Upon completion of this unit, you will be able to:
The following topics will be covered in this unit:
The format of the docker events command is:
docker events [options]
The following config options are available for docker events:
| Client Option | Description |
|---|---|
--since="" |
Display events since specified time |
--until="" |
Display events until specified time in future |
-f, --filter=[] |
Apply a filter to the events retrieved |
| Container Event | Description |
|---|---|
archive-path |
Files copied (as a tar archive) from a container to the host |
attach |
A client attaches to a running container |
commit |
An image is created from a container's filesystem |
create |
A container is created by the server |
destroy |
A container is removed by the server |
die |
A container stops (with or without error) |
exec_create |
An exec command is created for a running container |
exec_start |
An exec command is started in a running container |
export |
A container's filesystem is exported |
extract-to-dir |
Files are copied (as a tar archive) from the host to a container |
| Container Event | Description |
|---|---|
kill |
The server kills a running container |
oom |
A container exits due to being out of memory |
pause |
The server pauses a running container |
rename |
A container is renamed |
resize |
TTY for container process is resized |
restart |
A container is restarted |
start |
A container is started |
stop |
A container is sent the stop signal |
top |
Processes running in the container are listed |
unpause |
The server unpauses a running container |
| Image Event | Description |
|---|---|
delete |
An image layer is deleted from the local repository |
import |
An image is imported into a local repository |
pull |
An image is pulled from a registry |
push |
An image is pushed to a registry |
tag |
An image is tagged |
untag |
An image is untagged |
The format of the docker inspect command is:
docker inspect [options] container|image [container|image ...]
The docker inspect command allows for the retrieval of configuration data for containers and images
The following config options are available for docker inspect:
| Client Option | Description |
|---|---|
-f, --format=”” |
Golang text/template to apply to filter the data objects |
--type=container|image |
Specifies whether object is a container or an image |
-s, --size=false |
When type is container, return the size of its filesystem |
Retrieve the author of the busybox image:
# docker inspect -f '{{.Author}}' busybox
Jérôme Petazzoni<EMAIL_ADDRESS>Retrieve the network mode for the container with ID c263b0c06a99:
# docker inspect -f '{{.HostConfig.NetworkMode}}' c263b0c06a99
default
List all port bindings for the container with ID c263b0c06a99:
# docker inspect -f '{{range $p, $c := .NetworkSettings.Ports}} {{$p}} \
-> {{(index $c 0).HostPort}} {{end}}' c263b0c06a99
4100/tcp -> 49158 4200/tcp -> 49159
The format of the docker logs command is:
docker logs [options] container
The docker logs command only works with the json-file and journald logging drivers
The following config options are available for docker logs:
| Client Option | Description |
|---|---|
-f, --follow=false |
Follow the container's logs in real-time |
--since=false |
Display a container's logs from specific point in time |
-t, --timestamps=false |
Prepend a timestamp for each log entry |
--tail=”all” |
Display defined number of log entries from the end of the log |
The format of the docker stats command is:
docker stats [option] container [container ...]
The following config option is available for docker stats:
| Client Option | Description |
|---|---|
--no-stream=false |
Don't display real-time stats, return first sample |
Example docker stats output:
CONTAINER CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O
94580bf8e0e8 0.00% 0 B / 0 B 0.00% 0 B / 0 B 0 B / 0 B
nginx 0.03% 3.228 MB / 4.143 GB 0.08% 7.929 kB / 3.386 kB 127 kB / 0 B
node1 0.00% 35.91 MB / 4.143 GB 0.87% 9.586 kB / 1.809 kB 0 B / 0 B
node2 0.00% 37.81 MB / 4.143 GB 0.91% 10.95 kB / 1.559 kB 0 B / 4.096 kB
node3 0.00% 34.2 MB / 4.143 GB 0.83% 9.325 kB / 1.018 kB 0 B / 0 B
redis 0.18% 3.24 MB / 4.143 GB 0.08% 9.659 kB / 7.34 kB 28.67 kB / 0 B
The format of the docker wait command is:
docker wait container [container ...]
The format of the docker ps command is:
docker ps [options]
The following config options are available for docker ps:
| Client Option | Description |
|---|---|
-a, --all=false |
Display all containers irrespective of state |
-l, --latest=false |
Display the last created container |
-n=-1 |
Display the last 'n' created containers |
-s, --size=false |
Display the size of the container filesystem |
-q, --quiet=false |
Only display the truncated ID of containers and nothing else |
--no-trunc=false |
Use whole 256 digit container ID and links in output |
--before=”” |
Only display containers created before specified container |
--since=”” |
Only display containers created after specified container |
-f, --filter=[] |
Apply filter to output in key/value pair form |
--format=[] |
Format output based on Go text/template |
The docker ps output can be filtered according to:
| Filter Option | Description |
|---|---|
| id or name | Output filtered by container id or name |
| label | Label key or key/value pair provide filter |
| exited | Container exit status filter option |
| status | Current state of container provides filter |
| ancestor | Filtering based on ancestral image of container |
Combined filtering and formatting docker ps example:
$ docker ps -a -f status=running --format “table {{.Names}}:\t{{.Image}}\t{{.Status}}”
NAMES IMAGE STATUS
nginx: dockerworkflow_nginx Up About an hour
node1: dockerworkflow_node1 Up About an hour
node3: dockerworkflow_node3 Up About an hour
node2: dockerworkflow_node2 Up About an hour
redis: redis Up About an hour
The format of the docker diff command is:
docker diff container
The docker diff command details any file changes made to the container since it was created:
The format of the docker top command is:
docker top container [ps options]
Displays the process(es) running inside the container
Options associated with the 'ps' command can be passed to format output:
$ docker top 40565f5d2b81 -e
PID TTY TIME CMD
11101 ? 00:00:28 grunt
The format of the docker port command is:
docker port container [port[/tcp|udp]]
The docker port command provides the container to host port mappings
A docker port example:
$ docker port 4d9496628cad
4100/tcp -> <IP_ADDRESS>:49158
4300/tcp -> <IP_ADDRESS>:49157
The formats of the docker cp command are:
docker cp container:path hostpath|-
docker cp hostpath|- container:path
Some docker cp examples:
$ docker cp 199cfb97c339:/etc/nginx/nginx.conf .
$ docker cp ./nginx.conf 199cfb97c339:/etc/nginx/
$ docker cp 199cfb97c339:/data - | tar -xvf -
$ cat data.tar | docker cp - 199cfb97c339:/data


By including this file, all library files listed in the variable CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS will be installed with install(PROGRAMS ...) into bin for WIN32 and lib for non-WIN32. If CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS_SKIP is set to TRUE before including this file, then the INSTALL command is not called. The user can use the variable CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS to use a custom install command and install them however they want. If it is the MSVC compiler, then the microsoft run time libraries will be found and automatically added to the CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS, and installed. If CMAKE_INSTALL_DEBUG_LIBRARIES is set and it is the MSVC compiler, then the debug libraries are installed when available. If CMAKE_INSTALL_DEBUG_LIBRARIES_ONLY is set then only the debug libraries are installed when both debug and release are available. If CMAKE_INSTALL_MFC_LIBRARIES is set then the MFC run time libraries are installed as well as the CRT run time libraries. If CMAKE_INSTALL_OPENMP_LIBRARIES is set then the OpenMP run time libraries are installed as well. If CMAKE_INSTALL_SYSTEM_RUNTIME_DESTINATION is set then the libraries are installed to that directory rather than the default. If CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS_NO_WARNINGS is NOT set, then this file warns about required files that do not exist. You can set this variable to ON before including this file to avoid the warning. For example, the Visual Studio Express editions do not include the redistributable files, so if you include this file on a machine with only VS Express installed, you’ll get the warning.


Gateway
BOT
31-Oct-18 06:01 PM
[dscrdvrsc] <jimboscott> Hey - what does a cheat catcher payment look like in the GUI wallet?
[dscrdvrsc] <jimboscott> I think I got one, but not certain.
[dscrdvrsc] <jimboscott> The end result was a 24 VRSC block to a T address that has never been used in my wallet.
[dscrdvrsc] <dukeleto> if you don't have a -pubkey set, all block rewards go to a new taddr
[dscrdvrsc] <jimboscott> But I thought all staking rewards went to the address that holds the staking UTXO?
[dscrdvrsc] <jimboscott> I am not mining. Just staking and set up for cheat catcher blocks.
[dscrdvrsc] <jimboscott> So, this was a cheat catch it would seem...
[dscrdvrsc] <mavenL> @jimboscott known bug, check #community-support
[dscrdvrsc] <mavenL> Staking reward right now create new t-address
[dscrdvrsc] <jimboscott> But, the staking reward also involved a Z address as well...
[dscrdvrsc] <jimboscott> Never seen that before
[dscrdvrsc] <jimboscott> I cannot upgrade right now - OK to leave alone for a few hours?
[dscrdvrsc] <jimboscott> Why would a normally staked block get a Z address involved?


Who Was Voted Off 'Dancing With The Stars' Season 28 Week #10 Here? Find Out Here
We’re crying over who went home from Dancing With The Stars season 28 this week.
The seventh elimination of the competition happened just moments ago and was determined from the judges’ scores, and the weekly live-viewing audience vote tonight, following a ton of dances from the semi-finalists.
Fans were able to vote online at ABC.com, on the ABC app, and through text.
The five remaining celebs left in the competition are Lauren Alaina, Ally Brooke, Hannah Brown, Kel Mitchell, and James Van Der Beek.
Click inside to see which couple went home….
ACTOR JAMES VAN DER BEEK & EMMA SLATER
Supremes singer Mary Wilson, footballer Ray Lewis, basketball player Lamar Odom, model Sailor Brinkley-Cook, Karamo Brown, Kate Flannery, and Sean Spicer left the competition in the previous weeks.


