In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
return \
tf.placeholder(
tf.float32,
shape=[None, image_width, image_height,
image_channels],
name='image_input'
),\
tf.placeholder(
tf.float32,
shape=[None, z_dim],
name='input_z'
),\
tf.placeholder(
tf.float32,
name='learning_rate'
)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
alpha = 0.1
with tf.variable_scope('discriminator', reuse=reuse):
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
flat = tf.reshape(relu2, (-1, 7*7*128))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
x1 = tf.layers.dense(z, 7*7*128)
x1 = tf.reshape(x1, (-1, 7, 7, 128))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
x2 = tf.layers.conv2d_transpose(x1, 64, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_2 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_1 + d_loss_2
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
vars = tf.trainable_variables()
d_vars = [var for var in vars if var.name.startswith('discriminator')]
g_vars = [var for var in vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
return \
tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars),\
tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
img_w, img_h, img_chan = data_shape[1], data_shape[2], data_shape[3]
inp_real, inp_z, _ = model_inputs(img_w, img_h, img_chan, z_dim)
d_loss, g_loss = model_loss(inp_real, inp_z, img_chan)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
saver = tf.train.Saver()
sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
display_interval = 100
num_img_to_disp = 25
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
batch_images *= 2
#Random noise for generator
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
#Run optimizers
_ = sess.run(d_opt, feed_dict={inp_real: batch_images, inp_z: batch_z})
_ = sess.run(g_opt, feed_dict={inp_z: batch_z, inp_real: batch_images})
if steps % display_interval == 0:
train_loss_d = d_loss.eval({inp_real: batch_images, inp_z: batch_z})
train_loss_g = g_loss.eval({inp_z: batch_z})
print("Epoch: {} of {}".format(epoch_i, epoch_count),
"D-loss: {}".format(train_loss_d),
"G-loss: {}".format(train_loss_g))
show_generator_output(sess, num_img_to_disp, inp_z, img_chan, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


So, Reddit keeps the video and the audio in seperate files, and for the moment, doesn't keep a copy of the video with the sound still attached.
If you really need to download a file with sound, I suggest trying YouTubeDL, it's a command line tool that will download and merge both the video and the sound.


Mike Gilson: Web Development Examples and Resources
WEB110
BIG
Web Research
Accessibility
SMO
Forms
Gallery
Google Tools
SEO
Calendar
Map
Contact Mike
Lightbox II Gallery
A sample image gallery, using
Lightbox II
.
A healthy breakfast option.
A tasty lunch choice.
A yummy dinner selection.


Welcome to the Lift Web Framework
Lift provides the best features for building interactive
web applications:
-
Super simple and wicked powerful Ajax and
Comet coding. This lets you build more interactive, user-friendly
sites.
-
Amazingly concise code with the powerful type-safety
of Scala. This means
more time spent coding features
and less time writing tests or chasing
parameter mis-matches.
-
Runs on all standard JEE (Java) application servers
including Jetty, Tomcat, WebLogic, etc.
This means you get the performance, scalability and
compatibility of the best web infrastructure around.
-
Built-in security means more time focusing on your
application and less time being defensive about
parameter tampering, SQL injection, Cross Site Scripting and
other nasty attacks.
Lift is built on Scala, a hybrid Functional and
O-O language that compiles code down to the Java Virtual Machine.
Scala code can call any Java code and make use of all Java classes.
Java code can call some Scala code. Lift applications are packaged as WAR files and
can be deployed on any
Servlet 2.4 engine (e.g., Tomcat 5.5.xx, Jetty 6.0, etc.)
Lift code is as clean and brief as Rails, yet performs at least 6
times faster and
is multithreaded. Additionally, because Scala is strongly typed,
the compiler
catches type errors. For example:
User.find(By(User.email, "foo@bar.com")) // legal
User.find(By(User.birthday, new Date("Jan 4, 1975"))) // legal
User.find(By(User.birthday, "foo@bar.com")) // compiler error
Lift is an open source project distributed under an Apache License V2.0


chrissy ziccarelli
programmer
|
pro-choice
|
punk
i teach computer science with
girls who code
&
hear me code
i also make fun things like my
nickibot


Home
Contact Us
Subjects
Our Tutors
MAKE AN ENQUIRY
The Subjects we teach
Subject to changes.
Subject List
Name:
Contact:
Enquiry:


help bshell -------------------------------------------------------------------------------------------------------------------------------------------------
Title
bshell -- Stata module to run shell processes in all platforms and modes.
Syntax
bshell command
Description
This is a drop-in replacement for shell that works on all platforms and modes. Normally it just passes the command on to shell. But in batch-mode on Windows shell doesn't work (commands are ignored) and so in these cases we use a compiled plugin to launch the process using win32 API. If you need to invoke cmd (for example to handle redirections), you can call bshell cmd /c ..., though note that if ... starts and ends with double quotes, then cmd will strip them (so you might have to add extras).
Examples
Example using bshell:
bshell notepad


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Add New Store Information


Consent
Could not find Consent Task in your uploaded .bpmn files. Here is what you can
check to fix that:
- If you have Consent Task in one of your .bpmn files then check whether it has attached
property with name 'consent'.
- Remember, that order (is determined when .bpmn file is uploaded) of business processes is
crucial. Tool searches consent task in previous processes or previous tasks. That means
if Consent Task is in process with order 5, it will be ignored when analysing process with
order 4.
This task was marked as Consent Task, help me to identify some relevant attributes
of Consent class.
Show relevant GDPR articles
Definitions
-
[...] ‘consent’ of the data subject means any freely given,
specific, informed and unambiguous indication of the data subject’s wishes by
which he or she, by a statement or by a clear affirmative
action, signifies agreement to the processing of personal data relating to him or
her; [...]
Lawfulness of processing
-
Processing shall be lawful only if and to the extent that at least one of the following
applies:
-
the data subject has given consent to the processing of his or her personal data
for one or more specific purposes; [...]
Conditions for consent
-
Where processing is based on consent, the controller shall be able to demonstrate that
the
data subject has consented to processing of his or her personal data.
-
If the data subject’s consent is given in the context of a written declaration which
also
concerns other matters, the request for consent shall be presented in a manner which is
clearly distinguishable from the other matters,
in an intelligible and easily accessible form, using clear and plain language. Any part
of such a declaration which constitutes an infringement of this Regulation shall not be
binding.
-
The data subject shall have the right to withdraw his or her consent at any time. The
withdrawal of consent shall not affect the lawfulness of processing based on consent
before
its withdrawal. Prior to giving consent, the data subject
shall be informed thereof . It shall be as easy to withdraw as to give consent.
-
When assessing whether consent is freely given,
utmost account shall be taken of whether, inter alia, the performance of a contract,
including the provision of a service, is conditional on consent to the processing of
personal data that is not necessary for the performance of that contract.
Conditions applicable to child's consent in relation to information
society services
-
Where point (a) of Article 6(1) applies, in relation to the offer of information society
services directly to a child, the processing of the personal data of a child shall be
lawful where the child is at least 16 years old. Where the child is below the age of 16
years, such processing shall be lawful only if and to the extent that consent is given
or authorised by the holder of parental responsibility over the child.
Member States may provide by law for a lower age for those purposes provided that such
lower age is not below 13 years.
-
The controller shall make reasonable efforts to verify in such cases that consent is
given or authorised by the holder of parental responsibility over the child, taking into
consideration available technology.
-
Paragraph 1 shall not affect the general contract law of Member States such as the rules
on the validity, formation or effect of a contract in relation to a child.
* Here we assume that if data subject has given consent prior to activities of processing
personal data (Consent Task was before Processing Task), then data subject agreed on all
processing purposes of processing his or her personal data.
** When collecting consent from data subject you should also provide some additional information
in agreement. Please see Article 13
(Information to be provided where personal data are collected from the data subject)
and Article 14 (Information to be
provided where personal data have not been obtained from the data subject)
Consent attributes


Foam covers New Delhi river as pollution hits 'very unhealthy' levels
Pollution levels in India's New Delhi hit 'very unhealthy' levels on Thursday (November 7) as the city remained covered in thick smog.
The badly-polluted River Yamuna, which flows through the national capital, was covered by foam and froth churned up by the current.
Monuments such as the India Gate war memorial and India's presidential palace remained engulfed in smog on Thursday morning.


