One of the interesting tasks I have dealt with...
CSV - prepare and download using background process.
Serving a static file is easy, but when filters are applied on large data set and generating a file will add up load on server and the user experience isn't that good.
To overcome the above problem - I used delayed job gem as async process, you could choose any other gems as well. Here's how I went through...
The Synchronous way.
def download csv_string = CSV.generate do |csv| csv << ["ID", .....] @search.each do |site| csv << [site.id, site.so.on....] end end send_data csv_string, :type => 'text/csv', :filename => 'sites_list.csv', :disposition => 'attachment' end
Table size was huge, records around 50k+ when requested for all records(sites information here) it holds up my machine, so much was the load.
After research and research(that is googling and ..ing) I thought to myself gather yourself and deal with it, you will find a way.
The delayed job gem.
Having not dealt with background processes before, started with railscasts on delayed job.
Well having fixed it in my mind how to go about this task, I started, but easier said than done.
Plan was - Make use of background process and prepare the csv file, just like that the part was done and dusted.
def download ExportCsv.new(@search.to_a.map(&:id), current_user.id).delay.perform end
The GET request to download method should be an AJAX call.
download.js.erbalert('Preparing file to download, you will be notified once its complete...'); timeout('<%= SOME_DELAY %>'); # Will be explained.
The custom class for delayed job operations.
lib/export_csv.rbclass ExportCsv < Struct.new(:site_ids, :user_id) def perform sites = Site.where(id: site_ids) CSV.open("tmp/sites_xls/#{user_id}.csv", "w+") do |csv| csv << ["ID", .....] sites.each do |site| csv << [site.id, site.so.on....] end end end def after(job) User.find(user_id).update_attributes(csv_download: true) end handle_asynchronously :perform end
Add new column csv_download for users table.
Attribute will be set to true once after(method) is executed, which means asynchronously file is generated and residing on the server space.
So the preparation happens at background so there is no holding up server.
The poll function.
But why?
Once the csv preparation is complete the file seems to be in the application directory, so to notify for the client that the file is ready is download.
So what lies inside timeout function - polls rails method to check if file is ready to serve, when ready, pop a confirmation box to download.
function timeout(time) { setTimeout(function() { $.ajax({ url: "/controller/check_if_ready?", type : 'GET', dataType : 'json', success: function(response) { if(response.value === "success") { var value = confirm("File ready to download ?"); if (value == true) { window.location="/controller/download_csv"; } else if(value == false) { $.ajax({ url: "/controller/remove_file"}) } } else { timeout('4000') // wait 4 seconds } }, }); }, time); }
The Poll method - Ajax-ify.
check_if_ready?
def check_if_ready? if current_user.set_csv_download? && File.exist?(@file_path) render json: { value: "success" } else render json: { value: nil } end end
Navigation to different parts of application.
It seems everything's fine but what if the user navigates when the background process is working on CSV.
Update accordingly
download.js.erb<% unless session[:set_download] %> alert('Preparing file to download, you will be notified once its complete...'); <% session[:set_download] = 1 %> timeout('<%= INITIAL_DELAY %>'); // initial delay. <% else %> alert('Wait for the completion of earlier file...'); <% end %>
Call the function(be it globally) in whichever layout necessary.
%script - if session[:download] timeout('#{DELAY}');
Use a flag attribute(session[:download]) to make poll calls, once the download is complete you can toggle back to falsy value .
KickStart the background process.
RAILS_ENV=environment nice -n 15 script/delayed_job -n 3 restart
Final few points
- Delay value can be configured based on count of records to reduce number of ajax calls. But on navigation to different pages(from user) you might also want to have smaller delay, as there can be another request within that delay value. Having a count down timer is a best option.
- Delete the file and reset values afer the download.
- When multiple files are requested to download? I have proceeded by allowing user to make one download request at a time. You might also want to handle this situation.
- Important: On Capistrano deployment process, you will have to restart the delayed job process and make sure no download is in progress. If not handled - The Ajax call may get into loop and request to poll method may be endless, counter value can be used to fix this and kill the request.
- If user requests for a download and closes the session, it should not be a concern of method call to poll getting into loop, as we have handled calling of Javascript function based on session value.
Finally I'm done with this one, there are some pros and cons I mostly have covered them all in my points. I would love to have your feedback. Oh no comment section up here.


--- layout: post title: 'Big Data Visualization: Very Cool But Still Very Complex' date: '2012-03-02T11:42:34-08:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/18616170059/big-data-visualization-very-cool-but-still-very ---
http://bit.ly/wIbL5V


| METHOD | PATH | DESCRIPTION |
|---|---|---|
GET |
/users | Return all users from API |
GET |
/users/:id | Return an user by giving id |
POST |
/users | Create a new user |
PUT |
/users/:id | Update an user by giving id |
DELETE |
/users/:id | Delete an user by giving id |
| METHOD | PATH | DESCRIPTION |
|---|---|---|
GET |
/users | Return all posts from API |
GET |
/posts/:id | Return a post by giving id |
POST |
/posts | Create a new post |
PUT |
/posts/:id | Update a posts by giving id |
DELETE |
/posts/:id | Delete a posts by giving id |


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# Contributing to Apache Cordova
Anyone can contribute to Cordova. And we need your contributions.
There are multiple ways to contribute: report bugs, improve the docs, and
contribute code.
For instructions on this, start with the
[contribution overview](http://cordova.apache.org/contribute/).
The details are explained there, but the important items are:
- Sign and submit an Apache ICLA (Contributor License Agreement).
- Have a Jira issue open that corresponds to your contribution.
- Run the tests so your patch doesn't break existing functionality.
We look forward to your contributions!
The notes on [Commit Workflow](https://github.com/apache/cordova-coho/blob/master/docs/committer-workflow.md#commit-workflow) can be helpful even if you are not a committer.
## Running plugin tests
* clone and install [cordova-plugin-test-framework](https://github.com/apache/cordova-plugin-test-framework)
```
git clone git@github.com:apache/cordova-plugin-test-framework.git
```
* edit ```cordova-plugin-test-framework/www/assets/index.html``` and add the following line
```
```
* create test project
```
cordova create plugintest
cd plugintest
cordova platform add android
cordova plugin add ../cordova-plugin-inappbrowser
cordova plugin add ../cordova-plugin-inappbrowser/tests
cordova plugin add ../cordova-plugin-test-framework
```
* edit ```config.xml``` and replace `````` with ``````
* run application
```
cordova run
```