# Modified by Microsoft Corporation.
# Licensed under the MIT license.
# Various math calculations used by algorithms
import numpy as np
import torch
# general math methods
[docs]def normalize(v):
'''Method to normalize a rank-1 np array'''
v_min = v.min()
v_max = v.max()
v_range = v_max - v_min
v_range += 1e-08 # division guard
v_norm = (v - v_min) / v_range
return v_norm
[docs]def standardize(v):
'''Method to standardize a rank-1 np array'''
# assert len(v) > 1, 'Cannot standardize vector of size 1'
if len(v) == 1:
return v
v_std = (v - v.mean()) / (v.std() + 1e-08)
return v_std
[docs]def to_one_hot(data, max_val):
'''Convert an int list of data into one-hot vectors'''
return np.eye(max_val)[np.array(data)]
[docs]def venv_pack(batch_tensor, num_envs):
'''Apply the reverse of venv_unpack to pack a batch tensor from (b*num_envs, *shape) to (b, num_envs, *shape)'''
shape = list(batch_tensor.shape)
if len(shape) < 2: # scalar data (b, num_envs,)
return batch_tensor.view(-1, num_envs)
else: # non-scalar data (b, num_envs, *shape)
pack_shape = [-1, num_envs] + shape[1:]
return batch_tensor.view(pack_shape)
[docs]def venv_unpack(batch_tensor):
'''
Unpack a sampled vec env batch tensor
e.g. for a state with original shape (4, ), vec env should return vec state with shape (num_envs, 4) to store in memory
When sampled with batch_size b, we should get shape (b, num_envs, 4). But we need to unpack the num_envs dimension to get (b * num_envs, 4) for passing to a network. This method does that.
'''
shape = list(batch_tensor.shape)
if len(shape) < 3: # scalar data (b, num_envs,)
return batch_tensor.view(-1)
else: # non-scalar data (b, num_envs, *shape)
unpack_shape = [-1] + shape[2:]
return batch_tensor.view(unpack_shape)
# Policy Gradient calc
# advantage functions
[docs]def calc_returns(rewards, dones, gamma):
'''
Calculate the simple returns (full rollout) i.e. sum discounted rewards up till termination
'''
T = len(rewards)
rets = torch.zeros_like(rewards)
future_ret = torch.tensor(0.0, dtype=rewards.dtype)
not_dones = 1 - dones
for t in reversed(range(T)):
rets[t] = future_ret = rewards[t] + gamma * future_ret * not_dones[t]
return rets
[docs]def calc_nstep_returns(rewards, dones, next_v_pred, gamma, n):
'''
Calculate the n-step returns for advantage. Ref: http://www-anw.cs.umass.edu/~barto/courses/cs687/Chapter%207.pdf
Also see Algorithm S3 from A3C paper https://arxiv.org/pdf/1602.01783.pdf for the calculation used below
R^(n)_t = r_{t} + gamma r_{t+1} + ... + gamma^(n-1) r_{t+n-1} + gamma^(n) V(s_{t+n})
'''
rets = torch.zeros_like(rewards)
future_ret = next_v_pred
not_dones = 1 - dones
for t in reversed(range(n)):
rets[t] = future_ret = rewards[t] + gamma * future_ret * not_dones[t]
return rets
[docs]def calc_gaes(rewards, dones, v_preds, gamma, lam):
'''
Calculate GAE from Schulman et al. https://arxiv.org/pdf/1506.02438.pdf
v_preds are values predicted for current states, with one last element as the final next_state
delta is defined as r + gamma * V(s') - V(s) in eqn 10
GAE is defined in eqn 16
This method computes in torch tensor to prevent unnecessary moves between devices (e.g. GPU tensor to CPU numpy)
NOTE any standardization is done outside of this method
'''
T = len(rewards)
assert T + 1 == len(v_preds) # v_preds includes states and 1 last next_state
gaes = torch.zeros_like(rewards)
future_gae = torch.tensor(0.0, dtype=rewards.dtype)
# to multiply with not_dones to handle episode boundary (last state has no V(s'))
not_dones = 1 - dones
for t in reversed(range(T)):
delta = rewards[t] + gamma * v_preds[t + 1] * not_dones[t] - v_preds[t]
gaes[t] = future_gae = delta + gamma * lam * not_dones[t] * future_gae
return gaes
[docs]def calc_q_value_logits(state_value, raw_advantages):
mean_adv = raw_advantages.mean(dim=-1).unsqueeze(dim=-1)
return state_value + raw_advantages - mean_adv
# generic variable decay methods
[docs]def no_decay(start_val, end_val, start_step, end_step, step):
'''dummy method for API consistency'''
return start_val
[docs]def linear_decay(start_val, end_val, start_step, end_step, step):
'''Simple linear decay with annealing'''
if step < start_step:
return start_val
slope = (end_val - start_val) / (end_step - start_step)
val = max(slope * (step - start_step) + start_val, end_val)
return val
[docs]def rate_decay(start_val, end_val, start_step, end_step, step, decay_rate=0.9, frequency=20.):
'''Compounding rate decay that anneals in 20 decay iterations until end_step'''
if step < start_step:
return start_val
if step >= end_step:
return end_val
step_per_decay = (end_step - start_step) / frequency
decay_step = (step - start_step) / step_per_decay
val = max(np.power(decay_rate, decay_step) * start_val, end_val)
return val
[docs]def periodic_decay(start_val, end_val, start_step, end_step, step, frequency=60.):
'''
Linearly decaying sinusoid that decays in roughly 10 iterations until explore_anneal_epi
Plot the equation below to see the pattern
suppose sinusoidal decay, start_val = 1, end_val = 0.2, stop after 60 unscaled x steps
then we get 0.2+0.5*(1-0.2)(1 + cos x)*(1-x/60)
'''
if step < start_step:
return start_val
if step >= end_step:
return end_val
x_freq = frequency
step_per_decay = (end_step - start_step) / x_freq
x = (step - start_step) / step_per_decay
unit = start_val - end_val
val = end_val * 0.5 * unit * (1 + np.cos(x) * (1 - x / x_freq))
val = max(val, end_val)
return val


{% extends 'blog/base/auth.html' %} {% block title %} Login - {{ block.super }} {% endblock title %} {% block content %}
Your username and password didn't match. Please try again.
{% endif %} {% if next %} {% if user.is_authenticated %}Your account doesn't have access to this page. To proceed, please login with an account that has access.
{% else %}Please login to see this page.
{% endif %} {% endif %} {% endblock content %}


MEN IN BLACK (Columbia) Starring: Tommy Lee Jones, Will Smith, Linda Fiorentino, Vincent D'Onofrio, Rip Torn. Screenplay: Ed Solomon, based on the comic book by Lowell Cunningham. Producers: Walter F. Parkes and Laurie MacDonald. Director: Barry Sonnenfeld. MPAA Rating: PG-13 (profanity, violence) Running Time: 98 minutes. Reviewed by Scott Renshaw.
If BATMAN & ROBIN arrived as the summer film everyone was ready to hate, MEN IN BLACK arrived as the film everyone was ready to love. This was to be the antidote to a season filled with moribund sequels and lackluster high concepts, a cinematic life preserver for movie-goers drowning in over-promoted and under-developed bilge water blockbusters. It would bring together an appealing rising star, a savvy veteran character actor, an idiosyncratic director, a clever concept and fun visual effects. This, at last, would be something worth standing in line for.
MEN IN BLACK the movie -- as opposed to MEN IN BLACK the much-anticipated event -- isn't worth standing in line for. It's a frisky, fairly enjoyable summer fantasy, one which avoids the common big-budget trap of over-staying its welcome. At the same time, it's a bit disappointing, and for an unexpected reason. MEN IN BLACK has been edited to the point where scenes collide awkwardly, where attempts at sub-plots cry out for more development. It's a film which actually _under_-stays its welcome.
That says a lot for what is on the screen for just over 90 minutes. The story, based on the comic book series by Lowell Cunningham, deals with a top-secret government agency called the Men in Black. Their responsibility is overseeing the earth's alien population -- alien, as in the extra-terrestrial kind -- as well as keeping the existence of those aliens secret from the general population. Tommy Lee Jones plays a veteran MiB known only as Agent K who brings in New York cop James Edwards (Will Smith) as a new recruit. The newly-christened Agent J soon discovers that he has come aboard at a particularly bad time, as one foul-tempered alien visitor arrives on a mission which could spell the destruction of the planet.
When director Barry Sonnenfeld plays that premise for its most outrageous and witty comic possibilities, MEN IN BLACK is a whole lot of fun. Jones and Smith have a solid rapport in a variation on the standard buddy-cop pairing, turning in performances which are knowing without winking too obviously at the audience. Their supporting cast is sharp as well, particularly Vincent D'Onofrio in a hilarious turn as the sadistic alien Bug who inhabits the skin of a redneck farmer; D'Onofrio's physical movements are even more hideously other-worldly than Rick Baker's make-up effects. The grandest, goofiest moments involve MiB Headquarters, rendered by Bo Welch's production design and Eric Brevig's visual effects as a cross between Kennedy Airport, Ellis Island and Mos Eisley Spaceport. When a bank of television monitors show "known aliens" including Newt Gingrich, Dionne Warwick and Sylvester Stallone, you know that MEN IN BLACK will proceed with tongue firmly in cheek.
That wry charm, along with the collected talent in MEN IN BLACK, should have been enough to justify the hype. And so they might have been, if Sonnenfeld had put together a film with a momentum to match its moments. The plot races from place to place so quickly that you're never given a chance to piece it together. K's recruitment of J is handled awkwardly, attempts at creating an emotional core in K's pining over his lost love feel rushed and unnecessary, and Linda Fiorentino (as a coroner caught up in the alien shenanigans) never develops a character. As a collection of amusing individual scenes, MEN IN BLACK is just fine. It just never makes sense when precious minutes are devoted to half-hearted attempts at character development.
It's not fair to call MEN IN BLACK unsatisfying, because there's a lot of great stuff in it. It does, however, feel unfinished; it's great stuff not yet formed into a great movie. More to the point, it functions best as a set-up for -- oh bitter irony -- yet another big-budget summer sequel. Only when compared to its current competition is it the summer masterpiece everyone was ready for it to be.
On the Renshaw scale of 0 to 10 black operations: 6.
Visit Scott Renshaw's MoviePage http://www-leland.stanford.edu/~srenshaw Subscribe to receive reviews directly via email
"'Elitist' [is] a word _meaningless_ in a profession that tries to seek out and spotlight the best. 'Elite' is from the Old French root eslire, to choose, and choosing is what a critic does for a living. Calling a critic 'elitist' is like calling an accountant 'mathist.'" --Richard Von Busack
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