Let us create our first list! To construct a list you use the function list()
:
my_list <- list(comp1, comp2 ...)
The arguments to the list function are the list components. Remember, these components can be matrices, vectors, other lists, ...
Construct a list, named my_list
, that contains the variables my_vector
, my_matrix
and my_df
as list components.
# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Construct list with these different elements:
my_list <-
# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Construct list with these different elements:
my_list <- list(my_vector, my_matrix, my_df)
msg = "Do not remove or change the definition of the variables `my_vector`, `my_matrix` or `my_df`!"
test_object("my_vector", undefined_msg = msg, incorrect_msg = msg)
test_object("my_matrix", undefined_msg = msg, incorrect_msg = msg)
test_object("my_df", undefined_msg = msg, incorrect_msg = msg)
test_object("my_list",
incorrect_msg = "It looks like `my_list` does not contain the correct elements. Make sure to pass the variables `my_vector`, `my_matrix` and `my_df` to the `list()` function, separated by commas, in this order.")
success_msg("Wonderful! Head over to the next exercise.")
Use the list()
function with my_vector
, my_matrix
and my_df
as arguments separated by a comma.


This module is the web server for running the REST API of Hemlock.
Created on 20 August 2013 @author: Charlie Lewis
This class is responsible for initializing the urls and web server.
This class is responsible for all API actions that involve adding something to something else.
This class is responsible for changing the server that a schedule runs on.
This class is responsible for all API actions that involve creating someting.
This class is responsible for all API actions that involve deleting something.
This class is responsible for deregistering systems.
This class is responsible for rendering the favicon.
This class is responsible for all requests about data fields or schemas.
This class is responsible for all API actions that involve getting something.
This class is responsible for all API actions that involve listing a type of something.
This class is responsible for all API actions that involve listing something specific to something else.
This class is responsible for all data query requests.
This class is responsble for registering a system.
This class is responsible for all API actions that involve removing something.


Here are few miscellaneous rules for blocking various types of IP addresses. These rules may be adapted to allow the specified IP values by simply changing the Deny directive to Allow. Check ’em out (edit values to suit your needs):
# block a partial domain via network/netmask values
Deny from <IP_ADDRESS>/<IP_ADDRESS>
# block a single domain
Deny from <IP_ADDRESS>
# block domain.com but allow sub.domain.com
Order deny,allow
Deny from domain.com
Allow from sub.domain.com


My name is M. Elliot Frost and I am a Full Stack Web Developer.
I've had the pleasure of leading successful teams, projects, and producing innovative ideas and products for clients across the board including Educational Technology, Defense System Technology, and Startup Development.
A little more about me,
For one, I love to travel. I've been around the US and Canada, seen all of the UK, and traveled through plenty parts of continental Europe. I like to take a little piece of culture with me back from each place I go.
A few more of my hobbies include competitive lifting, cooking, and building projects.


by Curtis Edmonds --<EMAIL_ADDRESS>The James Bond movies are sometimes described as formulaic, but that's not quite right. A formula is more exact, more scientific. You mix so many cubic centimeters of one substance with so many milliliters of another substance and you get the same number of joules or watts or whatever-it-is every time. Bond movies are a lot more like recipes. You have the same ingredients every time, but if the ingredients are stale or you have too much of one thing and not enough of another, whatever it is you're cooking doesn't taste right.
Here, in The World is Not Enough, most of the ingredients are there for a successful Bond movie. In fact, you can almost run them down in a checklist -- Roger Ebert did exactly that in his Chicago Sun-Times review, in fact. We've got Brosnan as the best Bond of the decade, the redoubtable Dame Judith Dench as M, the new Iron Lady of the United Kingdom, and the winsome Samantha Bond as Moneypenny in the front office. John Cleese joins Q (Desmond Llewellyn) in the gadget shop (although the bad guys have the best gadget, a helicopter chain saw thingy that can even deal with the titanium armor on Bond's BMW). The clever product placements are still around (including a Visa card that gets Bond everywhere he wants to be). The sultry Sophie Marceau, whose only fault is that she is not Elizabeth Hurley, steams up the screen quite nicely as the Bond Girl. The chase scenes are here, too, but they're nothing special. An imaginative race inside an oil pipeline is the best of them.
Only one ingredient is missing from the Bond recipe here, and it's arguably the most important. The Bond villain is, at least in my cookbook, the key spice that makes the recipe work. Without a compelling bad guy to be Bond's foil, the movie doesn't work as well. Take away the bad guy, and 007 might just as well spend the movie doing paperwork.
The bad guy in The World is Not Enough is a schmuck. His name is Renard, (Robert Carlyle) and he has a bullet in his brain. Apparently, the bullet is destroying the part of his brain that makes him feel pain. One wonders, though, if the bullet also hit the part of his brain that makes him interesting. Sure, he can hold hot rocks in his hand, and he has a cool scar, but he's got no charisma, and takes no joy in his evil work. Most Bond villains are over-the-top, but Renard is about a hundred feet under the top and sinking fast. He's supposed to be a nihilistic terrorist, which should be interesting, but he's so blah and dull that he might as well be a crazed accountant. (Brad Pitt's character from Fight Club would be a welcome relief in this part.)
A better bad guy would have added to the movie immensely by counterbalancing some of the more ridiculous aspects of the movie -- that is, the plot. The plot is dreadful -- not interesting enough to be fun, just complicated enough to give you a headache. And the complications aren't there for any good reason. Like the nonsense about the Trade Federation at the start of the new Star Wars movie, the plot is just there to stitch the action sequences together.
It is arguable that the victory of Ronald Reagan and Margaret Thatcher and the West in the Cold War deprived James Bond movies of good plots. I think it runs deeper than that, though. The world today is still a scary place, even without the Soviets. But the scariness comes from many different parts of the world, most of which we're not familiar with. (The World is Not Enough spends a lot of time in the former Soviet republics of Azerbaijan and Kazakhstan -- and manages, somehow, to make believe that Baku is the Russian Riviera, complete with casinos and fancy villas.) And the geopolitical scariness is of a different order -- cyberterrorism and biological warfare and the like -- that the Bonds of this world are ill-equipped to deal with. It ought not to be that much of a stretch to come up with some plausible way to blow up the world, but the writers here can't figure it out.
The other flaws in the movie show up as well. Denise Richards is not given much to do as the second Bond Girl, and despite her supposed status as a rocket scientist (insert joke here), she's just given master-of-the-obvious lines like "The ship is flooding!" (She's given one line of Russian -- which I could actually follow, meaning that her character has the same bad accent and poor grasp of Russian that I do.) Bond is given a shoulder injury that only manifests itself when he is in the clutches of the bad guys. Robbie Coltrane, who would have made a fine replacement of Q, is only marginally effective as Bond's ally in the Russian Mafia. The opening chase scene is dull and overlong -- so much so that the credits seem out of place, almost as if Monty Python were doing the editing.
Anyone who has ever tried to replicate their grandmother's stuffing for Thanksgiving will sympathize with The World is Not Enough: a good recipe gone wrong. This holiday season, James Bond has served up a turkey.
-- Curtis Edmonds<EMAIL_ADDRESS>Movie Reviews: http://www.hsbr.org/buzz/reviewer/reviews/bdreviews.html http://www.epinions.com/user-curtisedmonds
"Oh, if life were like the movies, I'd never be blue." -- Alan Jackson, "Here In The Real World"
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.
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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


---
layout: sidebar
---
The Simple Theorem Prover
STP is a constraint solver for the theory of quantifier-free bitvectors that
can solve many kinds of problems generated by program analysis tools, theorem
provers, automated bug finders, cryptographic algorithms, intelligent fuzzers
and model checkers.
Features
- Easy to embed or run standalone
- Bindings for C, C++, and Python
- Supports multiple query input formats
- Open source and MIT licensed
Awards
STP's speed and accuracy compare favorably with other solvers in the bitvector (QF_BV) category.
-
1st Single Query — SMTCOMP 2023
-
1st Single Query UNSAT & Single Query 24sec Performance — SMTCOMP 2022
-
1st Cloud & Incremental — SMTCOMP 2021
-
2nd — SMTCOMP 2014
-
2nd — SMTCOMP 2011
-
1st — SMTCOMP 2010
-
1st — SMTCOMP 2006


Shopping
Samples and tutorials
Most of it is again in japanese... and as usual datasheet remains to be found (I only have the printed doc coming with the ship, I put as a pdf here under)!
YMZ285-akizuki.pdf PDF scan of the documentation coming with the ship (thank you Akizuki-denshi!)(big 2Mo!)


