public class AudioWrapper extends PlayerWrapper
| Constructor and Description |
|---|
AudioWrapper(Player player) |
| Modifier and Type | Method and Description |
|---|---|
void |
addSourceSettings(SourcePlayerSettings settings)
Adds the settings of a source to the
player . |
void |
clearSourceSettings()
Clears all the source settings of the
player . |
void |
disconnect()
Disconnects the
player from the
audio server. |
java.lang.String |
generateConnectURL(java.lang.String host)
Generates an URL containing the
player 's UUID and newly generated CID. |
java.util.UUID |
getCID()
Gets the CID of the
player . |
WebServerConnection |
getConnection()
Gets the connection of the
player . |
java.util.Collection<SourcePlayerSettings> |
getSourceSettings()
Gets all the settings of the
player . |
SourcePlayerSettings |
getSourceSettings(ISource source)
Gets the source settings of a source.
|
SourcePlayerSettings |
getSourceSettings(java.lang.String sourceID)
Gets the source settings of a source.
|
boolean |
hasSourceSettings(ISource source)
Checks if the
player has the
settings for a source. |
boolean |
hasSourceSettings(java.lang.String sourceID)
Checks if the player has the settings for a source.
|
boolean |
isConnected()
Checks if the
player is connected to
the audio server. |
void |
onUnload()
This is called once the wrapper gets unloaded.
|
void |
removeSourceSettings(ISource source)
Removes the source's settings from the player
player . |
void |
removeSourceSettings(java.lang.String sourceID)
Removes the source's settings from the player
player . |
void |
setConnection(WebServerConnection connection)
Sets the connection of the
player . |
getPlayer, onLoad
public AudioWrapper(Player player)
public void onUnload()
PlayerWrapper
onUnload
in class PlayerWrapper
public void clearSourceSettings()
player
.public boolean hasSourceSettings(ISource source)
player
has the
settings for a source.source
- The sourcepublic boolean hasSourceSettings(java.lang.String sourceID)
sourceID
- The ID of a sourcepublic void addSourceSettings(SourcePlayerSettings settings)
player
.settings
- The settings to addpublic void removeSourceSettings(ISource source)
player
.source
- The sourcepublic void removeSourceSettings(java.lang.String sourceID)
player
.sourceID
- The ID of a sourcepublic java.lang.String generateConnectURL(java.lang.String host)
player
's UUID and newly generated CID.host
- The host of the audio serverpublic void disconnect()
player
from the
audio server.public boolean isConnected()
player
is connected to
the audio server.public void setConnection(WebServerConnection connection)
player
.connection
- The connection to setpublic SourcePlayerSettings getSourceSettings(ISource source)
source
- The sourcepublic SourcePlayerSettings getSourceSettings(java.lang.String sourceID)
sourceID
- The ID of a sourcepublic java.util.Collection<SourcePlayerSettings> getSourceSettings()
player
.public WebServerConnection getConnection()
player
.public java.util.UUID getCID()
player
.


MetDecoders
Map Type
Map Type:
2D Projection
Globe
Debris Owneres
Countries:
Saudi Arabia
United States
United Kingdom
China
Jaban
Debris Status
Orbit
Landing
Impact
Roundtrip
Apogee
Perigee
Period
Inclination
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Cari Uang Buat Gunpla
Rp. 200.000.000 · 12 September 2020


Welcome to the world’s first Proof-of-Chain cryptocurrency! The CLAMs network was created with the widest digital value distribution in history. CLAMS were sent to all active users of the 3 most popular digital currencies. If you had Bitcoin, Litecoin or Dogecoin on May 12, 2014 then you probably have CLAMs waiting for you. We like to call this Digging up old CLAMs!


dpx::ElementReadStream Class Reference
#include <ElementReadStream.h>
List of all members.
Public Member Functions |
| | ElementReadStream (InStream *) |
| virtual | ~ElementReadStream () |
| virtual void | Reset () |
| virtual bool | Read (const dpx::Header &, const int element, const long offset, void *buf, const size_t size) |
| virtual bool | ReadDirect (const dpx::Header &, const int element, const long offset, void *buf, const size_t size) |
Protected Member Functions |
| void | EndianDataCheck (const dpx::Header &, const int element, void *, const size_t size) |
Protected Attributes |
| InStream * | fd |
Constructor & Destructor Documentation
| dpx::ElementReadStream::ElementReadStream |
( |
InStream * |
fd |
) |
|
| dpx::ElementReadStream::~ElementReadStream |
( |
|
) |
[virtual] |
Member Function Documentation
| void dpx::ElementReadStream::EndianDataCheck |
( |
const dpx::Header & |
dpxHeader, |
|
|
const int |
element, |
|
|
void * |
buf, |
|
|
const size_t |
size | |
|
) |
| | [protected] |
| bool dpx::ElementReadStream::Read |
( |
const dpx::Header & |
dpxHeader, |
|
|
const int |
element, |
|
|
const long |
offset, |
|
|
void * |
buf, |
|
|
const size_t |
size | |
|
) |
| | [virtual] |
| bool dpx::ElementReadStream::ReadDirect |
( |
const dpx::Header & |
dpxHeader, |
|
|
const int |
element, |
|
|
const long |
offset, |
|
|
void * |
buf, |
|
|
const size_t |
size | |
|
) |
| | [virtual] |
| void dpx::ElementReadStream::Reset |
( |
|
) |
[virtual] |
Member Data Documentation
The documentation for this class was generated from the following files:


A domain name is an identification string that defines a realm of administrative autonomy, authority or control within the Internet. Domain names are used in various networking contexts and for application-specific naming and addressing purposes.
Even if you have never hosted a web site before, you have undoubtedly used domain names. Every time you use a web browser to visit a web site by name, such as a2hosting.com, you are using domain names.
Domain names are organized hierarchically from right to left. The right-most portion of a domain name is called the top-level domain (TLD). The .com TLD is probably the most well-known, but there are many others, such as .net, .org, .edu, and .mil. (Country codes, such as .us and .fr, are also TLDs.) When you register a domain name, you can choose which TLD to use (though some TLD's, such as .edu and .gov, are restricted to certain types of organizations).
For example, the domain name Facebook.com consists of the website's name (Facebook) and the domain name extension (.com).
Domain names serve to identify Internet resources, such as computers, networks, and services, with a text-based label that is easier to memorize than the numerical addresses used in the Internet protocols. A domain name may represent entire collections of such resources or individual instances.
The Internet Corporation for Assigned Names and Numbers is an American multistakeholder group and nonprofit organization responsible for coordinating the maintenance and procedures of several databases related to the namespaces and numerical spaces of the Internet, ensuring the network's stable and secure operation.
ICANN was formed in 1998. It is a not-for-profit partnership of people from all over the world dedicated to keeping the Internet secure, stable and interoperable. It promotes competition and develops policy on the Internet's unique identifiers.
For example, the domain name "whois.icann.org" has three levels of hierarchy: "org" is the top-level, "icann" is the second-level, and "whois" is the third level. Remarkably, there can be up to 128 levels!