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001/* 002// This software is subject to the terms of the Eclipse Public License v1.0 003// Agreement, available at the following URL: 004// http://www.eclipse.org/legal/epl-v10.html. 005// You must accept the terms of that agreement to use this software. 006// 007// Copyright (C) 2001-2005 Julian Hyde 008// Copyright (C) 2005-2009 Pentaho and others 009// All Rights Reserved. 010*/ 011package mondrian.olap; 012 013import org.olap4j.AllocationPolicy; 014import org.olap4j.Scenario; 015 016import java.util.List; 017 018/** 019 * A Cell is an item in the grid of a {@link Result}. It is 020 * returned by {@link Result#getCell}. 021 * 022 * @author jhyde 023 * @since 6 August, 2001 024 */ 025public interface Cell { 026 /** 027 * Returns the coordinates of this Cell in its {@link Result}. 028 * 029 * @return Coordinates of this Cell 030 */ 031 List<Integer> getCoordinateList(); 032 033 /** 034 * Returns the cell's raw value. This is useful for sending to further data 035 * processing, such as plotting a chart. 036 * 037 *
 The value is never null. It may have various types:
 038 *
if the cell is null, the value is {@link Util#nullValue};
 039 *
if the cell contains an error, the value is an instance of 040 * {@link Throwable};
 041 *
otherwise, the type of this value depends upon the type of 042 * measure: possible types include {@link java.math.BigDecimal}, 043 * {@link Double}, {@link Integer} and {@link String}.
 044 *
 045 * 046 * @post return != null 047 * @post (return instanceof Throwable) == isError() 048 * @post (return instanceof Util.NullCellValue) == isNull() 049 */ 050 Object getValue(); 051 052 /** 053 * Return the cached formatted string, that survives an aggregate cache 054 * clear. 055 */ 056 String getCachedFormatString(); 057 058 /** 059 * Returns the cell's value formatted according to the current format 060 * string, and locale-specific settings such as currency symbol. The 061 * current format string may itself be derived via an expression. For more 062 * information about format strings, see {@link mondrian.util.Format}. 063 */ 064 String getFormattedValue(); 065 066 /** 067 * Returns whether the cell's value is null. 068 */ 069 boolean isNull(); 070 071 /** 072 * Returns whether the cell's calculation returned an error. 073 */ 074 boolean isError(); 075 076 /** 077 * Returns a SQL query that, when executed, returns drill through data 078 * for this Cell. 079 * 080 *
If the parameter {@code extendedContext} is true, then the query will 081 * include all the levels (i.e. columns) of non-constraining members 082 * (i.e. members which are at the "All" level). 083 * 084 *
If the parameter {@code extendedContext} is false, the query will 085 * exclude the levels (coulmns) of non-constraining members. 086 * 087 *
The result is null if the cell is based upon a calculated member. 088 */ 089 String getDrillThroughSQL(boolean extendedContext); 090 091 /** 092 * Returns true if drill through is possible for this Cell. 093 * Returns false if the Cell is based on a calculated measure. 094 * 095 * @return Whether can drill through on this cell 096 */ 097 boolean canDrillThrough(); 098 099 /** 100 * Returns the number of fact table rows which contributed to this Cell. 101 */ 102 int getDrillThroughCount(); 103 104 /** 105 * Returns the value of a property. 106 * 107 * @param propertyName Case-sensitive property name 108 * @return Value of property 109 */ 110 Object getPropertyValue(String propertyName); 111 112 /** 113 * Returns the context member for a particular dimension. 114 * 115 * The member is defined as follows (note that there is always a 116 * member):
 117 * 118 *
If the dimension appears on one of the visible axes, the context 119 * member is simply the member on the current row or column. 120 * 121 *
If the dimension appears in the slicer, the context member is the 122 * member of that dimension in the slier. 123 * 124 *
Otherwise, the context member is the default member of that 125 * dimension (usually the 'all' member).
 126 * 127 * @param hierarchy Hierarchy 128 * @return current member of given hierarchy 129 */ 130 Member getContextMember(Hierarchy hierarchy); 131 132 /** 133 * Helper method to implement {@link org.olap4j.Cell#setValue}. 134 * 135 * @param scenario Scenario 136 * @param newValue New value 137 * @param allocationPolicy Allocation policy 138 * @param allocationArgs Arguments for allocation policy 139 */ 140 void setValue( 141 Scenario scenario, 142 Object newValue, 143 AllocationPolicy allocationPolicy, 144 Object... allocationArgs); 145} 146 147// End Cell.java