Frequently Asked Questions
- Is it good practice to use a pack as an array?
- Why is the speed-up of my code not as expected?
- My code seg-faulted or crashed
- I tried to use a comparison operator and my code failed to compile
- How do I measure the speed-up of my code
- My AVX code is not twice as fast as SSE
- I disassembled my code, and the generated code is less than optimal.
- How can I use a certain intrinsic?
Is it good practice to use a pack as an array?
The main element of Boost.SIMD is the boost::simd::pack class. pack is an abstraction over a block of N
elements of type T
, quite similar to std::array
. The main semantic difference is that boost::simd::pack is implemented as the best hardware specific type able to store this amount of data which may be a simple scalar array, a single SIMD register or a tuple of SIMD registers depending on N
and T
.
In general, the good practice is to store data in regular, dynamic allocated data block and apply your algorithms over those data using pack and related functions.
Why is the speed-up of my code not as expected?
There are several factors which can reduce the speed-up obtained using Boost.SIMD.
Have you enabled compiler optimizations? You must enable all compiler optimizations as well as defining the
NDEBUG
flagCompiler Flag g++ -O3 clang++ -O3 Intel C++ Compiler -O3 Microsoft Visual Studio Enable Release mode - Have you compiled in 64bit mode? There is significant performance increase on architectures supporting 64 bit binaries.
Is you algorithm memory bound? Computational problems can often be classed as either compute-bound or memory-bound. Memory bound problems reach the limits of the system bandwidth transfering data between the memory and the processor, while compute-bound problems are limited by the processor's calculation ability.
Processors have often several hierarchies of caches. The time taken to transfer data from the cache to a register varies depending on the cache. When the required data is not in the cache, it is referred to as a cache miss. Cache-misses are very expensive. Data is transferred from memory to the cache in cache-lines sized chunks. On modern x86 machines, a cache-line is 64 bytes or twice the size of an AVX register. It is therefore highly advantageous to use all data loaded into cache.
The following loop is clearly memory bound as all of the time is spent loading and storing the data. The cost of the computation is negligible compared to that of the memory accesses.
for (int i = 0; i < size; i += pack_t::static_size) {pack_t v0(&data[i]);v0 = v0 * 2;bs::aligned_store(v0, &output[i]);}The following loop is compute-bound. As most of the time is spent calculating exp, significant speed-up is observed when this code is vectorized.
t0 = high_resolution_clock::now();for (int i = 0; i < size; i += pack_t::static_size) {pack_t v0(&data[i]);bs::aligned_store(v0, &output[i]);}- Is your code trivially vectorizable? Modern compilers can vectorize trivial code segments automatically. If you benchmark a trivial scalar code versus a vectorized code, the compiler may vectorize the scalar code, thereby giving similar performance to the vectorized version.
- Is your algorithm vectorizable?
- Have you used the fastest version of your function? Several Boost.SIMD functions, particularly more complex ones, come in several versions, where the user has a choice between accuracy and speed. This is achieved using decorators. Please refer to Decorators for more information.
Why did my code seg-faulted or crashed?
The most common cause of seg-faults in SIMD codes is accessing non-aligned memory. For best performance, all memory should be aligned on pack_t::alignment bytes. Boost.SIMD includes an aligned memory allocator to help you with this. Please refer to Memory Alignment for details on how to ensure that you memory is correctly aligned.
I tried to use a comparison operator and my code failed to compile
The most common reason for this is that the two packs being compared are not of the same type. Another common reason is that the return type is incorrect. Using auto is one way of preventing this error, however, it is best to be aware of the types you are using. Comparison operators in Boost.SIMD are of two types, either vectorized comparison, where the results is a vector of logical with the same cardinal as the input vectors, or a reduction comparison, where the result is a bool.
How to measure the speed-up of SIMD code?
There are several ways to measure the speed-up of your code. You may use the Boost.SIMD bench system to benchmark your code segment. This allows you to measure the execution time of your code in cycles per element or in units of time.
Otherwise, you may use standard timing routines such as those available in std::chrono
. In order to accurately benchmark your code, there are several points to consider.
- Your input should be sufficiently large. This is to eliminate cache effects.
- Your code is compiled in release mode, with all optimizations enabled and debug information not included.
- You should measure several times and use the average (or better the median).
A typical case where a benchmark could give inaccurate results is where the input is not large enough to fill the cache and a scalar and SIMD code segment are individually benchmarked, one after the other. In this case, all of the data will be loaded into the cache during the first bench, and will be available for the second bench, therefore decreasing the execution time of the second bench. Also, if you measure the code segment multiple times, the data from the first execution will already be in the cache.
My code compiled for AVX is not twice as fast as for SSE
Not all SSE instructions have an equivalent AVX instruction. Also, the cycles required for certain instructions are not equal on both architectures, for example, sqrt on SSE requires 10-14 cycles whereas sqrt on AVX requires 21-28 cycles. Please refer here for more information.
Very few integer operations are supported on AVX, AVX2 is required for most integer operations. If a Boost.SIMD function is called on an integer AVX register, this register will be split into two SSE registers and the equivalent instruction called on both register. In the case, no speed-up will be observed compared with SSE code. This is true also on Altivec, where double is not supported.
I disassembled my code, and the generated code is less than optimal.
- Have you compiled in release mode, with full optimizations with DNDEBUG defined?
- Have you used a 64 bit compiler?
- There are many SIMD related bugs across all compilers, and some compilers generate less than optimal code in some cases. Is it possible to update your compiler to a more modern compiler?
- We provide workarounds for all known compiler bugs, however, we may have missed some. You may also have found a bug in Boost.SIMD. Please report this through issues on our github with a minimal code example. We responds quickly to bug reports and do our best to patch them as quickly as possible.
How can I use a certain intrinsic?
If you require a certain intrinsic, you may search inside of Boost.SIMD for it and then call the relevant function.
In rare cases, the intrinsic may not be included in Boost.SIMD as we map the intrinsic wherever it makes sense semantically. If a certain intrinsic does not fit inside of this model, if may be excluded. In this case, you may call it yourself, however, this will not be portable.


| Methods' Details |
| attachNumberFormatsSupplier
|
| getNumberFormatsSupplier
|
detectNumberFormat
- Description
- detects the number format in a string which contains a formatted number.
|
|
convertStringToNumber
| double |
| convertStringToNumber( |
[in] long |
nKey, |
| [in] string |
aString ) |
| raises( |
NotNumericException ); |
- Description
- converts a string which contains a formatted number into a number.
If this is a text format, the string will not be converted.
|
|
convertNumberToString
| string |
| convertNumberToString( |
[in] long |
nKey, |
| [in] double |
fValue ); |
- Description
- converts a number into a string.
|
|
queryColorForNumber
| Color |
| queryColorForNumber( |
[in] long |
nKey, |
| [in] double |
fValue, |
| [in] Color |
aDefaultColor ); |
- Returns
- the color which is specified for the given value in the number format,
which is otherwise the value of aDefaultColor.
|
|
formatString
| string |
| formatString( |
[in] long |
nKey, |
| [in] string |
aString ); |
- Description
- converts a string into another string.
|
|
queryColorForString
| Color |
| queryColorForString( |
[in] long |
nKey, |
| [in] string |
aString, |
| [in] Color |
aDefaultColor ); |
- Returns
- the color which is specified for the given string in the number format,
which is otherwise the value of aDefaultColor.
|
|
getInputString
| string |
| getInputString( |
[in] long |
nKey, |
| [in] double |
fValue ); |
- Description
- converts a number into a string with the specified format.
This string can always be converted back to a number using the same
format.
|
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


{% extends 'base2.html' %} {% load staticfiles %} {% block content %}
Call Us :<PHONE_NUMBER>
Cancer is one of the scariest words in the English language. When you hear the word as part of a diagnosis, it's natural to feel many emotions, especially fear.
A cancer diagnosis can cause you and your family a great deal of stress, but you have many resources to help you. You owe it to yourself to learn as much as possible about your diagnosis and how it can be treated. Knowledge is power, and it can help you deal with this disease.
Many factors can cause the development of cancer in the body. Some of these factors, such as heredity (family members who have the disease) cannot be avoided. Others, such as lifestyle, can be controlled.
For instance, the use of tobacco is one of the main causes of cancer, especially lung cancer. Tobacco use, whether in the form of smoking, chewing, or exposure to second-hand smoke (smoking by others), can also cause cancer of the mouth and larynx, esophagus, throat, and many other parts of the body.


{% extends "base.html" %} {% block styles%} {{super()}} {% endblock %} {% block content %}
{{user.username}} has not uploaded an image or written any article
{{post.content[0:200] + "..."}}


Pax Shiro Role Test
has role admin.
lacks role admin.
has role goodguy or darklord.
has role admin.
lacks role admin.
has role goodguy or darklord.


ABOUT ME
I'm Thor, a web developer and designer living in the Bay Area, CA. I graduated from Reed College in 2016 and recently completed UC Berkeley Extension's full-stack coding bootcamp. I'm a passionate learner, and always looking for new ways to expand my knowledge and skill-set.
I'm interested in technology, design, philosophy, politics, teaching, and writing. I appreciate being able to work closely and creatively with people to come up with ideal strategies and solutions.
When I'm not coding, I like to rock climb at my local climbing gym. I love to travel, and have spent significant time living and exploring in other countries. I also built my desktop PC that I use for playing games or working with 3D rendering software like the Unity engine.
I am available for freelance work, so if you have a web project that's still in the idea phase send me an email and we can get started!
"Fast, affordable, and true to our specifications. Thor took our very old office site and dragged it into the 21st Century adding analytics, making it mobile-friendly, polishing the look and feel, but keeping it simple, as requested. It was like a window-washing in an old house you just can't leave. On another site, Thor supplied us with spot-on guidance to build a site aimed at eliciting public response for a study project. We love his fast, efficient, time on task service." — Brancart & Brancart, client
SKILLS
- keyboard_arrow_right React.js
- keyboard_arrow_right Semantic HTML5
- keyboard_arrow_right CSS3 and SCSS
- keyboard_arrow_right Javascript ES6/7+
- keyboard_arrow_right jQuery
- keyboard_arrow_right Bootstrap and Materialize
- keyboard_arrow_right Node.js
- keyboard_arrow_right NPM and a variety of packages
- keyboard_arrow_right Express routing
- keyboard_arrow_right MySQL (with Sequelize)
- keyboard_arrow_right MongoDB (with Mongoose)
- keyboard_arrow_right Working with RESTful API's
- keyboard_arrow_right Proficient with Squarespace, Wordpress, and other CMS's
- keyboard_arrow_right Git/Github branching and workflow
- keyboard_arrow_right Adobe XD/Figma prototyping
- keyboard_arrow_right Agile methodologies
- keyboard_arrow_right Writing and research