Drag either the sliders above or the markers on the map to select the portion of the track that you'd like to keep. Then click the Slice button below.
Select a GPX file from your computer that you'd like to edit.
Drag either the sliders above or the markers on the map to select the portion of the track that you'd like to keep. Then click the Slice button below.


--- layout: post title: Functional Living date: type: post parent_id: '0' published: false password: '' status: draft categories: - Random Thoughts tags: [] meta: _edit_last: '1965023' author: login: paradoxofmatrix email<EMAIL_ADDRESS>display_name: paradoxofmatrix first_name: '' last_name: '' ---
Am I dead, puzzeled Neo asks Morpheus. "Far from it" is the reply he gets. Why is far from being dead an achievement one might wonder. But pain doesn't allow him to think about anything else. Every cell of his body seems to be tied to some invisible string. He can't move his hands or legs or head. He can't even open his eyes.  What is this pull, these strings.. is it just gravity. Why can't he move freely the way his thoughts are moving right now?!


{% include info-block.html title="What are Design Principles?" description="Design Principles are a set of considerations that form the basis of any good product."%}
{% include info-block.html title="Why use them?" description="Design Principles help teams with decision making. A few simple principles or constructive questions will guide your team towards making appropriate decisions." %}
{% include info-block.html title="Who uses them?" description="Organisations, individuals and product teams have benefited from writing and following their principles. Here are a few examples:" %}
{% include block-grid-featured.html%}
{% include footer.html %}


Welcome to Daniekeys Official Page.
Are you here for the first time or you have been here before ? which ever way it comes, you are welcome to Daniekeys Official Website.
Just relax and leave everthing to us!
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Our website are very responsive and compatible with all forms device. we have got user experience from across 30+ devices
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As we all know that the world is revolving daily in terms of technologies ,so therefore at Daniekeys technology its our believe that learning never stops , in like of this we make of the latest reigning technologies for our website development
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Hello again! Thanks for wanting to read a little more about me. I was born in Japan and raised there until the young age of 5 and a half. We got up and moved to beautiful Seattle, Washington where I went to school and eventually graduated from University of Washington. I went to school for International Studies and after graduating I got up and moved my life over to Yokosuka, Japan. And since then I have been bitten by the travel bug.
I taught English at an elementary school in Japan for 3 year and aboslutely loved every minute of it. I suddenly decided to uproot my life again and move to Melbourne, Australia. I got a working holiday visa (which I suggest everyone under the age of 30 to go ahead and do) and worked while checking out all the great things Australia has to offer.
I ended up back in the Pacific Northwest in lovely Portland, Oregon. Close enough to home but far enough where the tranisition back to American living wasn't such a shock. I worked as a bartender at Bamboo Sushi and eventually got myself signed up at


main function file More...
#include "aldc.h"
#include "fio.h"
#include "datasets_paths.c"
Go to the source code of this file.
main function file
author: Palaiologos Efstathios
Definition in file aldc_encoder.c.
Definition at line 11 of file aldc_encoder.c.


Cofounder & CTO of eTobb.com Writer and Expert Mentor on CodeMentor.io Follow @jadjoubran Follow @JoubranJad
This tutorial contains 8 steps. Each step being in a different branch. We are currently on branch Master. Let's start the tutorial by switching to step1 and then press Done
git checkout -- . git checkout step1


| Top |
| gint | child-internal-pad-x | Read |
| gint | child-internal-pad-y | Read |
| gint | child-min-height | Read |
| gint | child-min-width | Read |
GObject ╰── GInitiallyUnowned ╰── GtkWidget ╰── GtkContainer ╰── GtkBox ╰── GtkButtonBox ├── GtkHButtonBox ╰── GtkVButtonBox
A button box should be used to provide a consistent layout of buttons throughout your application. The layout/spacing can be altered by the programmer, or if desired, by the user to alter the “feel” of a program to a small degree.
gtk_button_box_get_layout() and gtk_button_box_set_layout()
retrieve and
alter the method used to spread the buttons in a button box across the
container, respectively.
The main purpose of GtkButtonBox is to make sure the children have all the same size. GtkButtonBox gives all children the same size, but it does allow 'outliers' to keep their own larger size.
To exempt individual children from homogeneous sizing regardless of their 'outlier' status, you can set the non-homogeneous child property.
GtkWidget *
gtk_button_box_new (GtkOrientation orientation
);
Creates a new GtkButtonBox.
Since: 3.0
GtkButtonBoxStyle
gtk_button_box_get_layout (GtkButtonBox *widget
);
Retrieves the method being used to arrange the buttons in a button box.
gboolean gtk_button_box_get_child_secondary (GtkButtonBox *widget
,GtkWidget *child
);
Returns whether child
should appear in a secondary group of children.
Since: 2.4
gboolean gtk_button_box_get_child_non_homogeneous (GtkButtonBox *widget
,GtkWidget *child
);
Returns whether the child is exempted from homogenous sizing.
Since: 3.2
void gtk_button_box_set_layout (GtkButtonBox *widget
,GtkButtonBoxStyle layout_style
);
Changes the way buttons are arranged in their container.
void gtk_button_box_set_child_secondary (GtkButtonBox *widget
,GtkWidget *child
,gboolean is_secondary
);
Sets whether child
should appear in a secondary group of children.
A typical use of a secondary child is the help button in a dialog.
This group appears after the other children if the style
is GTK_BUTTONBOX_START
, GTK_BUTTONBOX_SPREAD
or
GTK_BUTTONBOX_EDGE
, and before the other children if the style
is GTK_BUTTONBOX_END
. For horizontal button boxes, the definition
of before/after depends on direction of the widget (see
gtk_widget_set_direction()
). If the style is GTK_BUTTONBOX_START
or GTK_BUTTONBOX_END
, then the secondary children are aligned at
the other end of the button box from the main children. For the
other styles, they appear immediately next to the main children.
widget |
||
child |
a child of |
|
is_secondary |
if |
void gtk_button_box_set_child_non_homogeneous (GtkButtonBox *widget
,GtkWidget *child
,gboolean non_homogeneous
);
Sets whether the child is exempted from homogeous sizing.
Since: 3.2
Used to dictate the style that a GtkButtonBox uses to layout the buttons it contains.
|
Buttons are evenly spread across the box. |
||
|
Buttons are placed at the edges of the box. |
||
|
Buttons are grouped towards the start of the box, (on the left for a HBox, or the top for a VBox). |
||
|
Buttons are grouped towards the end of the box, (on the right for a HBox, or the bottom for a VBox). |
||
|
Buttons are centered in the box. Since 2.12. |
||
|
Buttons expand to fill the box. This entails giving
buttons a "linked" appearance, making button sizes homogeneous, and
setting spacing to 0 (same as calling |
“layout-style”
property“layout-style” GtkButtonBoxStyle
How to lay out the buttons in the box. Possible values are: spread, edge, start and end.
Flags: Read / Write
Default value: GTK_BUTTONBOX_EDGE
“non-homogeneous”
child property“non-homogeneous” gboolean
If TRUE, the child will not be subject to homogeneous sizing.
Flags: Read / Write
Default value: FALSE
“secondary”
child property“secondary” gboolean
If TRUE, the child appears in a secondary group of children, suitable for, e.g., help buttons.
Flags: Read / Write
Default value: FALSE
“child-internal-pad-x”
style property“child-internal-pad-x” gint
The amount to increase a child's size on either side.
GtkButtonBox:child-internal-pad-x
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS padding instead.
Flags: Read
Allowed values: >= 0
Default value: 4
“child-internal-pad-y”
style property“child-internal-pad-y” gint
The amount to increase a child's size on the top and bottom.
GtkButtonBox:child-internal-pad-y
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS padding instead.
Flags: Read
Allowed values: >= 0
Default value: 0
“child-min-height”
style property“child-min-height” gint
The minimum height of buttons inside the box.
GtkButtonBox:child-min-height
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS min-height instead.
Flags: Read
Allowed values: >= 0
Default value: 27
“child-min-width”
style property“child-min-width” gint
The minimum width of buttons inside the box.
GtkButtonBox:child-min-width
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS min-width instead.
Flags: Read
Allowed values: >= 0
Default value: 85


