class: center, middle, inverse, title-slide # Statistical Modeling ### Abhijit Dasgupta ### November 7, 2018 --- layout: true
 PS 312, March 2019
 --- # Goals .pull-left[ + Using data models - Types and purpose + Common statistical models - Linear models - Logistic models + The "formula interface" in R to express models ] .pull-right[ + Learning to extract and use the results of models - The `broom` package - Graphical representations + What predictors/covariates are "important" + Model building and selection + Good practices ] --- class: inverse, middle, center .bigblockquote[All models are wrong, but some are useful] .right[G.E.P. Box] --- # Models Models are our way of understanding nature, usually using some sort of mathematical expression Famous mathematical models include Newton's second law of motion, the laws of thermodynamics, the ideal gas law All probability distributions, like Gaussian, Binomial, Poisson, Gamma, are models Mendel's laws are models **that result in** particular mathematical models for inheritance and population prevalence --- # Models We use models all the time to describe our understanding of different processes + Cause-and-effect relationships -- + Supply-demand curves -- + Financial planning -- + Optimizing travel plans (perhaps including traffic like _Google Maps_) -- + Understanding the effects of change - Climate change - Rule changes via Congress or companies - Effect of a drug on disease outcomes - Effect of education and behavioral patterns on future earnings --- # Data-driven models > Can we use data collected on various aspects of a particular context to understand the relationships between the different aspects? + How does increased smoking affect your risk of getting lung cancer? (causality/association) - Does genetics matter? - Does the kind of smoking matter? - Does gender matter? --- # Data-driven models > Can we use data collected on various aspects of a particular context to understand the relationships between the different aspects? + What is your lifetime risk of breast cancer? (prediction) - What if you have a sister with breast cancer? - What if you had early menarche? - What if you are of Ashkenazi Jewish heritage? .right[[The Gail Model from NCI](https://bcrisktool.cancer.gov/)] --- # Association models These are more traditional, highly interpretative models that look at **how** different predictors affect outcome. + Linear regression + Logistic regression + Cox proportional hazards regression + Decision trees Since these models have a particular known structure determined by the modeler, they can be used on relatively small datasets You can easily understand which predictors have more "weight" in influencing the outcome You can literally write down how a prediction would be made --- # Predictive models These are more recent models that primarily look to provide good predictions of an outcome, and the way the predictions are made is left opaque (often called a _black box_) + Deep Learning (or Neural Networks) + Random Forests + Support Vector Machines + Gradient Boosting Machines These models require data to both determine the structure of the model as well as make the predictions, so they require lots of data to _train_ on The relative "weight" of predictors in influencing the **predictions** can be obtained The effect of individual predictors is not easily interpretable, though this is changing They require a different **philosophic perspective** than traditional association models --- # Datasets We will use the `pbc` data from the `survival` package, and the in-built `mtcars` dataset. ```r library(survival) data(pbc) str(pbc) ``` ``` 'data.frame': 418 obs. of 20 variables: $ id : int 1 2 3 4 5 6 7 8 9 10 ... $ time : int 400 4500 1012 1925 1504 2503 1832 2466 2400 51 ... $ status : int 2 0 2 2 1 2 0 2 2 2 ... $ trt : int 1 1 1 1 2 2 2 2 1 2 ... $ age : num 58.8 56.4 70.1 54.7 38.1 ... $ sex : Factor w/ 2 levels "m","f": 2 2 1 2 2 2 2 2 2 2 ... $ ascites : int 1 0 0 0 0 0 0 0 0 1 ... $ hepato : int 1 1 0 1 1 1 1 0 0 0 ... $ spiders : int 1 1 0 1 1 0 0 0 1 1 ... $ edema : num 1 0 0.5 0.5 0 0 0 0 0 1 ... $ bili : num 14.5 1.1 1.4 1.8 3.4 0.8 1 0.3 3.2 12.6 ... $ chol : int 261 302 176 244 279 248 322 280 562 200 ... $ albumin : num 2.6 4.14 3.48 2.54 3.53 3.98 4.09 4 3.08 2.74 ... $ copper : int 156 54 210 64 143 50 52 52 79 140 ... $ alk.phos: num 1718 7395 516 6122 671 ... $ ast : num 137.9 113.5 96.1 60.6 113.2 ... $ trig : int 172 88 55 92 72 63 213 189 88 143 ... $ platelet: int 190 221 151 183 136 NA 204 373 251 302 ... $ protime : num 12.2 10.6 12 10.3 10.9 11 9.7 11 11 11.5 ... $ stage : int 4 3 4 4 3 3 3 3 2 4 ... ``` --- class: inverse, middle, center # The formula interface --- # Representing model relationships In R, there is a particularly convenient way to express models, where you have - one dependent variable - one or more independent variables, with possible transformations and interactions ``` y ~ x1 + x2 + x1:x2 + I(x3^2) + x4*x5 ``` -- `y` depends on ... -- - `x1` and `x2` linearly -- - the interaction of `x1` and `x2` (represented as `x1:x2`) -- - the square of `x3` (the `I()` notation ensures that the `^` symbol is interpreted correctly) -- - `x4`, `x5` and their interaction (same as `x4 + x5 + x4:x5`) --- # Representing model relationships ``` y ~ x1 + x2 + x1:x2 + I(x3^2) + x4*x5 ``` This interpretation holds for the vast majority of statistical models in R - For decision trees and random forests and neural networks, don't add interactions or transformations, since the model will try to figure those out on their own --- # Our first model ```r myLinearModel <- lm(chol ~ bili, data = pbc) ``` Note that everything in R is an **object**, so you can store a model in a variable name. This statement runs the model and stored the fitted model in `myLinearModel` R does not interpret the model, evaluate the adequacy or appropriateness of the model, or comment on whether looking at the relationship between cholesterol and bilirubin makes any kind of sense. -- .bigblockquote[It just fits the model it is given] --- # Our first model ```r myLinearModel ``` ``` Call: lm(formula = chol ~ bili, data = pbc) Coefficients: (Intercept) bili 303.20 20.24 ``` > Not very informative, is it? --- # Our first model ```r summary(myLinearModel) ``` ``` Call: lm(formula = chol ~ bili, data = pbc) Residuals: Min 1Q Median 3Q Max -565.39 -89.90 -35.36 44.92 1285.33 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 303.204 15.601 19.435 < 2e-16 *** bili 20.240 2.785 7.267 3.63e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 213.2 on 282 degrees of freedom (134 observations deleted due to missingness) Multiple R-squared: 0.1577, Adjusted R-squared: 0.1547 F-statistic: 52.8 on 1 and 282 DF, p-value: 3.628e-12 ``` > A little better ??? Talk about the different metrics in this slide --- # Our first model ```r broom::tidy(myLinearModel) ``` ``` # A tibble: 2 x 5 term estimate std.error statistic p.value <chr> <dbl> <dbl> <dbl> <dbl> 1 (Intercept) 303. 15.6 19.4 5.65e-54 2 bili 20.2 2.79 7.27 3.63e-12 ``` ```r broom::glance(myLinearModel) ``` ``` # A tibble: 1 x 11 r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> 1 0.158 0.155 213. 52.8 3.63e-12 2 -1925. 3856. 3867. # … with 2 more variables: deviance <dbl>, df.residual <int> ``` --- # Our first model We do need some sense as to how well this model fit the data .pull-left[ ```r # install.packages('ggfortify') library(ggfortify) autoplot(myLinearModel) ``` ] .pull-right[ ![](lecture_modeling_files/figure-html/unnamed-chunk-8-1.png) ] --- # Our first model Let's see if we have some strangeness going on -- .pull-left[ ```r ggplot(pbc, aes(x = bili))+geom_density() ``` We'd like this to be a bit more "Gaussian" for better behavior ] .pull-right[ ![](lecture_modeling_files/figure-html/unnamed-chunk-10-1.png) ] --- # Our first model Let's see if we have some strangeness going on .pull-left[ ```r ggplot(pbc, aes(x = log(bili)))+geom_density() ``` ] .pull-right[ ![](lecture_modeling_files/figure-html/unnamed-chunk-12-1.png) ] --- # Our first model ```r myLinearModel2 <- lm(chol~log(bili), data = pbc) summary(myLinearModel2) ``` ``` Call: lm(formula = chol ~ log(bili), data = pbc) Residuals: Min 1Q Median 3Q Max -440.07 -94.35 -21.07 42.67 1221.86 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 311.48 14.28 21.816 < 2e-16 *** log(bili) 98.80 12.07 8.186 9.42e-15 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 208.9 on 282 degrees of freedom (134 observations deleted due to missingness) Multiple R-squared: 0.192, Adjusted R-squared: 0.1891 F-statistic: 67.01 on 1 and 282 DF, p-value: 9.416e-15 ``` --- # Our first model ```r autoplot(myLinearModel2) ``` ![](lecture_modeling_files/figure-html/unnamed-chunk-14-1.png) --- # Just the residual plot, please ```r autoplot(myLinearModel2, which=1) ``` ![](lecture_modeling_files/figure-html/unnamed-chunk-15-1.png) -- > OR --- # Just the residual plot, please ```r d <- broom::augment(myLinearModel2) d ``` ``` # A tibble: 284 x 10 .rownames chol log.bili. .fitted .se.fit .resid .hat .sigma .cooksd <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 1 261 2.67 576. 28.1 -315. 0.0181 208. 2.13e-2 2 2 302 0.0953 321. 13.7 -18.9 0.00433 209. 1.79e-5 3 3 176 0.336 345. 12.8 -169. 0.00373 209. 1.23e-3 4 4 244 0.588 370. 12.4 -126. 0.00352 209. 6.41e-4 5 5 279 1.22 432. 14.6 -153. 0.00487 209. 1.33e-3 6 6 248 -0.223 289. 15.8 -41.4 0.00571 209. 1.14e-4 7 7 322 0 311. 14.3 10.5 0.00467 209. 5.98e-6 8 8 280 -1.20 193. 24.9 87.5 0.0142 209. 1.28e-3 9 9 562 1.16 426. 14.2 136. 0.00463 209. 9.84e-4 10 10 200 2.53 562. 26.6 -362. 0.0162 208. 2.51e-2 # … with 274 more rows, and 1 more variable: .std.resid <dbl> ``` --- # Just the residual plot, please ```r ggplot(d, aes(x = .fitted, y = .resid))+geom_point()+ geom_smooth(se=F)+ labs(x = 'Fitted values', y = 'Residual values') ``` ![](lecture_modeling_files/figure-html/unnamed-chunk-17-1.png) --- # Predictions ```r head(predict(myLinearModel2, newdata = pbc)) ``` ``` 1 2 3 4 5 6 575.6925 320.9006 344.7277 369.5578 432.3941 289.4371 ``` The `newdata` has to have the same format and components as the original data the model was trained on --- # Categorical predictors ```r myLM3 <- lm(chol ~ log(bili) + sex, data = pbc) broom::tidy(myLM3) ``` ``` # A tibble: 3 x 5 term estimate std.error statistic p.value <chr> <dbl> <dbl> <dbl> <dbl> 1 (Intercept) 283. 