Welcome to PySDL2’s documentation!¶
PySDL2 is a wrapper around the SDL2 library and as such similar to the
discontinued PySDL project. In contrast to PySDL, it has no licensing
restrictions, nor does it rely on C code, but uses ctypes
instead.
Contents¶
Further readings:


Panel title
Denomination:
Year:
Collection:
Condition:
Uncirculated
Brilliant
Very Good
Good
Used
Damaged
Gallery:
Name:
Name:
Name:
Name:
Name:
Name:


Eatables is an interactive recipe-book for browsing and maintaining your favorite recipes and storing your eating patterns. Let eatables enable your eating pleasure.


Thistle do nicely as Perth and Kinross winners scoop awards
The Old Manse of Blair in Blair Atholl was awarded the best hotel experience
by Douglas Dickie, https://www.dailyrecord.co.uk/authors/douglas-dickie/Businesses from across Perth and Kinross struck gold at the Scottish Thistle Awards regional finals.
The event for those in the tourism industry for Central, Fife and Tayside proved to be a fruitful for six nominees from the region.
The Old Manse of Blair in Blair Atholl was awarded the best hotel experience, sponsored by Fishers.
Judges praised the hotel for offering personal touches to guests and working in collaboration with the local community.
Straloch Highland Retreats in Blairgowrie received the best self catering accommodation experience award, sponsored by Bruce Stevenson Insurance Brokers.
Its emphasis on customer service and sustainability struck a chord with the judges.
Northlands B&B in Pitlochry was another accommodation winner, being named the most hospitable B&B/guest house, sponsored by Abbey UK.
The four-star accommodation was commended by judges for demonstrating real passion and purpose in its offering, with a customer at heart ethos.
Land Rover Experience Scotland, based in Dunkeld, received the best outdoor adventure experience award.
Judges commended the use of low emission vehicles and the steps to minimise the environmental impact. Staff empowerment was also praised by the judging panel.
WILDHOOD festival received the award for best cultural event or festival. The festival for children aged 12 and under took place in a 17th-centure castle near Kinross and included arts, crafts, theatre, circus, nature, outdoors and music. Last year’s festival welcomed more than 850 campers and thousands of day visitors in only its second year.
Finally, the Library at Innerpeffray received the best heritage tourism experience award, sponsored by Historic Environment Scotland.
The authentic and engaging experience was praised by judges, as well as the ambitious but sustainable plans for the future.
The awards, which were presented by TV star Jennifer Reoch, took place in Edinburgh.
The regional winners have earned their place at the prestigious national final in March.


Integrating Physics-Based Depletion into Fuel Cycle Simulation: When, How, and Why
Steve Skutnik
Abstract
Given the inherently dynamic nature of used fuel isotopic vectors resulting from reactor irradiation, the use of recipe-based approaches to isotopic tracking for fuel cycle scenario can become untenable under certain recycle-based scenarios. This effect is especially significant for material recycle back to thermal-spectrum reactors, which exhibit a more pronounced sensitivity of discharge isotopic compositions as a function of input isotopic vector.Thus, two key questions that should be addressed in fuel cycle simulation are, 1) How can we appropriately incorporate physics into reactor-based depletion for fuel cycle scenario analysis, and 2) When are these effects significant (and when can they be adequately neglected?) Thus, a key goal of this discussion will be to identify strategies for how to incorporate physics information into fuel cycle simulations and under what circumstances it contributes significantly to evaluation of fuel cycle metrics of interest.


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
input_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_real')
input_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return input_real, input_z, 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 generator, tensor logits of the generator).
def discriminator(images, reuse=False, alpha=0.1):
"""
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)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
layer_1 = tf.layers.conv2d(images, 64, 4, strides=2, padding='same')
relu_1 = tf.maximum(alpha * layer_1, layer_1)
# 14x14x64
layer_2 = tf.layers.conv2d(relu_1, 128, 4, strides=2, padding='same')
bn_2 = tf.layers.batch_normalization(layer_2, training=True)
relu_2 = tf.maximum(alpha * bn_2, bn_2)
# 7x7x256
layer_3 = tf.layers.conv2d(relu_2, 256, 2, strides=2, padding='same')
bn_3 = tf.layers.batch_normalization(layer_3, training=True)
relu_3 = tf.maximum(alpha * bn_3, bn_3)
# 4x4x256
layer_4 = tf.layers.conv2d(relu_3, 512, 2, strides=2, padding='same')
bn_4 = tf.layers.batch_normalization(layer_4, training=True)
relu_4 = tf.maximum(alpha * bn_4, bn_4)
# 2x2x512
flattened = tf.reshape(relu_4, (-1, 2*2*512))
# 1x2048
logits = tf.layers.dense(flattened, 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 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, alpha=0.1):
"""
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):
layer_1 = tf.layers.dense(z, 7*7*512)
layer_1 = tf.reshape(layer_1, (-1, 7, 7, 512))
bn_1 = tf.layers.batch_normalization(layer_1, training=is_train)
relu_1 = tf.maximum(alpha * bn_1, bn_1)
# 7x7x512
layer_2 = tf.layers.conv2d_transpose(relu_1, 256, 2, strides=2, padding='same')
bn_2 = tf.layers.batch_normalization(layer_2, training=is_train)
relu_2 = tf.maximum(alpha * bn_2, bn_2)
# 14x14x256
layer_3 = tf.layers.conv2d_transpose(relu_2, 128, 3, strides=2, padding='same')
bn_3 = tf.layers.batch_normalization(layer_3, training=is_train)
relu_3 = tf.maximum(alpha * bn_3, bn_3)
# 28x28x128
layer_4 = tf.layers.conv2d_transpose(relu_3, 64, 4, strides=1, padding='same')
bn_4 = tf.layers.batch_normalization(layer_4, training=is_train)
relu_4 = tf.maximum(alpha * bn_4, bn_4)
# Output layer
logits = tf.layers.conv2d_transpose(relu_4, out_channel_dim, 4, strides=1, 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)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_model_real)))
d_loss_fake = 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_real + d_loss_fake
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)
"""
# 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
# Model inputs
input_real, input_z, l_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
# Model losses
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
# Model optimizers
d_opt, g_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):
# TODO: Train Model
steps += 1
batch_images = 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_opt, feed_dict={input_real: batch_images, input_z: batch_z,
l_rate: learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z,
l_rate: learning_rate})
if steps % 10 == 0:
# Get the losses after every 10 batches and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}...".format(train_loss_g))
# Print sample from generator after every 100 batches
if steps % 100 == 0:
show_generator_output(sess, 25, 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 = 64
z_dim = 100
learning_rate = 0.0005
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 = 64
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.