The Lighthouse(Pharos) of Alexandria was built by Sostratus of Cnidus during the 3rd century B.C. on the island of Pharos off the coast of Egypt. It was destroyed by an earthquake in the 13th century.


|
Charters School "gives very good value for money."
|
The school was inspected by a team from Ofsted in September 1996. These are a number of quotations taken from the main findings of the full inspection report.
“Charters School is a very good school with some outstanding features which include its excellent atmosphere and very good examination results. The school has worked hard to achieve its deserved success.” To find outwhat the inspectors said about each aspect of our school, please click on the links below;
ExaminationResults / Pupils' Progress / Teaching / TheCurriculum / Pastoral / Management
“Results in the tests taken at age 14 at the end of Key Stage 3 (KS3) are well above the national level and it is clear that the school enhances its pupils’ attainment by this stage. This enhancement is further confirmed at the end of KS4 in its GCSE results, which are well above the national averages. Results have been improving over recent years, and reached their highest this year. The school maintains this high achievement in its A level results, which are well above average both for grades A and B and in all pass grades.”
“Pupils make good progress, at all ages and in most subjects. They profit from their lessons, and are alert, responsive and very supportive of their teachers and each other.”
“Behaviour and attendance are very good. Pupils value the education they receive. They are attentive and courteous around the school and give it their warm support.”
“Teaching is a strength of the school. It is assured and professional and is of good quality...........”
“It is good in the GCSE years, where examination demands bring out good planning, careful teaching and sound management skills.”
“.....there is much thoroughly good teaching at all levels, which helps pupils enjoy learning and make good progress. Teaching is characterised by the careful setting and marking of homework. The marking of work is regular and thorough.”
“The school’s curriculum is broad and well structured. Links with primary schools are well developed and careers guidance is very effective, with well constructed programmes of preparation for higher education.”
“The school provides well for the moral, social and cultural developmentof its pupils. The year system is ably led by committed year heads and their teams of tutors. Parents appreciate the care their children receive and the warm sense of community and purpose they perceive in theschool.”
“There is a good system of rewards and sanctions, and the school is an orderly, friendly place in which pupils know the difference between right and wrong, and act accordingly.”
“The Tirabad field study centre in mid-Wales plays a strong part in social development.”
“The welfare and health of pupils are well catered for. There is a sense of careful support at all levels, and very good links between the school and its community.”
“Management of the school’s financial resources is very efficient. Money is carefully managed and staff costings and deployment are sensible and economical. It manages all aspects of its life well, enabling its pupils to achieve very good results.”


## vikram chandra ### faber & faber, 2013
summary
much of the beginning of the books is about his personal history and encounter with software, and his explanation of what software is.
then in the code of beauty, he explains the unique features of the vedas and sanskrit, as a generative ruleset (compiled by a linguist named panini into a book called the Ashtadhyayi) which, given any morpheme and phoneme, can output gramatically valid sentences
rick briggs, doing AI at NASA considers sanskrit as what could be seen today as hardware-less computing, operating on natural language to generate semantic elements, but which could also just be a search for clear, unambiguous understanding that is inherent to any human being.
the Ashtadhyayi has crazy similarities with programming languages (context-free rules, recursion, inheritance, etc.)
the relationship between language, meaning and function. - how is meaning transferred? - how is it understood? - does language impel action?
enjoyment of poetry in sanskrit happens not through egotistical, subjective appreciation, but through “conoisseurship”, resulting from education, experience, temperament.
ugliness is interdependency (incoherence, incomprehemsibility, unpredictability)
software is complicated because it tries to mimick the world (cf. effective computation)S
according to Abhinavagupta, the self is just layers of memories as “latent impressions”. the aesthetic experience is the conjuring up of these impressions into the self. it results in a generalization, a trans-personalization, an inter-subjectivity.
unlike code, poetry has no success or failure, it just waits to manifest.
in art, if the regularity of form is essential, and if determinism is boring, it could imply that the potential for becoming art of source code is in the indeterminacy of its how, and not its what
poetry/code <-> affect/effect
tom christiansen: “the computer is the game”


Flapper is a jQuery plugin that replicates the split-flap (or "Solari") displays that used to be common in train stations and airports.
These displays used a rotating stack of letters and numbers printed on hinged panels. As the stack rotated, a new symbol would fall into place. Flapper recreates this effect using CSS transformations.
Flapper can display words or numbers, with or without formatting. It's ready to use with six sizes, two color themes, and two animation styles. Best of all, it's written entirely in HTML and CSS, so you can make it look however you'd like.
For best results, Flapper requires the numberformatter and transform jQuery plugins. But, it can also be used on its own with fewer special effects.
Two Animation Effects
Flapper's two animation modes allow you to design with special effects without affecting the performance of your page. Choose from gorgeous and detailed or quick and minimalistic.
Words and Numbers
Flapper can display any text or numeric sequence. It comes with presets for numeric, alphabetic, and alphanumeric displays. You can configure Flapper to display any symbols or international characters that you require.
Six Sizes and Two Colors
Flapper comes with six size presets and two color schemes, so you can quickly drop it into your page.
Themeable
Customize Flapper with your own fonts, colors, and gradients. Run the digits together, or space them far apart. Tweak Flapper's default styles, or create your own from scratch.
Easy-to-Use
Simply attach Flapper to any input
element anywhere on your page.
Flapper will hide the element and replace it with a numeric display. Then, it will listen
for change
events on the element's value and update the display automatically.
<input id="display" />
<script language="JavaScript">
$('#display').flapper(options).val(12345).change();
<script>
Any time the input's value changes, just call change()
to update the display.
Flapper takes care of the rest.
Start Using Flapper
Head over to Flapper's GitHub page to download the code and read the documentation. Flapper is released under the MIT license, which allows redistribution and modification with attribution.


LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform. In this project, I use LendingClub dataset that obtained from kaggle and this project is one of the project that I have done from the Python for Data Science and Machine Learning Bootcamp by Jose Portilla on Udemy.
Data Overview
Project Intro/Objective
Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model thatcan predict wether or nor a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan. Keep in mind classification metrics when evaluating the performance of your model!
The "loan_status" column contains our label.
Project Library
- Numpy
- Pandas
- Matplotlib
- Seaborn
- scikit-learn (sklearn)
- TensorFlow
- Keras
- random
Starter Code
This starter code function is to create feat_info() function that will help us to know the description of a column
Data and Setup
In this section, I want to show some of the data information, it includes Data Frame Info and Data Frame Head. First, we import the basic modules and read the lending_club_loan_two.csv file.
After that, we can see the data frame info and head.
Exploratory Data Analysis
In this section, I want to know which variables are important, view summary statistics, and visualize the data.
loan_status Countplot
Because I will be attempting to predict loan_status, I try to create a plot that will show the loan_status. From the countplot below, we can see that the majority of the borrower can fully paid their loan.
loan_status Histogram
Then, I create a histogram to see the distribuion of the loan mount. From the histogram below, we can see that amount of the borrower loan is between $5000 - $20000.
Variable HeatMap
After knowing the loan status count and histogram, I try to understand the data using correlation. First, I have to know the correlation of the variable within the data frame.
Then, I create a HeatMap using that correlated data frame.
From the HeatMap above, we can see that the most correlated data is loan_amnt and installment. We can check the description of loan_amnt and installment using the starter code.
I try to create a scatter plot to understand the relationship between loan_amnt and installment. From the scatter plot below, we can see that loan_amnt and installment correlated with each other.
Then I try to create a boxplot to understand the relationship between loan_status and loan_amnt. From the boxplot below, we can see the loan_amnt does not have effect on loan_status.
I calculate the summary statistics for the loan amount, grouped by the loan_status.
Then I try to explore the grade and subgrades columns that LendingClub attributes to the loans.
Then I create countplot to understand the relationship between loan_status and grade. From the countplot below, we can see grade E, F, and G has problem to pay their loan. So we can explore more on the subgrade to confirm this.
From the countplot below, we can see that grade F and G has problem to pay their loan. Then I try to isolate Grade F and G and create a new countplot.
Below is the countplot for grade F and G.
I create a new column called 'loan_repaid' which will contain a 1 if the loan status was "Fully Paid" and a 0 if it was "Charged Off".
Then I create a bar plot showing the correlation of the numeric features to the new loan_repaid column.
Data Preprocessing
In this section, I remove or fill any missing data, remove unnecessary or repetitive features, and convert categorical string features to dummy variables.
Missing Value
First, I want to know is there any missing value on LendingClub dataset, and if there is a missing value, I want to know the percentage of the missing value.
From the data above, we can see that empt_title and empt_length is the most columns that with missing value. So, we have to check the desctiption of this columns name.
We get the definition of emp_title and emp_length. We can check how many unique employment title are there.
There are 173105 unique employment title. Realistically there are too many unique job titles to try to convert this to a dummy variable feature. We can drop the emp_title.
Then, I create a count plot of the emp_length feature column. First, I have to count how many category of emp_length are, then sort using ascending. After that, I can create a countplot.
Then I creat a countplot using loan_status as hue. From the counplot below, still doesn't really inform us if there is a strong relationship between employment length and being charged off, what we want is the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan. So, I create a barplot that will give me information about the relationship emp_length and loan_status.
First, I have to calculate the number of employment charged off and fully paid based on their emp_length. Then I can calculate the percentage and create a barplot.
From the barplot below, we can see that there is no strong relationship between emp_length and loan_status, we can drop the emp_length from our data frame.
We can check our data frame again and see the other missing value. We can see that we have missing value on title, we can check this column and compare it with purpose column, is it a repetead information?
We can see that purpose and title contains the same informatin. So, we can drop the title column.
After dealing with emp_title, emp_length, and title, we can move to other column that has mising value. At mort_acc column, we there are many missing data, so we have to deal with this column.
I've got the definition of mort_acc using starter code, then we count the value of the mort acc. I can fill this missing data with linear model or mean/median of mort_acc, or simply drop this column. But, first I have to see the correlation of mort_acc with other column.
Looks like the total_acc feature correlates with the mort_acc , I will try using fillna() approach. I will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry. To get the result below:
I will fill in the missing mort_acc values based on their total_acc value. If the mort_acc is missing, then we will fill in that missing value with the mean value corresponding to its total_acc value from the Series we created above. This involves using an .apply() method with two columns.
There are two more column that has missing value, but they account for less than 0.5% of the total data. I can drop this two columns.
Categorical Variables and Dummy Variables
I have done working with missing data. Now I just need to deal with the string values due to the categorical columns. First, I have to know which one is the string data, then I go through all the string features to see what I should do with them.
term Feature
I have to convert this string to either a 36 or 60 integer numeric data type. I will use .apply() to convert this string to integer numeric.
grade Feature
I already know that grade is part of subgrade, I can drop this column.
Then I convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe.
verification_status, application_type, initial_list_status, purpose Feature
I convert these columns: ['verification_status', 'application_type', 'initial_list_status', 'purpose'] into dummy variables and concatenate them with the original dataframe.
home_ownership Feature
First, I want to teview the value_counts for the home_ownership column.
I convert these to dummy variables, but replace NONE and ANY with OTHER, so that I end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe.
address Feature
I can use the zip code from the address column, I can feature engineer it and create a column called 'zip_code' that extracts the zip code from the address column.
issue_d Feature
I do not know how to use this data, because I wouldn't know beforehand whether or not a loan would be issued when using my model, so in theory I wouldn't have an issue_date, I have to drop this feature.
issue_d Feature
This appears to be a historical time stamp feature. I will rxtract the year from this feature using a .apply function, then convert it to a numeric feature. I will set this new data to a feature column called 'earliest_cr_year'.Then I will drop the earliest_cr_line feature.
Model Preparation
Train Test Split
First, I import train test split from sklearn. Then drop the loan_status since its duplicate of loan_repaid column, and set X and y variables to the .values of the features and label.
After that, I perform train test split with test_size=0.2 and a random_state of 101.
Normalizing the Data
I will use a MinMaxScaler to normalize the feature data X_train and X_test. Then I only fit on the X_train data because I don't want data leakage from the test.
Creating the Model
Load TensorFlow and Keras
I will use tensorflow.keras.models to import Sequential and tensorflow.keras.layers to import Dense and Dropout.
For information, TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it’s built-in Python.
Before setting the model, I have to know the shape of X_train. Using the code above, I know that X_train have 78 output. I can set the a model that goes 78 --> 39 --> 19--> 1 output neuron. Because we have two hidden layers, we can called this model as Deep Learning.
I will use keras sequential model. Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
I will use keras dense layer. Dense layer is a neural network layer that is connected deeply, which means each neuron in the dense layer receives input from all neurons of its previous layer.
For activation function on Input layer and Hidden layers, I will use rectified linear activation function (relu). relu is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.
For activation function on Output layer and Hidden layers, I will use sigmoid. sigmoid is exists between 0 to 1, we can use this activation function to give me an output of 0 or 1.
I will set dropout layer to 0.2 in case of overfitting.
Because we want an output 0 or 1, I will use binary_crossentropy as loss.
I will use adam optimizer because adam optimizer is the most effective optimizer that we have right now.
In model.fit, I will fit the model to the training data for at least 25 epochs and add in the validation data for later plotting.
After done with model.fit, I can save my model.
Evaluating the Model
Valuation Loss versus Training Loss
To see how well my model perform, I can plot the valuation loss and training loss and see if there is much difference between this two value. If not, my model is good enough to use.
From the plot above, we can see that my training loss is not much different with valuation loss. I can say that my model is good enough but still has room for improvement.
Classification Report and Confusion Matrix
To create a classification report and confusion matrix, I have to import them from sklearn. Then I create a predictions variable using X_test then print it to see the result.
From the classification report, I got good result when predicting the fully paid person but got some problems with predicting the charged-off person, where I can see the recall and f1-socre value is not good enough. I have to explore more to the charged-off person.
From the confussion matrix, I got more False Positive than True Positive, but I got more True Negative than False Negative. From this confussion matrix, I can get information that my model is good enough to predict the charged-off person rather than fully paid person.
From the classification report and confussion matrix, I can conclude that I have to explore more on the charged-off person and try to manipulate the imbalance data to get more convincing results.
Additional Resources
- Header Backgrounds by Jason Leung at unsplash.com
- Kaggle Original Challenge Source
- For further explanation regarding python code, please kindly check this link.