36.6 7.71 2.14e-13 2 log(bili) 99.6 12.1 8.22 7.37e-15 3 sexf 32.5 37.8 0.858 3.92e- 1 ``` R has a somewhat unfortunate notation for categorical variables here, as `{variable name}{level}` --- class: inverse, middle, center # Logistic regression --- # The logistic transformation For an outcome which is binary (0/1), what is really modeled is the **probability** that the outcome is 1, usually denoted by _p_. However, we know `\(0 \leq p \leq 1\)`, so what if the model gives a prediction outside this range!! The logistic transform takes _p_ to $$ \text{logit}(p) = \log\left(\frac{p}{1-p}\right) $$ and we model _logit(p)_, which has a range from `\(-\infty\)` to `\(\infty\)` --- # Logistic regression Logistic regression is a special case of a **generalized linear model**, so the function we use to run a logistic regression is `glm` ```r myLR <- glm(spiders ~ albumin + bili + chol, data = pbc, family = binomial) myLR ``` ``` Call: glm(formula = spiders ~ albumin + bili + chol, family = binomial, data = pbc) Coefficients: (Intercept) albumin bili chol 2.3326484 -0.9954927 0.0995915 -0.0003176 Degrees of Freedom: 283 Total (i.e. Null); 280 Residual (134 observations deleted due to missingness) Null Deviance: 341.4 Residual Deviance: 315.2 AIC: 323.2 ``` - We have to add the `family = binomial` as an argument, since this is a special kind of GLM - All these models only use complete data; they kick out rows with missing data --- # Logistic regression ```r broom::tidy(myLR) ``` ``` # A tibble: 4 x 5 term estimate std.error statistic p.value <chr> <dbl> <dbl> <dbl> <dbl> 1 (Intercept) 2.33 1.30 1.80 0.0717 2 albumin -0.995 0.362 -2.75 0.00595 3 bili 0.0996 0.0344 2.89 0.00381 4 chol -0.000318 0.000615 -0.517 0.605 ``` -- ```r broom::glance(myLR) ``` ``` # A tibble: 1 x 7 null.deviance df.null logLik AIC BIC deviance df.residual <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> 1 341. 283 -158. 323. 338. 315. 280 ``` --- # Predictions from logistic regression ```r head(predict(myLR)) ``` ``` 1 2 3 4 5 6 1.10554163 -1.77506554 -1.04814132 -0.09414055 -0.93144911 -1.62851203 ``` -- These are on the "wrong" scale. We would expect probabilities -- ```r head(predict(myLR, type='response')) ``` ``` 1 2 3 4 5 6 0.7512970 0.1449135 0.2595822 0.4764822 0.2826308 0.1640343 ``` -- or you can use `plogis(predict(myLR))` for the inverse logistic transform --- class: inverse, middle, center # Model selection --- # How to get the "best" model Generally getting to the best model involves - looking at a lot of graphs - Fitting lots of models - Comparing the model fits to see what seems good Sometimes if you have two models that fit about the same, you take the smaller, less complex model (Occam's Razor) Generally it is not recommended that you use automated model selection methods. It screws up your error rates and may not be the right end result for your objectives -- > Model building and selection is an art --- # Clues to follow You can look at the relative weights (size of coefficient and its p-value) of different predictors - These weights will change once you change the model, so be aware of that -- You can trim the number of variables based on collinearities - If several variables are essentially measuring the same thing, use one of them -- You can look at residuals for clues about transformations -- You can look at graphs, as well as science, for clues about interactions (synergies and antagonisms) --- # Automated model selection ```r # install.packages('leaps') library(leaps) mtcars1 <- mtcars %>% mutate_at(vars(cyl, vs:carb), as.factor) all_subsets <- regsubsets(mpg~., data = mtcars1) all_subsets ``` ``` Subset selection object Call: regsubsets.formula(mpg ~ ., data = mtcars1) 16 Variables (and intercept) Forced in Forced out cyl6 FALSE FALSE cyl8 FALSE FALSE disp FALSE FALSE hp FALSE FALSE drat FALSE FALSE wt FALSE FALSE qsec FALSE FALSE vs1 FALSE FALSE am1 FALSE FALSE gear4 FALSE FALSE gear5 FALSE FALSE carb2 FALSE FALSE carb3 FALSE FALSE carb4 FALSE FALSE carb6 FALSE FALSE carb8 FALSE FALSE 1 subsets of each size up to 8 Selection Algorithm: exhaustive ``` --- # Automated model selection Which has the best R2? ```r ind <- which.max(summary(all_subsets)$adjr2) summary(all_subsets)$which[ind,] ``` ``` (Intercept) cyl6 cyl8 disp hp drat TRUE TRUE FALSE FALSE TRUE FALSE wt qsec vs1 am1 gear4 gear5 TRUE FALSE TRUE TRUE FALSE FALSE carb2 carb3 carb4 carb6 carb8 FALSE FALSE FALSE FALSE FALSE ``` --- # Using the tidyverse to run multiple univariate models Suppose I want to run a series of univariate regressions on the mtcars dataset, seeing how mpg is related to each of the continuous variables. Let's build this --- # Many models ```r mtcars <- as_tibble(mtcars) mtcars %>% select(mpg, disp:qsec) ``` ``` # A tibble: 32 x 6 mpg disp hp drat wt qsec <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 21 160 110 3.9 2.62 16.5 2 21 160 110 3.9 2.88 17.0 3 22.8 108 93 3.85 2.32 18.6 4 21.4 258 110 3.08 3.22 19.4 5 18.7 360 175 3.15 3.44 17.0 6 18.1 225 105 2.76 3.46 20.2 7 14.3 360 245 3.21 3.57 15.8 8 24.4 147. 62 3.69 3.19 20 9 22.8 141. 95 3.92 3.15 22.9 10 19.2 168. 123 3.92 3.44 18.3 # … with 22 more rows ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) ``` ``` # A tibble: 160 x 3 mpg variable value <dbl> <chr> <dbl> 1 21 disp 160 2 21 disp 160 3 22.8 disp 108 4 21.4 disp 258 5 18.7 disp 360 6 18.1 disp 225 7 14.3 disp 360 8 24.4 disp 147. 9 22.8 disp 141. 10 19.2 disp 168. # … with 150 more rows ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% group_by(variable) %>% lm(mpg~value, data=.) ``` ``` Call: lm(formula = mpg ~ value, data = .) Coefficients: (Intercept) value 21.28328 -0.01483 ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) ``` ``` # A tibble: 5 x 2 variable data <chr> <list> 1 disp <tibble [32 × 2]> 2 hp <tibble [32 × 2]> 3 drat <tibble [32 × 2]> 4 wt <tibble [32 × 2]> 5 qsec <tibble [32 × 2]> ``` --- # Many models ```r bl <- mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) bl$data[[1]] ``` ``` # A tibble: 32 x 2 mpg value <dbl> <dbl> 1 21 160 2 21 160 3 22.8 108 4 21.4 258 5 18.7 360 6 18.1 225 7 14.3 360 8 24.4 147. 9 22.8 141. 10 19.2 168. # … with 22 more rows ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.))) ``` ``` # A tibble: 5 x 3 variable data models <chr> <list> <list> 1 disp <tibble [32 × 2]> <S3: lm> 2 hp <tibble [32 × 2]> <S3: lm> 3 drat <tibble [32 × 2]> <S3: lm> 4 wt <tibble [32 × 2]> <S3: lm> 5 qsec <tibble [32 × 2]> <S3: lm> ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.)), outputs = map(models, ~tidy(.))) ``` ``` # A tibble: 5 x 4 variable data models outputs <chr> <list> <list> <list> 1 disp <tibble [32 × 2]> <S3: lm> <tibble [2 × 5]> 2 hp <tibble [32 × 2]> <S3: lm> <tibble [2 × 5]> 3 drat <tibble [32 × 2]> <S3: lm> <tibble [2 × 5]> 4 wt <tibble [32 × 2]> <S3: lm> <tibble [2 × 5]> 5 qsec <tibble [32 × 2]> <S3: lm> <tibble [2 × 5]> ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.)), outputs = map(models, ~tidy(.))) %>% select(variable, outputs) ``` ``` # A tibble: 5 x 2 variable outputs <chr> <list> 1 disp <tibble [2 × 5]> 2 hp <tibble [2 × 5]> 3 drat <tibble [2 × 5]> 4 wt <tibble [2 × 5]> 5 qsec <tibble [2 × 5]> ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.)), outputs = map(models, ~tidy(.))) %>% select(variable, outputs) %>% unnest() ``` ``` # A tibble: 10 x 6 variable term estimate std.error statistic p.value <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 disp (Intercept) 29.6 1.23 24.1 3.58e-21 2 disp value -0.0412 0.00471 -8.75 9.38e-10 3 hp (Intercept) 30.1 1.63 18.4 6.64e-18 4 hp value -0.0682 0.0101 -6.74 1.79e- 7 5 drat (Intercept) -7.52 5.48 -1.37 1.80e- 1 6 drat value 7.68 1.51 5.10 1.78e- 5 7 wt (Intercept) 37.3 1.88 19.9 8.24e-19 8 wt value -5.34 0.559 -9.56 1.29e-10 9 qsec (Intercept) -5.11 10.0 -0.510 6.14e- 1 10 qsec value 1.41 0.559 2.53 1.71e- 2 ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.)), outputs = map(models, ~tidy(.))) %>% select(variable, outputs) %>% unnest() %>% filter(term=='value') ``` ``` # A tibble: 5 x 6 variable term estimate std.error statistic p.value <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 disp value -0.0412 0.00471 -8.75 9.38e-10 2 hp value -0.0682 0.0101 -6.74 1.79e- 7 3 drat value 7.68 1.51 5.10 1.78e- 5 4 wt value -5.34 0.559 -9.56 1.29e-10 5 qsec value 1.41 0.559 2.53 1.71e- 2 ``` --- # Many models ```r mtcars %>% select(mpg, disp:qsec) %>% gather(variable, value, -mpg) %>% nest(-variable) %>% mutate(models = map(data, ~lm(mpg~value, data=.)), outputs = map(models, ~tidy(.))) %>% select(variable, outputs) %>% unnest() %>% filter(term=='value') %>% mutate_if(is.numeric, funs(round(., 3))) ``` ``` # A tibble: 5 x 6 variable term estimate std.error statistic p.value <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 disp value -0.041 0.005 -8.75 0 2 hp value -0.068 0.01 -6.74 0 3 drat value 7.68 1.51 5.10 0 4 wt value -5.34 0.559 -9.56 0 5 qsec value 1.41 0.559 2.52 0.017 ``` --- class: center, middle # Predictive modeling --- ```r library(tidyverse) library(caret) diamonds <- as_tibble(diamonds) diamonds <- diamonds %>% sample_frac(size=0.1) set.seed(12356) index_train <- sample(1:nrow(diamonds), 0.8 * nrow(diamonds)) diamonds_train <- diamonds[index_train,] diamonds_test <- diamonds[-index_train,] (nrow(diamonds) == (nrow(diamonds_train) + nrow(diamonds_test))) ``` ``` [1] TRUE ``` --- ```r diamonds ``` ``` # A tibble: 5,394 x 10 carat cut color clarity depth table price x y z <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> 1 1.58 Ideal J VVS1 61.5 56 10357 7.48 7.5 4.61 2 1.51 Very Good H SI2 60.2 59 8444 7.33 7.43 4.44 3 1 Very Good F SI2 61.3 61 4372 6.36 6.39 3.91 4 1.02 Good F SI2 63.8 53 4238 6.46 6.37 4.09 5 1.2 Premium G SI2 59.2 60 5278 6.92 6.9 4.09 6 1.51 Very Good G VS1 62.5 58 13686 7.23 7.27 4.53 7 1.22 Ideal H SI2 61.1 57 5807 6.9 5.92 4.22 8 0.54 Ideal E VS2 61.9 55 1754 5.2 5.24 3.23 9 0.81 Ideal G SI1 62.2 57 3192 5.97 6.02 3.73 10 1.51 Very Good H VVS2 63.1 59 11934 7.28 7.26 4.59 # … with 5,384 more rows ``` --- Let's fit a random forest to this data ```r rf_fit <- train(price ~ ., data=diamonds_train, method = 'rf') rf_fit ``` -- ```r diamonds_predict <- predict(rf_fit, diamonds_test) ``` -- I'm not showing results because it was taking too long