As of Oak 1.5.3 the default sync handler comes with an additional configuration option (see section Configuration that allows to enable dynamic group membership resolution for external users.
Enabling dynamic membership in the DefaultSyncConfig will change the way external groups are synchronized (see OAK-4101) and how automatic group membership is being handled (see OAK-4087)
The key benefits of dynamic membership resolution are:
With the default SyncHandler this configuration option will show the following effects:
Note: as a further improvement the PrincipalNameResolver interface was introduced in Oak 1.6.1 to allow for optimized resolution of a principal names from a given ExternalIdentityRef. In order to benefit from that shortcut a given implementation of ExternalIdentityProvider needs to also implement PrincipalNameResolver. See also OAK-5210.
The dynamic (principal) membership features comes with a dedicated PrincipalConfiguration implementation (i.e. [ExternalPrincipalConfiguration]) that is in charge of securing
the rep:externalPrincipalNames properties (see also section Validation and Configuration).
Additionally the [ExternalPrincipalConfiguration] provides a PrincipalProvider implementation which makes external (group) principals available to the repository’s authentication and authorization using the rep:externalPrincipalNames as a persistent cache to avoid expensive lookup on the IDP. This also makes external Principals retrievable and searchable through the Jackrabbit principal management API (see section Principal Management for a comprehensive description).
Please note the following implementation detail wrt accessibility of group principals: A given external principal will be accessible though the principal management API if it can be read from any of the rep:externalPrincipalNames properties present using a dedicated query.
As described above the dynamic membership option will effectively disable the synchronization of the complete external group account information into the repository’s user management feature but limit the synchronized information to the principal names and the membership relation between a given java.security.acl.Group principal and external user accounts.
The user management API will consequently no longer be knowledgeable of external group identities (exception: groups that have been synchronized before enabling the feature will remain untouched and will be synchronized according to the sync configuration).
While this behavior does not affect default authentication and authorization modules (see below) it will have an impact on applications that rely on full synchronization of external identities. Those application won’t be able to benefit from the dynamic membership feature until dynamic groups can be created with the Jackrabbit User Management API (see OAK-2687).
The authentication setup provided by Oak is not affected by the dynamic membership handling as long as the configured LoginModule implementations rely on the PrincipalProvider for principal resolution and the ExternalPrincipalConfiguration (“Apache Jackrabbit Oak External PrincipalConfiguration”) is properly registered with the SecurityProvider (see section Configuration).


How-to information and details concerning the annotation service can be found here.
ContigsThe target FASTA file containing the genome sequence to annotate.
Organism NameType or select an organism name. If the target species or strain is not listed select the most specific, accurate taxonomic level available.
Genetic CodeThe codon translation used in calling genes.
DomainThe taxonomic domain of the target organism: bacteria or archaea.
Output FolderThe workspace folder where results will be placed.
Output NameName used to uniquely identify results.


Stored Script Execution
One of the questions we get asked a lot is: when will I be able to run Map/Reduce and DistExec jobs over HotRod.
I’m happy to say: now !
Infinispan Server comes with Stored Script Execution which means that remote clients can invoke named scripts on the server. If you’re familiar with the concept of Stored Procedures of the SQL world, then you already have an idea of what this feature is about. The types of scripts you can run are those handled by Java’s scripting API. Out of the box this means Javascript (which uses either the Nashorn engine on JDK 8+), but you can add many more (Groovy, Scala, JRuby, Jython, Lua, etc). Scripts are stored in a dedicated script cache ("___scriptcache") so that they can be easily created/modified using the standard cache operations (put/get/etc.).
Here’s an example of a very simple script:
The script above just obtains the default cache, retrieves the value with key 'a' and returns it (the Javascript script engine uses the last evaluated expression of a script as its return value). The first line of the script is special: it looks like a comment, but, like the first line in Unix shell scripts, it actually provides instructions on how the script should be run in the form of properties.
The mode property instructs the execution engine where we want to run the script: local for running the script on the node that is handling the request and distributed for running the script wrapped by a distributed executor. Bear in mind that you can certainly use clustered operations in local mode.
Scripts can also take named parameters which will "appear" as bindings in the execution scope.
Invoking it from a Java HotRod client would look like this:
Server-side scripts will be evolving quite a bit in Infinispan 8.1 where we will add support for the broader concept of server-side tasks which will include both scripts and deployable code which can be invoked in the same way, all managed and configured by the upcoming changes in the Infinispan Server console.


This DFM action includes the following features.
*
Two values for Bridge size: minimal and optimal.
*
Separate settings for shaving coverage and bridge according to the
Pad type. You can disable coverage and bridge shaves according to the Pad type.
*
Clearance vs. Bridge compromise
*
A new algorithm enables replacing all individual clearances in a
row of SMD Pads into one large, continuous clearance.
*
Placing bridge shaves in locations other than in the middle
between two Pads
*
Ability to define Pad types to be ignored by the system
Current Solder Mask Optimization DFM action
New Solder Mask Optimization (Solder Mask Repair) DFM action
There are four (4) pad types in the action.

SM clearance that is larger in any one direction than its corresponding Pad. The clearance must be larger by a value larger than v_max_oversized_clearance .
In the Solder Mask Optimization action, you could define a special set of optimization values as a percentage of the Pad type and the distance to its neighbors. These user-defined values overwrote the parameters defined in the action screen. (For more details please look in Doc. 0602, Solder Mask Optimization .)
In the Solder Mask Repair action, the ability to define ranges was expanded. You can now define 14 different types of range variables. Each range is defined according to the possible paring of a Pad's type and its neighbor's pad type.
Following are the new range prefixes.
*
SMD Pad ranges: SMD2SMD, SMD2PTH, SMD2VIA, SMD2OTHER
*
PTH Pad ranges: PTH2PTH, PTH2VIA, PTH2SMD, PTH2OTHER
*
Via Pad ranges: VIA2PTH, VIA2VIA, VIA2SMD, VIA2OTHER
The Generic range, if defined, will be used if there is no specific range that fits the current distance.
A range is a set of 3 variables, arranged in the following format.
*
Each Range set can include up to 10 different sets of variables
for each type of variable: 10 border variables, 10 partition variables, and 10
bridge variables.
<Range
Name>_<Numeric counter>_< BORDERS> =
<Lower limit>:<Upper Limit>
Lower limit and Upper limit are real numbers. They can be in mils or in microns, depending on the unit of the model.
The border variable defines the
distance that this range refers to.
There should be no overlap between two border variables of the same Range sets.
<Range
Name>_<Numeric counter>_<PARTITION> =
<Min_Clearance>:<Opt_Clearance>
<Min_Coverage>:<Opt_ Coverage >
These numbers ( Min_Clearance, Opt_Clearance, Min_Coverage, Opt_ Coverage ) are expressed in percentage.
This variable defines the
minimal/optimal clearance and the
minimal/optimal coverage values as a percentage of the distance between two
copper features.
<Range
Name>_<Numeric counter>_< BRIDGE> =
<Min_Bridge>:<Opt_Bridge>
Like the partition variable, this variable defines the minimal/optimal bridge values as a percentage of the distance between two copper features.
When the action checks a Pad for optimization, it find its closet neighbor. Then, based on the distance between the two pads, the action looks for the relevant range that applies to this pair of pads. and gets the minimal/optimal clearance values from the selected range.
If a proper range is not found, the action uses the relevant parameters from the Action GUI Screen.
The SM Repair action runs a separate scan to locate coverage problems. When the action finds a coverage problem, it looks for the relevant range that applies to the distance between the selected pad and the feature that is causing the coverage problem. If a suitable range is found, its values for the coverage variables are used.
The bridge values are set according to the range that was chosen for the clearance operation.
Here is an example of one Range sets group, SMD2SMD, with 3 sets of Ranges variables.