Britain's KSI beats YouTube rival Logan Paul in boxing rematch
Britain’s KSI has won his six-round cruiserweight bout against fellow YouTube personality Logan Paul in Los Angeles.
The fight at the Staples Centre was won by the Watford man in a split decision – 55-56 57-54 56-55.
The pair first fought last year in Manchester but a “majority draw” was declared after six rounds.
“Last time I was scared to fight,” said KSI, whose real name is Olajide Olayinka Williams JJ Olatunji, after the match.
He couldn’t deal with my jabsKSI
“This time I just went in. I just kept going and going.”
“He couldn’t deal with my jabs. I got him.”
KSI – who has more than 20 million YouTube subscribers – was earlier forced to clarify comments he made ahead of the highly anticipated match, saying that he did not want to kill Paul.
The social media star took part in pre-fight banter with Paul at a press conference on Thursday, where he said to his rival: “You’re going to f****** die.”
“I don’t intend to kill Logan physically tomorrow, I only intend to kill his youtube career and spirit,” he tweeted.
“After this fight, I still want Logan to be alive and well.”
Paul – who has 19.9 million YouTube subscribers – did not touch gloves with his rival at the end of the match.
Their bout at Manchester Arena last year sold out to 20,000 fans and drew an audience of 2.5 million live viewers and more than 19 million subsequent views, making it the biggest internet event in history.
It was also named the most popular white-collar boxing match of all time.
However, viewers were left dissatisfied with the outcome of the fight.
Saturday’s fight had been expected to notch one million viewers on London-based pay-per-view service DAZN.


Shilen's Stone Statue:
The eldest daughter of Einhasad and Gran Kain is inquiring about you.
You stem from the Lizardman race, but you are now a part of the Elroki tribe that was born from the energy of water and prospered under the protection of the water goddess Shilen. Is this true?
Say yes
Say honestly that you are not a native Elrokian.


Medium.js keeps HTML code within contenteditable
semantic, simple, and clean. Medium.js also supports placeholders, automatic HR (or BR, or P) creation, events, hotkeys, simple & complex element injection, and more!
Medium.js is html5 compliant and has been tested in Firefox, Chrome, Safari, Opera, & IE8+. Browser support is constantly improving!
This script is inspired by Medium's beautiful content editor, something we have aspired to emulate since its inception.
This code is brought to you by:
(cmd/ctrl+[b,u,i])
input
or textarea
Simply include the dependencies, Medium.js library, CSS, and you are ready to rock.
his is Medium.j
The code within Medium.js editors are very semantic and organized, giving you peace of mind with using contenteditable
.
All options (excluding modifiers
, tags
, and cssClasses
) may be overridden with data-medium-*
attributes on the element.
element
: The element in which you want to apply Medium.js to.modifier
: The modifier you want for making things bold/italic etc. May be set to 'auto'
, 'cmd'
, or 'ctrl'
. placeholder
: An optional string that displays when the editor is empty autofocus
: Set the focus to the specified element automatically autoHR
: An HR tag will automatically be inserted if there are two empty paragraphs in succession mode
: Dictate the mode in which you want to allow editing. Currently there are three:
Medium.inlineMode
or "inline"
allows for no rich text editing, and no newlines. Similar to an input
element.Medium.partialMode
or "partial"
is like a textarea
, but allows support for paragraphs.Medium.richMode
or "rich"
allows styling of the text, paragraphs, and hr tags.Medium.inlineRichMode
or "rich"
allows styling of the text, but no new lines.maxLength
: The max size you want to allow for the editor modifiers[{Number}|{String}] = function(event, element) {};
:
Key modifiers from event.keyCode
that will be used to apply the respective styling changes or hook into events.
tags
: Dictates allowed tags. Can be set to null
for extreme freedom.tags.break
is the element that will be created for line breaks.tags.horizontalRule
is the element that will be created for a horizontal rule.tags.paragraph
is the element that will be created for paragraphs.tags.outerLevel
are the only elements allowed to replace a top-level tag.tags.innerLevel
are the only tags allowed within the parent tags. No additional tags are allowed unless included here.attributes
: Dictates attributes. Can be set to null
for extreme freedom.attributes.remove
are attributes to be removed.cssClasses
: Various classes used within Medium.js, customizable to your liking. beforeInvokeElement()
:
Triggered just before an element is invoked.
An element is invoked over a highlighted area.
The this
within the context of this function is Medium.Element
that is about to be invoked.
beforeInsertHtml()
:
Triggered just before html is added.
Inserted at cursor, but only if medium's element has focus.
The this
within the context of this function is Medium.Html
that is about to be inserted.
beforeAddTag(tag, shouldFocus, isEditable, afterElement)
:
Triggered just before a tag is told to be created from a keyboard action.
keyContext[{Number}|{String}] = function(event, element) {};
:
Triggered just before a keydown event.
Can return false or true to override existing functionality, or null to continue.
The element
is the element where the caret currently resides.
Methods provide functionality that can be used with buttons, toolbars, WYSIWYG editors, and the environment it is being used with.
destroy()
: Destroy the instantiated medium.js invokeElement({String}tagName, {Object} [attributes])
: invokes an element.insertHtml({String|HtmlElement} html, {Function} [callback])
: inserts html into the editor at the cursor.makeUndoable()
: makes the current state of the editor undoable/redoable. redo()
: redo an edit. undo()
: undo an edit. value({String} [value])
: set or get value from medium.Medium.js allows you to take total control over contenteditable
elements. This means that you can use contenteditable
without fearing your user is going to enter poor or invalid HTML. It keeps things tight, organized, and semantic.
Yes... Coming soon. The focus of Medium.js has been to make introducing a toolbar, any toolbar, YOUR toolbar, ultra easy. Take a look at wikiLingo for example. More to come!
Yup. This is because of you. But if you find a bug, please let us know or fork Medium.js and contribute so we can continue wide support!
Need commercial support for Medium.js? Currently provided by Visual Interop Development llc., drop us a line, we can't wait to see what we can do together.
MIT Licensing
Copyright (c) 2013 Jacob Kelley
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


A QOL interface to keep all the member types consistent.
Public Methods | |
| string | __toString () |
| string | getVisibility () |
| bool | isStatic () |
| Public Methods inherited from XMLWriterInterface | |
| void | write (RefXML $xml) |
| Reflector writes itself to the XML. More... | |
| string __toString | ( | ) |
<ClassFQN>::<identifier>
Implemented in RefMethod, AbstractMagicMember, RefProperty, and RefConstant.
| string getVisibility | ( | ) |
public
, protected
, private
Implemented in AbstractMagicMember.
| bool isStatic | ( | ) |
Implemented in RefConstant.


--- layout: article title: '_Levitation: Physics and Psychology in the Service of Deception_' authors: - ottaviani-jim excerpt: > The best magicians share their secrets, it seems. In this excerpt from Jim Ottaviani and Janine Johnston’s graphic novel, _Levitation_, a stage hand spills his guts on one of the greatest stage illusions ever invented: the Levitating Woman. permalink: /issues/2013/4/not-so-funny-pages-excerpt-levitation supernotes: /issues/1/2/ottaviani-levitation.json toc: 24 volume: 1 number: 2 ---
The science fiction author Arthur C. Clarke once proposed three laws for sorting through predictions of the future. The third is the most famous, for good reason: “Any sufficiently advanced technology is indistinguishable from magic.”
It’s a “magic” that can wear off fairly quickly, once the audience grasps that technology. But for historians of science, it’s a useful reminder of why people believe what their experience of the world tells them shouldn’t be true. And as such, it’s a damn good guide to the history of prestidigitation: stage magic.
Clarke’s law comes quickly to writer Jim Ottaviani when asked why he and the artist Janine Johnston made Levitation: Physics and Psychology in the Service of Deception, a comic book history about the late nineteenth- and early twentieth-century illusionists that invented, stole, and perfected one of the greatest tricks ever performed on stage: the levitating woman.
Ottaviani, a former nuclear plant operator and current librarian at the University of Michigan, turned to telling scientific history in comic book form after reading Richard Rhodes’s The Making of the Atomic Bomb. He and his many artistic collaborators have since delivered a steady stream of award-winning, New York Review of Books-praised fare, like his most recent work Feynman, about the famed Nobel-winning, Challenger-debunking, bongo-playing, Manhattan Project physicist. His next book, Primates: The Fearless Science of Jane Goodall, Dian Fossey, and Birute Galdikas, drawn by Maris Wicks, comes out this June.
Still, Ottaviani doesn’t feel like he strayed from ‘hard’ science to write Levitation. “Throughout the history of magic, science and technology that was not yet widely understood was used to baffle and amaze people,” he writes.
Ottaviani believes that the explanatory power of Levitation, like his other work, begins with his background in engineering: the ability to take “things—be they theories or stories—apart to find out how they work or where they came from and [put] them back together via pictures.” He’s quick to add, however, that readers don’t see his script, “so the success of a story hinges 100% on the skill of the artist every time.” The artist on Levitation, Johnston, sells the story with excellent face and body work, fin de siècle scenery, and, most importantly, the ability to convey both the physics, process, and wonder behind the levitation trick.
Levitation is an excellent piece of storytelling and explanation, delivered from the perspective of the stagehands that suffered the 1930s magician Howard Thurston. According to Thurston’s predecessor in the levitation trick, Harry Kellar, Thurston broke the illusion by inviting audience members up on stage to examine it. But did Thurston ruin the trick? Or did he just demonstrate another layer of its power?
We’re excited to excerpt its opening pages in this issue of The Appendix—and we should add, if you’re wondering, the full text doesn’t stint on the reveal.