Instrument
public protocol Instrument : AnyObject
A type that can communicated with via VISA.
-
The session associated with the instrument.
Declaration
Swift
var session: Session { get }
-
A unique identifier for the instrument.
Declaration
Swift
var identifier: String { get }
-
The time in seconds to wait before timing out when performing operations with the instrument.
Declaration
Swift
var timeout: TimeInterval { get set }
-
The delegate for the instrument – used for managing events.
Declaration
Swift
var delegate: InstrumentDelegate? { get }
-
The instrument’s dispatch queue that allows for running instrument code on another thread. Each instrument has a unique dispatch queue.
It is reccomended to run all code that interfaces with the instrument on this dispatch queue to prevent blocking the main thread when reading/writing data to/from the instrument. Doing so will prevent GUI hangs and also allows for communicating with multiple instruments at the same time. If one or more instruments need to wait on another instrument before doing work, you can run the code for the instruments on a single instrument’s dispatch queue.
Declaration
Swift
var dispatchQueue: DispatchQueue { get }
-
clear()
Extension methodPerforms an IEEE-488.1-style clear of the device.
Declaration
Swift
func clear() throws
-
close()
Extension methodCloses the specified insturment. After the instrument has been closed, no more operations can be performed with the instrument.
Declaration
Swift
func close() throws
-
lock(_:timeout:)
Extension methodLocks the instrument with the specified lock type and timeout.
Throws
One of the followingVISAError
errors:.invalidSession
.resourceLocked
.invalidLockType
.invalidAccessKey
.timeout
Note
Locks can be nested.Declaration
Swift
func lock(_ type: LockState.LockType, timeout: TimeInterval) throws
Parameters
type
The lock type to use.
timeout
The time in seconds to wait before timing out.
-
unlock()
Extension methodUnlocks the instrument if it is locked.
Note
Locks can be nested.Declaration
Swift
func unlock() throws


Wilted Woman’s Tropical Malaise film score released by Primitive Languages
by Natalia PanzerThe film was directed by Domingo Castillo.
Wilted Woman’s score for Miami-based artist Domingo Castillo’s film Tropical Malaise has been pressed to vinyl by Primitive Languages.
The experimental film is made up of sourced footage, text and sound to offer reflective commentary on “environmental and speculative development concerns in Miami, [Florida]”.
Wilted Woman’s A-side moves among tinny electronic vignettes, future-space synth swaths and unkempt, unstrung jams while the B-side presents a gristly quilt of stitched and seeping textures later revealed to conceal sonic fireworks that she shoots up into a dystopic night sky.
Earlier this year, Wilted Woman released Lon Lon Night Vision via Phantasy Sound and collaborated with Nick Klein on the album Café Kotti for Alien Jams.
The Tropical Malaise LP is available via Primitive Languages.
Read next: Ableton Loop 2018: A glimpse of music’s future amid climate change catastrophe
Related Stories
Friday, May 31
More from News
Friday, November 22
Friday, November 22
Friday, November 22
Thursday, November 21
Thursday, November 21
Thursday, November 21
Thursday, November 21
Thursday, November 21
Thursday, November 21
Thursday, November 21


Police hunt thug who beat and robbed woman in early morning attack
The suspect made off with a three-figure sum of money after pouncing at 6.45am.
by Eric McGowan, https://www.dailyrecord.co.uk/authors/eric-mcgowan/Police in Ayrshire are appealing for information after a woman was beaten and robbed in an early morning attack.
The 34-year-old victim was pounced on by a man in Eastern Avenue, at its junction with The Roundel, Largs at around 6.45am on Sunday, November 17.
Police say she was approached from behind, forced to the ground and robbed of a three-figure sum of cash. The thug then kicked her about the body before making off.
He is described as being around 5ft 8 and was wearing dark clothing and gloves.
The woman attended Inverclyde Royal Hospital where she was treated before being released.
Witnesses or anyone with other information that could assist police enquiries should contact CID at Saltcoats via telephone number 101 quoting incident number 1769 of 17 November 2019.
Alternatively calls can be made via Crimestoppers on 0800 555 111 where anonymity can be maintained.


BIRD A film review by Marcel-Franck Simon Copyright 1988 Marcel-Franck Simon
Charles Parker Jr. was a complicated man. He was by all accounts profoundly selfish, yet uncommonly generous; a self-pitying addict who urged people to stay away from drugs; a loving husband of several wives, some simultaneously, who chased every skirt in sight. Charles Parker Jr. was also a brilliant improviser and composer. He effected the second fundamental revolution in jazz music, the last one to have affected the whole music, from top to bottom. He was a genius, he was a failure, he was a light in the dark, he was an example of what not to be. He was all these things and more, at the same time, laughing through the inconsistencies with the same nonchalance he brought to his soloing.
The wonderful thing about the film portrayal of Parker, and for which we can give great thanks to director Clint Eastwood, is that these contradictions are laid out unflinchingly. BIRD is more interested in painting the whole picture more than accentuating the details. The result is perhaps the best jazz movie ever made, at least one that satisfies this casual film fan and dedicated jazz buff.
How to tackle the slippery topic of Charles Parker? Here was a man who was fiercely conscious of his artistic accomplishments and more than anything demanded respect, yet at the same time was regularly found passed out in the gutter, showed up late or not at all for gigs, behaved unpredictably, got sick on stage and generally acted in as self-destructive a manner as possible.
Eastwood's answer is to discard conventional, linear storytelling. The movie unfolds like a jazz solo, with ideas popping up seemingly at random, developing at a pace sometimes unrelated to that of others. The overall structure, and Eastwood's vision of his subject, emerges only gradually, almost as an afterthought. Since Bird's playing often follows the same patterns, this is most appropriate. BIRD moves along as a series of vignettes that jump haphazardly about in time, generally covering the period from 1946-55, with a couple of short jumps back to the mid-30s.
Eastwood seems not particularly interested in jazz history. In fact, several jazz fans have complained that his movie takes place "after everything has already happened." Indeed, little or no time is spent explaining *why* Parker's music was so revolutionary. Eastwood wants far more to describe Bird himself. Of course, deprived of such an important part of his context, the subject loses a crucial dimension.
Eastwood's solution is to anchor the movie around Bird's widow, Chan Parker. In fact, this is as much Chan's movie as anything. Probably as a result, their family scenes are finely detailed and revealing. But this goes too far. Chan's point of view is self-serving. She is the perfect wife, the perfect mix of resignation, tolerance and exasperation. Chan also ignores some important facts. For example, Bird had a common-law wife with whom he spent about as much time as with Chan, and who musicians report was much more time on the scene than Chan. This woman is absent from the movie, and all of Bird's other women are depicted as utterly dispensable. Even Baroness Pannonica de Koenigswarter, a great fan of jazz and patron of several jazz musicians (Thelonious Monk in particular; the pianist wrote the tune "Pannonica" for her), is treated as little more than a high class groupie.
On the other hand, Diane Venora gives a strong performance as Chan, expressing very well the contradictory pulls in a woman who wanted to keep her husband clean and pure by removing him from his milieu ("fleeing to West-ches-ter," in his sardonic phrase), yet realized that doing so would kill him just as surely.
Centering the movie on Chan has other, more subtle drawbacks. One that particularly annoyed me: where are the other musicians? Other than Dizzy Gillespie, played by Sam Wright, the only other musician mentioned is Red Rodney. Where are Miles Davis, Max Roach, Fats Navarro, Bud Powell, Monk, Al Haig, Oscar Pettiford, Don Byas, Kenny Clarke, Tommy Potter, Curly Russell, and all the others who helped forge bebop, and who worked often with Bird? And why give Red Rodney such prominence, when Rodney was at best an occasional collaborator? The story of Bird and Rodney's tour through the South, where Rodney was billed as Albino Red to avoid redneck wrath on an integrated band, while true, is blown way out of proportion, as Rodney himself has said.
The Buster allegory also bothers me. Buster in the movie is a (mythical) Kansas City alto player who throws his horn in the river after seening Bird play. He also seals Bird's defeat by hailing the coming of a simpler, more direct music called rock'n'roll. Little of the story makes sense. First because by 1946-7, when Buster is so shamed by Bird's brilliance, Parker was a celebrity among musicians. From that mid-30s session where a very young Bird was "gonged off" a jam session (by Papa Jo Jones), Parker joined the bands of Jay McShann, then Earl Hines, then Billy Eckstine. Dizzy and Clarke were in the latter, and that band was as much a catalyst for the new music as Minton's and other Harlem joints that have passed into legend. All that traveling made Bird a household name among musicians (as Gary Giddins has shown, it is also false to say that Bird and Dizzy first brought bebop to California; L.A. already had a strong bop scene, which included men such as Charles Mingus.) So it is extremely unlikely that in 1947 Buster would still know Bird as "Charlie, from just around."
The business about Buster pioneering rock'n'roll and thereby sealing Bird's defeat also makes no sense. Rhythm'n'blues from the late 30s codified the show-business approach to playing that in fact differentiates it from jazz. A hornman riding "one lousy B-flat" with honks, grunts, exaggerated gestures and sweat was common by the late 40s. Players from John Coltrane to Yusef Lateef to Dannie Richmond put in their apprenticeships in R'n'B bands. In fact, John Coltrane dedicated himself to jazz after being asked to "walk the bar" one time too many. As r'n'b people have bitterly pointed out, rock'n'roll is r'n'b played by white people who get on the radio and make all the money.
It shouldn't. But Buster's story is allegorical.  Bird explicitly rejected the idea that jazz should be mere entertainment; he felt it should command respect. This is why he was so keen on playing with strings; he felt the orchestral context gave his music legitimacy. So seeing his ideas stolen by Buster, stripped of their artistic aspirations, and fed back to the public as pure entertainment, would hurt Bird deeply.
This is especially true when coupled with the poignant scene where Bird drives by Stravinsky's house. Parker greatly admired Stravinsky, and would surely have wanted to connect with the composer on an equal footing, as a fellow innovator. But Stravinsky only saw a rumpled and dirty black man knocking on his door late at night, and quickly went back inside, to safety. Thus is the paradox of Bird laid out: here was a man who wanted nothing more than to be acknowledged by his country, but who did almost everything possible to avoid being respected. As Dizzy (played by Sam Wright) tells him, "no matter what they say, these people want the nigger to be unreliable. It tells them they are right in their hearts." Bird knew this to be true. That he at the same time never seriously attempted to prove it wrong is the essence of his contradiction, and Eastwood is to be congratulated for perceiving it, and making it the centerpiece of his movie.
Forest Whitaker is perfect as Bird. He has that air of a dissolute and irresponsible little boy. He is also to be praised for learning to play the instrument. The scenes in which he is shown playing are entirely convincing. The same cannot be said of Wright as Dizzy. In the one scene in which we see him playing the trumpet, he holds the instrument as if he'd never seen such a thing before.
No matter, the playing scenes are the best thing in the movie. The music jumps off the screen to grab the listener. The opening "Lester Leaps In," where a bent-over Bird hovers over the audience, dancing at top speed over the changes, is unforgettable. Excellent too are the California session in which Bird, unable to find heroin, sustained himself with benzedrine and alcohol, thereby precipitating a nervous breakdown (which led, ironically, to his writing such tunes as "Cool Blues" and "Relaxing at Camarillo"). The scene where Buster is knocked out is also a winner. In fact, music alone would be sufficient cause to see this movie.
So is Charles Parker an American hero? It depends on one's definition of hero. Eastwood apparently reaches the same conclusion. By painting the whole picture, and leaving the interpretations to the viewer, he has made more of a service to his subject than would have the fawning hero-worship tribute some people wanted. By keeping his own integrity, by showing the warts, he accords Bird the respect Charles Parker wanted.
And that's the way it should be.
Marcel-Franck Simon<EMAIL_ADDRESS>attunix!mingus
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


{include file="../application/index/view/public/_header.html"}
KOOA - Sales Management System
{$Think.lang.login}:
{$Think.lang.password}
Remember me
{$Think.lang.login}
{$Think.lang.forgot_pass}
{$Think.lang.forgot}
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.