--- layout: post title: 'London Games Festival: Dan Hon, Mindcandy' date: 2006-10-04 00:00:00 categories: digital-media interfaces-&-interaction play ---
Dan Hon of MindCandy, talking about PerplexCity.
No he's not - it's all about treasure, and everyone loves treasure.
They love puzzles and stories and treasure - PerplexCity is all these. Season 1: the story is about the disappearance of a fantastical object. Draws on the heritage of Masquerade (which really pissed off the National Trust, who objected to lots of middle class folks digging up England).
But Masquerade had an end.
Talks about the AI game, which many folks found more interesting than the film. One of the first examples of the ARG genre.
PerplexCity players have made a Google Earth-style scrollable map out of the backgrounds of the PC cards. PC has letters to the editor, articles, a newspaper written by players, etc. Giving the cards away for free today, because "they're a bit like crack, really". Some cards have heat-sensitive inks, UV inks, scratch and sniff, microdots, etc.
They do live events too - a conga around Trafalgar Square that got mixed up with an anti-war march ("the police don't like you doing human chains in Trafalgar Square"). Gender split in their players is 50/50, age range is 7 upwards, to pensioners ("the grey crowd").
They've also launched a board game.


Before Change
state = ((W, b, hidden, cell, X), (Y, gates))
state = jax.lax.fori_loop(0, X.shape[0], _lstm_stepper, state)
(W, b, hidden, cell, X), (Y, gates) = state
return Y, cell, gates
@jax_jit()
After Change
C = xp.zeros((nL+1, nB, nO), dtype="f")
// Set initial hidden and cell states. The Y and C will be shifted 1,
// so that we can have fewer arrays.
Y = index_update(Y, index[0], h0)
C = index_update(C, index[0], c0)
state = ((W, b, X), (Y, C, G))
state = jax.lax.fori_loop(0, X.shape[0], _lstm_stepper, state)
(W, b, X), (Y, C, G) = state


Copyright (c) Pedro Sena 1994
FILM TITLE: JERICHO DIRECTOR: LUIS ALBERTO LAMATA COUNTRY: VENEZUELA 1991 CINEMATOGRAPHY: ANDRES AGUSTI CAST: Cosme Cortazar, Francis Nueda, Alexander Milic SUPER FEATURES: Interesting Story
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Seen at the 16th Annual International Film Festival in Portland, Oregon.
Aside from the fact that this film appears ( technically ) to be a bit of an amateur effort, at least a few things standout as very good. The cinematography is actually very good and well thought out, and were it not for that this film would pass as a mediocre effort.
However, it isn't a bad film. It is hampered, and obviously so, by the lack of quality ( state of the art ) equipment with which a well done film could have been made. For an American audience, this will look a bit like the films of the fifties, in that almost the whole thing is simple, and straight forward. But it is the director's first film, and as such shows excellent promise for many years to come, despite a country whose economy is horrendous. There are times when the film appears to have been shot with rather old stock of film, which is about all they probably had.
 They don't seem to have the enthusiasm for the environment like he does, or seem to care about anything except their own expansion. And for this, he is sent away with a group of conquistadors into the jungle, hoping to find new people to deliver the message. And no sooner do they cross the first river, does he find the first obstacle. The river causes him to lose the books and most of his stuff. And after the first night, the group gets split when a band of thieves decides that following a German commander into the jungle is not what they want. The gold is stolen in the middle of the night, and the priest happens to overhear it and goes out to see it. He gets caught and has to go with the thieves or lose his life. He goes. This group begins dwindling one by one until they get attacked by a group of Indians, who kill all those running away. The friar, sits and prays. the Indians spare him, because they find him funny.
He is taken in, and begins his job of teaching the Indians Christianity. All is not well. They are not agreeable to learning a new word or language. So he bides his time, and watches the naked Indians walk around. One night, he can't sleep, and one of the couples is in the process of making love. The Indian sends him a woman. Fade to black, much to his embarrassment. He is laughed at, because he does not take the woman. Eventually, the tribe gets tired of having an outsider constantly trying to change them. So they do something about it. They inhale a mixture which makes them 'crazy' ( as do so many of those Indians in the area ) and this time they force it up his nose.
In the next event of the tribe they are seen going around a totem pole, and the priest is going nowhere. He gets in the middle and is moving in the wrong direction. He meets up with the woman who has taken a liking to him. The group continues its dance until we notice that the priest has gotten rid of his garb, and joined them in their dance. He has joined the tribe.
Later, he finally gets to meet another set of conquistadors, and they take him away. The priest is now rejoined with another order, and is encapsulated in a square mud room, where he must remain until he is rid of the Indian ways, and become a holy man again. He does not. Basically, he will die in this hut, rather than switch back to a faith that really doesn't care about anyone, except their own motives.
This film is not for the squeamish, for it does have a few scenes which are untactful, like a cannibalistic scene, and some violence, which is an obvious comment on the ugly invaders next to the peaceful Indians. And there is a good amount of violence, in that the conquistadors are merciless in many ways.
GOOD FILM. ONLY FOR THE FILM STUDENT. RATED RRR FOR ITS MMMM MOMENTS. FUNNY IN MANY SPOTS.
3.5 GIBLOONS Pedro Sena Member of the Internet Movie Critics Association
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


create html file containing only the contents; header, footer, and other stuff is generated by framework's component..
well, at least that is the plan.
AmidaMVC will searches for <title>~~</title> to use it as page title.
when title is found, it copies to title data, and the title tag will be removed from the page.


Are you interested in learning how we can help your business?
Do you want to develop that great idea you’ve always had in mind?
Do you need a consultation on the latest technologies and learn if you can benefit from them?
Contact us so we can have a chat! We speak English and Italian.
Get in touch: {{ settings.options.WebsiteSettings.email }}
Work with us: {{ page.job_email }}
View positions here
{{ settings.options.WebsiteSettings.name }}
{{ settings.options.WebsiteSettings.address|richtext }}
Phone: {{ settings.options.WebsiteSettings.phone }}
VAT: {{ settings.options.WebsiteSettings.vat }}


Relationship Between Semi-Major and Semi-Minor Axes
Look at the leftmost point on the ellipse:
The distance to the closer focal point must be
and the distance to the further focal point must be
Therefore, the sum of the distances to the focal points must be:
As, by definition, this value holds for every point on the ellipse, it must hold for the topmost point:
The two triangles formed between focal point, centre, and point on the ellipse must be right triangles and congruent. The line from the point on the ellipse to the focal point is the hypotenuse of the triangle and so the length must be a (because the two hypotenuses are equal and sum to 2a). By Pythagoras’s Theorem, we therefore have:
or alternatively:


[Example] Negative values w/ legend
Demonstating: rendering a map that have negative values.
The legend is enabled and shows to scales of positive and negative values
Open in a new window
View source code


EntityParent entity,parent[,global]
Parameters:
| entity - entity handle parent - parent entity handle global (optional) - true for the child entity to retain its global position and orientation. Defaults to true. |
Description:
| Attaches an entity to a parent. Parent may be 0, in which case the entity will have no parent. |
| None. |


