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Adapted from NCAA Tuning by Julia Silge @JuliaSilge
This example walks through how to tune and choose hyperparameters using this week’s #TidyTuesday dataset on NCAA women’s basketball tournaments. 🏀
Our modeling goal is to estimate the relationship of expected tournament wins by seed from this week’s #TidyTuesday dataset. This is similar to the “average” column in the FiveThirtyEight table in this article.
Let’s start by reading in the data.
suppressPackageStartupMessages({
library(tidyverse)
library(tidymodels)
library(splines)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())
ggplot2::theme_set(theme_jim(base_size = 12))
tt <- tidytuesdayR::tt_load("2020-10-06")
--- Compiling #TidyTuesday Information for 2020-10-06 ----
--- There is 1 file available ---
--- Starting Download ---
Downloading file 1 of 1: `tournament.csv`
--- Download complete ---
tournament <- tt$tournament
We can look at the mean wins by seed.
tournament %>%
group_by(seed) %>%
summarise(
exp_wins = mean(tourney_w, na.rm = TRUE),
.groups = "drop"
) %>%
ggplot(aes(seed, exp_wins)) +
geom_point(alpha = 0.8, size = 3) +
labs(y = "tournament wins (mean)")
Let’s visualize all the tournament results, not just the averages.
tournament %>%
filter(!is.na(seed)) %>%
ggplot(aes(seed, tourney_w)) +
geom_bin2d(binwidth = c(1, 1), alpha = 0.8) +
labs(
fill = "number of\nteams", y = "",
subtitle = "Tournament wins",
title = "Womens NCAA",
caption = "Data by FiveThirtyEight | Visualization by @jim_gruman"
) +
theme(
legend.position = c(0.9, 0.9),
legend.background = element_rect(color = "white")
)
We have a lot of options to deal with data like this (curvy, integers, all greater than zero) but one straightforward option is splines. Splines aren’t perfect for this because they aren’t constrained to stay greater than zero or to always decrease, but they work pretty well and can be used in lots of situations. We have to choose the degrees of freedom for the splines.
plot_smoother <- function(deg_free) {
p <- ggplot(tournament, aes(seed, tourney_w)) +
geom_bin2d(binwidth = c(1, 1), alpha = 0.8) +
geom_smooth(
method = lm, se = FALSE, color = "black",
formula = y ~ ns(x, df = deg_free)
) +
labs(
fill = "number of\nteams",
title = "Womens NCAA",
caption = "Data by FiveThirtyEight | Visualization by @jim_gruman",
subtitle = paste("Tournament Wins explained by ", deg_free, "spline terms")
) +
theme(
legend.position = c(0.9, 0.9),
legend.background = element_rect(color = "white")
)
print(p)
}
walk(c(2, 4, 6, 8, 10, 15), plot_smoother)
As the number of degrees of freedom goes up, the curves get more wiggly. This would allow the model to fit a more complex relationship, perhaps too much so. We can tune this hyperparameter to find the best value.
We can start by loading the tidymodels
metapackage, and splitting our data into training and testing sets.
set.seed(123)
tourney_split <- tournament %>%
filter(!is.na(seed)) %>%
initial_split(strata = seed)
tourney_train <- training(tourney_split)
tourney_test <- testing(tourney_split)
We are going to use resampling to evaluate model performance. A bootstrap sample is a sample that is the same size as the original data set that is made using replacement. This results in analysis samples that have multiple replicates of some of the original rows of the data.
set.seed(234)
tourney_folds <- bootstraps(tourney_train)
tourney_folds
# Bootstrap sampling
# A tibble: 25 x 2
splits id
<list> <chr>
1 <split [1561/545]> Bootstrap01
2 <split [1561/585]> Bootstrap02
3 <split [1561/595]> Bootstrap03
4 <split [1561/580]> Bootstrap04
5 <split [1561/578]> Bootstrap05
6 <split [1561/594]> Bootstrap06
7 <split [1561/567]> Bootstrap07
8 <split [1561/570]> Bootstrap08
9 <split [1561/597]> Bootstrap09
10 <split [1561/575]> Bootstrap10
# ... with 15 more rows
Next we build a recipe for data preprocessing. It only has one step!
First, we must tell the recipe()
what our model is going to be (using a formula here) and what our training data is.
For our first and only step, we create new spline terms from the original seed
variable. We don’t know what the best value for the degrees of freedom is, so we will tune()
it. We can set an id
value for the tuneable parameter to more easily keep track of it, if we want.
The object tourney_rec
is a recipe that has not been trained on data yet, and in fact, we can’t do this because we haven’t decided on a value for deg_free
.
tourney_rec <- recipe(tourney_w ~ seed, data = tourney_train) %>%
step_ns(seed, deg_free = tune("seed_splines"))
tourney_rec
Data Recipe
Inputs:
role #variables
outcome 1
predictor 1
Operations:
Natural Splines on seed
Next, let’s create a model specification for a linear regression model, and the combine the recipe and model together in a workflow.
lm_spec <- linear_reg() %>% set_engine("lm")
tourney_wf <- workflow() %>%
add_recipe(tourney_rec) %>%
add_model(lm_spec)
tourney_wf
== Workflow ====================================================================
Preprocessor: Recipe
Model: linear_reg()
-- Preprocessor ----------------------------------------------------------------
1 Recipe Step
* step_ns()
-- Model -----------------------------------------------------------------------
Linear Regression Model Specification (regression)
Computational engine: lm
This workflow is almost ready to go, but we need to decide what values to try for the splines. There are several different ways to create tuning grids, but if the grid you need is very simple, you might prefer to create it by hand.
spline_grid <- tibble(seed_splines = c(1:4, 6, 8, 10))
spline_grid %>%
knitr::kable(align = "l")
| seed_splines |
|---|
| 1 |
| 2 |
| 3 |
| 4 |
| 6 |
| 8 |
| 10 |
Now we can put this all together! When we use tune_grid()
, we will fit each of the options in the grid to each of the resamples.
all_cores <- parallelly::availableCores(omit = 1)
all_cores
system
11
future::plan("multisession", workers = all_cores) # on Windows
save_preds <- control_grid(save_pred = TRUE)
spline_rs <-
tune_grid(
tourney_wf,
resamples = tourney_folds,
grid = spline_grid,
control = save_preds
)
spline_rs
# Tuning results
# Bootstrap sampling
# A tibble: 25 x 5
splits id .metrics .notes .predictions
<list> <chr> <list> <list> <list>
1 <split [1561/545]> Bootstrap01 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [3,~
2 <split [1561/585]> Bootstrap02 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
3 <split [1561/595]> Bootstrap03 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
4 <split [1561/580]> Bootstrap04 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
5 <split [1561/578]> Bootstrap05 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
6 <split [1561/594]> Bootstrap06 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
7 <split [1561/567]> Bootstrap07 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [3,~
8 <split [1561/570]> Bootstrap08 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [3,~
9 <split [1561/597]> Bootstrap09 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
10 <split [1561/575]> Bootstrap10 <tibble [14 x 5]> <tibble [0 x 1]> <tibble [4,~
# ... with 15 more rows
We have now fit each of our candidate set of spline features to our resampled training set!
Now let’s check out how we did.
collect_metrics(spline_rs) %>%
knitr::kable()
| seed_splines | .metric | .estimator | mean | n | std_err | .config |
|---|---|---|---|---|---|---|
| 1 | rmse | standard | 0.9876010 | 25 | 0.0057964 | Preprocessor1_Model1 |
| 1 | rsq | standard | 0.4365488 | 25 | 0.0037990 | Preprocessor1_Model1 |
| 2 | rmse | standard | 0.9114383 | 25 | 0.0060283 | Preprocessor2_Model1 |
| 2 | rsq | standard | 0.5209721 | 25 | 0.0052959 | Preprocessor2_Model1 |
| 3 | rmse | standard | 0.8944212 | 25 | 0.0062435 | Preprocessor3_Model1 |
| 3 | rsq | standard | 0.5387421 | 25 | 0.0056062 | Preprocessor3_Model1 |
| 4 | rmse | standard | 0.8934482 | 25 | 0.0063220 | Preprocessor4_Model1 |
| 4 | rsq | standard | 0.5396353 | 25 | 0.0056399 | Preprocessor4_Model1 |
| 6 | rmse | standard | 0.8937110 | 25 | 0.0063760 | Preprocessor5_Model1 |
| 6 | rsq | standard | 0.5394114 | 25 | 0.0057790 | Preprocessor5_Model1 |
| 8 | rmse | standard | 0.8952845 | 25 | 0.0063011 | Preprocessor6_Model1 |
| 8 | rsq | standard | 0.5378269 | 25 | 0.0059841 | Preprocessor6_Model1 |
| 10 | rmse | standard | 0.8976755 | 25 | 0.0059911 | Preprocessor7_Model1 |
| 10 | rsq | standard | 0.5353777 | 25 | 0.0058777 | Preprocessor7_Model1 |
Looks like the model got better and better as we added more degrees of freedom, which isn’t too shocking. In what way did it change?
collect_metrics(spline_rs) %>%
ggplot(aes(seed_splines, mean, color = .metric)) +
geom_line(size = 1.5, alpha = 0.5) +
geom_point(size = 3) +
facet_wrap(~.metric, ncol = 1, scales = "free_y") +
labs(x = "degrees of freedom", y = NULL) +
theme(legend.position = "none")
The model improved a lot as we increased the degrees of freedom at the beginning, but then continuing to add more didn’t make much difference. We could choose the numerically optimal hyperparameter with select_best()
but that would choose a more wiggly, complex model than we probably want. We can choose a simpler model that performs well, within some limits around the numerically optimal result. We could choose either by percent loss in performance or within one standard error in performance.
select_by_pct_loss(spline_rs,
metric = "rmse",
limit = 5,
seed_splines
) %>%
knitr::kable()
| seed_splines | .metric | .estimator | mean | n | std_err | .config | .best | .loss |
|---|---|---|---|---|---|---|---|---|
| 2 | rmse | standard | 0.9114383 | 25 | 0.0060283 | Preprocessor2_Model1 | 0.8934482 | 2.013561 |
select_by_one_std_err(spline_rs,
metric = "rmse",
seed_splines
) %>%
knitr::kable()
| seed_splines | .metric | .estimator | mean | n | std_err | .config | .best | .bound |
|---|---|---|---|---|---|---|---|---|
| 3 | rmse | standard | 0.8944212 | 25 | 0.0062435 | Preprocessor3_Model1 | 0.8934482 | 0.8997702 |
Looks like 2 or 3 degrees of freedom is a good option. Let’s go with 3, and update our tuneable workflow with this information and then fit it to our training data.
final_wf <- finalize_workflow(tourney_wf, tibble(seed_splines = 3))
tourney_fit <- fit(final_wf, tourney_train)
tourney_fit
== Workflow [trained] ==========================================================
Preprocessor: Recipe
Model: linear_reg()
-- Preprocessor ----------------------------------------------------------------
1 Recipe Step
* step_ns()
-- Model -----------------------------------------------------------------------
Call:
stats::lm(formula = ..y ~ ., data = data)
Coefficients:
(Intercept) seed_ns_1 seed_ns_2 seed_ns_3
3.234 -1.886 -5.445 -1.858
We can predict from this fitted workflow. For example, we can predict on the testing data and compute model performance.
tourney_test %>%
bind_cols(predict(tourney_fit, tourney_test)) %>%
metrics(tourney_w, .pred) %>%
knitr::kable(align = "l")
| .metric | .estimator | .estimate |
|---|---|---|
| rmse | standard | 0.8302226 |
| rsq | standard | 0.5874205 |
| mae | standard | 0.5971794 |
Pretty good! We can also predict on other kinds of new data. For example, let’s recreate the “average” column in the FiveThirtyEight table on expected wins.
predict(tourney_fit, new_data = tibble(seed = 1:16)) %>%
knitr::kable()
| .pred |
|---|
| 3.2335079 |
| 2.6044101 |
| 2.0111317 |
| 1.4894923 |
| 1.0753114 |
| 0.7929041 |
| 0.6205691 |
| 0.5251009 |
| 0.4732938 |
| 0.4319423 |
| 0.3740070 |
| 0.2971133 |
| 0.2050530 |
| 0.1016177 |
| -0.0094010 |
| -0.1242113 |
It’s close! This isn’t a huge surprise, since we’re fitting curves to data in a straightforward way here, but it’s still good to see. You can also see why splines aren’t perfect for this task, because the prediction isn’t constrained to positive values.
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] vctrs_0.3.8 rlang_0.4.11 yardstick_0.0.8 workflowsets_0.1.0
[5] workflows_0.2.3 tune_0.1.6 rsample_0.1.0 recipes_0.1.16
[9] parsnip_<IP_ADDRESS>0 modeldata_0.1.1 infer_1.0.0 dials_<IP_ADDRESS>00
[13] scales_1.1.1 broom_0.7.9 tidymodels_0.1.3 forcats_0.5.1
[17] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[21] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 systemfonts_1.0.2
[4] plyr_1.8.6 selectr_0.4-2 tidytuesdayR_1.0.1
[7] listenv_0.8.0 usethis_2.0.1 digest_0.6.27
[10] foreach_1.5.1 htmltools_0.5.2 viridis_0.6.1
[13] fansi_0.5.0 magrittr_2.0.1 tzdb_0.1.2
[16] globals_0.14.0 modelr_0.1.8 gower_0.2.2
[19] extrafont_0.17 R.utils_2.10.1 vroom_1.5.4
[22] sysfonts_0.8.5 extrafontdb_1.0 hardhat_0.1.6
[25] colorspace_2.0-2 rvest_1.0.1 textshaping_0.3.5
[28] haven_2.4.3 xfun_0.25 crayon_1.4.1
[31] jsonlite_1.7.2 survival_3.2-11 iterators_1.0.13
[34] glue_1.4.2 gtable_0.3.0 ipred_0.9-11
[37] emojifont_0.5.5 R.cache_0.15.0 Rttf2pt1_1.3.9
[40] future.apply_1.8.1 DBI_1.1.1 Rcpp_1.0.7
[43] showtextdb_3.0 viridisLite_0.4.0 bit_4.0.4
[46] GPfit_1.0-8 lava_1.6.10 prodlim_2019.11.13
[49] httr_1.4.2 ellipsis_0.3.2 farver_2.1.0
[52] R.methodsS3_1.8.1 pkgconfig_2.0.3 nnet_7.3-16
[55] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[58] here_1.0.1 labeling_0.4.2 tidyselect_1.1.1
[61] DiceDesign_1.9 later_1.3.0 munsell_0.5.0
[64] cellranger_1.1.0 tools_4.1.1 cachem_1.0.6
[67] cli_3.0.1 generics_0.1.0 gifski_1.4.3-1
[70] evaluate_0.14 fastmap_1.1.0 ragg_1.1.3
[73] yaml_2.2.1 bit64_4.0.5 knitr_1.34
[76] fs_1.5.0 showtext_0.9-4 nlme_3.1-152
[79] future_1.22.1 whisker_0.4 R.oo_1.24.0
[82] xml2_1.3.2 compiler_4.1.1 rstudioapi_0.13
[85] curl_4.3.2 reprex_2.0.1 lhs_1.1.3
[88] bslib_0.3.0 stringi_1.7.4 highr_0.9
[91] gdtools_0.2.3 hrbrthemes_0.8.0 lattice_0.20-44
[94] Matrix_1.3-4 styler_1.5.1 conflicted_1.0.4
[97] pillar_1.6.2 lifecycle_1.0.0 furrr_0.2.3
[100] jquerylib_0.1.4 httpuv_1.6.2 R6_2.5.1
[103] promises_<IP_ADDRESS> gridExtra_2.3 parallelly_1.28.1
[106] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[109] proto_1.0.0 rprojroot_2.0.2 withr_2.4.2
[112] mgcv_1.8-36 parallel_4.1.1 hms_1.1.0
[115] grid_4.1.1 rpart_4.1-15 timeDate_3043.102
[118] class_7.3-19 rmarkdown_2.10 git2r_0.28.0
[121] pROC_1.18.0 lubridate_1.7.10