The purpose of this assignment is to get students hands on experience computing Procrustes Distance/Alignment and performing the Iterative Closest Points (ICP) algorithm (see Lecture 13 slides for more details). Because of the timing and the subject matter, this can be thought of as the programming part of the midterm, though it will still be grouped in and weighted in the Programming Assignments category
The code in this assignment is overall very short compared to other assignments you have done so far, but you need to be very careful about the finer points of matrix dimensions, transposes, and multiplication order to get this to work properly. This will also give everyone a first exposure to using numpy with geometry, which is needed in group assignment 2. Please have a look at the numpy demos we went over in class for some tricks that might help in this assignment and the next one.
This assigment is due at 11:55PM on Sunday 3/13. The first 5 points, which are setting up Python and taking a screenshot, are due on Wednesday 3/9, no exceptions and no partial credit (to make sure people are on track to setting up their Python environment).
Once you are finished, zip up your code and the sequence of images you generated in the examples section. Please also include a README with observations in the testing and examples sections, as well as any comments on the assignment and how many hours it took you.
To run the code, you will first need to make sure you have Python 2.7 installed, as well as some packages. The packages you need are
sudo apt-get install python-numpy python-scipy python-matplotlib python-opengl python-wxtools
Once you have these packages installed, you can run the file ICPView.py
, which is the entry point to running your code, which will be implemented in the file ICP.py
. ICPView.py
takes two command line arguments. The first argument is the path to the mesh that you want to aligned (referred to as "mesh X" from now on), and the second argument is the path to "target mesh" to which the first mesh will be aligned (called "mesh Y" or the "target mesh" from now on). For instance, if you run
then you should see something like this:
This GUI will help you to debug your code as you write it by showing you what correspondences you have found between two shapes, as well as what your Procrustes alignment step is doing. Left click + drag to rotate the camera, center click + drag to translate, and right click + drag to zoom. You can also compute an entire run of the ICP algorithm and play it back as an animation, and if you enter something into the text field "output prefix," then the program will output a sequence of PNG images that make up the movie. For example, here's what a working implementation would look like aligning the mesh candide.off
to the mesh NotreDameFrontHalf.off
candide2NotreDame.ogg |
An example of aligning candide.off to NotreDameFrontHalf.off |
ICPViewGLUT.py
. There are no textbox inputs enabled in this GUI, so you will have to pass a few more parameters along in the command line. The usage is as follows:
For example
Will start the program with the candide mesh and the Notre Dame mesh, capping ICP at a 200 iterations and saving the video frames with the prefix "candide." The GUI controls are as follows (most of the checkboxes and menu items in the ordinary GUI have been replaced with keyboard shortcuts)
pip uninstall PyOpenGL PyOpenGL-accelerate
)and try installing it instead from source. You'll have to download the three packages from here
conda install -c https://conda.anaconda.org/anaconda wxpython
ICPViewer.py
running by Wednesday 3/9. All or nothing on these points by that date
getCentroid(PC)
in ICP.py
to compute the centroid of a point cloud, where the points are along the columns of a 3 x N matrix. So you should return a 3 x 1 matrix representing the centroid. This should be one line of code (hint: use np.mean()
along some axis). You can test to make sure it's working properly in ICPViewer
by loading two shapes and clicking the "center meshes on centroids" button (Note that this is not exactly what happens in Procrustes alignment, since the centroids of the target mesh are computed on corresponding points only, but it can help you make sure your centroid step is correct). After the centroids are computed, be sure to re-center the camera by clicking the "Y Mesh" or the "X Mesh" views buttons.getCorrespondences(X, Y, Cx, Cy, Rx)
in ICP.py
. That is, given a centroid estimate for point clouds X and Y and a rotation matrix to align X with Y after centering both on their centroids, you need to compute the nearest neighbors on mesh Y to every point in mesh X. This example from class may be of some help. To complete that example, you need to fill in a matrix D where D[i, j] is the Euclidean or squared Euclidean distance between point i in X and point j in Y, and that code computes the index of the minimum distance along each row. You can use the "find correspondences" button in the GUI to help debug this:getProcrustesAlignment(X, Y, idx)
. To do this, you need to
np.linalg.svd
should come in handy here. Recall from lecture 13 that the SVD factorization of the matrix ABT, ignoring the scaling matrix, gives you the optimal rotation matrix to rotate point cloud B to align with point cloud A.
doICP(X, Y, MaxIters)
. You should loop as long as the correspondences are all different from the correspondences you got last time. If they are the same, this means that the process has converged. Again, you can check for this without using a loop (you will lose 2 points if you check this with a loop: try, for instance, using some boolean condition and np.sum()
). You only have to loop up to the maximum number of iterations, regardless of convergence.
ICPView.py
with ICPViewGLUT.py
if you're using that):
Next, try doing this the other way around; that is, aligning
What do you notice? Why is this happening? What does this say about the symmetry (or lack thereof) of the ICP algorithm?
In addition to aligning the face model to the statue, you should try this out on other examples. A variety of meshes are included in the meshes/
directory in the code distribution. Please submit with your assignment a sequence of images that show your program in action aligning one of those meshes to another one, by filling in a string in the "Output Prefix" field before clicking "Animate ICP" (or in the GLUT GUI by providing a fourth command line argument of that prefix). Note that you can move the camera before or during the animation to get a better view.
If you are creative about what you align to what, you can probably create some interesting concepts. Therefore, if you would like, you can also choose to submit your alignment video to the art contest for this assignment. Please indicate that you would like to submit your example and whether or not I can include your name on the course web site (and if not what your pseudonym should be). The winner will get two raffle points. Feel free to use any of the meshes in the Princeton Shape Database as you did for the image source assignment.
Finally, there has been a script provided that facilitates changing the geometry of different meshes before you align them. Check out the file manipulateGeometry.py
. That example shows applying a random rotation to an .off file, but you can do anything you want. Remember that the initial guess has a huge effect on ICP, so if you choose a different position or orientation that will influence where the algorithm converges


Bayo is a software developer with a diverse set of experience working on web, database and custom Alexa skills. His technical interests include voice enabled personal assistants, game development and data analysis.
Bayo is skilled in python, javascript and sql. He uses frameworks and tools such as the MEAN stack, Django, WordPress, Django, Amazon-Alexa and SQL Server Management Studio.