WITNESS LIST Business & Commerce Committee April 29, 2003 - 9:00A HB 543 FOR: Lee, Donald (Texas Conference of Urban Counties), Austin, TX Pardue, Craig (Dallas County), Dallas, TX ON: Cervenka, P.E., Edward R. (Texas Department of Housing and Community Affairs), Austin, TX Registering, but not testifying: For: French, Mike (Texas Manufactured Housing Association), Austin, TX Mitchell, Seth (Bexar County Commissioners Court), San Antonio, TX HB 849 Registering, but not testifying: For: Cernosek, Larry (Texas Towing and Storage Association), Pasadena, TX Rash, Jeanette (Texas Towing and Storage Association), Houston, TX Roper, Angela (Texas Towing and Storage Association), Anna, TX HB 1307 FOR: Lederer, John (Credit Union Legislative Coalition), Dallas, TX Parker, Gary (Texas Credit Union League and First University Credit Union), Waco, TX ON: Feeney, Harold (Credit Union Department), Austin, TX Pargaman, William (Self), Austin, TX Registering, but not testifying: For: Stegall, Melodie (Credit Union Legislative Coalition), Austin, TX HB 1531 FOR: Mann, James (Texas Pipeline Association), Austin, TX Registering, but not testifying: For: Nugent, Patrick (Texas Pipeline Association), Austin, TX Sebree, Ben (Texas Oil and Gas Association), Austin, TX On: Durso, Susan (Public Utility Commission), Austin, TX SB 92 FOR: Corrales, Sonia (Houston Area Women's Center), Houston, TX Figuero, Maria (Self), Houston, TX Leftwich, Theresa (Williamson County Crisis Center), Round Rock, TX McLendon, Barbra (Texas Council on Family Violence), Austin, TX ON: Mintz, David (Texas Apartment Association), Austin, TX SB 581 FOR: King, William Adrian (Cement and Concrete Promotion Council of Texas), Arlington, TX Waterfield, Dick (CCPC), Austin, TX SB 1278 FOR: Wood, Henry (South Texas Electric Cooperative), Victoria, TX SB 1429 FOR: Neeley, Karen (Independent Bankers Association of Texas), Austin, TX ON: Pettijohn, Leslie (Consumer Credit Commissioner), Austin, TX Schneider, Rob (Consumers Union), Austin, TX SB 1436 Registering, but not testifying: For: Camacho, Jose A. (Valor Telecom), Austin, TX Grover, Jim (Texas Telephone Association), Austin, TX SB 1519 FOR: Boldin, Jim (CEIK), Austin, TX Gonzalez, Julio (Self), San Antonio, TX Martin, Mark (Self), Beaumont, TX Sloan, Clyde (Self), Houston, TX Swift, David (CEIA and National Association of State Farm Agents), Austin, TX Taylor, Des (Texas Association of Insurance Financial Advisors), Austin, TX Wells, Jackie (Jackie Wells Agency), Temple, TX Zepeda, Johnny (Coalition of Exclusive Insurance Agents), San Antonio, TX AGAINST: Cantu, Ed (Ed Cantu Insurance Agency), Corpus Christi, TX Cardenas, Ruben (Ruben Cardenas Agency), Weslaco, TX Ellard, Les (Self), Deer Park, TX Hintz, John W. (State Farm Insurance Companies), Austin, TX Hopf, Jeff (Self), Llano, TX Hurst, David (David Hurst Insurance Agency Inc.), Houston, TX Irick, Joe (Self), Lewisville, TX Latta, Regan (Self), Houston, TX Shouse, Corbin (Corbin Shouse Agency), Spring, TX Wagner, Keith (Self), The Woodlands, TX ON: Ray, James Matthew (Matt) (Texas Department of Insurance), Austin, TX Registering, but not testifying: For: Aycock, Douglas (Texas Association of Insurance and Financial Advisors), Austin, TX Bhatia, Khelan (AARP), Austin, TX Cuellar, Edward (Self), San Antonio, TX Kelemen, George (AARP), Austin, TX Noble, Albert (Self), San Antonio, TX Warren, Mark (Texas Association of Insurance and Advisors), Austin, TX Against: Alkhazshvilly, Alex (Farmers Insurance), Mansfield, TX Avery, Elizabeth (Avery Insurance Agency - Farmers Insurance), Irving, TX Blancken, Russ (Blancken Insurance Group), Austin, TX Burris, Lisa (Lisa Burris Insurance Agency), McKinney, TX Collins, Gene (TX NAACP), Odessa, TX Gary, Jimmy (Jimmy Gary Insurance Agency), Arlington, TX Gary, Margo (Jimmy Gary Insurance Agency), Arlington, TX Hanson, Jr., Donald (National Association of Independent Insurers), Austin, TX Hatchett, Greg (Self), Lubbock, TX King, Richard (King Insurance and Financial Services), Midland, TX Kinney, Mark (Farmers Insurance), McKinney, TX Leibham, Phil (Farmers Insurance), Flower Mound, TX Lewis, Stephen (Self), Lufkin, TX Michelsen, Rob (Farmers Insurance), Arlington, TX Miller, Brian (Farmers Insurance - Brian Miller Agency), Bryan, TX Powen, Sandra (Farmers Insurance Group), San Antonio, TX Raines, Angela (Angela Raines Insurance Agency), Granbury, TX Rhea, Alan (Alan Rhea Insurance Agency), Waco, TX Richter, Kris (Kris Richter Insurance Agency, Inc.), San Antonio, TX Schoenig, Joe (Schoenig Insurance), Lubbock, TX Squyres, Rick (Self), Richmond, TX Stanley, Letitia (Farmers Insurance), Irving, TX Swartz, Bob (Bob Swartz Insurance Agency), Irving, TX Vestal, Ronald (Vestal Agency), College Station, TX


---
sort_order: 49
tei_id: rdx_191dd.p.idp6482256
annotation_count: 0
images:
small-thumbnail: https://readux.ecds.emory.edu/books/emory:191dd/pages/emory:rfds2/mini-thumbnail/
json: https://readux.ecds.emory.edu/books/emory:191dd/pages/emory:rfds2/info/
full: https://readux.ecds.emory.edu/books/emory:191dd/pages/emory:rfds2/fs/
page: https://readux.ecds.emory.edu/books/emory:191dd/pages/emory:rfds2/single-page/
thumbnail: https://readux.ecds.emory.edu/books/emory:191dd/pages/emory:rfds2/thumbnail/
title: Page 49
number: 49
---
{% raw %}DINNER. 49{% endraw %}
{% raw %}"There, little giri," said he, "you need not be so grateful{% endraw %}
{% raw %}to me; it is Providence has done U."{% endraw %}
{% raw %}" At aU events," she returned, " you are God's messenger to{% endraw %}
{% raw %}me."{% endraw %}
{% raw %}" Why, good-morning, Jlr. AUen, I didn't kuow you had{% endraw %}
{% raw %}come in. Don't you think our young lady is getting along{% endraw %}
{% raw %}first-rate! Just excuse me ; I'll be back again iu a minute ;"{% endraw %}
{% raw %}and Mrs. Wyman disappeared as soon as she had appeared.{% endraw %}
{% raw %}The good woman wanted to give AUen a chance to move a{% endraw %}
{% raw %}little further away from Alicia, without auy hurry or embar¬{% endraw %}
{% raw %}rassment—an opportunity he availed himself of, however, -^'ith{% endraw %}
{% raw %}some reluctance. But, -\vherever he moved or whatever he{% endraw %}
{% raw %}did, Alicia's eyes followed him constautly, -with a pleased,{% endraw %}
{% raw %}admiring expression, very flattering to the natural vanity of a{% endraw %}
{% raw %}young man.{% endraw %}
{% raw %}" Your ' young lady,' INIrs. Wymau, has been making a great{% endraw %}
{% raw %}lion of me," he said to the constable's wife on her reappear¬{% endraw %}
{% raw %}ance ; " she thinks I am a very -wonderful fellow, aud has{% endraw %}
{% raw %}made me quite vain of myself."{% endraw %}
{% raw %}" I guess it -won't do any hurt for her to think well of you,{% endraw %}
{% raw %}Mr. AUen. Some folks ain't easily spoiled."{% endraw %}
{% raw %}" Thank you. If flattery gives a man a good appetite for{% endraw %}
{% raw %}his dinner, I shall be able to do justice to yours, !Mrs. Wyman."{% endraw %}
{% raw %}" Well, I'm glad your appetite is ready, for the dinner is ;{% endraw %}
{% raw %}but Mr. Wymau hasu't come. He shouldn't have been late,{% endraw %}
{% raw %}to-day."{% endraw %}
{% raw %}"No, a man shouldn't be late on his wedding-day," cried{% endraw %}
{% raw %}the constable, coming in quietly, and saluting his wife on one{% endraw %}
{% raw %}cheek. " I reckon I wasn't late twenty-seveu years ago this{% endraw %}
{% raw %}blessed Thursday—was I, wife ?"{% endraw %}
{% raw %}" If you had been," returned Mrs Wymau, " my name -would{% endraw %}
{% raw %}not have been Mary Wymau."{% endraw %}
{% raw %}With a very soft " Hullo there !" the constable greeted his{% endraw %}
{% raw %}invalid guest, by w.ay oC welcoming her to the parlor. Then{% endraw %}
{% raw %}he shook hands with AUen, and afterward with her.{% endraw %}
{% raw %}" She looks right smart, uow, cousiderin', don't she, AUeu ?{% endraw %}
{% raw %}Only her hair is rather a short crop. She looks as if she'd{% endraw %}
{% raw %}been in the hands of the thief-whippers. There was three of{% endraw %}
{% raw %}them feUers catched up at Elktown yesterday, that got a{% endraw %}
{% raw %}Whipping and their heads shaved ; an' was told that if ever{% endraw %}