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Primitive Distributions¶
-
class
Distribution
(reparameterized=None)¶ Bases:
object
Base class for parameterized probability distributions.
Distributions in Pyro are stochastic function objects with .sample() and .log_pdf() methods. Pyro provides two versions of each stochastic function:
(i) lowercase versions that take parameters:
x = dist.bernoulli(param) # Returns a sample of size size(param). p = dist.bernoulli.log_pdf(x, param) # Evaluates log probability of x.
and (ii) UpperCase distribution classes that can construct stochastic functions with fixed parameters:
d = dist.Bernoulli(param) x = d() # Samples a sample of size size(param). p = d.log_pdf(x) # Evaluates log probability of x.
Note
Parameters and data should be of type torch.autograd.Variable and all methods return type torch.autograd.Variable unless otherwise noted.
Tensor Shapes:
Distributions provide a method .shape() for the tensor shape of samples:
x = d.sample(*args, **kwargs) assert x.shape == d.shape(*args, **kwargs)
Pyro distinguishes two different roles for tensor shapes of samples:
- The leftmost dimension corresponds to iid batching, which can be treated specially during inference via the .batch_log_pdf() method.
- The rightmost dimensions correspond to event shape.
These shapes are related by the equation:
assert d.shape(*args, **kwargs) == (d.batch_shape(*args, **kwargs) + d.event_shape(*args, **kwargs))
Distributions provide a vectorized .batch_log_pdf() method that evaluates the log probability density of each event in a batch independently, returning a tensor of shape d.batch_shape() + (1,):
x = d.sample(*args, **kwargs) assert x.shape == d.shape(*args, **kwargs) log_p = d.batch_log_pdf(x, *args, **kwargs) assert log_p.shape == d.batch_shape(*args, **kwargs) + (1,)
Distributions may also support broadcasting of the .log_pdf() and .batch_log_pdf() methods, which may each be evaluated with a sample tensor x that is larger than (but broadcastable from) the parameters. In this case, d.batch_shape() will return a shape that is broadcastable to d.batch_log_pdf().shape. For example:
x = d.sample() xx = torch.stack([x, x]) d.batch_log_pdf(xx) + torch.zeros(d.batch_shape() + (1,)) # Broadcast.
Implementing New Distributions:
Derived classes must implement the following methods: .sample(), .batch_log_pdf(), .batch_shape(), and .event_shape(), . Discrete classes may also implement the .enumerate_support() method to improve gradient estimates and set .enumerable = True.
Examples:
Take a look at the examples[link] to see how they interact with inference algorithms.
-
analytic_mean
(*args, **kwargs)¶ Analytic mean of the distribution, to be implemented by derived classes.
Note that this is optional, and currently only used for testing distributions.
Returns: Analytic mean. Return type: torch.autograd.Variable. Raises: NotImplementedError if mean cannot be analytically computed.
-
analytic_var
(*args, **kwargs)¶ Analytic variance of the distribution, to be implemented by derived classes.
Note that this is optional, and currently only used for testing distributions.
Returns: Analytic variance. Return type: torch.autograd.Variable. Raises: NotImplementedError if variance cannot be analytically computed.
-
batch_log_pdf
(x, *args, **kwargs)¶ Evaluates log probability densities for each of a batch of samples.
Parameters: x (torch.autograd.Variable) – A single value or a batch of values batched along axis 0. Returns: log probability densities as a one-dimensional torch.autograd.Variable with same batch size as value and params. The shape of the result should be self.batch_size(). Return type: torch.autograd.Variable
-
batch_shape
(*args, **kwargs)¶ The left-hand tensor shape of samples, used for batching.
Samples are of shape d().shape == d.batch_shape() + d.event_shape().
Returns: Tensor shape used for batching. Return type: torch.Size
-
enumerable
= False¶
-
enumerate_support
(*args, **kwargs)¶ Returns a representation of the parametrized distribution’s support.
This is implemented only by discrete distributions.
Returns: An iterator over the distribution’s discrete support. Return type: iterator
-
event_shape
(*args, **kwargs)¶ The right-hand tensor shape of samples, used for individual events.
Samples are of shape d().shape == d.batch_shape() + d.event_shape().
Returns: Tensor shape used for individual events. Return type: torch.Size
-
log_pdf
(x, *args, **kwargs)¶ Evaluates total log probability density of a batch of samples.
Parameters: x (torch.autograd.Variable) – A value. Returns: total log probability density as a one-dimensional torch.autograd.Variable of size 1. Return type: torch.autograd.Variable
-
reparameterized
= False¶
-
sample
(*args, **kwargs)¶ Samples a random value.
For tensor distributions, the returned Variable should have the same .size() as the parameters.
Returns: A random value or batch of random values (if parameters are batched). The shape of the result should be self.size(). Return type: torch.autograd.Variable
-
shape
(*args, **kwargs)¶ The tensor shape of samples from this distribution.
Samples are of shape d().shape == d.batch_shape() + d.event_shape().
Returns: Tensor shape of samples. Return type: torch.Size
Bernoulli¶
-
class
Bernoulli
(ps=None, logits=None, batch_size=None, log_pdf_mask=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Bernoulli distribution.
Distribution over a vector of independent Bernoulli variables. Each element of the vector takes on a value in {0, 1}.
This is often used in conjunction with torch.nn.Sigmoid to ensure the ps parameters are in the interval [0, 1].
Parameters: - ps (torch.autograd.Variable) – Probabilities. Should lie in the interval [0,1].
- logits – Log odds, i.e. log (p / (1 - p)). Either ps or logits should be specified, but not both.
- batch_size – The number of elements in the batch used to generate a sample. The batch dimension will be the leftmost dimension in the generated sample.
- log_pdf_mask – Tensor that is applied to the batch log pdf values as a multiplier. The most common use case is supplying a boolean tensor mask to mask out certain batch sites in the log pdf computation.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
enumerable
= True¶
-
enumerate_support
()¶ Returns the Bernoulli distribution’s support, as a tensor along the first dimension.
Note that this returns support values of all the batched RVs in lock-step, rather than the full cartesian product. To iterate over the cartesian product, you must construct univariate Bernoullis and use itertools.product() over all univariate variables (may be expensive).
Returns: torch variable enumerating the support of the Bernoulli distribution. Each item in the return value, when enumerated along the first dimensions, yields a value from the distribution’s support which has the same dimension as would be returned by sample. Return type: torch.autograd.Variable.
-
event_shape
()¶
-
sample
()¶
-
shape
(x=None)¶
Beta¶
-
class
Beta
(alpha, beta, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Univariate beta distribution parameterized by alpha and beta.
This is often used in conjunction with torch.nn.Softplus to ensure alpha and beta parameters are positive.
Parameters: - alpha (torch.autograd.Variable) – Lower shape parameter. Should be positive.
- beta (torch.autograd.Variable) – Upper shape parameter. Should be positive.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
sample
()¶ Un-reparameterizeable sampler.
-
shape
(x=None)¶
Categorical¶
-
class
Categorical
(ps=None, vs=None, logits=None, one_hot=True, batch_size=None, log_pdf_mask=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Categorical (discrete) distribution.
Discrete distribution over elements of vs with P(vs[i]) proportional to ps[i]. If one_hot=True, .sample() returns a one-hot vector; otherwise .sample() returns the category index.
Parameters: - ps (torch.autograd.Variable.) – Probabilities. These should be non-negative and normalized along the rightmost axis.
- logits (torch.autograd.Variable.) – Non-normalized log probability values. Either ps or logits should be specified but not both.
- vs (list or numpy.array or torch.autograd.Variable) – Optional list of values in the support.
- one_hot – Whether sample() returns a one_hot sample. Defaults to False if vs is specified or True if vs is not specified.
- batch_size (int) – Optional number of elements in the batch used to generate a sample. The batch dimension will be the leftmost dimension in the generated sample.
-
batch_log_pdf
(x)¶ Evaluates log probability densities for one or a batch of samples and parameters. The last dimension for
ps
encodes the event probabilities, and the remaining dimensions are considered batch dimensions.ps
andvs
are first broadcasted to the size of the datax
. The data tensor is used to to create a mask overvs
where a 1 in the mask indicates that the corresponding value invs
was selected. Since,ps
andvs
have the same size, this mask when applied overps
gives the probabilities of the selected events. The method returns the logarithm of these probabilities.Returns: tensor with log probabilities for each of the batches. Return type: torch.autograd.Variable
-
batch_shape
(x=None)¶
-
enumerable
= True¶
-
enumerate_support
()¶ Returns the categorical distribution’s support, as a tensor along the first dimension.
Note that this returns support values of all the batched RVs in lock-step, rather than the full cartesian product. To iterate over the cartesian product, you must construct univariate Categoricals and use itertools.product() over all univariate variables (but this is very expensive).
Parameters: - ps – numpy.ndarray where the last dimension denotes the event probabilities, p_k, which must sum to 1. The remaining dimensions are considered batch dimensions.
- vs – Optional parameter, enumerating the items in the support. This could either
have a numeric or string type. This should have the same dimension as
ps
. - one_hot – Denotes whether one hot encoding is enabled. This is True by default.
When set to false, and no explicit
vs
is provided, the last dimension gives the one-hot encoded value from the support.
Returns: torch variable or numpy array enumerating the support of the categorical distribution. Each item in the return value, when enumerated along the first dimensions, yields a value from the distribution’s support which has the same dimension as would be returned by sample. If
one_hot=True
, the last dimension is used for the one-hot encoding.Return type: torch.autograd.Variable or numpy.ndarray.
-
event_shape
()¶
-
sample
()¶ Returns a sample which has the same shape as
ps
(orvs
), except that ifone_hot=True
(and novs
is specified), the last dimension will have the same size as the number of events. The type of the sample is numpy.ndarray if vs is a list or a numpy array, else a tensor is returned.Returns: sample from the Categorical distribution Return type: numpy.ndarray or torch.LongTensor
-
shape
(x=None)¶
Cauchy¶
-
class
Cauchy
(mu, gamma, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Cauchy (a.k.a. Lorentz) distribution.
This is a continuous distribution which is roughly the ratio of two Gaussians if the second Gaussian is zero mean. The distribution is over tensors that have the same shape as the parameters mu`and `gamma, which in turn must have the same shape as each other.
This is often used in conjunction with torch.nn.Softplus to ensure the gamma parameter is positive.
Parameters: - mu (torch.autograd.Variable) – Location parameter.
- gamma (torch.autograd.Variable) – Scale parameter. Should be positive.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
sample
()¶
-
shape
(x=None)¶
Delta¶
-
class
Delta
(v, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Degenerate discrete distribution (a single point).
Discrete distribution that assigns probability one to the single element in its support. Delta distribution parameterized by a random choice should not be used with MCMC based inference, as doing so produces incorrect results.
Parameters: v (torch.autograd.Variable) – The single support element. -
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
enumerable
= True¶
-
enumerate_support
(v=None)¶ Returns the delta distribution’s support, as a tensor along the first dimension.
Parameters: v – torch variable where each element of the tensor represents the point at which the delta distribution is concentrated. Returns: torch variable enumerating the support of the delta distribution. Return type: torch.autograd.Variable.
-
event_shape
()¶
-
sample
()¶
-
shape
(x=None)¶
-
Normal¶
-
class
Normal
(mu, sigma, batch_size=None, log_pdf_mask=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Univariate normal (Gaussian) distribution.
A distribution over tensors in which each element is independent and Gaussian distributed, with its own mean and standard deviation. The distribution is over tensors that have the same shape as the parameters mu and sigma, which in turn must have the same shape as each other.
This is often used in conjunction with torch.nn.Softplus to ensure the sigma parameters are positive.
Parameters: - mu (torch.autograd.Variable) – Means.
- sigma (torch.autograd.Variable) – Standard deviations. Should be positive and the same shape as mu.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶ Diagonal Normal log-likelihood
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
reparameterized
= True¶
-
sample
()¶ Reparameterized Normal sampler.
-
shape
(x=None)¶
Exponential¶
-
class
Exponential
(lam, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Exponential parameterized by scale lambda.
This is often used in conjunction with torch.nn.Softplus to ensure the lam parameter is positive.
Parameters: lam (torch.autograd.Variable) – Scale parameter (a.k.a. lambda). Should be positive. -
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
reparameterized
= True¶
-
sample
()¶ Reparameterized sampler.
-
shape
(x=None)¶
-
Gamma¶
-
class
Gamma
(alpha, beta, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Gamma distribution parameterized by alpha and beta.
This is often used in conjunction with torch.nn.Softplus to ensure alpha and beta parameters are positive.
Parameters: - alpha (torch.autograd.Variable) – Shape parameter. Should be positive.
- beta (torch.autograd.Variable) – Shape parameter. Should be positive. Shouldb be the same shape as alpha.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
sample
()¶ un-reparameterized sampler.
-
shape
(x=None)¶
HalfCauchy¶
-
class
HalfCauchy
(mu, gamma, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Half-Cauchy distribution.
This is a continuous distribution with lower-bounded domain (x > mu). See also the Cauchy distribution.
This is often used in conjunction with torch.nn.Softplus to ensure the gamma parameter is positive.
Parameters: - mu – mean (tensor)
- gamma – scale (tensor (0, Infinity))
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
sample
()¶
-
shape
(x=None)¶
LogNormal¶
-
class
LogNormal
(mu, sigma, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Log-normal distribution.
A distribution over probability vectors obtained by exp-transforming a random variable drawn from
Normal({mu: mu, sigma: sigma})
.This is often used in conjunction with torch.nn.Softplus to ensure the sigma parameters are positive.
Parameters: - mu (torch.autograd.Variable) – log mean parameter.
- sigma (torch.autograd.Variable) – log standard deviations. Should be positive.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
reparameterized
= True¶
-
sample
()¶ Reparameterized log-normal sampler.
-
shape
(x=None)¶
Multinomial¶
-
class
Multinomial
(ps, n, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Multinomial distribution.
Distribution over counts for n independent Categorical(ps) trials.
This is often used in conjunction with torch.nn.Softmax to ensure probabilites ps are normalized.
Parameters: - ps (torch.autograd.Variable) – Probabilities (real). Should be positive and should normalized over the rightmost axis.
- n (int) – Number of trials. Should be positive.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
expanded_sample
()¶
-
sample
()¶
-
shape
(x=None)¶
Poisson¶
-
class
Poisson
(lam, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Poisson distribution over integers parameterizeds by scale lambda.
This is often used in conjunction with torch.nn.Softplus to ensure the lam parameter is positive.
Parameters: lam (torch.autograd.Variable) – Mean parameter (a.k.a. lambda). Should be positive. -
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
sample
()¶
-
shape
(x=None)¶
-
Uniform¶
-
class
Uniform
(a, b, batch_size=None, *args, **kwargs)¶ Bases:
pyro.distributions.distribution.Distribution
Uniform distribution over continuous interval [a, b].
Parameters: - a (torch.autograd.Variable) – lower bound (real).
- b (torch.autograd.Variable) – upper bound (real). Should be greater than a.
-
analytic_mean
()¶
-
analytic_var
()¶
-
batch_log_pdf
(x)¶
-
batch_shape
(x=None)¶
-
event_shape
()¶
-
reparameterized
= False¶
-
sample
()¶
-
shape
(x=None)¶