File | Batch Analysis offers a partially automated approach to analyzing multiple sets of existing measurements with new declaration values. This operation expects a batch script prepared earlier as input. See Batch Script Generation and Input files - Batch Script File Format for details on batch script preparation and content. The operation locates a batch script file on the file system using a file dialog, parses the script for analysis entries, runs the analysis entries found in the script, producing analysis results for each script entry.
The new analyses created by executing the batch script do not modify or change any previous analyses present in the INCC database.
Invoking the File | Batch Analysis command opens a file dialog. Use the file dialog to locate a batch script file. When the batch script file is located, select “Open” on the file dialog. The selected batch script file is then opened by INCC, and parsed for measurement commands. No errors or warnings are generated when the script is parsed for batch processing. Malformed script entries are unceremoniously skipped.
For each valid measurement entry discovered in the batch script file, the existing measurement data is retrieved and then analyzed using the new values provided in the script entry. You should see the successive analysis results pop up as each valid batch script line is processed. Each newly analyzed measurement produces a standard results window. A log window recording the progress of the processing results is also created.


When you that we were different, completely I kinda always knew Id end up your ex-girlfriend I kinda always knew Id end up your ex-girlfriend I hope I hold a special place with the rest of them I kinda always knew Id end up your ex-girlfriend Im another ex-girlfriend on your list But I should have thought of that before we kissed Your wildness scares me So does your freedom You say you cant stand the restrictions I find myself trying to change you If you were meant to be my lover I wouldnt have to And I feel so mean, I feel in between Cause Im about to give you away I kinda always knew Id end up your ex-girlfriend (for someone else to take) I kinda always knew Id end up your ex-girlfriend (am I making a mistake?) I hope I hold a special place with the rest of them (all the time that we spent) I kinda always knew Id end up your ex-girl, friend Im another ex-girlfriend on your list But I should have thought of that before we kissed Im another ex-girlfriend on your list But I should have thought of that before we kissed Im about to give you away for someone else to take Im about to give you away for someone else to take We keep repeating mistakes for souvenirs And weve been in between the days for years And I know that when I see you Im going to die I know Im going to want you and you know why Its going to kill me to see you with the next girl Cause Im the most gorgeously jealous kind of ex-girl But I should have thought of that before we kissed I kinda always knew Id end up your ex-girlfriend I kinda always knew Id end up your ex-girlfriend I hope I hold a special place with the rest of them I kinda always knew Id end up your ex-girlfriend Im another ex-girlfriend on your list But I should have thought of that before we kissed Im another ex-girlfriend on your list But I should have thought of that before we kissed |


Your username
Your email
Your password
Please confirm your password
Already a member ? Go and log in


Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics ('PSID') delivered raw data. Downloads data directly from the PSID server using the 'SAScii' package. 'psidR' takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples).
| Version: | 1.9 |
| Imports: | data.table, RCurl, foreign, SAScii, openxlsx, futile.logger |
| Suggests: | testthat |
| Published: | 2018-10-18 |
| Author: | Florian Oswald |
| Maintainer: | Florian Oswald <florian.oswald at gmail.com> |
| License: | GPL-3 |
| URL: | https://github.com/floswald/psidR |
| NeedsCompilation: | no |
| Citation: | psidR citation info |
| Materials: | NEWS |
| In views: | Econometrics |
| CRAN checks: | psidR results |
| Reference manual: | psidR.pdf |
| Package source: | psidR_1.9.tar.gz |
| Windows binaries: | r-devel: psidR_1.9.zip, r-release: psidR_1.9.zip, r-oldrel: psidR_1.9.zip |
| OS X binaries: | r-release: psidR_1.9.tgz, r-oldrel: psidR_1.9.tgz |
| Old sources: | psidR archive |
Please use the canonical form https://CRAN.R-project.org/package=psidR to link to this page.


 It never has an edge to it, therefore it seems more like a sitcom than a comedy/drama. Drama and characterization are thrown out the window, and contrivances rule the day... which allow the home fries to become too tasteless to enjoy.
Drew Barrymore as the not too swift red-headed Sally, the girl with a golden heart, who works in a small-town Texas hamburger fast-food place, is pregnant and falls for the clean-cut stepson, Dorian (Luke), of the married man (Chris Ellis) who fathered her unborn child. Dorian and his maniacal bother Angus (Busey) are helicopter Air National Guard pilots who attempt to scare their cheating stepfather for their mother's (Catherine O'Hara) sake, but go too far in scaring him and cause him to have a heart attack when they hover over his car in their helicopter and force him to flee the car on a darkened country road. The only problem with the perfect murder, is that the obedient sons are afraid that the voices they heard on their helicopter's radio might be witnesses to the murder. They figure out that those voices came from the hamburger place, so Dorian gets a job there in order to see what any one there knows about the murder, which the police are not even suspicious of.
Sally and her family are made to appear like caricatures from the Beverly Hillbillies, living in a run-down shack, with a father (Flaherty) who is a loud- mouth drunkard with pangs of violence. The film touches bases with the Oedipal problem, sibling favoritism, normalcy, a dysfunctional family, and violence in rural areas. It hones in on the life of fast food workers and the low quality of life in the small town, hoping to make a satire on that lifestyle. But the film just can't get above the absurdity of its bizarre tale to say much more about why everyone is so screwed up or make the romance between the shy Dorian (the good brother) and Sally feel like it belongs in the film. These two are not believable as lovers, as I saw no sparks flying from them. Also the murder plot is just not that funny, and when it becomes more involved, it just becomes a sick joke that goes on for too long; and, the film ends with a one-too many helicopter scene that couldn't be more ridiculous. The film had all the hints of going somewhere in its opening premise, but it seemed to get stuck, not knowing how to tell a story about what the film really should have been about, the quest of Dorian and Sally for a normal life despite their dysfunctional up-bringing.  Being a weirdo does not make one necessarily an interesting character. That is a lesson that this first-time director should take away from this failed effort at dark humor.
It should be mentioned that there was one appetizing scene in the movie, as Dorian escorts Sally to Lamaze class and we see them go through those exercises; it is still a good scene even if it is still not clear why he does so. I just had the feeling he felt sorry for her, rather than that he was in love with her, as I presume the director wanted us to believe.
This is a film that might please those who frequent fast-food places and don't worry about their cholestral count and indigestion. In other words, the film moved along at a fast pace, there wasn't much to think about, and might be just the thing for those who would consider this a date movie and not be concerned that it didn't deliver a heavier meal. As for me, I try to eat junk food only when I'm stuck on the road and am starving to death. In this case, since I wasn't stuck on the road, my excuse for eating this junk food was that I was enticed to try something new they were advertising on the menu, but after tasting it, I felt it was just like the same old stuff they always serve in those places, and I just wasn't hungry enough to appreciate that.
REVIEWED ON 9/17/99 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


Multi purpose demo page
Some content
Click here
to navigate with LuigiClient to /help
Click here
to send a custom message to Core, which
asks if you want to navigate to /help, removes dirty state
and triggers a core navigation function to go there.


Cure Console 3000
Cure name:
Basic Resources
Collect Credits
Credits:
0
0
/s
Synthesize Medicine
Medicine:
0
-1
/s
People
Current Team Size:
0
Hire Worker
10
credits
: Hire a new worker
Jobs
Unassigned:
0
-
Scientists:
0
+
Uses Centrifuge to make medicine
-
Doctors:
0
+
Sells medicine for Credits
Lab Equipment
Buy Centrifuge
Centrifuges:
0
1 Scientist,
10
Credits
: +1 medicine/s


Utopian.io has just been listed on SteemProjects by @noisy.
Utopian.io believes many more projects should follow this example in terms of effort and quality. SteemProjects is still in its infancy, some minor issues may be there, but the UX, the UI and the coding effort in this project are SUPER AWESOME!.
SteemProjects by @noisy
It is a platform to list Steem Projects but it is actually much more than just that. Project owners can do many things, like managing a timeline, sharing the status of the project, present the project in beautiful details and much more.
Have a look to the [pre-RELEASE][Tutorial] How to add/edit a project on SteemProjects.com to learn how you can submit your project there.
Utopian.io is already there and PROUD OF THAT!
A Special Thanks to The Sponsors
| @freedom @misterdelegation @ned @transisto @wackou @xeldal (Vote for witness) @lafona-miner @steempty @good-karma (Vote for witness) @thecryptodrive (Vote for witness) @furion (Vote for witness) @cnts @javybar @minnowbooster @netuoso (Vote for witness) @tarekadam @jerrybanfield (Vote for witness) @billbutler @ajvest @elear @masterthematrix @materia @yabapmatt (Vote for witness) @delegate.lafona (Vote for witness) @agoric.systems (Vote for witness) @flauwy @teamsteem (Vote for witness) @deanliu @lexiconical @masterofcoin @cmorton @johnsmith @jerzy @dragosroua (Vote for witness) @unosuke @gokulnk @o1o1o1o @techslut @jenkinrocket @mother2chicks | @rival (Vote for witness) @jomeszaros @steemj @oendertuerk @sircork (Vote for witness) @shreyasgune @lykkejay @hadimemories @kemal13 @soushi888 @dodybireuen @ragepeanut @jatinhota (Vote for witness) @xplosive @littleboy @eastmael @dimitrisp (Vote for witness) @koinbot @katamori @eliel @evildido @alexsm @allowisticartist @doodlebear @dubem-eu @fakire1sadaka @steemfunding @lusanamaya @knowledges @callmetoxy @nehomar @rizal.konoha2 @tanvirrahman @bluetechno @yandot @ilvstranger @raserrano @robin-ho |
A Special Thanks to The Moderators
@espoem @jefpatat @arie.steem @ruah @themanualbot @stoodkev @favcau @deveerei @mcfarhat @mooncryption @cnts @shreyasgune @knowledges @zeartul @lykkejay @vladimir-simovic @gktown
This page is synchronized from the post: Utopian Is Now On SteemProjects By @Noisy. A Project That Deserves More Exposure!