Event Location:
Microsoft
400 George St
Brisbane, , Australia
Abstract:
In this session, I make the case for Self-Service Analytics as one part of a broader reporting and analytics strategy. On its own, Self-Service Analytics struggles to deliver business users the insights they need, but when combined with a corporate BI platform, the power of Self-Service Analytics shines, and the often toxic relationship between the IT department and the Business changes to one of mutual respect and working together to achieve common goals. After laying the foundations, we'll launch into a range of demos covering PerformancePoint dashboards, SSRS Report Alerts, Report Builder Report Parts, Power View, Pivot PowerPivot. All demos will show how corporate BI solutions can be extended and enhanced with Self-Service Analytics.
Speaker(s): Rod Colledge,
Abstract:
The traditional approach to database scalability has been to simply buy a bigger box. However with the ongoing data explosion that is occurring in organisations today there is a need to re-think how to achieve scalability for the database platform. In this session we will explore how to extend the scalability of Microsoft SQL Server by using HDInsight and how to achieve scalability as a Hybrid solution using the Azure platform. With nothing more than a credit card and the Azure platform you will see how it is now possible to create highly-available, infinitely scalable SQL Server environments that support automated deployment and elastic scale.
Speaker(s): Peter Ward,
Abstract:
There seems to be a perception that working with Windows Azure SQL Databases is just a case of redirecting your connection strings to the cloud. In this session, Greg will explain why that isn't the case, explain what is and isn't provided by WASD, and detail lessons that he has learned from working with this environment.
Speaker(s): Greg Low,
Abstract:
In this session, I will introduce the audience to Microsoft Data Explorer for Excel. "Data Explorer" is an Excel add-in that enhances the self-service Business Intelligence experience in Excel by simplifying data discovery and access. It has often been referred to as the end-user ETL tool, but I believe that it will also be a valuable tool for all technical staff. This demo rich presentation will expose many great features of the newest addition to the Microsoft self-service offering.
Speaker(s): Craig Bryden,
Abstract:
In this fast paced world there is an ever increasing need to squeeze extra performance out of your SQL Server environment. SQL Server will always run well when there are no applications running on it – but when you need to scale the database to thousands of transactions a second what do you do? In this session Peter Ward from WARDY IT Solutions (www.wardyit.com) will empower you with numerous tips and tricks on to finding the “SQL Server Go-Faster Switch” based on real-world experience from some of the largest SQL Server implementations in APAC.
Speaker(s): Peter Ward,
Abstract:
This presentation will introduce big data analytics for the business analyst with PowerPivot and Power View. It will be of interest to those new to the concept of big data, new to self-service data modelling with PowerPivot, and those interested to understand what is new for PowerPivot and Power View in Excel 2013. Demonstrations will include creating a big data solution by using HDInsight Server; producing a PowerPivot model based on the big data solution; using PowerPivot to integrate big data with local data; and, analyzing the PowerPivot model data by using Power View.
Speaker(s): Peter Myers,
Abstract:
You can't escape hardware or the laws of physics. Ultimately your SQL Server instance will be using the processor, memory, disk and network subsystems! In this session Victor Isakov (MCA, MCM, MCT, MVP) will go through the various subsystems and examine how the SQL Server database engine works with them. Along the way he will discuss how to tune both the hardware and SQL Server for optimal performance.
Speaker(s): Victor Isakov,
Abstract:
AlwaysOn Availability Groups became available in SQL Server 2012. They provide a more granular approach to providing High Availability for you databases. In this session Warwick will introduce you to how you can utilise your AGs with Replication, Change Tracking Change Data Capture.
Speaker(s): Warwick Rudd,
Abstract:
There are a lot of misconceptions in the community about how indexes do and don't work in SQL Server. In this session, Greg will make sure that you have a solid understanding of the core concepts and will dispel many of the common myths.
Speaker(s): Greg Low,
Abstract:
This presentation introduces the upgraded features of SQL Server 2012 Master Data Services (MDS) and the new service, SQL Server 2012 Data Quality Services (DQS). MDS is a solution for master data management. First released in SQL Server 2008 R2, this upgraded version includes many new features and enhancements, including a new web interface developed with Silverlight, an Excel add-in to support simplified model creation and easier data updates and management. DQS is a new knowledge-driven data cleansing solution, and it supports building knowledge bases that provide correction and de-duplication of data.
Speaker(s): Peter Myers,
Abstract:
Windows 2012 is out! And it potentially completely changes how you deploy your SQL Server infrastructure. As a DBA, you should understand Microsoft's new operating system and its important set of features that impact SQL Server and your high availability solution design. In this session Victor Isakov (MCA, MCM, MCT, MVP) will present a session on the benefits that Windows 2012 brings to SQL Server, how to install and configure Windows 2012 for a SQL Server deployment, and explore new features that will affect your virtualization and high availability architectures.
Speaker(s): Victor Isakov,
Abstract:
In a world where executives/CXOs are keen to perform analytics and data consolidation with lightning speed, there is a strong push towards incorporating the principles of Agile manifesto in the BI world to deliver solutions faster than ever before. In this session, I will share techniques that will help a BI consultant deliver BI solutions without having a formalised requirements document and how these techniques ultimately add more value to any given business. The session will involve case studies and practical demos using Team Foundation Server and SQL Server 2012's BI tools - SSIS, SSRS, SSAS (tabular) and Excel PowerPivot.
Speaker(s): Mohamed Kabiruddin,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: https://www.linkedin.com/in/craigbryden/
Craig has been working with SQL Server for over 20 years and currently holds several SQL Server based certifications. He has also previously held an MCT certification. Craig's contribution to the SQL Server community was recognised in 2018 with a Microsoft MVP award. He was involved with Microsoft Learning to set some of the current SQL server Exams. Craig is the Chief Data Strategist at BiTQ. Craig is an organiser of the QLD Power BI User Group
LinkedIn: http://linkedin.com/in/peterjsmyers
Peter Myers is a consultant, trainer and presenter, and has worked with Microsoft database and development products since 1997. Today he specializes in all Microsoft Business Intelligence products and also authors training course content for Microsoft products and services. He has a broad business background supported by a bachelor’s degree in applied economics and accounting, and he extends this with extensive experience backed by current MCSE certifications. He has been a Data Platform MVP since 2007.
LinkedIn: http://au.linkedin.com/in/petersward/
Contact: http://wardyit.com/blog
Peter Ward is WARDY IT Solutions Chief Technical Architect. Peter's credentials and achievements include being a highly regarded speaker at SQL Server events; a sought after SQL Server consultant and trainer providing solutions for some of the largest SQL Server sites in Australia. Peter has been recognised as a Microsoft Most Valuable Professional for his technical excellence and commitment to the SQL Server Community.
LinkedIn: http://www.linkedin.com/in/greglow
Contact: http://blog.greglow.com
Greg Low is well-known in the SQL Server community as a mentor, trainer, author, and community leader. He is a certified master for SQL Server, was an instructor in that program, and holds a PhD in Computer Science. Microsoft often calls on Greg to develop data-related content, and to train their staff. He regularly speaks at tier-one conferences internationally. Greg is best known for his SQL Down Under podcast, SDU Tools, and his eBooks and whitepapers. He heads up a boutique consultancy called SQL Down Under in Melbourne Australia. Greg has been awarded as an MVP since 2001.
LinkedIn: http://au.linkedin.com/in/petersward/
Contact: http://wardyit.com/blog
Peter Ward is WARDY IT Solutions Chief Technical Architect. Peter's credentials and achievements include being a highly regarded speaker at SQL Server events; a sought after SQL Server consultant and trainer providing solutions for some of the largest SQL Server sites in Australia. Peter has been recognised as a Microsoft Most Valuable Professional for his technical excellence and commitment to the SQL Server Community.
LinkedIn: http://www.linkedin.com/in/greglow
Contact: http://blog.greglow.com
Greg Low is well-known in the SQL Server community as a mentor, trainer, author, and community leader. He is a certified master for SQL Server, was an instructor in that program, and holds a PhD in Computer Science. Microsoft often calls on Greg to develop data-related content, and to train their staff. He regularly speaks at tier-one conferences internationally. Greg is best known for his SQL Down Under podcast, SDU Tools, and his eBooks and whitepapers. He heads up a boutique consultancy called SQL Down Under in Melbourne Australia. Greg has been awarded as an MVP since 2001.
LinkedIn: http://www.linkedin.com/in/victorisakov
Contact: http://www.victorisakov.com
Victor Isakov (MCA, MCM, MCSM, MCT, MVP) is a Database Architect and trainer who provides consulting and training services to various organizations in the public, private, and NGO sectors globally. He regularly speaks at international conferences such as Microsoft TechEd/Ignite, PASS Summit, IT/Dev Connections, and SQLSaturdays. Victor has written a number of books on SQL Server and has worked with Microsoft to develop the SQL Server exams and certification. In 2017 he authored "Exam Ref 70-764 Administering a SQL Database Infrastructure (1st Edition)". He is one of only three non-Microsoft, Microsoft Certified Architects in the world.
LinkedIn: http://au.linkedin.com/pub/mohamed-kabiruddin/32/639/8b8/
Contact: http://www.nobrainerlab.com
Mohamed is a Cloud Solutions Architect with extensive experience in designing BI / Data Analytics solutions with expertise in analysis, design, modelling and implementation. He is a keen data enthusiast with a flair for managing and consolidating data.
LinkedIn: http://www.linkedin.com/in/victorisakov
Contact: http://www.victorisakov.com
Victor Isakov (MCA, MCM, MCSM, MCT, MVP) is a Database Architect and trainer who provides consulting and training services to various organizations in the public, private, and NGO sectors globally. He regularly speaks at international conferences such as Microsoft TechEd/Ignite, PASS Summit, IT/Dev Connections, and SQLSaturdays. Victor has written a number of books on SQL Server and has worked with Microsoft to develop the SQL Server exams and certification. In 2017 he authored "Exam Ref 70-764 Administering a SQL Database Infrastructure (1st Edition)". He is one of only three non-Microsoft, Microsoft Certified Architects in the world.
Contact: http://www.jnrit.com.au/Blog.aspx
Warwick Rudd is currently employed as a SQL Server Consultant and Certified Trainer for WARDY IT Solutions, Australia’s leading SQL Server consultants, trainers and Business Intelligence specialists. Warwick is a frequent speaker at local SQL Server user groups and SQLSaturday events in Australia. When not playing with the SQL stack, Warwick likes to get away to the cold and spend time snowboarding.
LinkedIn: http://linkedin.com/in/peterjsmyers
Peter Myers is a consultant, trainer and presenter, and has worked with Microsoft database and development products since 1997. Today he specializes in all Microsoft Business Intelligence products and also authors training course content for Microsoft products and services. He has a broad business background supported by a bachelor’s degree in applied economics and accounting, and he extends this with extensive experience backed by current MCSE certifications. He has been a Data Platform MVP since 2007.
Contact: http://www.stratadb.com
Rod Colledge is a director and founder of StrataDB, a leading Australian-based provider of SQL Server solutions. With 15+ years of SQL Server experience, Rod's commitment and depth of knowledge was recognized in 2010 with a Microsoft MVP award for SQL Server. Rod leads StrataDB's Business Intelligence practice and has authored or contributed to three books, including the “SQL Server MVP Deep Dives” series. He's a sought-after speaker at both local and international SQL Server conferences.
The following is a list of sponsors that helped fund the event.