The following document contains the listing of user tags found in the code. Below is the summary of the occurrences per tag.
| Tag Class | Total number of occurrences | Tag strings used by tag class |
|---|---|---|
| @todo | 0 | @todo |
| TODO | 25 | TODO |
Each tag is detailed below:
Number of occurrences found in the code: 25
| com.github.davidmoten.rx2.Actions | Line |
|---|---|
| make holder | 50 |
| com.github.davidmoten.rx2.BiFunctions | Line |
| make holder | 39 |
| make holder | 50 |
| com.github.davidmoten.rx2.BiPredicates | Line |
| make holder | 14 |
| make holder | 25 |
| make holder | 36 |
| com.github.davidmoten.rx2.Callables | Line |
| make holder | 22 |
| com.github.davidmoten.rx2.Consumers | Line |
| make holder | 57 |
| make holder | 147 |
| use custom exception | 153 |
| com.github.davidmoten.rx2.Functions | Line |
| make holder | 39 |
| make holder | 50 |
| com.github.davidmoten.rx2.ObservablesTest | Line |
| assert stuff about closing closeable.close(); closeable.close(); | 68 |
| com.github.davidmoten.rx2.RetryWhen | Line |
| remove this cast when rxjava 2.0.3 released because signature of retryWhen will be fixed | 64 |
| unit test | 92 |
| com.github.davidmoten.rx2.buffertofile.Serializers | Line |
| use holder | 23 |
| use holder | 28 |
| use holder | 33 |
| com.github.davidmoten.rx2.internal.flowable.DelimitedStringLinkedList | Line |
| might need GC Nepotism protection | 170 |
| com.github.davidmoten.rx2.internal.flowable.FlowableRepeatingTransform | Line |
| what to do here? cancelWholeChain(); | 235 |
| --No comment-- | 578 |
| com.github.davidmoten.rx2.internal.flowable.FlowableStateMachine | Line |
| --No comment-- | 72 |
| com.github.davidmoten.rx2.internal.flowable.TransformerStringSplit | Line |
| is this check needed? | 62 |
| com.github.davidmoten.rx2.internal.flowable.buffertofile.PageListTest | Line |
| test rewrite marking | 72 |
| com.github.davidmoten.rx2.internal.flowable.buffertofile.PagedQueue | Line |
| optimize for BigEndian as well | 106 |