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)
"""
# TODO: Implement Function
real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels))
z_input = tf.placeholder(tf.float32, (None, z_dim))
learning_rate = tf.placeholder(tf.float32)
return (real_input, z_input, 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 variables 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, alpha=0.2):
"""
Create the discriminator network
:param images: 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)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
#Input layer is 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
relu1 = tf.maximum(x1, alpha * x1)
#14x14x64
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(bn2, alpha * bn2)
#7x7x128
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(bn3, alpha * bn3)
#4x4x256
flat = tf.reshape(relu3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
output = tf.sigmoid(logits)
return (output, 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 variables 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, alpha=0.2):
"""
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
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse=not is_train):
x1 = tf.layers.dense(z, 7*7*256)
x1 = tf.reshape(x1, (-1, 7, 7, 256))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(x1, alpha * x1)
#7x7x256
x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(x2, alpha * x2)
#14x14x128
x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(x3, alpha * x3)
#28x28x64
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
#28x28x3
output = tf.tanh(logits)
return output
"""
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)
"""
# TODO: Implement Function
gen_model = generator(input_z, out_channel_dim, is_train=True, alpha=0.2)
disc_model_real, disc_logits_real = discriminator(input_real, alpha=0.2)
disc_model_fake, disc_logits_fake = discriminator(gen_model, reuse=True, alpha=0.2)
disc_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_real, labels=tf.ones_like(disc_model_real)))
disc_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_fake, labels=tf.zeros_like(disc_model_fake)))
gen_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_logits_fake, labels=tf.ones_like(disc_model_fake)))
disc_loss = disc_loss_real + disc_loss_fake
return (disc_loss, gen_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)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return (d_train_opt, g_train_opt)
"""
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")
"""
# TODO: Build Model
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
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
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
if steps % 10 == 0:
# At the end of each 10 epochs, get the losses and print them out
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 100 == 0:
# At the end of each 100 epochs, show generator output
show_generator_output(sess, 16, input_z, data_shape[3], 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.


About me
I am Mahmudul Hasan Misbah, From Sylhet, Bangladesh. over the internet I introduced as Hasan Misbah since 2012. I am a freelance Web Designer and Developer specially in WordPress with a passion for responsive design.
my aim to write the best front-end code. My HTML5 is validated and search engine optimized, WordPress sites are easily updatable and everything is beautifully responsive.


Indent with a tab character
Indent with 2 spaces
Indent with 3 spaces
Indent with 4 spaces
Indent with 8 spaces
Braces with control statement
Braces on own line
End braces on own line
Preserve empty lines?
Keep array indentation?
Beautify GS2
(ctrl-enter)
IE6. You must be kidding me.


Hi, I'M Tan Kai Xuan
programmer
1998.08.20
016-7465392
<EMAIL_ADDRESS>No 2, Jalan 4, Taman Sri Berlian, 83000 Batu Pahat Johor
About Me |
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Hi, I'M
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May 2017-May 2018 City and Guilds Diplima in IT Software Developer NSIT Academy |
Work Experience |
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May 2017-May 2018 Sea View Restaurant Waiter |


Modify options of the $mdAria
service, which will be used by most of the Angular Material
components.
You are able to disable $mdAria
warnings, by using the following markup.
$mdAriaProvider.disableWarnings();
Disables all ARIA warnings generated by Angular Material.


{% extends "get_together/base.html" %} {% load i18n %} {% block content %}
{% blocktrans with email=request.user.email %}An email has been sent to {{email}}. Click the link it contains to confirm that you have access to this email address.{% endblocktrans %}
{% blocktrans with expires=confirmation.expires %}This confirmation request will expire on {{expires}}{% endblocktrans %}


Healing energy radiates from you in an aura with a 30-foot radius. Until the spell ends, the aura moves with you, centered on you. You can use a bonus action to cause one creature in the aura (including you) to regain 2d6 hit points.
Healing energy radiates from you in an aura with a 30-foot radius. Until the spell ends, the aura moves with you, centered on you. You can use a bonus action to cause one creature in the aura (including you) to regain 2d6 hit points.


RUNNING ON EMPTY A film review by Randy Parker Copyright 1996 Randy Parker
RATING: ***1/2 (out of ****)
(Review written in 1988)
Imagine moving to a new town and assuming a new identity every year because you are on the run from the F.B.I. for a crime you did not commit. Now imagine having these problems at 17, that difficult age of college applications, first love, and transition from adolescence to adulthood. In director Sidney Lumet's RUNNING ON EMPTY, River Phoenix finds himself in just this predicament.
His parents are Arthur and Annie Pope, left wing radicals who blew up a napalm factory in 1971 as part of the war protest; the explosion blinded and paralyzed a janitor who was not scheduled to be in the building at the time. To avoid imprisonment, the Popes went underground with their then two year old son and cut off all ties to family and friends. RUNNING ON EMPTY is set 15 years later; their son, played by Phoenix, is now a high school senior who has lived underground for as long as he can remember.
RUNNING ON EMPTY opens with the Pope family fleeing from their current home because the FBI is on the verge of discovering their whereabouts. The Popes assume new identities and move to a new town. As they settle in, Phoenix's character is the focal point of the story. The music teacher at the local high school takes interest in Phoenix's piano playing and encourages him to apply to Julliard. In the mean time, Phoenix falls in love with, and bares his soul to, his teacher's daughter--a rebellious free spirit played by Martha Plimpton.
RUNNING ON EMPTY examines the conflict that arises when the time comes for the Pope family to move on once again. Phoenix does not want to leave his family, but staying with them means separating from Plimpton and abandoning his plans to attend college and study music. On the other hand, if Phoenix does leave his family, he may never see them again. Phoenix's father, played by Judd Hirsch, opposes his college plans. Arthur Pope is a stubborn man deeply committed to his family, which he views as an inseparable unit; he cringes at the idea of it breaking apart. In contrast, Phoenix's mother, Annie, sees gross injustice in making Phoenix give up his future for the crime she and Arthur committed. One of the most underrated actresses in the business, Christine Lahti--who was last seen in the unjustly neglected HOUSEKEEPING--plays Annie Pope.
Lumet's portrayal of the Pope family packs a powerful emotional punch. In fact, I recommend bringing a large supply of Kleenex since RUNNING ON EMPTY is likely to bring you to tears several times. For example, the scene of Lahti visiting her father for the first time since going underground is excruciatingly painful to watch. Under Lumet's sensitive direction, Lahti, Hirsch, Plimpton, and the rest of the cast give first-rate performances, but RUNNING ON EMPTY really belongs to Phoenix. As the conflicted young pianist, Phoenix is riveting in an Oscar-calibre performance. It's hard to believe that this is the same actor who just two years ago played one of the young boys in STAND BY ME. With his performance in RUNNING ON EMPTY, Phoenix has successfully bridged the transition from child acting to adult roles. This heartbreaking drama is one of the year's best films.
--- Randy Parker<EMAIL_ADDRESS>http://www.shoestring.org
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