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The vast majority of us need 7-8 hours of sleep each 24-hour period to maintain optimal performance and alertness. A small percentage of us can maintain optimal performance and alertness on less than 7-8 hours, and some of us require more than 7-8 hours. Are you obtaining the amount of sleep that is optimal for you?


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THE TIMING SOLUTION FOR COACHES AND ATHLETES AT RACES AND PRACTICE
Gain Freedom from the Stopwatch
With our hardware capture splits and view them anywhere where cellular service is available. Anytime an athlete runs by wearing a tag, a split is captured. View your entire team's data instantly and graph it with the click of a button.
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Intelligent Automation Where it Counts
Automate the data capture and allow us to help you in the analysis. TRAC supports multiple analysis methods, allowing you to dial in the right level of support. Analyze the effectiveness of each workout from the increased number of data points that can now be captured, or let us highlight points that could be concerning for you.
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Data can now be viewed via mobile app, or on your personal computer anywhere in the world. You can compare runners across seasons or across workouts, head to head, instantly. Upload old workouts to our site to make the comparison easier, or download current ones into Excel for a personal touch.
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This site is the final project of the course. The site contains many implementations such as: float menu to switch pages, header, footer, divisions on page to fit the text, embedded multimedia (local and from YouTube) and pictures, zoom in on pictures, pictures legends and forms. In addition to that, there are too many CSS3 configurations on header, texts, links, etc. The code is available HERE


The last step in the add process is to save the user security record. Before you select Save on the Add User Record page, the new record can still be cancelled if you click the Cancel button. It is not added until you check the Approval box and click Save. This navigation page gives you the opportunity to go back and double-check or correct any of the information previously entered in the Add Wizard before you save the record. You can do so by selecting the relevant tabDetails, Institution IDs, Applications, Special Roles, Availability, and Tokensreview or make your change, and click the OK button to return to this page.
Note: The Access Rights page is accessible from within the Applications page. After you have provided all relevant information for the new user, check the Approval box and click Save. A confirmation page displays with the new user ID. See the Security manual for further details.
Click the following links for more information:
| First Name | First name of the user being added. |
| Last Name | Last name of the user being added. |
| Institution ID | 6-digit identifier assigned by Card Services for the selected institution. |
| Approval | Prior to saving the record, you must accept the statement by checking the box. For further information, see to the Security manual or contact your client services representative. |
| Save |
After the Approval box has been checked, the Save button adds the new user security record, initiates processing of the token request (if necessary), and takes you to a confirmation page. Clicking Save from the Add User Record page takes you to the Confirmation page. |
| Cancel |
Clicking Cancel from the Add User page rejects the add, discards all information entered, and displays the following message: Click OK to exit this user. |