A B C D E F G H I J L M N O P Q R S T U W Y
| tis-package | Time Indices and Time Indexed Series |
| adjustableTierChart | Create tier charts |
| aggregate.tis | Compute Summary Statistics of Time Series Subsets |
| as.character.jul | Convert Time Index or Jul to Character |
| as.character.ti | Convert Time Index or Jul to Character |
| as.data.frame.tis | Coerce to a Data Frame |
| as.Date.jul | Convert ti or jul objects to Dates |
| as.Date.ti | Convert ti or jul objects to Dates |
| as.jul | Julian Date Objects |
| as.list.jul | Construct a List from a Time Index or Jul Object |
| as.list.ti | Construct a List from a Time Index or Jul Object |
| as.matrix.tis | Create a Matrix from a Time Indexed Series |
| as.POSIXct.jul | Date-time Constructor Functions |
| as.POSIXct.ti | Date-time Constructor Functions |
| as.ssDate | ssDate Objects |
| as.ti | Time Index Objects |
| as.tis | Time Indexed Series |
| as.tis.default | Time Indexed Series |
| as.tis.tis | Time Indexed Series |
| as.tis.ts | Time Indexed Series |
| as.tis.zoo | Time Indexed Series |
| as.ts.tis | Convert a Time Indexed Series to a Time Series |
| asClassyList | Convert a vector into a list of objects with the same class |
| asJul | Julian Date Objects |
| assignList | Assign Values In a List to Names |
| asTi | Time Index Objects |
| barplot.tis | Barplot for Time Indexed Series |
| barplot2 | Horizontally Shifted Bar Plots |
| basePeriod | Time Index Frequencies and Periods |
| basis | Optional tis attributes |
| basis<- | Optional tis attributes |
| between | Check for Inclusion in a Closed Interval |
| blanks | Blanks |
| capitalize | Capitalize strings |
| cbind.tis | Combine Series Into a Multivariate (Matrix) Time Indexed Series |
| columns | Rows and Columns of a Matrix |
| convert | Time scale conversions for time series |
| couldBeTi | Time Index Objects |
| csv | Writes a CSV (comma separated values) file. |
| cummax.tis | Cumulative Sums, Products, and Extremes |
| cummin.tis | Cumulative Sums, Products, and Extremes |
| cumprod.tis | Cumulative Sums, Products, and Extremes |
| cumsum.tis | Cumulative Sums, Products, and Extremes |
| currentApril | Current Period Time Indexes |
| currentAugust | Current Period Time Indexes |
| currentDecember | Current Period Time Indexes |
| currentFebruary | Current Period Time Indexes |
| currentFriday | Day of Week Time Indexes |
| currentHalf | Current Period Time Indexes |
| currentJanuary | Current Period Time Indexes |
| currentJuly | Current Period Time Indexes |
| currentJune | Current Period Time Indexes |
| currentMarch | Current Period Time Indexes |
| currentMay | Current Period Time Indexes |
| currentMonday | Day of Week Time Indexes |
| currentMonth | Current Period Time Indexes |
| currentMonthDay | Day positions in Time Index Periods |
| currentNovember | Current Period Time Indexes |
| currentOctober | Current Period Time Indexes |
| currentQ4 | Current Period Time Indexes |
| currentQMonth | Current Period Time Indexes |
| currentQuarter | Current Period Time Indexes |
| currentSaturday | Day of Week Time Indexes |
| currentSeptember | Current Period Time Indexes |
| currentSunday | Day of Week Time Indexes |
| currentThursday | Day of Week Time Indexes |
| currentTuesday | Day of Week Time Indexes |
| currentWednesday | Day of Week Time Indexes |
| currentWeek | Current Period Time Indexes |
| currentYear | Current Period Time Indexes |
| dateRange | Start and End Time Indices for a Series |
| day | Extract parts of various Date-Time Objects |
| dayOfMonth | Day positions in Time Index Periods |
| dayOfPeriod | Day positions in Time Index Periods |
| dayOfWeek | Day positions in Time Index Periods |
| dayOfYear | Day positions in Time Index Periods |
| description | Description and Documentation Attributes |
| description<- | Description and Documentation Attributes |
| documentation | Description and Documentation Attributes |
| documentation<- | Description and Documentation Attributes |
| easter | Holidays |
| end.tis | Starting and ending time indexes |
| evalOrEcho | Evaluate an Argument If Possible |
| fanSeries | Constant Growth Series |
| federalHolidays | Holidays |
| firstBusinessDayOf | Day positions in Time Index Periods |
| firstBusinessDayOfMonth | Day positions in Time Index Periods |
| firstDayOf | Day positions in Time Index Periods |
| format.jul | Convert Time Index or Jul to Character |
| format.ti | Convert Time Index or Jul to Character |
| fortify.tis | Fortify a tis object |
| frColors | FRB Color Palettes |
| goodFriday | Holidays |
| growth.rate | Growth Rates of Time Series |
| growth.rate<- | Growth Rates of Time Series |
| hms | Hours, Minutes and Seconds from a Time Index or Jul |
| holidays | Holidays |
| holidaysBetween | Holidays |
| hourly | R support for Intraday frequencies |
| ilspline | Linear Spline Integration |
| inaugurationDay | Holidays |
| inferTi | Create a Time Index to Match a Vector of DateTimes |
| interpNA | Interpolate missing values in a Time Indexed Series |
| is.jul | Julian Date Objects |
| is.ssDate | ssDate Objects |
| is.ti | Time Index Objects |
| is.tis | Time Indexed Series |
| isBusinessDay | Holidays |
| isEaster | Holidays |
| isGoodFriday | Holidays |
| isHoliday | Holidays |
| isIntradayTif | Check for Intraday Time Index Frequency |
| isLeapYear | Check Leap Year |
| jul | Julian Date Objects |
| jul.Date | Julian Date Objects |
| jul.default | Julian Date Objects |
| jul.IDate | Julian Date Objects |
| jul.ti | Julian Date Objects |
| jul.yearmon | Julian Date Objects |
| jul.yearqtr | Julian Date Objects |
| Lag | Lag a Time Series |
| lag.tis | Lag a Time Series |
| Lags | Lag a Time Series |
| lags | Lag a Time Series |
| lastBusinessDayOf | Day positions in Time Index Periods |
| lastBusinessDayOfMonth | Day positions in Time Index Periods |
| lastDayOf | Day positions in Time Index Periods |
| latestApril | Most Recent Period Time Indexes |
| latestAugust | Most Recent Period Time Indexes |
| latestDecember | Most Recent Period Time Indexes |
| latestFebruary | Most Recent Period Time Indexes |
| latestFriday | Day of Week Time Indexes |
| latestHalf | Most Recent Period Time Indexes |
| latestJanuary | Most Recent Period Time Indexes |
| latestJuly | Most Recent Period Time Indexes |
| latestJune | Most Recent Period Time Indexes |
| latestMarch | Most Recent Period Time Indexes |
| latestMay | Most Recent Period Time Indexes |
| latestMonday | Day of Week Time Indexes |
| latestMonth | Most Recent Period Time Indexes |
| latestMonthDay | Day positions in Time Index Periods |
| latestNovember | Most Recent Period Time Indexes |
| latestOctober | Most Recent Period Time Indexes |
| latestQ4 | Most Recent Period Time Indexes |
| latestQuarter | Most Recent Period Time Indexes |
| latestSaturday | Day of Week Time Indexes |
| latestSeptember | Most Recent Period Time Indexes |
| latestSunday | Day of Week Time Indexes |
| latestThursday | Day of Week Time Indexes |
| latestTuesday | Day of Week Time Indexes |
| latestWednesday | Day of Week Time Indexes |
| latestWeek | Most Recent Period Time Indexes |
| latestYear | Most Recent Period Time Indexes |
| lines.tis | Plotting Time Indexed Series |
| lintegrate | Linear Spline Integration |
| mergeSeries | Merge Time Indexed Series |
| minutely | R support for Intraday frequencies |
| month | Extract parts of various Date-Time Objects |
| naWindow | Exclude NA and Zero Observations |
| nberDates | Plotting NBER Recesssions |
| nberShade | Plotting NBER Recesssions |
| nberShade.default | Plotting NBER Recesssions |
| nextBusinessDay | Holidays |
| observed | Optional tis attributes |
| observed<- | Optional tis attributes |
| period | Time Index Frequencies and Periods |
| plotWindow | Set up Coordinates for Graphics Window |
| points.tis | Plotting Time Indexed Series |
| POSIXct | Date-time Constructor Functions |
| POSIXct.default | Date-time Constructor Functions |
| POSIXct.jul | Date-time Constructor Functions |
| POSIXct.numeric | Date-time Constructor Functions |
| POSIXct.ti | Date-time Constructor Functions |
| POSIXlt | Date-time Constructor Functions |
| POSIXlt.default | Date-time Constructor Functions |
| POSIXlt.jul | Date-time Constructor Functions |
| POSIXlt.ti | Date-time Constructor Functions |
| previousBusinessDay | Holidays |
| print.ti | Print a Time Index |
| print.tis | Printing Time Indexed Series |
| quarter | Extract parts of various Date-Time Objects |
| romerLines | Plotting NBER Recesssions |
| RowMeans | Form Row Sums and Means |
| RowMeans.default | Form Row Sums and Means |
| RowMeans.tis | Form Row Sums and Means |
| rows | Rows and Columns of a Matrix |
| RowSums | Form Row Sums and Means |
| RowSums.default | Form Row Sums and Means |
| RowSums.tis | Form Row Sums and Means |
| scatterPlot | Produce high-quality scatter plots |
| screenPage | Page Setup for Plots |
| secondly | R support for Intraday frequencies |
| setColors | Set the Graphics Palette and Default Colors |
| setDefaultFrequencies | Return Known Time Index Frequencies, Change Default Frequencies |
| solve.tridiag | Solve a Tridiagonal System of Equations |
| ssDate | ssDate Objects |
| start.tis | Starting and ending time indexes |
| start<- | Starting and ending time indexes |
| stripBlanks | Strip Blanks |
| stripClass | Remove part of a class attribute |
| stripTis | Remove part of a class attribute |
| t.tis | Matrix Transpose |
| ti | Time Index Objects |
| ti.Date | Time Index Objects |
| ti.default | Time Index Objects |
| ti.jul | Time Index Objects |
| ti.ssDate | Time Index Objects |
| ti.ti | Time Index Objects |
| ti.tis | Time Index Objects |
| ti.yearmon | Time Index Objects |
| ti.yearqtr | Time Index Objects |
| tiBusiness | Daily and Business Day Time Indexes |
| tiDaily | Daily and Business Day Time Indexes |
| tierChart | Create tier charts |
| tif | Time Index Frequencies and Periods |
| tif.default | Time Index Frequencies and Periods |
| tif.ti | Time Index Frequencies and Periods |
| tif.tis | Time Index Frequencies and Periods |
| tif.ts | Time Index Frequencies and Periods |
| tif2freq | Periods Per Year for Time Index Frequencies |
| tifList | Return Known Time Index Frequencies, Change Default Frequencies |
| tifName | Time Index Frequencies and Periods |
| tifName.default | Time Index Frequencies and Periods |
| tifName.ti | Time Index Frequencies and Periods |
| tifName.tis | Time Index Frequencies and Periods |
| tis | Time Indexed Series |
| tisFilter | Linear Filtering on a Time Series |
| tisFromCsv | Read time series from Comma Separated Values (.csv) file |
| tisLegend | Add a legend to a tisPlot or scatterPlot |
| tisPlot | Plot time indexed series (tis objects) |
| today | Time Index for the Current Date |
| tunnelSeries | Constant Growth Series |
| updateColumns | Update lists and time series |
| updateList | Update lists and time series |
| window.tis | Time windows for Time Indexed Series |
| year | Extract parts of various Date-Time Objects |
| ymd | Extract parts of various Date-Time Objects |
| ymd.default | Extract parts of various Date-Time Objects |
| ymd.jul | Extract parts of various Date-Time Objects |
| ymd.ssDate | Extract parts of various Date-Time Objects |
| ymd.ti | Extract parts of various Date-Time Objects |
| ymdShade | Shading Date Ranges |
| ymdXy | Shading Date Ranges |