{% seo %} {% if jekyll.environment == 'production' and site.google_analytics_key != '' %} {% endif %} HOME ABOUT OUR FAITH EVENTS BLOG CONTACT
©2019 Stanford Christian Students


Thank you for visiting my website. This is a place for firsts, first project, first blog, first tweet, all firsts, but made from scratch with the best of intentions.
I invite you to take the journey with me. This is the place where I'll be adding my projects and blogging about what I've learned and about my experience at DEVbootcamp.


Retinoblastoma protein expression leads to reduced Oct-1 DNA binding activity and enhances interleukin-8 expression.
Abstract
Tumor cell lines with a defective retinoblastoma gene are unable to transcribe the HLA class II genes in response to IFN-gamma treatment, and reconstitution of functional Rb rescues IFN-gamma-induced class II gene expression. However, the molecular mechanism of Rb rescue of the class II genes is unknown. We have examined the effect of Rb expression on the activation of the promoter for HLA-DRA, the prototype class II gene. Oct-1, a POU domain transcription factor, was identified as a repressor of HLA-DRA promoter activity in the Rb-defective cells. Rb expression led to phosphorylation of Oct-1, thus relieving its repressive effect. Oct-1 has also been shown to repress interleukin 8 promoter activity. Consistent with reduced levels of Oct-1 DNA binding activity in the Rb-transformed cell lines, interleukin 8 expression is higher in these cell lines.


This demo shows the integration between Lightstreamer JavaScript Client library and Vue.js.
To learn more visit the project page on github and explore the source code of the application.
To learn more visit the project page on github and explore the source code of the application.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 2
Try this chicken and vegetable balti for a healthy curry that is quick and easy to prepare.
As part of an
Intermittent diet
plan, 1 serving provides:
1 of your 3 daily low-fat dairy portions
2 of your 6 daily vegetable portions
This meal provides
341 kcal, 40g protein, 30.5g carbohydrate (of which 20.5g sugars), 6g fat (of which 1.5g saturates), 9g fibre and 0.6g salt per portion.
Spray a large, deep, non-stick frying pan or wok with oil and place over a medium heat. Add the onion and cook for five minutes, stirring regularly until well softened and lightly browned.

Add the chicken and peppers into the pan with the onion and cook for three minutes, turning occasionally.
Meanwhile, in a small bowl, mix the cornflour with 2 tablespoons cold water and stir in the yoghurt until thoroughly mixed.
Sprinkle the curry powder over the chicken and vegetables, add the garlic and cook for 30 seconds.
Tip the tomatoes into the pan, add the yoghurt mixture, 150ml/3½fl oz of water and coriander.
Bring to a gentle simmer and cook for 20-25 minutes, stirring occasionally until the chicken is tender and the sauce is thick. Season with freshly ground black pepper to taste and garnish with coriander.


You can use the take
and skip
prefilters to control the queried data
With skip
you omit the given count of values.
With take
you limit the queried data.
With first
you can query only the first element.
Take
, Skip
, Select
, Count
, First
and Last
will require the collection data to always get queried from the database on every change. Keep that in mind when creating performance critical tasks.
In this example only the first 5 values are queried.
In this example the first 5 values are omitted.
In this example the first value is selected.


Configuration options for Actions
.
Static Properties
Prebuffer trivial files into memory, up to the given number of bytes across all uploaded files.
Handy for handling small file uploads (such as user avatars) in an application where file
uploads are not a significant feature.
@member/
Require that the Agent be logged into the application to use access this Action.
Require that the Agent have same-origin access priveleges in the client context to use this Action. This secures against XSS attacks in the browser.
If true, accept unknown content types and pass them, as well as multipart/form-data
requests,
to the Action as streams. The passed request.body
will be a ReadableStream
instance.
When binaryStreams
is set and so is neverBuffer
, the [request body]
(.Request.body) is always a stream
.
Member Properties
Specify a minimum authentication profile to access this Action. Unauthenticated requests are automatically served a blank 403 response.
A JSON Schema document used to restrict request body content. filth form requests are mapped
to simple flat Objects and JSON bodies are validated as-are. Failure to validate will cause a
406
response to be automatically sent. When bodySchema
is present, an Action's
request.body
can always be assumed to conform. Finally, the parent server will automatically
respond to OPTIONS requests with { VERB:{ body:bodySchema, query:querySchema }, ...
.
Schemata are pre-compiled with likeness for
rapid validation. This allows $ref
to validate much more quickly, however, it will load
references when the server starts and does not keep them up to date (yet).
When generating html with a template but never when generating a JSON response, response
content
is non-destructively deep-merged over this Object. To put
it another way, context
sets default content for html pages.
A JSON Schema document used to restrict request url query terms. The query is mapped as a simple
flat Object and the field _domestic
is automatically stripped before validation. Failure to
validate will cause a 406
response to be automatically sent. When querySchema
is present, an
Action's request.query
can always be assumed to conform. The parent server will automatically
respond to OPTIONS requests with { VERB:{ body:bodySchema, query:querySchema }, ...
.
Schemata are pre-compiled with likeness for
rapid validation. This allows $ref
to validate much more quickly, however, it will load
references when the server starts and does not keep them up to date (yet).
As an alternative to setting the route when activating the Action
you
may set it in the configuration. Routes specified in addAction
override configured routes.
When a route is set as a String, it is prepended with a forward slash if none is present and
converted to a regular expression of the form /pathname(?:/(.*))?
.
Methods
Called during the parent server's initialization stage, before the server has begun accepting requests. It's usually the best time to establish database access. Two arguments are passed:
Define one or more template Functions to convert a context Object to an html string. If a
Function is used, all html responses are generated by this Function. If an Object is provided,
http response codes are mapped to templates. If a response code cannot be matched exactly, it
falls back to the 200
template, then attemps an exact match and finally the 200
template on
the global templates
.
Template Functions may be synchronous or asynchronous. The context Object and a callback Function are passed as arguments.
When a client uses this Action over the REST transport, a template will be executed if it is supplied. Specify either a single template Function or a map of response codes to template Functions. Wildcards may be used i.e. "2xx" or "30x". Most specific response code wins.