No.164, Galle Road, Dehiwala, Sri Lanka.
Price(Rs.): 500.00
Preperation Time (min) :12
Description :Regular/Medium
Price(Rs.): 200.00
Preperation Time (min) :20
Description : Regular/ Medium


Name: brother
Input: Single entry of arbitrary data type.
Output: Set of entries of arbitrary data type.
Definition: Short description of the function, style of the old system manual
Argumentlist:
Argument No 1 (required) Keyword:
Argument No 2 (optional) Numeric Range.
Discussion: Detailed explanation and examples


Manager
Employee ID: 468421
Office Number: 807
Engineer
Employee ID: 368611
Intern
Employee ID: 654815
School: UNiversity of Alabama


I have always had a great love for music. There is something relaxing about music. I love anything from old time Jazz to new age pop. I grew up in a house filled with music. From the age of 8 I have played at least one instrument.
With so much going on around the world, I believe that it is important to be informed. Even from a young age I remember going into my parent’s room to watch the evening news. I loved learning and experiencing the news. That love has continued throughout my life, as I continue to find enjoyment in keeping up on this ever changing world.
There is something about sports. I definitely love playing sports, but I also seem to love watching sports. I grew up playing sports and being outside hiking. I attribute my ability to work as a team, and my ability to communicate to the great sports of my childhood.


Notifications
{{x.senderName}}
{{x.senderEmailId}}
Message Received
Invitation
Connection Established
{{x.createdAt | date: 'medium' }}
delete


More than 150 million Americans live in areas that don't meet federal air requirements. Pollution from cars, corporations, and other carbon sources contributes to this.
Thankfully, trees have our backs. They provide many meaningful environmental benefits, including improving air quality.
In this game, you will fight against the cars' pollution using trees near you!
This app uses the City of Urbana's tree database to query the trees that are actually closest to you! Click here to see the open dataset on Socrata.
Join the Arbor Day Foundation today to recieve 10 trees to plant near you. The Foundation will let you select trees that grow in your local habitable zone.
The City of Urbana's Tree Commission works hard to maintain its trees and raise awareness about their importance. Click here to learn more about their work.
Created by Vinesh Kannan and Nick Li | Illinois Institute of Technology


Welcome to the society!
Message from the President
Hey Queen,
Mission: “Uplifting the lives of young queens through a creative platform”
Motto: “For Queens, By Queens!”
If you need anything, such as more information, please feel free to contact us on any of our social medias!
-Angel & Sarah :)
Apr
14
Society Charity Bake Sale
The Hubs, Paternoster Row
Loading...
This is a test event for the homepage. There probably isn't going to be a charity bake sale and this is all just filler text used to test the formatting rules of the webpage. Apologies for the lack of cake.
Society of Queens is a female collective that hosts networking events and opportunities for young women. We pride ourselves on diversity and inclusivity. We have created a platform for personal and business elevation.
We want women to be able to navigate themselves within a professional setting and to assist them along their entrepreneurial journeys. Since launching in February 2019, we have facilitated many events ranging from self-care to sexual health.
Ultimately, we want to make the transition from education to the professional world smoother for our members.
Take a look at this video of us on BBC radio!
Instagram
Twitter
Facebook
Paypal


{% extends "competition/competition_base.html" %} {% load url from future %} {% load competition_tags %} {% block content %}


Apple Music Previews
Listen song & playlist
previews of your favourite Artists
Search
5
10
15
20
All
Songs
Playlists
Please load some music here ;)


obis_match_habitat.Rd
function to get OBIS records for a given species and match to IAP and Bio-Oracle temperatures if save_all_recs == TRUE, this full matched dataset will be saved before summarising function returns a summary of temperature affinity of the species
obis_match_habitat( sp_id, layers, fields = c("decimalLongitude", "decimalLatitude", "minimumDepthInMeters", "maximumDepthInMeters", "depth", "eventDate", "year", "month", "scientificName", "aphiaID"), check_match = FALSE, validate_sp_id = FALSE, geometry = NULL, ignore_failures = FALSE, overwrite = FALSE )
| sp_id | validates species Aphia ID |
|---|---|
| layers | character vector of habitat layers to match |
| fields | fields to return from OBIS query. Defaults to |
| check_match | whether to check validity of |
| validate_sp_id | whether to check for validity of aphiaID on OBIS |
| geometry | an sf object. Queries will be faster if the geometry is simple and restricted in space. |
| ignore_failures | whether to through an error or return NULL for queries that return no obis records. |
| overwrite | whether to re-download and overwrite already downloaded layers |
an sf
object, one row for each species occurence returned from obis.
Column include requested OBIS metadata through fields
argument plus a column
for each layer environmental layer requested containing data retuned for each
occurence location.
if (FALSE) { obis_match_habitat(sp_id = 325567, layers = c( "BO_bathymean", "BO2_phosphateltmax_bdmean", "BO2_salinitymin_ss", "rock50cm", "april", "march" )) }


It will be small displays. Every button will have it own chip as touch bar has now. So, keyboard will switch to lanuage which you need right now. Main idea that you will see symbols of selected language.