Q: What are the two overlaying protocols that control the sending and receiving of information on the internet?
A: TCP (Transmission Control Protocol) and IP (Internet Protocol)
Q: What are distributed applications and why are they considered distributed?
A: Distrubted applications are applications which run on end systems. That is to say, these applications run on separate hosts and use the internet to send and receive data with applications running on other remote host machines. They are therefore considered distributed because they communicate across a network as opposed to internally with applications on the same machine.
Q: In our context, what is a protocol?
A: A protocol defines the format and the order of messages exchanged between two or more communicating entities, as well as the actions taken on the transmission and/or receipt of a message or other event.
Q: Name three physical mediums for transmitting data on the internet.
A: Possibilities: Fiber Optic Cable, Coaxial Cable, Twisted Pair Copper Wire (Insulated and Unshielded), Satellite Radio Channels, Terrestrial Radio Channels.
Q: What is meant by the phrase "A Network of Networks"?
A: This is part of our theme of layers of abstraction. There are routers and communication links which span the entire globe, and this in itself is a network. However, there is not one single ISP which manages this entire global network. Instead, ISPs charge and segment networks on an individual basis, or sometimes in small chunks of routers. These are each networks as well, and we therefore call this system a network of interconnected networks.
Q: What are the different types of delay at a node in a packet-switch network?
A: A node in a packet-switch network may suffer from processing, queuing, transmission, and propogation delays together make up what is called total nodal delay.
Q: For what kind of application is it critical to have low delay, and for what kind is it not ciritical for low delays, but desirable for high throughput?
A: Internet telephony, video calling, and gaming requires low delay because the real-time information is critical. For file transfering it is not critical to have low delays, but it is desirable to have high throughput to get the entire file quickly.
Q: Please name the two network application architectures and give one advantage of each.
A: The two architectures are client-server and peer to peer or P2P. Client-server models have many advantages, the primary one being a simple structure which allows end users to access the entire network by connecting solely to the central server, providing ease of access as well as relative security. Peer to peer has the advantage of a direct connection to other users, so that the network connections are not dependent on a single point of failure.
Leonard Kleinrock is an important American computer
scientist who, among other things, worked very closely with the development of the first
pieces of hardware that comprised the internet in the early days. He graduated from
MIT with a doctorate in computer science in 1963, and then joined the faculty at UCLA where he
remains today.
Kleirock's most recognized and significant work was
done on queuing theory, which is instrumental to packet switching and in turn the concepts of
computer networks that we are learning about in this class. In his PhD research
Kleinrock describes his work as being to, "establish a mathematical theory of packet networks...".
This work would become critical not only in his work at MIT on the first ARPANET
message, but also for the operation of the world wide web today.
We saw in the movie "Lo and Behold" the machine that
Charley Kline used to send the first message on October 29th, 1969. Kleinrock oversaw this
operation and after the first system crash the system was recovered and a month later the first
permanent ARPANET link between Stanford and MIT was established.
Leonard Kleinrock has since made a number of contributions
to the bureaucratic acceptance of the use of the internet for personal reasons, and was inducted into
the Internet Hall of Fame in 2012, among receiving a number of other nationally significant awards.
These include the National Medal of Science, which he received in 2008, and an induction into IEEE-Eta
Kappa Nu in 2011. This man has lead an incredible life, and his work has undoubtedly help shape the
landscape of modern society.
GitHub is a version control software application which can be accessed both as a web application or
a desktop application. This means that it is very useful for teams of individuals who are working
in parallel on a project who need a way to keep track of changes that are made to the project or authorship
of individual portions of the work.
To get started with GitHub you will need to create an account.
This process is quite simple and can be done at github.com by filling out the sign-up form.
Now that you have an account, we can learn about repositories. Repositories are the way that
GitHub delineates different projects or packages of files. A repository has branches and is created with
a single master branch which represents the official project. The purpose of branches is to allow users who
are working on the project to create new branches of the project where they can experiment with making changes
without affecting the integrity of the master branch; the official project.
Creating branches is as simple as the click of a button, and
requires only that you name the branch and give a brief description of its purpose. The only
tricky part of operating with these different branches is the push and pull functionality, along with the
ability to clone branches. GitHub stores its data in a cloud, on a remote server. When you "pull"
a branch off of the master branch, it does not create a physical copy of the files on your machine. To do
this, you will need to "clone" the project, or download it to your local machine. You can then make changes
to those files as you see fit.
The key to getting your changes incorporated into the master branch
involve "committing" your changes to your own branch. GitHub will automatically track the differences between
your version of the files in the project, and those of your branch and the master branch. Once changes have been
made, you can click on the "commit" button, merely give a description of the changes you made, and they will
become the new version of your branch. When you are ready to make your changes part of the master branch, you will
create a "pull request" which is the same as a commit except that the administrator of the project will have to
approve the changes. Once they do, your changes will become part of the master project. Congratulations, you
can now perform basic operations in GitHub!