Java Regex The Java Regex or Regular Expression is an API to define a pattern for searching or manipulating strings. It is widely used to define the constraint on strings such as password and email validation. After learning Java regex tutorial, you will be able to test your regular expressions by the Java Regex Tester Tool. Java Regex API provides 1 interface and 3 classes in java.util.regex package. java.util.regex package The Matcher and Pattern classes provide the facility of Java regular expression. The java.util.regex package provides following classes and interfaces for regular expressions.
- MatchResult interface
- Matcher class
- Pattern class
- PatternSyntaxException class
Matcher class It implements the MatchResult interface. It is a regex engine which is used to perform match operations on a character sequence.
| No. |
Method |
Description |
| 1 |
boolean matches() |
test whether the regular expression matches the pattern. |
| 2 |
boolean find() |
finds the next expression that matches the pattern. |
| 3 |
boolean find(int start) |
finds the next expression that matches the pattern from the given start number. |
| 4 |
String group() |
returns the matched subsequence. |
| 5 |
int start() |
returns the starting index of the matched subsequence. |
| 6 |
int end() |
returns the ending index of the matched subsequence. |
| 7 |
int groupCount() |
returns the total number of the matched subsequence. |
Pattern class It is the compiled version of a regular expression. It is used to define a pattern for the regex engine.
| No. |
Method |
Description |
| 1 |
static Pattern compile(String regex) |
compiles the given regex and returns the instance of the Pattern. |
| 2 |
Matcher matcher(CharSequence input) |
creates a matcher that matches the given input with the pattern. |
| 3 |
static boolean matches(String regex, CharSequence input) |
It works as the combination of compile and matcher methods. It compiles the regular expression and matches the given input with the pattern. |
| 4 |
String[] split(CharSequence input) |
splits the given input string around matches of given pattern. |
| 5 |
String pattern() |
returns the regex pattern. |
Example of Java Regular Expressions There are three ways to write the regex example in Java.
Test it Now Output
Regular Expression . Example The . (dot) represents a single character.
Test it Now
Regex Character classes
| No. |
Character Class |
Description |
| 1 |
[abc] |
a, b, or c (simple class) |
| 2 |
[^abc] |
Any character except a, b, or c (negation) |
| 3 |
[a-zA-Z] |
a through z or A through Z, inclusive (range) |
| 4 |
[a-d[m-p]] |
a through d, or m through p: [a-dm-p] (union) |
| 5 |
[a-z&&[def]] |
d, e, or f (intersection) |
| 6 |
[a-z&&[^bc]] |
a through z, except for b and c: [ad-z] (subtraction) |
| 7 |
[a-z&&[^m-p]] |
a through z, and not m through p: [a-lq-z](subtraction) |
Regular Expression Character classes Example
Test it Now
Regex Quantifiers The quantifiers specify the number of occurrences of a character.
| Regex |
Description |
| X? |
X occurs once or not at all |
| X+ |
X occurs once or more times |
| X* |
X occurs zero or more times |
| X{n} |
X occurs n times only |
| X{n,} |
X occurs n or more times |
| X{y,z} |
X occurs at least y times but less than z times |
Regular Expression Character classes and Quantifiers Example
Test it Now
Regex Metacharacters The regular expression metacharacters work as shortcodes.
| Regex |
Description |
| . |
Any character (may or may not match terminator) |
| \d |
Any digits, short of [0-9] |
| \D |
Any non-digit, short for [^0-9] |
| \s |
Any whitespace character, short for [\t\n\x0B\f\r] |
| \S |
Any non-whitespace character, short for [^\s] |
| \w |
Any word character, short for [a-zA-Z_0-9] |
| \W |
Any non-word character, short for [^\w] |
| \b |
A word boundary |
| \B |
A non word boundary |
Regular Expression Metacharacters Example
Test it Now
Regular Expression Question 1
Test it Now Regular Expression Question 2
Test it Now Java Regex Finder Example
Output:
Enter regex pattern: java
Enter text: this is java, do you know java
I found the text java starting at index 8 and ending at index 12
I found the text java starting at index 26 and ending at index 30
|


%include("/common/_head.html"){}%>
我的想法 -飞趣
<% var user = c.currentUser(request, servlet.response); var isLogin = false; if(user != null){ isLogin=true; } include("/common/_headwithlogin.html",{user:user,isLogin:isLogin}){} %>
<%include("/common/_home_nav.html",{user:user}){}%>
<%for(var thought in thoughtList){%>
删除
${thought.nickname}
${thought.area}
${printTime(thought.createTime)}
${emojiShow(thought.thoughtContent)}
${thought.collected?'
取消收藏
': '
收藏
'}
${thought.likeCount!0}
回复
${thought.commentCount!0}
条回复
<%}%>
<%include("/common/_footor.html"){}%> <%include("/common/_scripts.html",{user:user}){}%>


A class (inheriting from Builder) that takes parsed documents and performs an action on them. Normally, builders translate the documents to an output format, but it is also possible to use the builder builders that e.g. check for broken links in the documentation, or build coverage information.
See Available builders for an overview over Sphinx’ built-in builders.


What happens if my opponent doesn’t have enough cards in their deck to scrap?
In that case, your opponent scraps all the cards in their deck. Then, setting those cards aside, they shuffle all the other cards in their scrap pile into their deck. Then they scrap enough cards from their reformed deck to scrap the total number of cards they were supposed to. In most cases, however many cards they were supposed to scrap will be the number of cards in their scrap pile after Megatron’s ability is finished. Sorry, after Lord Megatron’s ability is finished.
What does “reshuffles their deck” mean?
 If you shuffle your deck for another reason, that’s not “reshuffling your deck.” That’s just shuffling your deck.
Although it’s unlikely, let’s say my opponent’s deck runs out of cards, but there are no cards in their scrap pile. What happens?
If there are no cards in their scrap pile, they can’t reshuffle their deck, so Megatron . . . sorry, Lord Megatron’s ability won’t trigger.


#include <stBasicObjects.h>
Public Member Functions | |
| stBasicStringObject () | |
| stBasicStringObject (const DataType *str) | |
| stBasicStringObject (stBasicStringObject *str) | |
| virtual | ~stBasicStringObject () |
| stSize | GetSize () |
| stSize | GetLength () |
| DataType & | operator[] (int idx) |
| DataType | Get (int idx) |
| OIDType | GetOID () |
| void | SetOID (OIDType oid) |
| void | SetString (const DataType *str) |
| const DataType * | GetString () |
| stBasicStringObject * | Clone () |
| stSize | GetSerializedSize () |
| const stByte * | Serialize () |
| void | Unserialize (const stByte *data, stSize dataSize) |
| bool | IsEqual (stBasicStringObject *obj) |
This class template implements an string object that can be indexed by all metric trees in this library.
This object holds a null terminated string of some type and a OID. The OID may be used to hold an additional information about the object (like the primary key in a database) and will never be used by the distance functions.
The template parameter DataType defines the data type of each unit of the array. It may be any C/C++ standard numeric types (integers, characters and floating points). The template parameter OIDType defines the type of the OID to be used. As DataType, it may assume any numeric type. By default, it will always be and int value.
The Serialized version of this class is organized as follows:
+--—+---—+
| OID | Data |
+--—+---—+
Since Data has a variable size (associated with the string object), the serialized form will also have a variable number of bytes.
This class template was designed to work with the metric evaluators defined in stBasicMetricEvaluators.h.
Definition at line 352 of file stBasicObjects.h.
|
inline |
Default constructor. It creates an array with 0 elements. Use SetSize() to change the number of entries before use this instance.
This constructor is required by the stObject interface.
Definition at line 360 of file stBasicObjects.h.
Referenced by stBasicStringObject< DataType, OIDType >::Clone().
|
inline |
Creates a new instance of this class and initializes it with a given null terminated string.
| str | A null terminated string. |
Definition at line 374 of file stBasicObjects.h.
References stBasicStringObject< DataType, OIDType >::SetString().
|
inline |
Creates a new instance of this class and initializes it with a given null terminated string.
| str | A null terminated string. |
Definition at line 391 of file stBasicObjects.h.
|
inlinevirtual |
Disposes this instance and releases all associated resources.
Definition at line 405 of file stBasicObjects.h.
|
inline |
This method must create a clone (copy) of this class. The new instance must have the same behaviour of the original instance but can not keep any links to its creator (they are independent). In other words, the clone will not reflect any changes done in the original class.
Definition at line 506 of file stBasicObjects.h.
References stBasicStringObject< DataType, OIDType >::GetOID(), stBasicStringObject< DataType, OIDType >::SetOID(), and stBasicStringObject< DataType, OIDType >::stBasicStringObject().
|
inline |
Gets the value of the element at a given position.
| idx | The index of the desired entry. |
| out_of_range | If the index is out of range. |
Definition at line 444 of file stBasicObjects.h.
|
inline |
Returns the length of this string. It is a synonym to GetSize().
Definition at line 423 of file stBasicObjects.h.
|
inline |
Returns the OID associated with this instance.
Definition at line 457 of file stBasicObjects.h.
Referenced by stBasicStringObject< DataType, OIDType >::Clone().
|
inline |
This method must return the size of the serialized version of this object in bytes.
Definition at line 515 of file stBasicObjects.h.
|
inline |
Returns the length of this string. It is a synonym to GetLength(). This name is used by the basic metric evaluators templates.
Definition at line 416 of file stBasicObjects.h.
|
inline |
Returns the contents of this object as a null terminated string.
Definition at line 498 of file stBasicObjects.h.
| bool stBasicStringObject< DataType, OIDType >::IsEqual | ( | stBasicStringObject< DataType, OIDType > * | obj | ) |
|
inline |
This operator can be used to access all positions of the array for reading or writing.
| idx | The index of the element. |
Definition at line 434 of file stBasicObjects.h.
|
inline |
This method must create a serialized version of this instance. The serialized version is an array of bytes that will hold all information required to rebuild this object. For example, if the object is a string, the serialized form may be the string itself without the terminator character (since it is a sequence of bytes).
This array will be used by the SlimTree to store the object into a disk page and to recover it (by calling Unserialize()) and its size given by GetSerializedSize().
The effect of the of A->Unserialize(B->Serialize(), B->GetSerializedSize()) must be the same of A=B->Clone().
Implementation note: The SlimTree will not claim the ownership of the returned array. The object must dispose it when it is not required. In case of the serialized version be a pointer to an internal array, no copy operation will be necessary.
Definition at line 522 of file stBasicObjects.h.
|
inline |
Sets the OID associated with this instance.
| OID | The new OID value. |
Definition at line 472 of file stBasicObjects.h.
Referenced by stBasicStringObject< DataType, OIDType >::Clone().
|
inline |
Copies a null terminated string to this instance.
| str | A null terminates string. |
Definition at line 484 of file stBasicObjects.h.
Referenced by stBasicStringObject< DataType, OIDType >::stBasicStringObject().
|
inline |
This method must rebuild the object from its serialized version. The serialized version is an array of bytes that holds all information required to rebuild an object. For example, if the object is a string, the serialized form may be the string itself without the terminator character (since it is a sequence of bytes).
This method will be used by the SlimTree to rebuild the object to compare it with another object (the sample object in a query or another rebuilt object).
The effect of the of A->Unserialize(B->Serialize(), B->GetSerializedSize()) must be the same of A=B->Clone().
Implementation note: The SlimTree is the owner of the data array. This means that this array may vanish at any time after the call of Unserialize(). There is also another restriction. The Unserialize() method must never modify the contents of the data array.
| data | The serialized object. |
| datasize | The size (in bytes) of the data array. |
Definition at line 529 of file stBasicObjects.h.