Ethernet
Interface to get the MAC address of the first ethernet adapter.
File information
Logging
To enable logging for IEthernet, the flag LOG_ETHERNET must be set in the managers diagnostic settings.
Classes
IEthernet
class IEthernet {
public:
static bool GetMACAddress(byte * mac, class IInstanceLog * const log);
static bool GetIpAddress(char * buffer, size_t bufferLen, bool ipv6, class IInstanceLog * const log);
};
Public functions
GetMACAddress (static function)
-
The function writes the MAC address of the first ethernet adapter into the byte array of size MAC_ADDRESS_SIZE.
Parameters
| byte * mac | A byte pointer to store the MAC address bytes. It needs to be of size MAC_ADDRESS_SIZE (6 bytes) |
| IInstanceLog * log | An IInstanceLog pointer to the logging interface. |
byte mac[MAC_ADDRESS_SIZE];
IEthernet::GetMACAddress(mac, log);
GetMACAddress (static function)
-
The function writes the IP address of the first ethernet adapter into the char array of size IP_ADDRESS_SIZE.
Parameters
| char * buffer | A byte pointer to store the IP address. It needs to be of size IP_ADDRESS_SIZE (46 bytes) |
| size_t bufferLen | The length of the char pointer. |
| bool ipv6 | If true, the IPv6 address is fetched. |
| IInstanceLog * log | An IInstanceLog pointer to the logging interface. |
Data types
MAC_ADDRESS_SIZE
#define MAC_ADDRESS_SIZE 6
Overview
The #define MAC_ADDRESS_SIZE
is used to define the size of the byte * mac array.
IP_ADDRESS_SIZE
#define IP_ADDRESS_SIZE 46
Overview
The #define IP_ADDRESS_SIZE
is used to define the maximum length of the char array for an IP address.


I created an example of custom street view with Google Maps API v3. I explain how to create this street view at on my site: Google Maps Playground - The way of creating custom streetview.


--- layout: page title: "WebSub" description: "An open, simple, web-scale and decentralized pubsub protocol. Anybody can play. As opposed to more developed (and more complex) pubsub specs like Jabber Publish-Subscribe this spec's base profile (the barrier-to-entry to speak it) is dead simple. The fancy bits required for high-volume publishers and subscribers are optional. The base profile is HTTP-based, as opposed to XMPP (see more on this below). To dramatically simplify this spec in several places where we had to choose between supporting A or B, we took it upon ourselves to say \"only A\", rather than making it an implementation decision. We offer this spec in hopes that it fills a need or at least advances the state of the discussion in the pubsub space. Polling is extremely wasteful and high latency. We think a decentralized pubsub layer is a fundamental, missing layer in the Internet architecture today and its existence, more than just enabling the obvious lower latency feed readers, would enable many cool applications, most of which we can't even imagine. But we're looking forward to decentralized social networking." ---
| Document Name: | W3C TR http://www.w3.org/TR/websub |
|---|---|
| Document URI: |
http://www.w3.org/TR/websub
|
| Online Version: |
http://www.w3.org/TR/websub
|
| Organization: | World Wide Web Consortium (W3C) |
| Series: | Technical Report (TR) |
| Abstract: | An open, simple, web-scale and decentralized pubsub protocol. Anybody can play. As opposed to more developed (and more complex) pubsub specs like Jabber Publish-Subscribe this spec's base profile (the barrier-to-entry to speak it) is dead simple. The fancy bits required for high-volume publishers and subscribers are optional. The base profile is HTTP-based, as opposed to XMPP (see more on this below). To dramatically simplify this spec in several places where we had to choose between supporting A or B, we took it upon ourselves to say "only A", rather than making it an implementation decision. We offer this spec in hopes that it fills a need or at least advances the state of the discussion in the pubsub space. Polling is extremely wasteful and high latency. We think a decentralized pubsub layer is a fundamental, missing layer in the Internet architecture today and its existence, more than just enabling the obvious lower latency feed readers, would enable many cool applications, most of which we can't even imagine. But we're looking forward to decentralized social networking. |
hub
Return to ( Series | Organization | all Specifications )


Players
Online
Player
Age
Gender
# Played
# Won
{{po.username}}
{{po.gender}}
{{po.played}}
{{po.won}}
New challenge for {{newChg.askedPlayer}}
×
Search the songs and select
4 options
to build up the question and toggle the one you want to show the lyrics of
Answer
Options
Remove
Search songs:
{{sl.trackName}}
{{sl.trackArtist}}
Tr.1
Tr.2
Tr.3
Tr.4


Definition at line 22 of file Directory.php.
| addFile |
( |
|
$name, |
|
|
array |
$coverageData, |
|
|
array |
$testData, |
|
|
|
$cacheTokens |
|
) |
| |
Returns the number of files in/under this node.
- Returns
- integer
Definition at line 119 of file Directory.php.
Returns the child nodes of this node.
- Returns
- array
Definition at line 215 of file Directory.php.
Returns the classes of this node.
- Returns
- array
Definition at line 225 of file Directory.php.
Returns the directories in this directory.
- Returns
- array
Definition at line 195 of file Directory.php.
Returns the files in this directory.
- Returns
- array
Definition at line 205 of file Directory.php.
Returns the functions of this node.
- Returns
- array
Definition at line 267 of file Directory.php.
Returns an iterator for this node.
- Returns
- RecursiveIteratorIterator
Definition at line 137 of file Directory.php.
Returns the LOC/CLOC/NCLOC of this node.
- Returns
- array
Definition at line 288 of file Directory.php.
Returns the number of classes.
- Returns
- integer
Definition at line 346 of file Directory.php.
| getNumExecutableLines |
( |
| ) |
|
Returns the number of executable lines.
- Returns
- integer
Definition at line 310 of file Directory.php.
Returns the number of executed lines.
- Returns
- integer
Definition at line 328 of file Directory.php.
Returns the number of functions.
- Returns
- integer
Definition at line 454 of file Directory.php.
Returns the number of methods.
- Returns
- integer
Definition at line 418 of file Directory.php.
Returns the number of tested classes.
- Returns
- integer
Definition at line 364 of file Directory.php.
| getNumTestedFunctions |
( |
| ) |
|
Returns the number of tested functions.
- Returns
- integer
Definition at line 472 of file Directory.php.
Returns the number of tested methods.
- Returns
- integer
Definition at line 436 of file Directory.php.
Returns the number of tested traits.
- Returns
- integer
Definition at line 400 of file Directory.php.
Returns the number of traits.
- Returns
- integer
Definition at line 382 of file Directory.php.
Returns the traits of this node.
- Returns
- array
Definition at line 246 of file Directory.php.
The documentation for this class was generated from the following file:
- vendor/phpunit/php-code-coverage/src/CodeCoverage/Report/Node/Directory.php


We are committed to building profitable business partnerships for the economic growth of the Federal Capital Territory.
Investments Company Limited (AICL) is a leading business development and investment holding company based in the Capital City – Abuja want to know why people chooses us read below The Abuja Taxi scheme is a new transport initiative of the Federal Capital Territory Administration (FCTA), Subsidy Reinvestment and Empowerment.
Abuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala MohammedAbuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala Mohammed The Abuja Taxi scheme is a new transport initiative of the Federal Capital Territory Administration (FCTA), Subsidy Reinvestment and Empowerment. More...
Abuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala MohammedAbuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala Mohammed The Abuja Taxi scheme is a new transport initiative of the Federal Capital Territory Administration (FCTA), Subsidy Reinvestment and Empowerment. More...
Abuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala MohammedAbuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala Mohammed The Abuja Taxi scheme is a new transport initiative of the Federal Capital Territory Administration (FCTA), Subsidy Reinvestment and Empowerment. More...
Abuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala MohammedAbuja Investments Company Limited was proud to join the Churchgate Group in welcoming the FCT Minister H.E Sen Bala Mohammed The Abuja Taxi scheme is a new transport initiative of the Federal Capital Territory Administration (FCTA), Subsidy Reinvestment and Empowerment. More...
Investment


HyAPI Machine Learning Toolkit
Workflow Details
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Status:
Choose Dataset Archive:
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Chiang Rai: first stop in northern Thailand
by Monika Suchoszek
We started our day with the early bus to Wat Rong Khun, known among tourists as the White Temple. We used a local bus leaving from the Chiang Rai Bus Terminal 1 (just next to the night bazaar). It was very easy to find the correct bus stop because it was labeled with the name of the temple and time schedule. We bought a ticket on the bus without any problems (you just need to have a change). After about 20 min of the bus ride, the woman selling the tickets was pointing the place outside the window with a huge number of tourists in front of it and we knew it’s time to get out.
 It took us maybe 1 hour to visit White Temple and it made a huge impression on us :) It’s really well maintained, all small details look just perfect ! You can clearly notice that artist, Ajarn Chalermchai Kositpipat and 120 of his followers, devoted their lives to create this magical place. His plan is to make modern Thai Buddhist art famous all over the world and his ambitious vision needs to be carried on by his followers since he assumes that completing this project will take another 60 to 90 years of hard work. Freedom is very important for the author, therefore he doesn’t accept any donations from the government or politicians and only the money from selling the tickets is used to finance his project.
First we walked through the main temple (no photos allowed inside) to see on one of the walls the paintings of multiple super heroes (e.g. Superman), the World Trade Center on fire during the terrorist attack with the portrait of the main offender, Osama Binladen in the company of George Bush, both looking at us with their evil eyes. It’s a symbol of hate which led to the war and death of innocent people, both Muslims and Americans. To get inside, we first had to cross a bridge above the sea of sculptures in the shape of people’s arms, trying to reach us.
There are a few more buildings, one gold and one white temple, both still under construction. However, another building (close to the exit) caught our attention! The huge golden house turned out to be … the restroom! That was definitely the most beautiful toilet I have ever used :) This place was in general really lovely, the tiny mirrors decorating the sculptures and domination of two colors made this place very harmonized and unique.
After we got back to the main bus station, we immediately jumped on another bus in the direction of Maesai to visit the Black House, also called the Baandam Museum. The woman selling tickets told us again where we needed to get off and from there it was maybe 800m of walk to reach the place (direction clearly marked with the signs). It’s an open-air museum dedicated to Thawan Duchanee with around 40 buildings. Inside each of them you can find a collection of paintings, sculptures, old furniture but unfortunately a large portion of the collection were skins, horns and animal bones. Thawan Duchanee got his degree in Painting, Monument and Urban Planning, continuing his education in the Netherlands where he got his Ph.D. in Metaphysics. He has acquired fame and praise all over the world, winning many prizes, however for many people (including me) this place would be controversial.
Besides many temples in the city center of Chiang Rai, The Golden Clock Tower caught our attention. This Clock was designed by the same artist that designed the White Temple. We were forced to spend the rest of the afternoon in our apartment since it rained heavily. In the evening, we walked again to the night market to grab a delicious smoothie and to look for interesting souvenirs. We also discovered a very tasty, soft type of steamed bun called in Thailand Baozi or bao, filled with meat or taro but my favourite kind was with a sweet, yellow custard filling :)
Practical info:
- how we got here was described in the previous post, Luang Namtha
- entrance ticket to the White Temple - 50 THB per person, visiting time around 1h
- entrance ticket to the Baandam Museum (Black House) - 80 THB per person, visiting time around 1.5h
- bus ticket to the White Temple/Baandam Museum - 20 THB per person
- breakfast on the bus station - 40 THB per person (fried rice with egg)
- lunch/dinner - around 75 THB for both of us
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Mike Doe Last Update Date: 2/10/2014
- Status:
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- Toto Lara
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Download deployer.phar (6.0.5)
or download a different version
Run the following commands in the console:
curl -LO https://deployer.org/deployer.phar
mv deployer.phar /usr/local/bin/dep
chmod +x /usr/local/bin/dep
Or you can install it with composer:
composer require deployer/deployer
Deployer is packed with time-saving features and optimisations, best for performance and development.
Create your own deployment script based on our modular blocks called "recipes".
Deployer has very clean and well tested code. The repository is watched by many code quality tools, and every pull request is also checked by QA tools.
If something is wrong, a simple rollback to the previous release is possible.
Prepare codebase, warm cache, do other stuff, and then deploy them with symlinks!
Deployer can run tasks in parallel, no other extension is required!
Deployer keeps consistency between servers, even in parallel mode! If one task fails, subsequent tasks will not be executed on all servers.
If you are having problems, you can ask for help on our community chats.
Deployer is an open source project. If you want to support the development of Deployer visit our patreon page.