Several families, who had been commuting from the south of Johannesburg to Sandton Bible Church in the Fourways area, had the vision to start a new Bible church closer to home. These families asked American missionaries with Biblical Ministries Worldwide, who had been serving at Sandton Bible Church, if they would lead the new church plant in the south.
The initial efforts began with a series of Bible studies, and as God worked through relationships, the group expanded, and on 31 October 2004, Sunday morning services began in Glenanda.
MountainView moved to the Sha-Mani Conference Centre in Alberton in 2005 and met there for several years. As children, youth, and ladies' and men's ministries grew, the church to almost 80 believers.
We saved up for years toward the purchase of our own building, and in August of 2010, we purchased a house in Glenanda (four houses away from where MountainView began in 2004!) that had a massive living area that we could renovate into a church hall. The house has become home and is now a very busy place! God has continued to provide leadership for the church through the years from missionaries and local men. As we continue to grow, both in numbers and financially, we look forward to moving into a larger facility in the future.


MT
3/4 BP
BS
BO
2LM
CP
ShareSizeCalculator
Entry Price
Stop Loss
Risk Amount
(Set before entering Entry or Stop Loss)
Number of Shares
1R
2R
3R


bat
Print and concatenate files.
A `cat` clone with syntax highlighting and Git integration.
More information: home page
- Print the contents of a file to the standard output:
bat path/to/file
- Concatenate several files into the target file:
bat file1 file2 > target_file
- Append several files into the target file:
bat file1 file2 >> target_file
- Number all output lines:
bat --number path/to/file
- Syntax highlight a JSON file:
bat --language json file.json
- Display all supported languages:
bat --list-languages


Before Change
stream_counter = len(Streamlines([i for j in all_streams for i in
j]).data)
except BaseException:
if ix > 10:
raise ValueError("No streamlines identified for consecutive"
" sampling iterations...")
else:
print("0 or Invalid streamlines encountered. Skipping...")
ix += 1
print(
"%s%s%s%s"
% (
"\nCumulative Streamline Count: ",
After Change
all_streams = []
ix = 0
while float(stream_counter) < float(target_samples) and float(ix) < 5:
out_streams = Parallel(n_jobs=nthreads, verbose=10, backend="loky",
mmap_mode="r+", max_nbytes=1e6)(
delayed(run_tracking)(


No device features could be found.This is usually resolved by having an Android device online before starting UI Sleuth.


Welcome
We are a professional organization dedicated to improving the visibility of Asian American/Pacific Islander issues in the media, promoting and training AAPIs in the newsroom and strengthening ties between news networks and our AAPI communities in Oregon. We are a local chapter of the Asian American Journalists Association.


...imagine combining the best of Java or Processing with the best of visual node-based systems like Node-RED;
imagine components defined as code fragments, so you're never constrained by what comes built-in;
imagine forking components or creating new ones all while your project is running...
Real-time processing
Forest-of-actors architecture for low-latency, data processing, media & embedded. Optional graphics module with support for Processing, GStreamer and OpenGL. Optional audio with Pipes and JACK.
Intuitive graphical patching
Node-based actor graph editing for fast visual project building. Drag & drop components, draw in connections. Edit everything live - instant feedback without interrupting flow.
Extend at runtime
Fork components on-the-fly, or create new ones from scratch, with real-time code reload. Integrated live Java editor and compiler. Export and share, or import from a growing library.
Distributed by design
Built from the ground up for working with multiple pipelines using a distributed architecture. Option to run projects transparently across processes or machines.


A working copy is a local checked out copy of a repository. Where a repository offers access to files and folders for every revision, a working copy behaves just like a normal folder on your hard disk that contains one particular revision of these items. You can use any application to modify the items in a working copy, just like you can in any other folder. Subversion keeps track of which file in the working copy belongs where in a repository, so when you create a new file in your working copy, it will initially be seen by Subversion as an unversioned item. When you delete a file or folder from your working copy, Subversion will see it as missing.
To actually add a new (unversioned) file or folder to your repository, you first schedule it for addition. When you commit your changes, the item will be added to the repository, creating a new revision. To delete a file, you schedule it for deletion and commit your changes. Because Subversion keeps track of previous revisions, you can always retrieve deleted files, as long as they were once part of the repository.
Normally you’ll just delete a file from your working copy and it will show as missing in Versions, making it easier to spot which file you wanted to get rid of.
To store any kind of change in your repository, you need to explicitly tell Subversion to do so. Versions make this easier by letting you clearly see, review and then commit all changes in your working copy at once. You will still have to add or delete files explicitly for them to show up in, or be removed from, the repository when you commit.


{% translate "you shall not pass!"|capfirstletter %}
{% translate "'cause page returned 403" %}...
{% translate "it seems you are trying to perform an action for which you are not allowed"|capfirst %}...
{% translate "maybe you should"|capfirst %} {% translate "go back" %}?


My name is Christopher Sayles, Full Stack Application Developer. I am an avid coder who loves to expand my knowledge base and push my brain to the limit. Being able to convert what the mind can dream of into a real world application has me hooked on programming and development.
"I've learned that fear limits you and your vision. It serves as a blinder to what may be just a few steps down the road for you. The journey is valuable, but believing in your talents, your abilities, and your self-worth can empower you to walk down an even brighter path."-Soledad O'Brien


This is an example inline link.
This link has no title attribute.
See my About page for details.
This is the simple case
This is the [simple case with space] {.example}
link