Testyourlog.in will create WebdriverIO script customized to your site's login/registration process. It will include installation instructions and be tailored to your specific website, including URLs, elements and messages.
The code is still in development. In the meantime, you can sign up to be notified when it's ready to use.
Sponsored by learn.webdriver.io


Page heading goes here
This page example includes a selection of styles and components available to the prototype kit.
For more styles, code snippets and guidance on components visit the
NHS digital service manual.
Level heading two
{% set pastDayContent %}
{{ table({
responsive: true,
panel: false,
caption: "Past day",
firstCellIsHeader: false,
head: [
{
text: "Case manager"
},
{
text: "Cases opened"
},
{
text: "Cases closed"
}
],
rows: [
[
{
header: "Case manager",
text: "David Francis"
},
{
header: "Cases opened",
text: "3"
},
{
header: "Cases closed",
text: "0"
}
],
[
{
header: "Case manager",
text: "Paul Farmer"
},
{
header: "Cases opened",
text: "1"
},
{
header: "Cases closed",
text: "0"
}
],
[
{
header: "Case manager",
text: "Rita Patel"
},
{
header: "Cases opened",
text: "2"
},
{
header: "Cases closed",
text: "0"
}
]
]
}) }}
{% endset -%} {% set pastWeekContent %}
{{ table({
responsive: true,
panel: false,
caption: "Past week",
firstCellIsHeader: false,
head: [
{
text: "Case manager"
},
{
text: "Cases opened"
},
{
text: "Cases closed"
}
],
rows: [
[
{
header: "Case manager",
text: "David Francis"
},
{
header: "Cases opened",
text: "24"
},
{
header: "Cases closed",
text: "18"
}
],
[
{
header: "Case manager",
text: "Paul Farmer"
},
{
header: "Cases opened",
text: "16"
},
{
header: "Cases closed",
text: "20"
}
],
[
{
header: "Case manager",
text: "Rita Patel"
},
{
header: "Cases opened",
text: "24"
},
{
header: "Cases closed",
text: "27"
}
]
]
}) }}
{% endset -%} {% set pastMonthContent %}
{{ table({
responsive: true,
panel: false,
caption: "Past month",
firstCellIsHeader: false,
head: [
{
text: "Case manager"
},
{
text: "Cases opened"
},
{
text: "Cases closed"
}
],
rows: [
[
{
header: "Case manager",
text: "David Francis"
},
{
header: "Cases opened",
text: "98"
},
{
header: "Cases closed",
text: "95"
}
],
[
{
header: "Case manager",
text: "Paul Farmer"
},
{
header: "Cases opened",
text: "122"
},
{
header: "Cases closed",
text: "131"
}
],
[
{
header: "Case manager",
text: "Rita Patel"
},
{
header: "Cases opened",
text: "126"
},
{
header: "Cases closed",
text: "142"
}
]
]
}) }}
{% endset -%} {% set pastYearContent %}
{{ table({
responsive: true,
panel: false,
caption: "Past year",
firstCellIsHeader: false,
head: [
{
text: "Case manager"
},
{
text: "Cases opened"
},
{
text: "Cases closed"
}
],
rows: [
[
{
header: "Case manager",
text: "David Francis"
},
{
header: "Cases opened",
text: "1380"
},
{
header: "Cases closed",
text: "1472"
}
],
[
{
header: "Case manager",
text: "Paul Farmer"
},
{
header: "Cases opened",
text: "1129"
},
{
header: "Cases closed",
text: "1083"
}
],
[
{
header: "Case manager",
text: "Rita Patel"
},
{
header: "Cases opened",
text: "1539"
},
{
header: "Cases closed",
text: "1265"
}
]
]
}) }}
{% endset -%}
{{ tabs({
items: [
{
label: "Past day",
id: "past-day",
panel: {
html: pastDayContent
}
},
{
label: "Past week",
id: "past-week",
panel: {
html: pastWeekContent
}
},
{
label: "Past month",
id: "past-month",
panel: {
html: pastMonthContent
}
},
{
label: "Past year",
id: "past-year",
panel: {
html: pastYearContent
}
}
]
}) }}
{{ list({
"title": "Do",
"type": "tick",
"items": [
{
"item": "cover blisters that are likely to burst with a soft plaster or dressing"
},
{
"item": "wash your hands before touching a burst blister"
},
{
"item": "allow the fluid in a burst blister to drain before covering it with a plaster or dressing"
}
]
}) }}
{{ list({
"title": "Don't",
"type": "cross",
"items": [
{
"item": "burst a blister yourself"
},
{
"item": "peel the skin off a burst blister"
},
{
"item": "pick at the edges of the remaining skin"
},
{
"item": "wear the shoes or use the equipment that caused your blister until it heals"
}
]
}) }}
Level heading three
Body text:
-
The quick brown
-
fox jumps over
-
the lazy dog
{{ warningCallout({
"heading": "School, nursery or work",
"HTML": "
Stay away from school, nursery or work until all the spots have crusted over. This is usually 5 days after the spots first appeared.
"
}) }}
Page last reviewed: 15 March 2021
Next review due: 15 March 2024


Firefighters called to major fire involving three houses in Plympton
Smoke and flames were seen coming from the building
by Rom Preston-EllisFirefighters are at the scene of a major house fire in Plympton.
Smoke can be seen for miles around following the blaze which is reported to involve three properties in the Merafield Road area.
Devon and Somerset Fire and Rescue Service has been contacted for more information.
Police and ambulance are also on scene although there are not reported to be any injuries.
Eyewitness Debbie Benwell said: "Fire in Plympton. It’s three cottages in Merafield Road. So sad."
A Devon and Somerset Fire and Rescue Service spokesperson said: "We were called at 8.15am to reports of smoke coming from a chimney of a property on Merafield Road.
"Smoke and flames were seen coming from the buildings and there were concerns that a roof might collapse.
"Police and ambulance are on scene but there are no injuries reported.
"The fire is in the roof of three connected terraced properties."


The Cantus Ultimus project applies the latest OMR technologies to plainchant manuscripts in order to transform the existing CANTUS database (directed by Lacoste) of nearly 400,000 chant records into a state-of-the-art research environment in which both music and text are fully searchable. Within the past year, we have processed and made publicly available online two of the oldest surviving chant manuscripts, the late 10th-c. St. Gall manuscripts (CH-SGs 390 and 391), and completed the OMR of the Salzinnes antiphonal.
Diva is a web-based, open-source digital document viewer, developed by Hankinson (postdoctoral researcher), Wendy Liu, and Evan Magoni, and managed by Fujinaga and Pugin. Diva was designed for websites of libraries, archives, and museums so that they could present high-resolution images of documents in a user-friendly interface optimized for speed and flexibility. The new version, released in August 2015, supports the International Image Interoperability Framework (IIIF). The IIIF is an important new initiative committed to developing a set of common interfaces that support interoperability between image repositories, facilitating horizontal integration across libraries and archives all over the world.
The ELVIS Database (https://elvisproject.ca/) is an open, crowd-sourced database of music in symbolic notation, maintained by MIRAI. This summer, undergraduate student Alex Parmentier improved the search capabilities of the database, making it more powerful and adaptable, and added new functions allowing users to upload new pieces and modify pieces they have uploaded. In collaboration with our project manager and graduate students in musicology, he clarified guidelines for data entry and made the interface more attractive and user friendly. Our continued building of the database (now totalling over 6,000 movements and pieces) has made large-scale corpus studies possible.
Our open-source software for analyzing counterpoint, VIS, and its corresponding web application (https://counterpoint.elvisproject.ca/) make large-scale corpus studies accessible to music theorists and musicologists without programming experience. Within the last year, we have made significant improvements to the stability and flexibility of VIS. In summer 2015, Ryan Bannon (undergraduate student and lead programmer on VIS) began integrating VIS into Rodan, a workflow engine developed by Hankinson. This integration involved the low-level code integration of VIS tasks into Rodan such that they can be realized as individual atomic workflow tasks, and the development of a user-friendly web application that allows researchers to generate VIS-based workflows via a graphical user interface. We are on schedule to release our web application in spring 2016.
jSymbolic is a software tool developed by McKay for analyzing symbolic music files by extracting a range of characteristic statistical information (called “features”) relating to musical elements such as pitch, rhythm, harmony, instrumentation, dynamics, and texture. In 2015, McKay and Tristano Tenaglia (undergraduate student) developed a new version that can extract features from MEI files (Music Encoding Initiative: an open-source, computer-readable music encoding format) and from windowed sections of a score (rather than the score in its entirety). Additionally, jSymbolic is now able to export features into WEKA ARFF, which will be useful for machine learning, facilitating such tasks as composer, style, or genre recognition.
McKay has also developed a new Java application and library called jMei2Midi, which can convert MEI files to the widely recognized MIDI file format. This allows us to process music accessible only in MEI with software not yet able to read MEI files. jMei2Midi’s general parsing libraries will also provide a useful resource for developers in the process of incorporating MEI-parsing capabilities into their own software.


Born in Minnesota, lived three different places in Illinois, 5 years in Detroit, high school in South Korea, University in Wyoming and New Zealand, Colorado and California for a bit, now back in Detroit. I'd like to say it's full circle, but there's a little extra bit hanging off there so it's really more of a balloon.
I've been walking only slightly longer than I could read, writing for only mildly shorter than I could walk. If you blended me up and analyzed my base components, it would be a little bit of everything and a whole lot of words. And I toss 'em 'bout passing fair.
I have experience in:
Writing is so much more than the stringing together of pretty words. It requires vision mixed with a vivid imagination. A knack for brevity. Thorough appreciation of the eloquence and grace that drive our language to the heights of its beauty.
And a feel for when a good visual can do all the work.
Check here for my old baby site.