ObjectCreationTest
CreateAnonymousObjects
Results
CreateSimpleObjects
Results
CreateSimpleObjectsParams
Results
CreateZmEventObjects
Results


Ruth A. Melendez
Democrat
Special Education Teacher, Colorado Springs Charter Academy, Colorado Springs See attached resume for other teaching experience
B.S. - Elementary Education M.A. - Special Education
80919-1116
Democratic Caucus Lead - Fall, 2016 Campaign volunteer for Hillary Clinton - 2016
○ Volunteer, Marion Soup Kitchen, 2017-2018 ○ Volunteer for Colorado Springs CityServe - 2017-2019 ○ Trail clean-up volunteer - 2020
● Black Lives Matter peaceful protests - downtown Colorado Springs, Summer, 2020 ● During the 2016 presidential campaign, I was asked to be a caucus leader for a local precinct.
● I am White. I was fortunate to be married for 35 years to a Hispanic man. He brought to our marriage diverse thinking and embracing other ethnic groups. My grown son looks very much like his father. During his high school years, he faced moments of prejudice. We all faced a high school that was largely upper middle class white students, and were able to build bridges at the administrative level through open dialogue. ● I would be honored to work towards more positive discussions at the local and state level, reaching across party lines to develop consensus to create a fair map. It is critical this be done across our land.
I have been a public educator for 35 years. Twelve of the last years of my career were spent teaching in a Title One school. I learned how to work with parents of minority students. One of the powerful experiences I learned was these parents wanted nothing but the best for their child. Sometimes that was not overtly apparent because the parents were so busy trying to put food on the table and keep a roof over their family’s head. Understanding that motivated me to do what I could at school to educate their children. ● For two years, I worked at my district’s central office as a math coach. One of the controversial topics at the time was unpleasant conversations between a 5th grade elementary teacher and a middle school math teacher about the topic of students learning their math facts. (That may not sound important to some, but for 2 passionate educators, it was huge.) Ugly words were exchanged in several district wide committee meetings, and the Assistant Superintendent charged me with bringing the sides together. I used a discussion protocol that gave each person the opportunity to speak, ending with problem solving. Part of the protocol involved listening for understanding. As facilitator, I summarized both the teacher's viewpoint, and fortunately, we were able to move to problem solving. I believe my experiences of diving deeply into the Hispanic culture in my marriage, my work with students of color, and my “seek to understand” philosophy would be an asset to the commission.
The last 10 years of my teaching career emphasized data-driven instruction. Working alongside 2 teammates, we would analyze test results, form groupings, and plan instruction to meet the students’ needs. Analyzing and understanding data, as well as appropriate methodology was at the forefront of every planning discussion. This collective work would not have been possible with a high level of collaboration and teamwork.
Female


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Using Hourglass with the Beanstack mobile app can give you more insight into your child’s reading. View detailed stats about reading sessions, including badges earned, books completed, total time spent reading, and more. You can even switch between multiple family members’ accounts with a quick tap.


"The two-dimensional space Z3×Z3
contains nine points: (0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0),
(2,1), and (2,2). The 48 invertible 2×2 matrices over Z3
form the general linear group known as GL(2, 3). They act
on Z3×Z3 by matrix multiplication modulo
3, permuting the nine points. More generally, GL(n, p)
is the set of invertible n×n matrices over the field Zp,
where p is prime. With (0, 0) shifted to the center, the matrix
actions on the nine points make symmetrical patterns."
Citation data from Wolfram.com:
"GL(2,p) and GL(3,3) Acting on Points"
from The Wolfram Demonstrations Project,
http://demonstrations.wolfram.com/GL2PAndGL33ActingOnPoints/,
Contributed by: Ed Pegg Jr"
As
well as displaying Cullinane's 48 pictures of group actions from 1985,
the Pegg program displays many, many more actions of small finite
general linear groups over finite fields. It illustrates Cullinane's
1985
statement:
"Actions of GL(2,p) on a p×p
coordinate-array have the same sorts of symmetries, where p is
any odd prime."
Pegg's program also illustrates actions on a
cubical array-- a
3×3×3 array acted on by GL(3,3). For some other actions on
cubical arrays, see Cullinane's
Finite Geometry of the Square and Cube.


Content Management
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Jason Monberg (2010/03/01 1:04pm)
Created
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We provide highly personalized logistics services to help you meet the demands of your freight program. Every shipment we move is managed by seasoned professionals and experts who will serve you day-in and day-out by personally routing each and every shipment, handling contingencies, and consulting with you via phone or email.
We call this commitment to sonalized service and making your our esteemed client.
Excepteur ut occaecccat proident, sunt in culpa quis. Phasellus lacinia id erat risus quis.
Excepteur ut occaecat proident, sunt in culpa quis. Phasellus lacinia id erat risus quis.
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Excepteur ut occaecat proident, sunt in culpa quis. Phasellus lacinia id erat risus quis.
At Logistics, we continue to provide you wtih transoprt services and essentially tracking services from the manufacturer all the way to the consumer...
We manage high-volume, market-driven, retail products for industries that expect maximum performance and flexible infrastructure. We handle B2B and e-commerce fulfillment and distribution, supporting the top 100 retailers countrywide...
Our pharmaceutical warehouse facilities are fully licensed and accredited, maintaining cGMP standards and VAWD licensing, enabling us to provide compliant cold chain storage for finished and WIP pharmaceutical products...
We offer forms of commerce that are quickly altering traditional supply chains, by offering consumers the opportunity to make purchases via the web, mobile devices, catalogs and retail stores.
At Logistics, we believe in naturing our partners and client's business to grow to great proportion. That means providing services and options that will provide the seed which will result in overall turn around of the organization.
© 2019 Digital. All Rights Reserved | Design by Ness Inc.
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×Sed ut perspiciatis unde omnis iste asdasd asdaszzc scaca natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo quasi architecto beatae vitae dicta sunt explicabo.
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YouTube is a platform which promotes various young content developers to outsource their talent. YouTube Creator's Analytics aims to provide a tool which can record the number of users at different watch times in the video so that the youtubers can check their content engangement time for various users and to analyse whether their risky/intuitive element of YouTube worked or not?(By seeing the analytics, one can determine the graph between time points and number of users).
Made by Shubhendra Pal Singhal. This website is not an official website of YouTube.


{% extends "base.html" %} {% block content %}
Please scan the following QR code into your authentication device or manually enter the information below:


This tool was developed in accordance with the research paper titled- "Increase Apparent Public Speaking Fluency By Speech Augmentation"
Know more!   Use our tool!Either choose from following samples or upload a speech recording. Make sure to record with minimal background noise for best results. Wait for a while for the audio to get processed after uploading!


Welcome to my first webpage!
My name is Dinah A. Eric
This is my journey towards becoming a developer
Education:High school level
I interacted with details of Moringa School through the internet and developed an interest.


Description
NanoICQ is a small and highly portable ICQ library and client written in pure Python. Current version of GUI uses wxPython, based on wxWidgets C++ library.
Status
It's not yet released as standalone package, however you can download sources using CVS or SVN repositary to look at the current state.Development page
This is a NanoICQ default development page.Screenshots
A couple of screenhots of alpha version. Main window and message window.TODO
Main goals at this moment:- Complete buddy list handling when user changes his status
- Proxy support


In computational and experimental work, we know only a list of values h(tj) sampled at discrete values of tj. Let us suppose that h(t) is sampled with uniform interval between consecutive points:
|
| (113) |
The sampling rate is deﬁned by fs = 1∕Δ. The sampling theorem states that: If the Fourier transformation of function h(t), H(f), has the following property
|
| (114) |
then sampling h(t) with the sampling rate fs ≥ 2fc (i.e., Δ ≤ 1∕(2fc)) will completely determine h(t), which is given explicitly by the formula
|
| (115) |
We will not concern us here with the proof of the sampling theorem and simply start working with Eq. (115) to derive the concrete expression for the Fourier transformation of h(t). Substituting the expression (115) for h(t) into the Fourier transformation (104), we obtain the explicit form of the Fourier transformation of h(t):
With the help of Wolfram Mathematica, the integration in Eq. (116) is evaluated analytically, giving|
| (117) |
Using this, Eq. (116) is written
|
| (118) |
which shows that H(f) = 0 for |f| > fc, which is consistent with the assumption of sampling theorem, i.e., H(f) has the property given in Eq. (114). The second line of Eq. (118) is identical to Eq. (112) except that Eq. (118) in this case is exact while Eq. (112) is only approximate. In other words, if Δ ≤ 1∕(2fc), then the Fourier transformation is exactly given by Eq. (118), where fc is the largest frequency contained in h(t) (i.e., H(f) = 0 for |f| > fc).