A quick tutorial on functions and creating custom functions
What is a function? Very simply, a function is a block of reusable code that is used to perform an action.
In R, we can store a function as an object and thus be able to recall the function repeatedly and perform an operation (or several) very quickly and easily with a single command. Let’s break it down a little bit more.
Here’s an example of a function
mean_function <- function(variable) {
mean(variable)
}
First by using the “<-” we are assigning the function to an object name. Note that the name of the function is meaningful, that is, we can guess what it does by it’s name. This is important if you create a lot of functions, as you may loose track of the purpose of each. Furthermore, it helps make your code more readable to others.
Our example function is very simple, it calculates the mean of a variable. Now, we could just do this with mean(variable), but I wanted to use a simple example for our first function. The input to this function would be a variable, the output would be the mean of that variable. You can test this example function by entering different variables from our dataset.
Next, a function is composed of three parts:
Body: this is the code inside the function (inside the curly brackets {}). In our example, the ‘body’ is the part within the curly brackets, in this case: mean(variable).
Formals: these are the arguements supplied to the function - controls how we can call the function In our example, the formal, the arguement we supply to the function, is ‘variable’. The formals are listed in the parantheses right after function. Your function can have multiple formals.
So for example, let’s say we wanted to have a function that calculates the mean of two variables. In this case, we’d need to provide two formals, variable 1 and variable 2 (see example below). Note that you can call the formals whatever you want. In this case we used ‘variable1’ and ‘variable2’. We could have also used ‘x’ and ‘y’, ‘cat’ and ‘dog’, anything really. The names are arbitrary, but it is most helpful to give the formals names that are meaninful so you can remember what type of information you need to provide it.
mean_function2 <- function(variable1, variable2) {
(mean(variable1) + mean(variable2))/2
}
Let’s try out our functions using our movie dataset (link to download above) which I named ‘data’. First let’s try out the ‘mean_function’. You can supply the function with a variable and it should output the mean. Let’s try it out.
#Here's our function
mean_function <- function(variable) {
mean(variable)
}
#What is the average imdb score for the films in our dataset?
mean_function(data$imdb_score)
[1] 6.442138
We called the function using the name we assigned it, supplied the input(our variable), and received the output (our mean) of 6.44!
Hooray!
Let’s try another. Let’s find out what the average film duration is.
mean_function(data$duration)
[1] NA
Uh oh! Our result was NA? Why do you think that happened?
When we look at our dataframe, we see that the variable ‘duration’ does have missing data. We need to remove that to get our function to work. To do that, let’s just add it to our function!
#Let's write over our previous 'mean_function' to remove missing data so it works on more of our variables
mean_function <- function(variable) {
mean(variable, na.rm=TRUE)
}
#Now let's try to find out the average film duration again
mean_function(data$duration)
[1] 107.2011
Great it worked! The average film duration is 107 minutes.
Now let’s try out our second function. Let’s combine the number of facebook likes for actor1 and actor2 and see what the overall average is. But first, let’s modify the function to remove missing data.
#modify the function to exclude missing data
mean_function2 <- function(variable1, variable2) {
(mean(variable1, na.rm=TRUE) + mean(variable2, na.rm=TRUE))/2
}
#Remember we have to provide two variables for our second function
mean_function2(data$actor_1_facebook_likes, data$actor_2_facebook_likes)
[1] 4105.901
The average number of facebook likes is 4,105! Great job!
Now that you have some basic function knowledge, let’s take a try at creating some more complex functions. Let’s first try to make a function that is very specific to this dataset. Let’s try something fun. Let’s write a function that will tell us the average imdb score for a given actor!
#We first want to pick a meaningful name for our function, so it is clear what it does. Also what's the arguements we need to provide to the function? The actor's name right? So we'll have one formal for this function.
average_imdb <- function(actor) {
#Now the dataset in it's current form isn't ideal for this question, but luckily we can change the dataset within the function. Notice that nothing is saved to our global environment when we do this, it remains in the function.
#There are three columns of actors in the dataset - actor1, 2, and 3. We will want to gather these all into one column first. I also used select here to select just the relevant variables
df <- data %>%
select(actor_1_name, actor_2_name, actor_3_name, imdb_score) %>%
gather(number, actor, -imdb_score )
#Next, we want to aggregate the data such that when there are multiple instances of an actor, we calculate their overall mean
df <- aggregate(imdb_score ~ actor, df, mean)
#Next, so that we can call up the actor, we give the rows names of the actors.
row.names(df) <- df$actor
#Lastly, we call up the imdb score for the given actor
df[actor,"imdb_score"]
}
#And that's our function. So you can see it more clearly, here it is written out without the comments:
average_imdb <- function(actor) {
df <- data %>%
select(actor_1_name, actor_2_name, actor_3_name, imdb_score) %>%
gather(number, actor, -imdb_score )
df <- aggregate(imdb_score ~ actor, df, mean)
row.names(df) <- df$actor
df[actor,"imdb_score"]
}
Now let’s try the function out!
average_imdb("Tom Cruise")
[1] 7.013793
average_imdb("Johnny Depp")
[1] 6.736585
average_imdb("Will Smith")
[1] 6.73
Great! This worked, it reported the average imdb for each actor input we gave it.
Now, what if I misspelled an actor’s name, what would the function do then? Let’s try it.
average_imdb("tom cruz")
[1] NA
The function returned ‘NA’. This makes sense given that there isn’t a ‘tom cruz’ in our list, but that may not be clear to the user of our function. One thing we can do to make our function even more effective is to add helpful messages, warnings, and errors. Let’s try adding a more descriptive return message to our average imdb function when we receive ‘NA’.
average_imdb <- function(actor) {
df <- data %>%
select(actor_1_name, actor_2_name, actor_3_name, imdb_score) %>%
gather(number, actor, -imdb_score )
df <- aggregate(imdb_score ~ actor, df, mean)
row.names(df) <- df$actor
name <- df[actor,"imdb_score"]
#We can add 'if' statements to our functions to add conditions to our function. See more below.
if(is.na(name)) {
return("This actor is not in our database. Did you check your spelling? Make sure to capitalize the first letter of the first and last name")
}
else {
return(name)
}
}
average_imdb("Tom Crude")
[1] "This actor is not in our database. Did you check your spelling? Make sure to capitalize the first letter of the first and last name"
This time we receive not ‘NA’ but a helpful message that tells us that our name is not in the database or that we misspelled it.
In the previous case, we included a conditional return message, but you can also provide custom messages, warnings, and errors. To create a custom error, you will enter the conditional information, and use ‘stop’ instead or ‘return’. Stop ‘stops’ the function and outputs the error.
if(what is the condition) { stop("Error Message) }
Warning is very similar, except you’d use ‘warning’ instead or ‘stop’. In this case the function won’t stop running, put will provide the output as well as the error message.
if(conditional statement) { warning(“warning message”) }
We do this by adding ‘If’ statements within our function. If statements provide conditions through which the function operates differentially given certain circumstances. In the case above, we created an ‘If’ statement that was based on the output we received. You could also create ‘If’ statements that affect how the input is dealt with. For example:
Input_if <- function(number) {
if(number >= 0) {
number * 5
}
if(number < 0) {
abs(number) * 5
}
}
#Try it out
Input_if(4)
Input_if(-4)
[1] 20
In our example function above, we used ‘if’ statements to make it so the function performed differently if the input(a number) was greater than or less than 0.
You can use as many ‘if’ statements as you’d like in a function. You can also use ‘else’ if you want the function to perform one way for a given condition but respond another way given any other input that doesn’t match that condition. For example, instead of using two ‘if’ statements in our function above, we could have used ‘else’.
Input_if <- function(number) {
if(number >= 0) {
number * 5
}
else {
abs(number) * 5
}
}
#Try it out
Input_if(4)
[1] 20
Input_if(-4)
[1] 20
Our ‘average_imdb’ function was fun and worked efficiently, however, that function would only be relevant to that particular dataset. Let’s make a function that is more generalizable and could be used with any dataset.
Let’s make a function that outputs descriptives for a given variable. This is useful and can be used with any dataset that has a numerical variable. To make the function effective, we should include a meaningful name, useful error messages, and make it generalizable to any dataset.
First, we want to think about what descriptives we want the function to output. Let’s go with some standards: mean, standard deviation, minimum value, maximum value, total number of observations, and number of missing data points.
We will also want to make sure to account for missing data.
Let’s give it a try.
descriptives <- function(variable) {
if(!is.numeric(variable)) {
stop("Variable supplied is not numeric!", call. = FALSE)
}
data.frame(mean = mean(variable, na.rm = TRUE),
std_dev = sd(variable, na.rm = TRUE),
min_value = min(variable, na.rm = TRUE),
max_value = max(variable, na.rm = TRUE),
total_n = length(variable),
missing_data = sum(is.na(variable)))
}
#Test if it works
descriptives(data$imdb_score)
mean std_dev min_value max_value total_n missing_data
1 6.442138 1.125116 1.6 9.5 5043 0
#Let's make sure our error message works too
descriptives(data$color)
Error: Variable supplied is not numeric!
Great! Our function works as expected and it correctly delivers a useful error message!
I hope you enjoyed this brief introduction to functions. There’s always more to learn, check out these great resources below:
Additional Resources on Functions:
https://adv-r.hadley.nz/functions.html#function-fundamentals
https://swcarpentry.github.io/r-novice-inflammation/02-func-R/


Gets or sets the report viewer component to interact with.
Gets or sets the report viewer component to interact with.
Gets or sets whether to show the filter pane in the report.
Gets or sets whether to show the filter pane in the report.
Gets or sets whether to show the navigation tabs in the report.
Gets or sets whether to show the navigation tabs in the report.
Pages to the next page in the report shown in the associated viewer component.
Toggles the filter pane in the associated report viewer.
Toggles the nav pane in the associated report viewer.
Event handler handling the 'next page' button. Call to navigate one page forward.
Event handler handling the 'previous page' button. Call to navigate one page back.
Generated using TypeDoc
Provides a component that can navigate through the pages in a power bi report for a given report viewer component.
ReportNavigation


27 May 2010 - On 25 January 2010, the Ministry of Health (MoH), Cameroon reported one case of yellow fever in Kumba district in the country's south-western region, close to the border with Nigeria's Cross River State.
The index case was identified as a 16 year-old female student in Kake high school. She presented with a clinical picture of fever and jaundice. Onset of symptoms started on 11 January with fever. She had no history of yellow fever vaccination and survived. The index case was identified through routine yellow fever surveillance system.
Laboratory specimens from this case were found to be IgM positive by ELISA method at the National Laboratory in the Institut Pasteur, Yaoundé, Cameroon and confirmed positive by plaque reduction neutralization test (PRNT)- a more specific test for yellow fever- at the regional reference laboratory for yellow fever at the Institut Pasteur in Dakar, Senegal.
Following identification of the index case, an outbreak investigation was conducted in the area. The investigation identified one person as symptomatic (fever). Laboratory tests by ELISA method of serum samples for this and 50 other contact were found to be negative.
Cameroon conducted a yellow fever mass vaccination campaign in 62 at-risk-districts covering a population of 7.4 million in May 2009. The two districts of Kumba and Muyuka were however, not part of the campaign as they had no history of yellow fever cases in the past 50 years.
The Ministry of Health plans to vaccinate 345,592 people in Kumba and Muyuka districts. The country has requested support from the International Coordinating Group on Yellow Fever Vaccine Provision (YF-ICG), which manages the global emergency stockpile of yellow fever vaccine. The ICG has approved support for the vaccination campaign, which will be funded by the GAVI Alliance through the World Health Organization. This support is part of the global Yellow Fever Initiative which aims to prevent devastating yellow fever epidemics and secure adequate yellow fever vaccine supply for Africa.


There are two Snap! blocks for searching for a good architecture and choices for training methods and parameters.
The full-featured version looks like:
The block repeatedly makes a copy of the current model with random changes.
It keeps track of the one with the best score.
When it completes you can either use the best parameters found in your model and its training or
use the replace model named ... block to change the current model
to the best one found.
There are many parameters for controlling what parameters are changed during the search:
Number of experiments.
This controls how many times a new model is created and trained.
Number of samples.
This controls how many times each experiment is repeated.
Due to the random initial weights in the neural network the exact same parameters can produce different results.
The scores of each sample are averaged.
Initial number of training cycles.
This controls how many learning steps to take for each experiment.
Note that this is a parameter that can be changed during search by default.
Optimization method.
The optimization method controls how weights are adjusted during training.
For more information see this Wikipedia article.
Loss function.
This is used during training to determine how to improve the neural net's weights.
Note for categorical training (i.e. labelling things) the system picks the best one so no need to
for it to consider variants.
Activation function.
This is a function that is applied to the output of each artificial neuron.
Relu is often the best.
Read this Wikipedia article to learn more.
Shuffle data.
If true, the data is re-ordered after each training cycle.
Number of training cycles.
If enabled, the search will explore different numbers of training steps.
Validation split.
If enabled, the search will explore different fractions to split the data for training and for validation
(and evaluation).
Note that if instead validation data has been sent
using the send validation data ... block.
Dropout rate.
The dropout rate controls what fraction of the inputs to each artificial neuron are "dropped"
(i.e. treated as zero).
This helps prevent the model learning aspects of the data that specific to the training set.
It helps make the model be more general.
Number of layers.
If enabled, the search will consider both deeper and shallower models.
It will also explore wider and narrower layers.
The search needs to determine if one model variant is better than another.
The following parameters control how different metrics are weighed.
Loss weight.
For non-categorical models this is typically the most important measure.
The search algorithm uses the negative log of the validation loss.
Accuracy.
Only applies to categorical models and measures the fraction of times the model gave the right
label to elements of the validation dataset.
Duration of training.
All else being equal we prefer models that train quickly.
The search algorithm uses the negative log of the duration.
Size of model
Larger model take longer to load and to generate predictions.
The negative log of the number of parameters is used by the algorithm.
Standard deviation of the score
If the number of samples is greater than one the results of training a model
with the same hyperparameters can differ. A good set of parameter values should consistently produce good results.
The negative of the standard deviation is used to penalize parameters that lead to a range of outcomes.