To establish a set of skills in a growing industry that will allow me to work for myself.
The goal is to understand the programming industry, then work for a start up company, and eventually start my own company in this field.
Within 5 years I would like to work from home, but set up an office in Auckland where I can meet clients and hold staff meetings.
Patience with myself, take my time to fully understand. Don’t rush into the next task. Also, I would like to work on my emotional intelligence.
One of my strengths of happily getting things done can be a limitation as I struggle to be patient due to me wanting to move on to the next task.
As nice as it is having a skill to improvise as I go, it may not be that helpful in this course, as having a plan to follow is one of the problem solving strategies.
However, improvising may be quite helpful if one of the problem solving strategy involves searching for new ideas.
One of my strength of being able to make decisions quickly based on logic will be helpful in my learning journey as I will be less likely to make emotional decisions which I later may regret.
Bootcamp challenges will be learning at a fast face with another person next to me. I worry either I or the other person may be too slow to understand.
I hope we dont hold each other back in any way and are patient with one another.
I hope to develop skills in bootcamp such as being able to reflect on my progress, being more self aware and to develop leadership skills.
It is important for me to graduate this course, to learn to code and to complete all of the technical aspects.
Foundations: To move swiftly towards the next task once I understand the previous one fully. I will achieve this by doing the stretch exercises if I finish early.
To have a plan to follow when it comes to solving a problem. I will achieve this by referring back to the newly introduced process of solving a problem – this is saved on my desktop.
To work on one problem at a time. Take my time. I will achieve this by not getting behind in my work, and by taking regular breaks.
If I am stuck on a problem for longer than 30 minutes, I will seek help through getting in touch with my course facilitator and members of my team either through Slack or by direct conversation in class.
Bootcamp: I will manage my time well by arriving on time and being fully present when I am there. If I start to get frustrated I will take a five minute break, or go for a walk.
I will be more patient with myself and others. I will listen to all of the advice offered and I will try to do my best in all expected areas.
Foundatins: I expect the facilitation team to be able to answer all of my technical questions, unless the question is not relevant to the course work.
I also expect them to be able to provide the answer within a reasonably time frame. For example, within an hour or two Mon-Fri.
Bootcamp expectations: To be able to find one on one help when I really need it.  If I cannot find answers there, I will ask a fascilitator.
At the end of each day, I will do an hour or two of coding every weekday in the evening after dinner, as well as four to six hours in the weekend.
Expectations from myself: To be fully present in the work and not get distracted. To follow all of the advice such as meditation and do the yoga as prescribed for better health of mind.


{% load staticfiles %}
Basic Panel - jQuery EasyUI Demo
Home
Exit
Edit
Ok
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About
Try jQuery EasyUI to build your modern, interactive, javascript applications.
west content
east region
south region
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Product
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Please note that the host name being entered has to match completely in order to work.
Entering e.g. youtube.com won't work as the site runs as www.youtube.com,
which is technically another host.
You can, however, insert a base domain such as youtube.com and include it's sub-domains
by checking the appropriate checkbox.
But be careful when toggling the JS state via icon or contextual menu then as it will remove the parent entry
and not create a custom one for the sub-domain!
Complete inheritance is not supported yet!


To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
Word Bank


TWICE Dahyun's airport fashion has netizens squealing over her cuteness
by sl278TWICE's Dahyun's adorable airport has made trending headlines on Korean news sites.
The group was seen arriving back to Korea after finishing their schedules in Japan.
 Netizens have been coo-ing over her adorable look, stating:
"She's so cute! Like a little penguin."
"It's our tofu!"
"Wow, she's really too cute for words."
What do you think of Dahyun's look?


If the process topology is a Cartesian structure, an MPI_SENDRECV operation may be used along a coordinate direction to perform a shift of data. As input, MPI_SENDRECV takes the rank of a source process for the receive, and the rank of a destination process for the send. If the function MPI_CART_SHIFT is called for a Cartesian process group, it provides the calling process with the above identifiers, which then can be passed to MPI_SENDRECV. The user specifies the coordinate direction and the size of the step (positive or negative). The function is local.
| MPI_CART_SHIFT(comm, direction, disp, rank_source, rank_dest) | |
| IN comm | communicator with Cartesian structure (handle) |
| IN direction | coordinate dimension of shift (integer) |
| IN disp | displacement (> 0: upwards shift, < 0: downwards shift) (integer) |
| OUT rank_source | rank of source process (integer) |
| OUT rank_dest | rank of destination process (integer) |
int MPI_Cart_shift(MPI_Comm comm, int direction, int disp, int *rank_source, int *rank_dest)
MPI_Cart_shift(comm, direction, disp, rank_source, rank_dest, ierror)
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, INTENT(IN) :: direction, disp
INTEGER, INTENT(OUT) :: rank_source, rank_dest
INTEGER, OPTIONAL, INTENT(OUT) :: ierror
MPI_CART_SHIFT(COMM, DIRECTION, DISP, RANK_SOURCE, RANK_DEST, IERROR)
INTEGER COMM, DIRECTION, DISP, RANK_SOURCE, RANK_DEST, IERROR
The direction argument indicates the coordinate dimension to be traversed by the shift. The dimensions are numbered from 0 to ndims-1, where ndims is the number of dimensions.
Depending on the periodicity of the Cartesian group in the specified coordinate direction, MPI_CART_SHIFT provides the identifiers for a circular or an end-off shift. In the case of an end-off shift, the value MPI_PROC_NULL may be returned in rank_source or rank_dest, indicating that the source or the destination for the shift is out of range.
It is erroneous to call MPI_CART_SHIFT with a direction that is either negative or greater than or equal to the number of dimensions in the Cartesian communicator. This implies that it is erroneous to call MPI_CART_SHIFT with a comm that is associated with a zero-dimensional Cartesian topology.
Example
The communicator, comm, has a two-dimensional, periodic, Cartesian
topology associated with it. A two-dimensional array of REALs is stored
one element per process, in variable A. One wishes to skew this array,
by shifting column i (vertically, i.e., along the column) by
i steps.
.... ! find process rank CALL MPI_COMM_RANK(comm, rank, ierr) ! find Cartesian coordinates CALL MPI_CART_COORDS(comm, rank, maxdims, coords, ierr) ! compute shift source and destination CALL MPI_CART_SHIFT(comm, 0, coords(2), source, dest, ierr) ! skew array CALL MPI_SENDRECV_REPLACE(A, 1, MPI_REAL, dest, 0, source, 0, comm, & status, ierr)
Advice to users.
In Fortran, the dimension indicated by DIRECTION
= i has
DIMS(i+1) nodes, where
DIMS is the array that
was used to create the grid. In C, the dimension
indicated by direction = i is the dimension specified by
dims[i].
( End of advice to users.)


I² Dashboard
Statistics:
Current Timestamp:
Raw Data Stream:
Linechart Result Stream:
Football field Result Stream:
Player Selection:
Linechart
Live Football field:


All Classes
AbsKeyFactory
AESEncryptorFactory
AESKeyFactory
AliceBobNetworkTest
DESEncryptorFactory
DESKeyFactory
DHPeer
ECDHExportTest
ECDHPeer
Encryptor
EncryptorFactory
Entropy
FileEncryptor
KeyAgreementPeer
KeyAgreementTest
KeyFactory
MaxAllowedKeyLengthTest
MessageTest
OneTimePad
StreamTest
TextEncryptor
UtilTest


Provides classes and interfaces for the inheritable event model. The model supports inheritable events and a flexible way of registering and unregistering event listeners. It's a fundamental building block of Vaadin, and as it is included in {@link com.vaadin.ui.AbstractComponent}, all UI components automatically support it.
The core of the event model is the inheritable event class hierarchy, and the {@link com.vaadin.event.EventRouter EventRouter} which provide a simple, ubiquitous mechanism to transport events to all interested parties.
The power of the event inheritance arises from the possibility of
receiving not only the events of the registered type, but also the
ones which are inherited from it. For example, let's assume that there
are the events GeneralEvent
and SpecializedEvent
so that the latter inherits the former. Furthermore we have an object
A
which registers to receive GeneralEvent
type
events from the object B
. A
would of course
receive all GeneralEvent
s generated by B
, but in
addition to this, A
would also receive all
SpecializedEvent
s generated by B
. However, if
B
generates some other events that do not have
GeneralEvent
as an ancestor, A
would not receive
them unless it registers to listen for them, too.
The interface to attaching and detaching listeners to and from an object works with methods. One specifies the event that should trigger the listener, the trigger method that should be called when a suitable event occurs and the object owning the method. From these a new listener is constructed and added to the event router of the specified component.
The interface is defined in {@link com.vaadin.event.MethodEventSource MethodEventSource}, and a straightforward implementation of it is defined in {@link com.vaadin.event.EventRouter EventRouter} which also includes a method to actually fire the events.
All fired events are passed to all registered listeners, which are of type {@link com.vaadin.event.ListenerMethod ListenerMethod}. The listener then checks if the event type matches with the specified event type and calls the specified trigger method if it does.