As team leader, I am responsible for general DevOps and product deployment management. The natural overlap between leading a team and managing communication, morale, and scheduling resulted in my taking over the position of scrum master as well. This freed up more of my team members to focus on development tasks and not have to worry about the minutiae of the development process or interfacing with the client. While the development of tech-based products is new to my repertoire, I have a decade of experience in naval engineering, product line management, and scheduling of multiple platforms' equipment.


Hotel Reservation System
Home
Administrator
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Total $
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Expiration
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01
02
03
04
05
06
07
08
09
10
11
12
/
yy
17
18
19
20
21
Code
Name On Card
Billing Addr1
Checkout


| [[logo.html]] |
{{ meta_title }}
BC Registries and Online Services
|
| Renewal Registration Number: {{ renewalRegistrationNumber }} |
| Registration Date and Time: | {{createDateTime}} |
| Base Registration Number: | {{baseRegistrationNumber}} |
| Registration Life: | {% if lifeInfinite is defined and lifeInfinite %} Infinite {% elif expiryDate is defined and expiryDate == 'Never' %} Infinite {% elif lifeYears is not defined or lifeYears == 0 %} 180 Days {% elif lifeYears is defined %} {{lifeYears}} {% if lifeYears != 1 %} Years {% else %} Year {% endif %} {% endif %} |
| New Expiration Date and Time: | {% if lifeInfinite is defined and lifeInfinite %} Never {% else %} {{expiryDate}} {% endif %} |
{% if courtOrderInformation is defined %}
[[registration/courtOrderInformation.html]]
{% endif %}
[[registration/registeringParty.html]]


A dessert respected all over
Added by Larrystone
Priceless vegetable meal from Mali
Added by Rowland
Nothing taste better...
Added by Shayo
Everyone loves dodo!
One of the best Nigerian delicacy


Syllabus
PENNSYLVANIA DEPARTMENT OF CORRECTIONS
et al.
v.
YESKEY
Respondent Yeskey was sentenced to 18 to 36 months in a Pennsylvania correctional facility, but was recommended for placement in a Motivational Boot Camp for first-time offenders, the successful completion of which would have led to his parole in just six months. When he was refused admission because of his medical history of hypertension, he sued petitioners, Pennsylvanias Department of Corrections and several officials, alleging that the exclusion violated the Americans with Disabilities Act of 1990 (ADA), Title II of which prohibits a public entity from discriminating against a qualified individual with a disability on account of that disability, 42 U. S. C. §12132. The District Court dismissed for failure to state a claim, holding the ADA inapplicable to state prison inmates, but the Third Circuit reversed.
Held:<unicode value="8194"> State prisons fall squarely within Title IIs statutory definition of public entity, which includes any … instrumentality of a State … or local government. §12131(1)(B). Unlike the situation that obtained in Gregory v. Ashcroft, 501 U. S. 452, there is no ambiguous exception that renders the coverage uncertain. For that reason the plain-statement requirement articulated in Gregory, if applicable to federal intrusion upon the administration of state prisons, has been met. Petitioners attempts to derive an intent not to cover prisons from the statutory references to the benefits of programs, and to qualified individual are rejected; some prison programs, such as this one, have benefits and are restricted to qualified inmates. The statutes lack of ambiguity also requires rejection of petitioners appeal to the doctrine of constitutional doubt. The Court does not address the issue whether applying the ADA to state prisons is a constitutional exercise of Congresss power under either the Commerce Clause or the Fourteenth Amendment because it was addressed by neither of the lower courts. Pp. 26.
118 F. 3d 168, affirmed.
Scalia, J., delivered the opinion for a unanimous Court.


WITHOUT LIMITS (M). (New Line Cinema/Village Roadshow) Director: Robert Towne Stars: Billy Crudup, Donald Sutherland, Monica Potter, Jeremy Sisto, Matthew Lillard, Gabe Olds, Judith Ivey, Dean Norris, Billy Burke, Adam Setliff, Nicholas Oleson, William Mapowther Running time: 118 minutes.
For a long time, Tom Cruise has been interested in making a biopic about Steve Prefontaine, the champion American distance runner who was killed in a car crash at the age of 24. In the early pre- production stages, Cruise was even attached to the role. However, his involvement here is limited to that merely of producer.
Like James Dean, Prefontaine was something of a cult hero, a popular idol, who died tragically young while still at the start of his promising career. At the time of his death, Prefontaine remained unbeaten in America, and many of the records he set 25 years ago remain unbroken. The film touches on his clashes with the sport's governing body, his strong desire to win, and his crushing defeat in the Munich Olympics. His college romance with writer Mary Marckx (Monica Potter, from Patch Adams, etc) is the least interesting element of the film.
The most fascinating part of Without Limits centres on the complex and volatile relationship between the cocky, arrogant and strong willed Prefontaine and his brilliant coach and mentor Bill Bowerman (Donald Sutherland). Although the two men had different philosophies about how to run a distance race, they slowly established a mutual respect for each other's abilities. Without Limits opens during the 10,000 metres final at the 1972 Munich Olympics, and unfolds in flashback, seen mainly from Bowerman's perspective.
Writer/director Robert Towne, who last ventured into this territory with Personal Best, brings some genuine suspense to the running sequences. It is the numerous race sequences that bring some excitement and energy to this otherwise routine biopic.
Billy Crudup (from Inventing The Abbotts, etc) brings a brashness, vitality and raw sex appeal to his performance as the charismatic Prefontaine. So often cast as the villain or the dry authority figure, Sutherland rarely gets a chance to show what a superb performer he is when given a decent role. As Pre's coach he gets a rare opportunity to deliver a subtle and insightful performance, and he brings a spark to the film.
Without Limits is actually the second film to depict the life of the famous runner, following the lacklustre Pre, which disappeared straight to video. This stylish and superbly crafted biopic is very American in tone, and is ultimately more concerned with worshipping Prefontaine's achievements rather than offering personal insights into the man. It is also ultimately a story of particular appeal only to American audiences, with its international appeal limited mainly to those with an interest in athletics and sports biographies.
**1/2 greg king http://www.netau.com.au/gregking
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Featured work: Boop KIDS
TBA
Platform for pre-scholars and families! Boop Kids will have games, videos, cute interactions and animations, as well as all sorts of content for children and family alike.
Worked on the project from pre-production, and participated on the app orignal idea, flow and design. Was responsible for the avatar creation system as well as the marketing and social media materials, including product branding. Participated on the production and post-production of the first two animated espisodes of Boop Kids.
Take a look at Boop Kid’s Instagram account (work in progress).


This page is being reserved for a .js script to submit a photograph of a face. Using the trained TensorFlow library of images, the system should then make calculations determining what emotion is being displayed in the image.


Movies Disclaimer
Why?
Really two reasons for putting this list together:
- A co-worker mentioned that he has a friend that made the statement "No good movies were made before 2000". Another co-worker responded (later) with "were there any good movies made after 2000". This statement bugged me for a number of days until it boiled over into me making my own list.
- At the end of my life, I (probably) won't have a vast fortune to bequeath to my son. However, I do love (and am passionate) movies and would want my son to understand and at least recognize those movies which I consider good. He would probably say "buy me a hamburger and we'll call it even" but here goes...
Why isn't the list ranked by favorites?
On the internet, there are millions of lists ranking the best movies. My opinion is that, like music, people's tastes change on any number of factors: time of day, time of year, seasons, political climate, technology changes, etc. My feeling is that any list I created but be "stale" almost immediately. My goal was to simply list the movies that I liked and expect the agreements//disagreements that followed.
Criteria
The movies on this list had to meet the following criteria/rationale:
- All movies must be able to be watched/re-watched an infinite amount of times without loosing (significant) appreciation. Obviously, you can't (really) forget plot twists and surprise endings, but movies on my list can be watched over and over with little bits of nuance added each time they're watched.
- Movies on this list should be independent of the year they were made. They should be timeless. Humor, commentary, and content should span the passage of time. Some leniency (but not much) for the quality of special effects is granted.
- Some movies are included because they represent landmarks or game changers.


Location
Contact
Hours
Spartanburg Library - The Green Room
Practice yoga at the library every week at this instructor-led class!
Spartanburg Library - The Green Room
Practice yoga at the library every week at this instructor-led class!
Spartanburg Library - The Green Room
Practice yoga at the library every week at this instructor-led class!


RelProxy provides a simple approach for hot class reload for Java and Groovy
Source code of RelProxy and these examples are HERE


Input File Parquet
Input File Parquet as the name suggests accepts a parquet file as input source and is present under the Input category in the component palette.
This component is used to read a parquet file. It has a single output port.
The Input File Parquet component can be easily distinguished in the component palette.
An enlarged version of the same is depicted when dragged on the canvas.
For further reference click on the links below:


Fake asynchronous call anwsers in 2s after page load.
Open console and see logs or inspect window.dataLayer for more information


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{{< layout}} {{$pageTitle}}Check your photo and submit{{/pageTitle}} {{$header}}
Make sure:
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Your photo must:
Our automated check suggests
If you decide to use this photo we’ll check it manually. We’ll ask for another photo if something doesn’t look right.