Zarzis is just south of the Island of Djerba and is an ideal place from which to explore the southern part of Tunisia and in particular the timeless sands of the Sahara.
Position 33° 28'36"N, 11° 07'42"E (harbour entrance)
This is an expanding commercial port with 60 berths for yachts. Water, electricity, fuel and repairs are available.


The login button will open a new popup. Once you are logged in successfull we will redirect you to the squidex management portal.
Proudly made by Quaisar Ahmad & Sebastian Stehle, 2016-2017


| Reliable Multicast Pipes |
|
|
|
Introduction (skip intro)
The JXTA open network computing platform is designed for peer-to-peer (P2P) computing and enables application developers to build and deploy interoperable services and content. The platform is divided in three layers:
The platform layer provides some very important peer group [core] services:
JXTA provides an abstract transport layer through the endpoint service that is independent of the underlying network transport layers [there are many implementations even for Bluetooth transport]. Peers, use the Pipe Service to exchange messages [Pipe = a virtual link between 2 or more (for propagate type) peers ]. Pipes beneath are using endpoint Messengers for sending – receiving messages for each hop of the virtual channel. Multicasting in JXTA is implemented in a different way. Firstly we have to understand the difference between propagation of a message and multicasting. Multicasting is a very popular term, and it has a very strict meaning. It uses UDP to send a message to a Multicast Group [IP class D address] and everyone that is joined to that receive the message. Propagation is a mechanism of JXTA that is used to send messages to multiple peers of a peer group and is built on top of the rendezvous service and is implemented inside the pipe service [propagate pipes]. The correlation between Multicasting & propagation is that propagate pipes are using sometimes multicast packets to send messages to the LAN, in case that some peers of the same peer group exist inside the same LAN. That’s it, multicasting is not used in JXTA except on the special occasion that was previously discussed. In terms of JXTA multicasting most of the times has the meaning of sending a message to multiple recepients through propagate pipes and more rarely the multicasting of messages inside the LAN.
|
|
|
In project Reliable Multicast Pipes (RMP ) the objective is to build a service that will reliably send messages [in order, without losses etc.] through the existing transport of JXTA, to multiple recipients. If someone uses a packet sniffer to monitor the messages that are sent when a JXTA application is running, he will notice that the majority of the packets that are exchanged between the peers (under and not over the JXTA transport layer) use TCP and only a few of them use UDP [only for multicasting inside the LAN]. One might think that it is very strange to talk about reliability if TCP is used as the transport protocol, because of the reliable nature of the last. A simple example might be helpful for understanding the need for such a reliable protocol :
Let us suppose that we have peers A, B and C. A and C can only communicate through peer B. If A has a reliable connection to B [e.g. TCP] and C has a reliable connection to B then a message sent from A to C going through B could still be lost if B fails to deliver to C a message received from A. B could discard the message due to load issues, buffer space, quota management etc. The most common reason is that C didn’t ask for its messages in a timely manner and B discarded the message and A will never know that C didn’t get the message since end2end ACK/NACK is not used [not supported for propagate pipes]. So, reliable transmission would involve the creation of a buffer space which would be helpful in the case of message losses. That is what JxtaSocket does when reliable = true is specified. All messages are saved until ACKed [similarly for JxtaBiDiPipes also]. The problem is the lack of a protocol – service – mechanism for reliable transmission for propagating messages.
This is exactly the objective of the RMP project : Reliable transmission of messages to multiple receivers using JxtaMulticastSockets (one of the same with propagate pipes), inside a peer group.
The way RMP works is influenced by the TRAM protocol of JRMS. A quick preview of the algorithm follows :
i. provided threshold is not exceeded because a root node can have only a predefined number of “children”/peers attached to it ii. root node has already created a JxtaServerPipe and it’s pipeID is also included inside the response.
Every node that has the RMP service running, uses a kind of message storage [like a buffer] to hold the history of messages that are received. If the node happens to be a root node then the buffer must be of greater capacity. Every message has some meta-fields indicating information like sender id, channel ID, sequence number etc. Let us suppose that a peer/node happens to loose a message, e.g. receives messages with seq_nums #100, #101, #103, #104, #105 [#102 is lost]. Given that it doesn’t receive #102 after #window_size receipts occur, it sends a NACK with the sequence number of the lost message to the root node that the peer is attached to. If root doesn’t have msg #102 it sends in turn also a request for to its own root/parent. This procedure continues recursively till a request reaches the original sender. This way the sender doesn’t get overwhelmed with ACKs from receivers and the network is not over-utilized because a great portion of retransmissions is done by the local repair heads, the root nodes. |