--- layout: default title: Aaron Grainer cover: 'assets/images/LQqfap.jpg' class: 'home-template' logo2: 'assets/images/logo/grainer-logo.png' current: home --- {% if page.logo %}{% endif %} {{ site.name }} {{ site.description }} Scroll Down {% include skills.html %} {% include mini-projects.html %} {% include timeline.html %} {% include author.html %}


Chapter 1 Linear Regression
1.1 Learning objectives
In this part, we cover the linear regression model. The linear model is commonly applied and versatile enough to be suitable for most tasks. We will use a dataset from the 1990 US Census which provides demographic and socio-economic data. The dataset includes observations from 1994 communities with each observation identified by a state
and communityname
variable. Before we start analyzing, we load the dataset and do some pre-processing.
We load a part of the census data using the read.csv()
function and confirm that the state
and communityname
are present in each dataset. The dataset is named communities.csv
and is included on your memory stick. You can copy it over to your computer and set the working directory in R to work in that folder. Alternatively, you can download the dataset here.
We assign the dataset to an object that resides in working memory. Let’s call that object communities
.
The stringsAsFactors
argument stops R from converting text variables into categorical variables called factors in R. The dataset is rather large and we are only interested in a few variables. In the following, we introduce a new package for data manipulation.
1.1.1 Dplyr package
The dplyr
package is useful for data manipulation. We install it by running install.packages("dplyr")
. We only install a package once. To update the package, run update.packages("dplyr")
. Loading multiple packages can cause clashes if packages include functions with similar names. In order to avoid such clashes, we will not load the package into the session with the library()
function but instead call dplyr functions directly from the package like so: dplyr::function_name()
. We demonstrate this as we go along.
<IP_ADDRESS> The dplyr::select()
function
Since our dataset has more columns (variables) than we need, let’s select only a few and rename them using more meaningful names. An easy way to accomplish this is using dplyr::select()
. The function allows us to select the columns we need and rename them at the same time.
communities <- dplyr::select(
communities,
state,
community = communityname,
UnemploymentRate = PctUnemployed,
NoHighSchool = PctNotHSGrad,
white = racePctWhite)
Note that the first argument in dplyr::select
is the name of the dataset (communities
in our case). The remaining arguments are the variables that we keep. The first variable state
has a meaningful name and does not need to be renamed. The second variable communityname
could be shorter and we rename it to community
. Similarly, we rename PctUnemployed
, PctNotHSGrad
and racePctWhite
.
1.1.2 Visualizing a relationship b/w two continuous variables
A good way gauge whether two variables that both continuous are related is to draw a scatter plot. We do so for the unemployment rate and for the lack of high school education. Both variables are measured in percent, where NoHighSchool
is the percentage of adults without high school education in a community.
plot(
x = communities$NoHighSchool,
y = communities$UnemploymentRate,
xlab = "Proportion of adults without high school education",
ylab = "Unemployment rate",
bty = "n",
pch = 16,
col = "gray")
Use ?plot()
or google R plot
for a description of the arguments.
It looks like communities with lower education levels suffer higher unemployment. To assess (1) whether that relationship is systematic (not a chance finding) and (2) what the magnitude of the relationship is, we estimate a linear model with the lm()
function. The two arguments we need to provide to the function are described below.
| Argument | Description |
|---|---|
formula |
The formula describes the relationship between the dependent and independent variables, for example dependent.variable ~ independent.variable In our case, we’d like to model the relationship using the formula: UnemploymentRate ~ NoHighSchool |
data |
This is simply the name of the dataset that contains the variable of interest. In our case, this is the merged dataset called communities . |
For more information on the lm()
function, run ?lm()
. Let’s run the linear model.
The lm()
function models the relationship between UnemploymentRate
and NoHighSchool
and we’ve assigned the estimated model to the object m1
. We can use the summary()
function on m1
for the key results.
Call:
lm(formula = UnemploymentRate ~ NoHighSchool, data = communities)
Residuals:
Min 1Q Median 3Q Max
-0.42347 -0.08499 -0.01189 0.07711 0.56470
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.078952 0.006483 12.18 <2e-16 ***
NoHighSchool 0.742385 0.014955 49.64 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1352 on 1992 degrees of freedom
Multiple R-squared: 0.553, Adjusted R-squared: 0.5527
F-statistic: 2464 on 1 and 1992 DF, p-value: < 2.2e-16
The output from lm()
might seem overwhelming at first so let’s break it down one item at a time.
| # | Description |
|---|---|
The dependent variable, also sometimes called the outcome variable. We are trying to model the effects of NoHighSchool on UnemploymentRate so UnemploymentRate is the dependent variable. |
|
The independent variable or the predictor variable. In our example, NoHighSchool is the independent variable. |
|
| The differences between the observed values and the predicted values are called residuals. | |
The coefficients for the intercept and the independent variables. Using the coefficients we can write down the relationship between the dependent and the independent variables as: UnemploymentRate = 0.078952 + ( 0.7423853 * NoHighSchool ) This tells us that for each unit increase in the variable NoHighSchool , the UnemploymentRate increases by 0.7423853. |
|
| The p-value of the model. Recall that according to the null hypotheses, the coefficient of interest is zero. The p-value tells us whether can can reject the null hypotheses or not. | |
| The standard error estimates the standard deviation of the coefficients in our model. We can think of the standard error as the measure of precision for the estimated coefficients. | |
| The t statistic is obtained by dividing the coefficients by the standard error. | |
