/images/header.jpg"
og_image: "/assets/images/og-default-header.jpg"
excerpt: "
"
feature_row:
- image_path: assets/images/mobile_dev_thumb.jpg
alt: "Blog"
title: "Blog"
excerpt: "Android, Community and Public Speaking"
url: "/blog"
- image_path: assets/images/header_talks.jpg
alt: "Talks"
title: "Talks"
excerpt: "Slides & Videos"
url: "/talks"
- image_path: assets/images/about.jpeg
alt: "About"
title: "About"
excerpt: "Hello! My name is Anna"
url: "/about"
---
All modern mobile: iOS, Android, cross-platform
{% include feature_row %}
{{ site.data.ui-text[site.locale].recent_posts | default: "Recent Posts" }}
{% for post in paginator.posts %}
{% include archive-single.html %}
{% endfor %}
{% include paginator.html %}


Code, Art, or Both?
WEBSITE
base10studios.com
EMAIL
<EMAIL_ADDRESS>PHONE
913-626-2105
PERFORMANCE
&
ELEGANCE
are not exclusive nor optional; They are complimentary & essential. In all my work, I strive to prove this.
EDUCATION
University of Oklahoma
Set to graduate with a Master's in Computer
Science in the Spring of 2020 with a GPA of 3.45. Active member of
OU's Software Studio and Game Developer's Association. Member of the Honors College, and
recipient of the National Merit scholarship.
SKILLS
Languages
JavaScript, Java, C++, Haskell, HTML, CSS
Tools
Angular JS, OpenGl, Git, FireBase, Linux
PAST EXPERIENCE
Plow Technologies - Student Intern, Summer 2016
Onping, Plow Technologies’ online product, presents live wellsite information
to clients through custom dashboards. I expanded the web-API of Onping, created
several new pages, and renovated its mobile presence. Onping’s data is stored in
no-SQL databases, served by Haskell, and presented through Angular JS with Twitter Bootstrap.
I have experienced application development from many angles- from the servers and database management to the markup and styling of pages, even down to the rendering steps. I have worked under the constraints of native and web applications alike. asdf adf asdfas fasdfas asdfa fasd fasdf asdf adf asdf asdf sadf a sdfa sdfasdf a sd f dsa.
CURRENT PROJECTS
Roam - Lead Developer (Software Studio)
Roam is a hybrid application for discovering and curating events based on location. Built
around Angular 2, Firebase, and Materialize.css, Roam’s front end is designed to be
high-performance while targeting multiple platforms.
Gloo Kit - Lead Developer
Gloo Kit is an OpenGL powered set of tools for developing native applications of all kinds.
Built around concurrency and the observer pattern, it is designed for performance and
responsiveness without hair-pulling.


| 1.1.0 |
Clean ups, refactorings, misc. bug fixes. |
| 1.0.0 |
Remove deprecated Device and Connection syntax, add Basestar#respond method |
| 0.22.2 |
Bug-fix for Registry loader |
| 0.22.1 |
Remove lodash, misc. bug fixes |
| 0.22.0 |
API extraction, new devices syntax. |
| 0.21.2 |
Update Robeaux version |
| 0.21.1 |
Add back debug logging for starting/connecting devices/connections |
| 0.21.0 |
Remove Connection/Device objects, update Robot connection/device syntax, fluent syntax updates |
| 0.20.2 |
Correct API issues, possible issue with test setups |
| 0.20.1 |
Revert accidental scrict handling of param in driver initializer |
| 0.20.0 |
Browser support, new module loading, log level support, misc. development changes |
| 0.19.1 |
Correct issue with dynamic method proxying |
| 0.19.0 |
Fluent syntax, improved start/halt, various other updates |
| 0.18.0 |
Updates Robot and Driver commands structure |
| 0.17.0 |
Updates to API to match CPPP-IO spec |
| 0.16.0 |
New IO Utils, removal of Utils#bind, add Adaptor#_noop method. |
| 0.15.1 |
Fixed issue with the API on Tessel |
| 0.15.0 |
Better halting, cleaner startup, removed 'connect' and 'start' events, and misc other cleanups/refactors. |
| 0.14.0 |
Removal of node-namespace and misc. cleanup |
| 0.13.3 |
Fixes bug with disconnect functions not being called. |
| 0.13.2 |
Use pure Express, adds server-sent-events, upd API. |
| 0.13.1 |
Add API authentication and HTTPS support |
| 0.13.0 |
Set minimum Node version to 0.10.20, add utils to global namespace and improve initialization routines |
| 0.12.0 |
Extraction of CLI tooling |
| 0.11.2 |
bugfixes |
| 0.11.0 |
Refactor into pure JavaScript |
| 0.10.4 |
Add JS helper functions |
| 0.10.3 |
Fix dependency issue |
| 0.10.2 |
Create connections convenience vars, refactor config loading |
| 0.10.1 |
Updates required for test driven robotics, update Robeaux version, bugfixes |
| 0.10.0 |
Use Robeaux UX, add CLI commands for helping connect to devices, bugfixes |
| 0.9.0 |
Add AngularJS web interface to API, extensible commands for CLI |
| 0.8.0 |
Refactored Adaptor and Driver into proper base classes for easier authoring of new modules |
| 0.7.0 |
cylon command for generating new adaptors, support code for better GPIO support, literate examples |
| 0.6.0 |
API exposes robot commands, fixes issues in driver/adaptor init |
| 0.5.0 |
Improve API, add GPIO support for reuse in adaptors |
| 0.4.0 |
Refactor proxy in Cylon.Basestar, improve API |
| 0.3.0 |
Improved Cylon.Basestar, and added API |
| 0.2.0 |
Cylon.Basestar to help develop external adaptors/drivers |
| 0.1.0 |
Initial release for ongoing development |


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