PATCH ADAMS
Reviewed by Harvey Karten, Ph.D. Universal Pictures Director: Tom Shadyac Writer: Steve Oedekerk Cast: Robin Williams, Monica Potter, Daniel London, Philip Seymour Hoffman, Bob Gunton, Peter Coyote
If you are a reluctant public speaker, you are advised by a recent study to avoid having your fans in the audience. Mothers, dads, sweetheart and all others who might be rooting for you could disrupt your talk.  Sounds strange? The explanation is that you will be especially nervous in front of your devotees for fear of letting them down. Could we stretch this concept a bit and apply it to medicine? Sure. If you are performing life- threatening surgery on a member of your own family, you may be particularly jittery, since a mistake could cost you someone you hold dear. In other words, it's best for doctors not to be emotionally involved with the patients they are treating. Some physicians, though, go to the other extreme and are absolute cold fish. You want the best that technology has to offer if you're in the hospital, but you also want human beings to treat you as a person and not just as a disease. "I'm wheeling in the appendix from room 303," just doesn't cut it, so to speak.
Now, "Patch Adams" is about a physician who violates the rule against emotional involvement, an infraction which brings him in conflict with the administrator of a hospital and dean of his medical school. By the conclusion of the film he has gained the enthusiastic approval of everyone else--the girl of his dreams who was repelled by him at first meeting, the roommate who challenged his way of thinking, the kids in the cancer ward, the nurses who pretty much said "let Patch be Patch," even the head of a medical disciplinary board that considered expelling him from school.  He clowns around wildly with the deadly sick kids, using an enema bulb to make a red nose; he insists that a diabetic being studied by a group of medical students be called by her name and not "the diabetic," when making the round he wears shirts that Hawaiians would consider loud. He's a genuine human being. But alas, he's not the sort of guy you'd want for your own physician. While he pricks the pretensions of the pompous practitioners, he goes to the other extreme instead of finding the golden mean. You want your professional to be highly sympathetic, even get somewhat emotionally involved with you and your background. But for laughter, you'd best do what the late editor of Saturday Review magazine, Norman Cousin, did when he was hospitalized with a fatal illness.  But you don't want your doctor wear a Pinocchio nose or an enema bulb, do you?
Though Patch Adams may not be your cup of curative chicken soup, the movie, which is based on an actual person, is involving and emotionally fulfilling, if quite a bit over- sentimental. Conventionally directed like a TV story by Tom Shadyac and based on Hunter Doherty Adams's own story, "Patch Adams" begins in 1969 when Adams voluntary checks himself into a psychiatric hospital, a suicidal wreck. There he discovers that he has the gift to reach his fellow patients, something the head psychiatrist is unable to do since the latter does not even look his clients in the eye.  Though middle-aged, he decides at that point that he wants to help people and, with the aid of his brilliant mind, is able to enroll in medical school and attain super grades with a minimum of studying. He falls in love with one of the few women in the class (the adorable Monica Potter who looks about twenty years his junior) and with the help of an empathetic classmate, Truman Schiff (Daniel London), he converts that ultra-serious student to his philosophy. His ideology can be summed up, "You treat a disease, you win you lose...you treat a patient and I guarantee you'll win."
Much of the film is taken up shtick performed by the title character, played wonderfully and with sincerity by Robin Williams, who has sprung back from his role in the disastrous "What Dreams May Come." As Adams, Mr. Williams finds that he can draw laughter from people of all walks of life and ages, as he demonstrates in speaking to a convention of meat packers where he jokes, "In New Zealand they found a new use for sheep...wool." As the school's dean, Bob Gunton is appropriately villainous, a man whose serious stature is threatened by this great bag of fun. Their personality clash leads to Adams's near dismissal from the school for violating just about every rule of proper behavior in the book, but Adams brings out the humanity of other doctors, such as one played by Josef Sommer (who is, by the way, exactly the sort of physician I would like to have rather than Adams) and the head honcho played by Harve Presnell.
The major drawback of the work is its conventional story- telling, not too different in structure from what you might see on the tube. But given the hostility that so many people have against the presumptuousness of the medical profession on whom we all depend so much, you will be drawn to Robin Williams's temperament however much you may find it excessively good-natured and whimsical. After all, his character did ultimately establish a large, free clinic for uninsured patients called the Gesundheit Hospital on 105 acres of countryside. "Patch Adams" should have been released years ago where it could have served as effective advertising for the Clinton medical plan.
Rated PG-13. Running Time: 110 minutes. (C) 1998 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Navigation
index
modules
|
next
|
OLD 1.0.0 documentation
»
Welcome to OLD’s documentation!
Contents:
1 OLD User Guide
2 About the OLD
3 How to Get the OLD
4 How to Use the OLD
5 Forms
6 Files
7 Collections
8 People
9 Tags
10 Sources
11 Memory
12 Dictionary
13 Application Settings
14 Character Encoding
15 Fonts
16 Regular Expressions
17 OLD Markup
18 reStructuredText
19 Markdown
20 Keyboard Shortcuts
21 Browser Support
22 Glossary of Terms
23 Future Features
Developer Guide
API Documentation
Indices and tables
Index
Module Index
Search Page


One way of characterizing the complexity of various decision problems in formal logic involves looking at the nesting structure of the quantifier prefix in their prenex normal form.
In particular, alternations in the nesting structure are relevant: positions where one or more universal quantifiers are followed by one or more existential quantifiers, or vice-versa.
This is a fairly large topic itself but for our purposes, it's only relevant that:
- The number and order of quantifier alternations matter to the complexity of analyzing a formula. The restrictions of the various logical fragments of interest in Ivy are almost all about limiting discourse to specific alternations (for example EPR is
exists.forall.
) - Depending on the writer, the alternation structure of a formula may be abbreviated a few equivalent ways:
- The universal quantifier is written
forall
in Ivy but∀
in formal logic and simplyA
in a fair amount of literature. That is, a writer might refer to an "A formula". - The existential quantifier is written
exists
in Ivy but∃
in formal logic and implyE
in a fair amount of literature. That is, a writer might refer to an "E formula". - Therefore people may write about, for example,
∀∃∀
formulas for those formulas that have a prefix that alternates between universal, existential, and finally universal quantification. Other authors might just call this an "AEA formula". - In many contexts, sequences of quantifiers of the same type (universal or existential) are abbreviated by an asterisk, for example
∀*∃∀
might refer to formulas that have multiple universal quantifiers before the first alternation to an existential quantifier. In other contexts the asterisks might be omitted, and an∀∃∀
formula might really mean "any formula with any number of repetitions of each quantifier type".
- The universal quantifier is written


Rubeus namespace
The Rubeus Engine namespace.
Contents
- Reference
Namespaces
- namespace AudioComponents
- The Symphony Audio Engine.
- namespace Awerere
- The Awerere Physics Engine namespace.
- namespace GraphicComponents
- The Graphic Components of Rubeus Engine namespace.
- namespace UtilityComponents
- The Utility Components of Rubeus Engine namespace.
Classes
- struct AudioModifier
- Custom POD structure to be sent with messages as audio commands.
- struct Message
- Declares the message objects that systems use to communicate.
- class REngine
- A class the contains all components that make up Rubeus.
- class RGameObject
- A game object. Main class responsible for player interactions.
- class RInputManager
- The Input manager. Manages key strokes, mouse buttons, and cursor/scroll inputs.
- class RLevel
- A single container from all access to a level's contents.
- class RMasterComponent
- A master component. Serves as parent class for all components.
- class RMessageSystem
- A message system class that handles message posting and adding.
- class RWorld
- The container for all gameobjects. Synonymous to Scene graph.
Enums
- enum class EMouseButtons { __MOUSE_BUTTON_1 = 0, __MOUSE_BUTTON_2 = 1, __MOUSE_BUTTON_3 = 2, __MOUSE_BUTTON_4 = 3, __MOUSE_BUTTON_5 = 4, __MOUSE_BUTTON_6 = 5, __MOUSE_BUTTON_7 = 6, __MOUSE_BUTTON_8 = 7, __MOUSE_BUTTON_LAST = __MOUSE_BUTTON_8, __MOUSE_BUTTON_LEFT = __MOUSE_BUTTON_1, __MOUSE_BUTTON_RIGHT = __MOUSE_BUTTON_2, __MOUSE_BUTTON_MIDDLE = __MOUSE_BUTTON_3 }
- Mouse buttons supported.
- enum class EKeyboardKeys { __UNKNOWN = -1, __SPACE = 32, __APOSTROPHE = 39, __COMMA = 44, __MINUS = 45, __PERIOD = 46, __SLASH = 47, __0 = 48, __1 = 49, __2 = 50, __3 = 51, __4 = 52, __5 = 53, __6 = 54, __7 = 55, __8 = 56, __9 = 57, __SEMICOLON = 59, __EQUAL = 61, __A = 65, __B = 66, __C = 67, __D = 68, __E = 69, __F = 70, __G = 71, __H = 72, __I = 73, __J = 74, __K = 75, __L = 76, __M = 77, __N = 78, __O = 79, __P = 80, __Q = 81, __R = 82, __S = 83, __T = 84, __U = 85, __V = 86, __W = 87, __X = 88, __Y = 89, __Z = 90, __LEFT_BRACKET = 91, __BACKSLASH = 92, __RIGHT_BRACKET = 93, __GRAVE_ACCENT = 96, __WORLD_1 = 161, __WORLD_2 = 162, __ESCAPE = 256, __ENTER = 257, __TAB = 258, __BACKSPACE = 259, __INSERT = 260, __DELETE = 261, __RIGHT = 262, __LEFT = 263, __DOWN = 264, __UP = 265, __PAGE_UP = 266, __PAGE_DOWN = 267, __HOME = 268, __END = 269, __CAPS_LOCK = 280, __SCROLL_LOCK = 281, __NUM_LOCK = 282, __PRINT_SCREEN = 283, __PAUSE = 284, __F1 = 290, __F2 = 291, __F3 = 292, __F4 = 293, __F5 = 294, __F6 = 295, __F7 = 296, __F8 = 297, __F9 = 298, __F10 = 299, __F11 = 300, __F12 = 301, __F13 = 302, __F14 = 303, __F15 = 304, __F16 = 305, __F17 = 306, __F18 = 307, __F19 = 308, __F20 = 309, __F21 = 310, __F22 = 311, __F23 = 312, __F24 = 313, __F25 = 314, __KP_0 = 320, __KP_1 = 321, __KP_2 = 322, __KP_3 = 323, __KP_4 = 324, __KP_5 = 325, __KP_6 = 326, __KP_7 = 327, __KP_8 = 328, __KP_9 = 329, __KP_DECIMAL = 330, __KP_DIVIDE = 331, __KP_MULTIPLY = 332, __KP_SUBTRACT = 333, __KP_ADD = 334, __KP_ENTER = 335, __KP_EQUAL = 336, __LEFT_SHIFT = 340, __LEFT_CONTROL = 341, __LEFT_ALT = 342, __LEFT_SUPER = 343, __RIGHT_SHIFT = 344, __RIGHT_CONTROL = 345, __RIGHT_ALT = 346, __RIGHT_SUPER = 347, __MENU = 348, __DEACTIVATED = 0, __ACTIVATED = 1 }
- Keyboard keys supported.
- enum EMessageCode { system_ok, change_window_title, load_image, get_loaded_image, modify_audio, load_level }
- Defines all message codes the game requires.
- enum EAudioEffect { fade_out, fade_in }
- Defines audio commands allowed to send to the audio manager.
Functions
- void mouseButtonCallback(GLFWwindow* window, int button, int action, int mods)
- Callback function which sets the flag corresponding to a mouse button.
- void scrollCallback(GLFWwindow* window, double xoffset, double yoffset)
- Callback function which sets mouse scroll value.
- void keyCallback(GLFWwindow* window, int key, int scancode, int action, int mods)
- Callback function which sets the flag corresponding to a key.
- void cursorPositionCallback(GLFWwindow* window, double xpos, double ypos)
- Callback function which sets mouse position value.
Variables
Function documentation
void Rubeus:: mouseButtonCallback(GLFWwindow* window,
int button,
int action,
int mods)
Callback function which sets the flag corresponding to a mouse button.
| Parameters | |
|---|---|
| window | GLFW specified window object. |
| button | GLFW specified mouse button pressed or released. |
| action | GLFW specified key action.(Release, Press or Repeat) |
| mods | Modifier key pressed along with. |
void Rubeus:: scrollCallback(GLFWwindow* window,
double xoffset,
double yoffset)
Callback function which sets mouse scroll value.
| Parameters | |
|---|---|
| window | GLFW specified window object. |
| xoffset | X value of scroll |
| yoffset | Y value of scroll |
void Rubeus:: keyCallback(GLFWwindow* window,
int key,
int scancode,
int action,
int mods)
Callback function which sets the flag corresponding to a key.
| Parameters | |
|---|---|
| window | GLFW specified window object. |
| key | GLFW specified key which was pressed or released. |
| scancode | |
| action | GLFW specified key action.(Release, Press or Repeat) |
| mods | Modifier key pressed along with. |
void Rubeus:: cursorPositionCallback(GLFWwindow* window,
double xpos,
double ypos)
Callback function which sets mouse position value.
| Parameters | |
|---|---|
| window | GLFW specified window object. |
| xpos | X value of position |
| ypos | Y value of position |