| The R-squared and adjusted R-squared tell us how much of the variance in our model is accounted for by the independent variable. The adjusted R-squared is always smaller than R-squared as it takes into account the number of independent variables and degrees of freedom. |
<IP_ADDRESS> Predictions
We are often interested in predicting values for the dependent variable based on a values for the independent variable. For instance, what is the predicted unemployment rate given 50 percent of the adults without high school education? We use the predict()
function to assess this. Instead of making the forecast for the case were 50 percent do not have high school education, we make a prediction for each level of low education.
We create a sequence of values for low education using the sequence function first seq()
. We create 100 values from 0 to 1.
We now define a dataset where the variable names are called exactly the same as in our regression model m1
. Let’s check the name of the independent variable in m1
by calling the object. We then copy and paste the variable name to make sure that we don’t have a typo in our code.
Call:
lm(formula = UnemploymentRate ~ NoHighSchool, data = communities)
Coefficients:
(Intercept) NoHighSchool
0.07895 0.74239
We now use the predict()
function to make a prediction for each of the 100 edu
values.
We create a new dataset including the education values from 0 to 1 and the predictions. In the predict()
function, we set the argument se.fit
to TRUE. This returns a standard error for our prediction and lets us quantify our uncertainty. IN the dataset, we will save the point estimates (the best guesses) as well as values for the upper and lower bound of our confidence intervals
out <- data.frame( NoHighSchool = edu,
predicted_unemployment_rate = preds$fit,
lb = preds$fit - 1.96 * preds$se.fit,
ub = preds$fit + 1.96 * preds$se.fit)
Let’s inspect the first ten values of our data.
NoHighSchool predicted_unemployment_rate lb ub
1 0.00000000 0.07895202 0.06624469 0.09165936
2 0.01010101 0.08645087 0.07400458 0.09889715
3 0.02020202 0.09394971 0.08176286 0.10613655
4 0.03030303 0.10144855 0.08951944 0.11337766
5 0.04040404 0.10894739 0.09727420 0.12062058
6 0.05050505 0.11644623 0.10502702 0.12786544
We now add our prediction to the scatter plot.
lines( x = edu, y = out$predicted_unemployment_rate, lwd = 2)
lines( x = edu, y = out$lb, lwd = 2, lty = "dashed")
lines( x = edu, y = out$ub, lwd = 2, lty = "dashed")
As the plot shows, the precision of our estimates is quite good (the 95 percent confidence interval is narrow).
Returning to our example, are there other variables that might explain unemployment rates in our communities dataset? For example, is unemployment rate higher or lower in communities with different levels of minority population?
We first create a new variable called Minority
by subtracting the percent of White
population from 1. Alternatively, we could have added up the percent of Black, Hispanic and Asians to get the percentage of minority population since our census data also has those variables.
Next we fit a linear model using Minority
as the independent variable.
Call:
lm(formula = UnemploymentRate ~ Minority, data = communities)
Residuals:
Min 1Q Median 3Q Max
-0.45521 -0.12189 -0.02369 0.10162 0.68203
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.257948 0.005506 46.85 <2e-16 ***
Minority 0.428702 0.015883 26.99 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.173 on 1992 degrees of freedom
Multiple R-squared: 0.2678, Adjusted R-squared: 0.2674
F-statistic: 728.5 on 1 and 1992 DF, p-value: < 2.2e-16
Now let’s see how this model compares to our first model. We can show regression line from model2
just like we did with our first model.
# plot
plot(communities$Minority, communities$UnemploymentRate,
xlab = "Minority population rate",
ylab = "Unemployment rate",
bty = "n",
pch = 16,
col = "gray")
# predict outcomes
minority.seq <- seq(from = 0, to = 1, length.out = 100)
preds2 <- predict(m2, newdata = data.frame(Minority = minority.seq), se.fit = TRUE)
out2 <- data.frame(Minority = minority.seq,
predicted_unemployment_rate = preds2$fit,
lb = preds2$fit - 1.96 * preds2$se.fit,
ub = preds2$fit + 1.96 * preds2$se.fit)
lines( x = minority.seq, y = out2$predicted_unemployment_rate, lwd = 2)
lines( x = minority.seq, y = out2$lb, lwd = 2, lty = "dashed")
lines( x = minority.seq, y = out2$ub, lwd = 2, lty = "dashed")
Does m2
offer a better fit than m1
? Maybe we can answer that question by looking at the regression tables instead. Let’s print the two models side-by-side in a single table with the screenreg()
function contained in the texreg
package.
Let’s install texreg
first like so:
We now compare the models using the texreg()
function like so:
======================================
Model 1 Model 2
--------------------------------------
(Intercept) 0.08 *** 0.26 ***
(0.01) (0.01)
NoHighSchool 0.74 ***
(0.01)
Minority 0.43 ***
(0.02)
--------------------------------------
R^2 0.55 0.27
Adj. R^2 0.55 0.27
Num. obs. 1994 1994
RMSE 0.14 0.17
======================================
*** p < 0.001, ** p < 0.01, * p < 0.05
Contemplate the output from the table for a moment. Slope coefficients (everything except the intercept) are always the effect of a 1-unit change of the independent variable on the dependent variable in the units of the dependent variable. Both our independent variables are proportions. Hence a 1-unit change covers the entire ranges of our independent variables (0 to 1). Model 1 suggests that the unemployment rate is 74 percent larger in a district where no one has a high school degree than in a district where everyone has a high school degree. Similarly, model 2 suggests that in a district where everyone has a minority background (making everyone is a minority an oxymoron), the unemployment rate 43 percent higher than in a community where no one is. Please note that these predictive models should not be mistaken to capture causal relationships.
These are the two plots that we created earlier. In the model using NoHighSchool
the points which are the actual unemployment rates are much closer to our prediction (the regression line) than in the model using Minority
. This means that variation in NoHighSchool
better explains variation in UnemploymentRate
than variation in Minority
. This is captured in the R^2
and Adj. R^2
. Both R^2
and Adj. R^2
are measures of model fit. The difference between them is that Adj. R^2
is a measure that penalizes model complexity (more variables). In models with more than one independent variable, we rely on Adj. R^2
and in models with one independent variable, we use R^2
, i.e. here we would use R^2
.