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Index
A B C D E F G H I K L M N O P R S T U W
All Classes All Packages
All Classes All Packages
All Classes All Packages
T
- TestClassifier - Class in main.tests
-
CLASS USED ONLY FOR TESTING PURPOSES - THIS IS BASICALLY A UNIT TEST OF THE CODE A class used to test the score of the classifier
- TestClassifier() - Constructor for class main.tests.TestClassifier
- TestConfusionMatrix - Class in main.tests
-
CLASS USED ONLY FOR TESTING PURPOSES - THIS IS BASICALLY A UNIT TEST OF THE CODE A class used to test the ConfusionMatrix class
- TestConfusionMatrix() - Constructor for class main.tests.TestConfusionMatrix
- TestDataHolderFacade - Class in parser.facades
-
A class that implements the DataHolder interface.
- TestDataHolderFacade() - Constructor for class parser.facades.TestDataHolderFacade
- TestGraph - Class in main.tests
-
CLASS USED ONLY FOR TESTING PURPOSES - THIS IS BASICALLY A UNIT TEST OF THE CODE A class used to test the SimpleWeightedGraph class
- TestGraph() - Constructor for class main.tests.TestGraph
- TestTrainingModel - Class in main.tests
-
CLASS USED ONLY FOR TESTING PURPOSES - THIS IS BASICALLY A UNIT TEST OF THE CODE A class used to test the trainingModel class
- TestTrainingModel() - Constructor for class main.tests.TestTrainingModel
- theta_c - Variable in class classifier.TreeAugmentedNayveBayes
-
An list of double to save the values of theta_c
- theta_ijkc - Variable in class classifier.TreeAugmentedNayveBayes
-
An hashmap to save the calculations of the theta_ijkc
- TNPerClass - Class in metrics.perclass
-
This class extends AbstractAuxiliaryScores and implements the method doCalculateScore using the formula to calculate the number of True Negatives on multi-class problems
- TNPerClass(int[][]) - Constructor for class metrics.perclass.TNPerClass
-
Constructor for the TNPerClass, receives a matrix of int (int[][] - confusion matrix) used to calculate the scores of individual classes
- toString() - Method in class classifier.TreeAugmentedNayveBayes
-
Redefinition of the toString() method that returns a string with the tree structure of the classifier
- toString() - Method in class datamodel.categories.ModelCategory
-
Redefinition of the toString() method.
- toString() - Method in class datamodel.classifications.ModelClassfication
-
Redefinition of the toString() method, printing the information on the classes, the labels and the counts of different classes
- toString() - Method in class datamodel.TrainingModel
-
Redefinition of the toString() method.
- toString() - Method in class graph.edge.SimpleWeightedEdge
-
Redefinition of the toString method
- toString() - Method in class graph.SimpleWeightedGraph
-
Redefinition of the toString() method in order to print if the graph is directed, the number of vertices/nodes and the adjencyList of edges.
- toString() - Method in class metrics.AbstractMultipleCategoryScore
-
Redefinition of the toString() method that returns Scorename:[label1:score, label2:score, ..., avg]
- toString() - Method in class metrics.ConcreteConfusionMatrix
-
Redefinition of the toString() function that prints the matrix in the terminal along with the corresponding labels
- totalNumberLabels - Variable in class datamodel.categories.ModelCategory
-
The total number of different labels this category has: example- 0,1,2 - totalNumberLabels = 3
- TPPerClass - Class in metrics.perclass
-
This class extends AbstractAuxiliaryScores and implements the method doCalculateScore using the formula to calculate the number of True Positives on multi-class problems
- TPPerClass(int[][]) - Constructor for class metrics.perclass.TPPerClass
-
Constructor for the TPPerClass, receives a matrix of int (int[][] - confusion matrix) used to calculate the scores of individual classes
- train() - Method in class classifier.AbstractBayes
-
A method that invokes the doTrain() method inside.
- train() - Method in interface classifier.Classifier
-
A method that when invoked should train a classifier
- TrainingDataHolderFacade - Class in parser.facades
-
A class that implements the DataHolder interface.
- TrainingDataHolderFacade() - Constructor for class parser.facades.TrainingDataHolderFacade
- trainingModel - Variable in class classifier.AbstractBayes
-
A DataModel of the same generic type of the classifier with counts and labels extracted from training data
- TrainingModel<T> - Class in datamodel
-
A class that implements the DataModel interface.
- TrainingModel(String[]) - Constructor for class datamodel.TrainingModel
-
The constructor for the TrainingModel class.
- TrainingModelFacade - Class in parser.facades
-
A class that implements the DataHolder interface.
- TrainingModelFacade() - Constructor for class parser.facades.TrainingModelFacade
- tree - Variable in class classifier.TreeAugmentedNayveBayes
-
An int array that inside contains the parent of each node.
- TreeAugmentedNayveBayes<T> - Class in classifier
-
A class that extends the AbstractBayes class.
- TreeAugmentedNayveBayes(Score, DataModel<T>) - Constructor for class classifier.TreeAugmentedNayveBayes
-
The constructor for the TreeAugmentedNayveBayes class.
All Classes All Packages