The Capital Markets Company (UK) Ltd.
Position:
Technology Consulting Intern
Team:
Project Specific Team
Type:
Internship
Duration:
September 2021-Current
- Analyzed previous release defects and compiled client-facing presentations to maintain healthy communication with stakeholders
- Contributing to the migration of the In-Production solution to a new release with added features and bug fixes (In Progress)


COVID- Marche daily number infected virus {border-style px;} transmission #ffffff;} cases incidences distribution epidemic reproduction case disease Figure outbreak region cumulative estimated incidence provinces day individual people study values (.â€".) (â€"), April growth interval measures mitigation (.â€".), Italy days forecasting new onset px; {font-family estimate exponential font-size infection model spread time â€" Marche, Pesaro computed coronavirus countries date double; function information likelihood median population rates serial value Ascoli CI), CI, Elsevier European However, Infect Italian Italy, Poisson average bootstrap border-color border-width data days. dynamics early equals health including mentioned observed possible prediction solid; study, symptom transmissibility used weeks {font-style {font-weight â€‰ (Figure Ancona, China Fermo, [.â€".] achieved analysis assessment based bold calculated centre contact control courier; em; factors high knowledge like lockdown mean necessity novel numbers outbreak, present projections pt;} public quarantine rate resource terms using


FurAffinity Extender
The FurAffinity Extender extension by Kauko.
Follow us on Twitter
- Fork us on GitHub
FurAffinity Extender is open source! Visit our GitHub project.
Features
FurAffinity Extender has the following features.
- Adds a download button to image pages
- Adds an open gallery or submissions page in tabs link to open up all images in a gallery or submission page at once (with optional delay to avoid timeouts)
- Adds an option to enable hotkeys - left/p moves previous, right/n moves next, f adds to favorites, s saves the file
- Highlight submissions and journals by title or username
- Open source
Have a suggestion? Send a Twitter DM,
a note on FA
or file an issue on Github.


Questionable Questions
Week 9
The Harm of Poorly Posed Questions
November 13, 2014
As I heard many times before starting Dev Bootcamp – and have since come to believe on my own – being a programmer means knowing how to learn quickly. With the tech industry constantly changing and developers being responsible for knowing many languages and technologies, being a fast learner is a crucial aspect of being a programmer. And learning is dependent on asking good questions. Good questions are an amazing tool when you consider how often questions are asked in an unproductive way and how quickly bad questions can get you off track.
Good questions are specific, well explained, contextualized, and don’t make assumptions. That means, for example, that when you’re asking a question you need to explain the language you’re coding in, the problem you’re trying to solve, the approach you took, and any error messages you might have received. Not making assumptions is a very important point. You may have been wrestling with a problem for hours and are very familiar with it, but you have to keep in mind others are coming to it cold and will need to digest it first. You can’t assume they know what you know. It’s your job to help them understand it quickly so you can both get to solving it. This can’t be done passively, for example by just showing them the chunk of code you’re working on and expecting them to parse it instantly. Instead, you need to walk them through your thought process.
Sometimes I err on the side of being brief so as not to bog people down with details. I know how frustrating it can be to spend a lot of time trying to understand the problem before you even start solving it. At times I realize I was too brief and didn’t include enough details. For example, once I asked on our cohort Google+ community how to write driver test code to test for an argument error message. I got a few answers that didn’t exactly address my problem, and it soon dawned on me that my question wasn’t detailed enough. I didn’t explain what challenge I was working on, how I had tried to solve it, or what my thoughts were; I just asked the question. I realize now I should have explained more at the outset. I’m often wary of my question being too long or unwieldy and people ignoring it. But my cohort and DBC’s larger community has been very helpful and understanding, so I realized I shouldn’t be afraid to ask questions.
Sometimes I also have a tendency to want to work through programming challenges entirely on my own. While I think this is a necessary trait for programmers at times, it definitely has its downsides and needs to be balanced. Phase 0 has helped me get past that mental block of wanting to figure out problems on my own and has taught me how productive collaboration really is. There have been many times I was hopelessly stuck and forced to ask a question I had been toiling over. When I saw that other people had been struggling with the same problem, I realize how stubborn I had been and that I should have asked for help earlier. The important thing is not about who’s right, it’s about making sure you learn the material.
Phase 0 has undoubtedly made me better at asking questions and learning quickly, and it’s given me a good crash-course start to my journey to becoming a team-oriented developer with soft skills and hard skills. When I start Phase 1 on Monday, I’ll definitely take these lessons with me so I can learn as effectively as possible. With the pace and expectations of DBC, I know I’ll have no time to waste on poorly posed questions.


Police find young woman reported missing
A young woman reported missing from a Queen St, Brisbane, apartment, has been found safe.
Police earlier issued an alert appealing for information to help locate the woman.
She has since been found safe and well.


I have no dietary restrictions. Re: drinks, if there is a supply of diet coke or coke light or something then I'm generally able to keep functioning all day. Alcohol: I'm not a huge drinker of alcoholic beverages, but I'll drink an occasional beer or sake or margarita or something in an appropriate social context :smiley:


LAST LIEUTENANT, THE (Secondloitnanten)(director/writer: Hans Petter Moland; screenwriter: Axel Hellstenius; cinematographer: Harald Paagard; editor: Einar Egeland; cast: Espen Skjonberg (Thor Espedal), Bjorn Sund-quist (Krogh), Rut Tellefsen (Anna), Lars Andreas Larssen (Bjelland), Gard B. Eidsvold (Merstad), 1993-Norway)
Reviewed by Dennis Schwartz
A fine production about the fall of Norway during Germany's invasion in 1940. It tells the historical tale of Norway's quick capitulation to the Nazi forces. It questions the military leader's decision not to even fire a shot, though outmanned and sure to be defeated, but offering no resistance or support to their countrymen. That seemed hard to believe, considering they had an armed force of some three thousand. It is seen through the eyes of a senior citizen in his sixties, Thor Espedal (Espen Skjonberg), who just retired from the Merchant Marines. He is spurred on to action by the death of his friend at the hands of the Nazis and his patriotism, as he astonishes his wife Anna (Rut) and the younger soldiers by his enlistment. The result is a straightforward, old-fashioned drama that shows his mettle and crustiness, in a film noted solely for his sterling performance.
The film ably depicts the peaceful nature of the Norwegian people and how they found war to be a very difficult position for them to be in, and how confused everyone was at the time, as there was a lack of leadership from the top.
Thor makes love to his wife and then goes to Army HQ. There is mass confusion and no strong leadership present there. But that doesn't stop the men from laughing at Thor, who is dressed in his outdated uniform and is so much older than anyone else at headquarters.
He wins the soldiers over when he detonates a bridge and stays on to fight a Resistance campaign in the hills with a cowardly captain (Larssen), who decides to leave when the commanding general surrenders to the Germans. Thor takes over command, putting together a purely volunteer amateur army. He is joined by a socialist, Krogh (Sund-quist), who in civilian life is an engineer. Krogh has recently returned from Spain, where he fought the fascists in the Civil War, and has lost his citizenship because of that.
The only battle scene that is emphasized, is when the Resistance fighters ambush a German patrol and capture 150 Germans. But after that victory, reality sets in and the men are fearful that the Germans will send in a brigade and destroy them. Thor gives the volunteers a choice of going home or fighting. The volunteers have lost their taste for battle, so they scatter and return to their homes. Thor remains alone, waiting for the Germans to come. The scene was carefully done, showing the unsaid fear that swept over their camp when the men realized they might become martyrs if they stayed.
The Last Lieutenant doesn't have anything new to say, but what it says about one's loyalty to country and how its military leaders were faced with difficult decisions, was deftly handled by first-time director Hans Petter Moland. It is a film for those who want to see a different approach to war films, one where the study of character is more important than the battle scenes and the arguments of ideology. The film told a decent story. It's only problem was, that it just wasn't that exciting a picture.
REVIEWED ON 3/28/2000 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Bela Tarr's Sátántangó sounds like my kind of film. Indeed, I'm astonished that it has only just come to my attention, given that it was released in 1994. Clearly I am not keeping up with things as energetically as I ought to be. Sátántangó is seven and a half hours long, in black and white, set on a collapsing collective farm in Hungary, and the opening shot, which lasts for almost eight minutes, follows a herd of Hungarian cows trudging around a collapsing collective farmyard. I have not yet seen it, but I suspect I will adore it.


The page you requested could not be found, either contact your webmaster or try again.