Pizza-Empire is a pizza haven that would wills surely leave you coming back for more. With customer satisfaction a major priority in our business we make pizza delicasies that you will find nowhere else!
"Pizza-Empire, Where quality matters!"
We provide original mouth watering recipes that make you taste buds wiggle1
Pizza-Empire
Not only are our recipes original bu also are the pizza specials healthy with no that extra fat.
Pizza-Empire
Delivery is fast cause as we all know pizza is best served when hoot!
Pizza-Empire
Customers are our number 1 priority hence they are treated with utermost respect and proffesionalism.
Pizza-Empire
Pizza-Empire
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WITNESS LIST Human Services Committee February 19, 2001-2:00P HB 474 For: Bingham, Katherine (Associated Catholic Charities) Hagert, Celia (Center for Public Policy Priorities) Kratochvil, Susan (Self) Lashley, Barbara (Christ the Good Shepard Catholic Community) Sanders, Jan (TX IMPACT and Greater Dallas Community of Churches) Registering, but not testifying: For: Bower, Bruce (Texas Welfare Reform Organization) Cole, Pat (National Training Center on Domestic and Sexual Violence) Corrigan, Jennifer (Texas Council on Domestic Violence) Craven, Susan (Texans Care for Children) DelGallo, Krista (Self) Flynn, Charlotte (Gray Panthers) Johnson, Karen (United Ways of Texas) Rushing, Linda (Texas Catholic Conference) Smith, Clinton (Self and Austin Gray Panthers) Smith, Colleen (Self) On: Denton, Judy L. (TX Department of Human Services) HB 475 For: Bingham, Katherine (Associated Catholic Charities) Hagert, Celia (Center for Public Policy Priorities) Kratochvil, Sue (Self) Lashley, Barbara (Christ the Good Shepherd Catholic Community) Registering, but not testifying: For: Bower, Bruce (Texas Welfare Reform Organization) Cole, Pat (National Training Center on Domestic and Sexual Violence) Corrigan, Jennifer (Texas Council on Family Violence) Craven, Susan (Texans Care for Children) DelGallo, Krista (Self) Flynn, Charlotte (Gray Panthers) Johnson, Karen (United Ways of Texas) Rushing, Linda (Texas Catholic Conference) Smith, Clinton (Self and Austin Gray Panthers) Smith, Colleen (Self) On: Denton, Judy (TX Department of Human Services) HB 477 For: Bresette, Patrick (Center for Public Policy Priorities) On: Temple, Larry E. (TX Workforce Commission) Registering, but not testifying: For: Cole, Pat (National Training Center on Domestic and Sexual Violence) Corrigan, Jennifer (TX Council on Family Violence) Craven, Susan (Texans Care for Children) Flynn, Charlotte (Gray Panthers) Rushing, Linda (TX Catholic Conference) Smith, Clinton (Self and Austin Gray Panthers) Smith, Colleen (Self) On: Denton, Judy L. (TX Department of Human Services) HB 478 For: Bresette, Patrick (Center for Public Policy Priorities) Sanders, Jan (TX IMPACT and Greater Dallas Community of Churches) Registering, but not testifying: For: Corrigan, Jennifer (Tx Council on Family Violence) Craven, Susan (Texans Care for Children) Flynn, Charlotte (Gray Panthers) Rushing, Linda (Texas Catholic Conference) Smith, Clinton (Self and Austin Gray Panthers) Smith, Colleen (Self) On: Denton, Judy (TX Department of Human Services) HB 1213 For: Evans, David (Self) Horton, Colleen (Self and Tx Advocates Supporting Kids with Disabilities) Montague Kiely, Sharon (Self and Handle with Care, Inc) Murphree, Susan (Advocacy, Inc.) Registering, but not testifying: For: Borel, Dennis (Self and Coalition of Texas with disabilities) Cernoch, Jennifer (Self) Cole, Pat (Self) Daly, Richard (Self) Drake, Margaret (Self and Handle with Care, Inc.) Flynn, Charlotte (Gray Panthers) Hernandez, Leslie (National Association of Social Workers) Kafka, Bob (ADAPT of Texas) Lovelace, Joe (NAMI T X) Marshall, Susan (The Arc of Tx) McPherson, Kim (The Mental Health Association in Texas) Routon, Randy (Self) Schmitt, Cyndie (Tx Council of Community MHMR Centers, Inc.) Torres, Lorena (Self) On: Beattie, Terry (Heath and Human Services Commission) Cohen, Sharon (Tx Dept. of Health) Gold, Marc (Tx. Dept. of Health) O'Hanlon, Mary Beth (Health and Human Services Commission) Wagle, Reena (Tx Dept. of Health) HB 1218 For: Cadena, Adriana (Texas Immigrant and Refugee Coalition) Escobar, Felicia (National Council of La Raza) Hagert, Celia (Center for Public Policy Priorities) On: Denton, Judy L. (TX Department of Human Services) Registering, but not testifying: For: Corrigan, Jennifer (TX Council on Family Violence) Flynn, Charlotte (Gray Panthers) Rushing, Linda (Texas Catholic Conference) HB 1478 For: Murphree, Susan (Children's Long Term Care Policy Council) Registering, but not testifying: For: Flynn, Charlotte (Gray Panthers) Hooser, Greg (Private Providers Association of Texas) Horton, Colleen (TX Advocates Supporting Kids with Disabilities) Marshall, Susan (The Arc of Texas) Schmitt, Cyndie (TX Council of Community MHMR Centers, Inc.) On: Beattie, Terry (Health and Human Service Commission) Cohen, Sharon (Tx Dept. of Health) O'Hanlon, Mary Beth (Health and Human Services Commission) Wagle, Reena (Texas Dept. of Health)


To use the ON_RESET, ON_STOP, ON_GO, and IDLE_MODE features, you must define a set of memory accesses to perform on those event(s). The Mentor Embedded Sourcery Probe provides various access list descriptors for defining the access sequence for each case. The cmd_desc.maj command file provides primitives for describing these access sequences. The cmd_desc.maj file should be read in the target initialization command file for your board if you intend to use any of these features.
| Note | |
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Do NOT change the cmd_desc.maj command file. It is read during initialization to define the primitives you require to define access sequences appropriate for your target. |
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The commands to preset these sequences are added to the target initialization file. Each descriptor is a maximum of 16 words long, in the following format:
ew MEP_ON_STOP_CMD = en, { @op, addr [ , data [ , mask ] ] }...
ew MEP_ON_GO_CMD = en, { @op, addr [ , data [ , mask ] ] }...
ew MEP_ON_RESET_CMD = en, { @op, addr [ , data [ , mask ] ] }...
ew MEP_IDLE_MODE_CMD = int, { @op, addr [ , data [ , mask ] ] }...
Where:
Table 3-13. Descriptor Components
Examples
ew MEP_ON_RESET_CMD = 1, @$ucd_rd8, FFF00003
Defines an On-Reset command that reads the byte at 0xFFF00003 after the probe resets the target.
ew MEP_ON_STOP_CMD = 1, @$ucd_rmw16, 80000000, C00, F00
ew MEP_ON_GO_CMD = 1, @$ucd_rmw16, 80000000, 300, F00
Defines On-Stop and On-Go commands that read a 16-bit value from 80000000, masks off bits 11..8, sets those bits to 1100 (On-Stop) or 0011 (On-Go), then writes the result back to 80000000. Other bits in that register are not changed by these commands.
ew MEP_IDLE_MODE_CMD = 0n250, @$ucd_wr32, 0x40000000, 0n5000
Defines an Idle-Mode command that writes the 32-bit value 5000 (decimal) to the register at 0x40000000 every 250 milliseconds.
ew MEP_ON_RESET_CMD = 0
ew MEP_ON_STOP_CMD = 0
ew MEP_ON_GO_CMD = 0
ew MEP_IDLE_MODE_CMD = 0
Disables the On-Reset, On-Stop, On-Go, and Idle-Mode command descriptors.


CashMoneyAP Drops "All I Know" With Rich The Kid & Stunna 4 Vegas
CashMoneyAP releases new heat with Rich The Kid and Stunna 4 Vegas on the track.
by Arielle LondonAll I Know
CashMoneyAP Feat. Rich The Kid & Stunna 4 Vegas
Editor Rating: HOTTTTT
User Rating: n/a - 0% (0)
You know when you hear that CashMoneyAP tag at the beginning of a song you're in for something dope. Chances are dancing, downloading and adding the song to your playlist could be involved. Well "All I Know" is no exception. The newly released single is easy listening meets hype. CashMoneyAP's production with Rich The Kid and Stunna 4 Vegas rhyming over the beat is solid artistry combined. Both rappers flow with confidence over the talented producer's musical creation to give listeners some fire before the weekend.
The single artwork gives you an idea of the content of the song with a baddie in a mask throwing up money in the air. Getting money is at the root of the song and well, everything if we're being honest. Listen to the new track below.
Quotable Lyrics
Getting to the money cause that's all I know (What)
I can't trust these n****s and they sell my soul (My soul)
Racking up these hunnids they don't never fold (Fold)
N****s they be lying, you already told (Woo)


{% extends "twiglet/showcases/base_showcase_popup.html" %} {% block title %}Peter Sestoft's disclaimer{% endblock %} {% block close %}{% endblock %} {% block bodycontent %}
There is no license given with the code, just the following announcement that you can find on that page.


Easily access to all posts for the JSON representation available at https://jsonplaceholder.typicode.com/posts/


This is the default room. If any mobile user joins, your will lose control and this app will mirror that mobile user's screen. Click to join a random room.


This is a demo of Ultimate ResT API (URSA - the bear). URSA is composed of two components:
-
.net server side ReST API alternative with self documentation abilities base on
HYpermedia DRiven Application (Hydra) Core Vocabulary.
-
angularJS client side ReST API that utilises an API documentation generated by the server side,
builds necessary views on the fly and runs the machiner in motion.
If asked for credentials you can use these:
- User name
- guest
- Password
- guest


Schtromm.github.io
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