{% extends "base.html" %} {% load user_list %} {% load comment_list %} {% load markdown %} {% block title %} Testsolving {{ session.puzzle }} {% endblock %} {% block main %}
Participants:
| User | Fun rating | Difficulty rating | Hours spent | Done? |
|---|---|---|---|---|
| {{ participation.user.username }} | {{ participation.fun_rating|default:"n/a" }} | {{ participation.difficulty_rating|default:"n/a" }} | {{ participation.hours_spent|default_if_none:"n/a" }} | {% if participation.ended %}done{% endif %} |
Session notes
{{ session.notes|markdown }} {% if sheets_enabled %} {% endif %} {% if participation %} {% if spoiled %}You are testsolving a puzzle you are spoiled on.
{% else %}You are in this testsolving session.
{% endif %} {% if participation.ended %}You finished this testsolve at {{ participation.ended }}
{% endif %} {% if session.joinable %} {% else %} {% endif %} {% if not answers_exist %}No answers have been assigned to this puzzle in Puzzlord, so the answer checker will not mark any answers as correct.
{% endif %}Done with the puzzle? (You can confirm and leave feedback after clicking the link)
{% else %}You are spoiled on {{ session.puzzle }}.
{% endif %} {% if guesses %} Past guesses:You are neither participating in this testsolving session nor spoiled on the puzzle. Would you like to join this session?
{% endif %} {% endblock %}


Interface for associating a SAX event with a document location
Interface for associating a SAX event with a document location.If a SAX parser provides location information to the SAX application, it does so by implementing this interface and then passing an instance to the application using the document handler's setDocumentLocator method. The application can use the object to obtain the location of any other document handler event in the XML source document.
Note that the results returned by the object will be valid only during the scope of each document handler method: the application will receive unpredictable results if it attempts to use the locator at any other time.
SAX parsers are not required to supply a locator, but they are very strong encouraged to do so. If the parser supplies a locator, it must do so before reporting any other document events. If no locator has been set by the time the application receives the startDocument event, the application should assume that a locator is not available.
This will be the public identifier
If the system identifier is a URL, the parser must resolve it
fully before passing it to the application.
alphabetic index hierarchy of classes
|
Xerces-C XML Parser for C++ Version 1.1 |
|
|
generated by doc++


{% extends 'base.html' %} {% block content %}
Hello! We're making a new django-based web forum of a new breed. {% endblock %}


SteamControllerButton
Objective-C
enum SteamControllerButton : uint32_t {}
Swift
struct SteamControllerButton : OptionSet
Represents a physical push button input (or combination thereof) from the Steam Controller.
The values of this enumeration are the same ones used by the controller’s bluetooth protocol.
-
The button on the right underside of the controller.
Declaration
Objective-C
SteamControllerButtonRightGrip = 0x000001
Swift
static var rightGrip: SteamControllerButton { get }
-
Press on the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadClick = 0x000002
Swift
static var leftTrackpadClick: SteamControllerButton { get }
-
Press on the right trackpad.
Declaration
Objective-C
SteamControllerButtonRightTrackpadClick = 0x000004
Swift
static var rightTrackpadClick: SteamControllerButton { get }
-
Touch on the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadTouch = 0x000008
Swift
static var leftTrackpadTouch: SteamControllerButton { get }
-
Touch on the left trackpad.
Declaration
Objective-C
SteamControllerButtonRightTrackpadTouch = 0x000010
Swift
static var rightTrackpadTouch: SteamControllerButton { get }
-
Press on the analog stick.
Declaration
Objective-C
SteamControllerButtonStick = 0x000040
Swift
static var stick: SteamControllerButton { get }
-
Press in the up area of the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadClickUp = 0x000100
Swift
static var leftTrackpadClickUp: SteamControllerButton { get }
-
Press in the right area of the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadClickRight = 0x000200
Swift
static var leftTrackpadClickRight: SteamControllerButton { get }
-
Press in the left area of the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadClickLeft = 0x000400
Swift
static var leftTrackpadClickLeft: SteamControllerButton { get }
-
Press in the down area of the left trackpad.
Declaration
Objective-C
SteamControllerButtonLeftTrackpadClickDown = 0x000800
Swift
static var leftTrackpadClickDown: SteamControllerButton { get }
-
The left pointing button to the left of the Steam button.
Declaration
Objective-C
SteamControllerButtonBack = 0x001000
Swift
static var back: SteamControllerButton { get }
-
Steam Button. The big round one in the middle of the controller.
Declaration
Objective-C
SteamControllerButtonSteam = 0x002000
Swift
static var steam: SteamControllerButton { get }
-
The right pointing button to the right of the Steam button.
Declaration
Objective-C
SteamControllerButtonForward = 0x004000
Swift
static var forward: SteamControllerButton { get }
-
The button on the left underside of the controller.
Declaration
Objective-C
SteamControllerButtonLeftGrip = 0x008000
Swift
static var leftGrip: SteamControllerButton { get }
-
A full press on the right trigger (the button below the right bumper).
Declaration
Objective-C
SteamControllerButtonRightTrigger = 0x010000
Swift
static var rightTrigger: SteamControllerButton { get }
-
A full press on the left trigger (the button below the left bumper).
Declaration
Objective-C
SteamControllerButtonLeftTrigger = 0x020000
Swift
static var leftTrigger: SteamControllerButton { get }
-
The right bumper button, also known as right shoulder button.
Declaration
Objective-C
SteamControllerButtonRightBumper = 0x040000
Swift
static var rightBumper: SteamControllerButton { get }
-
The left bumper button, also known as left shoulder button.
Declaration
Objective-C
SteamControllerButtonLeftBumper = 0x080000
Swift
static var leftBumper: SteamControllerButton { get }
-
The button marked A on the front of the controller.
Declaration
Objective-C
SteamControllerButtonA = 0x800000
Swift
static var A: SteamControllerButton { get }
-
The button marked B on the front of the controller.
Declaration
Objective-C
SteamControllerButtonB = 0x200000
Swift
static var B: SteamControllerButton { get }
-
The button marked X on the front of the controller.
Declaration
Objective-C
SteamControllerButtonX = 0x400000
Swift
static var X: SteamControllerButton { get }
-
The button marked Y on the front of the controller.
Declaration
Objective-C
SteamControllerButtonY = 0x100000
Swift
static var Y: SteamControllerButton { get }