Starr: It's 'Been One of Those Bombshell Days' | Breitbart
by Ian HanchettDuring the Fox News Channel’s coverage of Wednesday’s impeachment hearings, Fox News Contributor and former Whitewater Independent Counsel Ken Starr reacted to the testimony of U.S. Ambassador to the E.U. Gordon Sondland by stating that it “has been one of those bombshell days.”
Starr said that President Trump “may have covered himself by saying, no quid pro quo, the record is muddled. So, we have Gordon Sondland’s understanding. It doesn’t look good for the president, substantively.”
He later added, “[T]here will be articles of impeachment. I think we’ve known that. … And then, substantively, what we heard from the chairman just now is, it’s over. We now know — I mean, this is his position — we now know that the president, in fact, committed the crime of bribery. … I think articles of impeachment are being drawn up if they haven’t already been drawn up. And so, it depends, will it be bipartisan, and so forth. So, this, obviously, has been one of those bombshell days.”
Follow Ian Hanchett on Twitter @IanHanchett


BiMDP¶
CodeSnippet
You can download all the code on this page from the code snippets directory
BiMDP defines a framework for more general flow sequences, involving
top-down processes (e.g. for error backpropagation) and loops. So
the bi in BiMDP primarily stands for bidirectional. It also adds
a couple of other features, like a standardized way to transport
additional data, and a HTML based flow inspection utility. Because BiMDP
is a rather large addition and changes a few things compared to
standard MDP it is not included in mdp
but must be imported
separately as bimdp
(BiMDP is included in the standard MDP
installation)
>>> import bimdp
Warning
BiMDP is a relatively new addition to MDP (it was added in MDP 2.6). Even though it already went through long testing and several refactoring rounds it is still not as mature and polished as the rest of MDP. The API of BiMDP should be stable now, we don’t expect any significant breakages in the future.
Here is a brief summary of the most important new features in BiMDP:
Nodes can specify other nodes as jump targets, where the execution or training will be continued. It is now possible to use loops or backpropagation, in contrast to the strictly linear execution of a normal MDP flow. This is enabled by the new
BiFlow
class. The newBiNode
base class adds anode_id
string attribute, which can be used to target a node.The complexities of arbitrary data flows are evenly split up between
BiNode
andBiFlow
: Nodes specify their data and target using a standardized interface, which is then interpreted by the flow (somewhat like a very primitive domain specific language). The alternative approach would have been to use specialized flow classes or container nodes for each use case, which ultimately comes down to a design decision. Of course you can (and should) still take that route if for some reason BiMDP is not an adequate solution for your problem.In addition to the standard array data, nodes can transport more data in a message dictionary (these are really just standard Python dictionaries, so they are
dict
instances). The newBiNode
base class provides functionality to make this as convenient as possible.An interactive HTML-based inspection for flow training and execution is available. This allows you to step through your flow for debugging or add custom visualizations to analyze what is going on.
BiMDP supports and extends the
hinet
and theparallel
packages from MDP. BiMDP in general is compatible with MDP, so you can use standard MDP nodes in aBiFlow
. You can also useBiNode
instances in a standard MDP flow, as long as you don’t use certain BiMDP features.
The structure of BiMDP closely follows that of MDP, so there are
submodules bimdp.nodes
, bimdp.parallel
, and bimdp.hinet
. The
module bimdp.nodes
contains BiNode
versions of nearly all MDP nodes.
For example bimdp.nodes.PCABiNode
is derived from both BiNode
and mdp.nodes.PCANode
.
There are several examples available in the mdp-examples
repository,
which demonstrate how BiMDP can be used. For example backpropagation
demonstrates how to implement a simple multilayer perceptron, using
backpropagation for learning. The example binetdbn
is a
proof-of-concept implementation of a deep belief network. In addition
there are a couple of smaller examples in bimdp_examples
.
Finally note that this tutorial is intended to serve as an introduction, covering all the basic aspects of BiMDP. For more detailed specifications have a look at the docstrings.
Targets, id’s and Messages¶
In a normal MDP node the return value of the execute
method is
restricted to a single 2d array. A BiMDP BiNode
on the other hand can
optionally return a tuple containing an additional message dictionary
and a target value. So in general the return value is a tuple (x, msg,
target)
, where x
is a the usual 2d array. Alternatively a
BiNode
is also allowed to return only the array x
or a 2-tuple
(x, msg)
(specifying no target value). Unless stated otherwise the
last entry in the tuple should not be None
, but all the other values
are allowed to be None
(so if you specify a target then msg
can
be None
, and even x
can be None
).
The msg
message is a normal Python dictionary. You can use it to
transport any data that does not fit into the x
2d data array. Nodes
can take data from to the message and add data to it. The message is
propagated along with the x
data. If a normal MDP node is contained
in a BiFlow
then the message is simply passed around it. A
BiNode
can freely decide how to interact with the message (see the
BiNode section for more information).
The target value is either a string or a number. The number is the
relative position of the target node in the flow, so a target value of 1
corresponds to the following node, while -1 is the previous node. The
BiNode
base class also allows the specification of a node_id
string in the __init__
method. This string can then be used as a
target value.
The node_id
string is also useful to access nodes in a BiFlow
instance. The standard MDP Flow
class already implements
standard Python container methods, so flow[2]
will return the third
node in the flow. BiFlow
in addition enables you to use the
node_id
to index nodes in the flow, just like for a dictionary. Here is
a simple example
>>> pca_node = bimdp.nodes.PCABiNode(node_id="pca")
>>> biflow = bimdp.BiFlow([pca_node])
>>> biflow["pca"]
PCABiNode(input_dim=None, output_dim=None, dtype=None, node_id="pca")
BiFlow¶
The BiFlow
class mostly works in the same way as the normal Flow
class. We already mentioned several of the new features, like support
for targets, messages, and retrieving nodes based on their node_id
.
Apart from that the only major difference is the way in which you can
provide additional arguments for nodes. For example the FDANode
in
MDP requires class labels in addition to the data array (telling the
node to which class each data point belongs). In the Flow
class the
additional training data (the class labels) is provided by the same
iterable as the data. In a BiFlow
this is no longer allowed, since
this functionality is provided by the more general message mechanism. In
addition to the data_iterables
keyword argument of train
there
is a new msg_iterables
argument, to provide iterables for the
message dictionary. The structure of the msg_iterables
argument must
be the same as that of data_iterables
, but instead of yielding
arrays it should yield dictionaries (containing the additional data
values with the corresponding keys). Here is an example
>>> samples = np.random.random((100,10))
>>> labels = np.arange(100)
>>> biflow = bimdp.BiFlow([mdp.nodes.PCANode(), bimdp.nodes.FDABiNode()])
>>> biflow.train([[samples],[samples]], msg_iterables=[None,[{"labels": labels}]])
The _train
method of FDANode
requires the labels
argument, so
this is used as the key value. Note that we have to use the BiNode
version of FDANode
, called FDABiNode
(alomost every MDP node has
a BiNode
version following this naming convention). The BiNode
class provides the cl
value from the message to the _train
method.
In a normal Flow
the additional arguments can only be given to the
node which is currently in training. This limitation does not apply to a
BiFlow
, where the message can be accessed by all nodes (more on this
later). Message iterators can also be used during execution, via the
msg_iterable
argument in BiFlow.execute
. Of course messages can
be also returned by BiFlow.execute
, so the return value always has
the form (y, msg)
(where msg
can be an empty dictionary). For example:
>>> biflow = bimdp.nodes.PCABiNode(output_dim=10) + bimdp.nodes.SFABiNode()
>>> x = np.random.random((100,20))
>>> biflow.train(x)
>>> y, msg = biflow.execute(x)
>>> msg
{}
>>> # include a message that is not used
>>> y, msg = biflow.execute(x, msg_iterable={"test": 1})
>>> msg
{'test': 1}
Note that BiNode
overloads the plus operator to create a BiFlow
.
If iterables are used for execution then the BiFlow
not only
concatenates the y
result arrays, but also tries to join the msg
dictionaries into a single one. Arrays in the msg
will be
concatenated, for all other types the plus operator is used.
The train
method of BiFlow
also has an additional argument
called stop_messages
, which can be used to provide message iterables
for stop_training
. The execute
method on the other hand has an
argument target_iterable
, which can be used to specify the initial
target in the flow execution (if the iterable
is just a single array
then of course the target_iterable
should be just a single node_id
).
BiNode¶
We now want to give an overview of the BiNode
API, which is mostly an
extension of the Node
API. First we take a look at the possible return
values of a BiNode
and briefly explain their meaning:
execute
x
or(x, msg)
or(x, msg, target)
. Normal execution continues, directly jumping to the target if one is specified.
train
None
terminates training.x
or(x, msg)
or(x, msg, target)
. Means that execution is continued and that this node will be reached again to terminate training. Ifx
isNone
and no target is specified then the remainingmsg
is dropped (so it is not required to “clear” the message manually in_train
for custom nodes to terminate training).
stop_training
None
doesn’t do anything, like the normal MDPstop_training
.x
or(x, msg)
or(x, msg, target)
. Causes an execute like phase, which terminates when the end of the flow is reached or whenEXIT_TARGET
is given as target value (just like during a normal execute phase,EXIT_TARGET
is explained later).
Of course all these methods also accept messages. Compared to Node
methods they have a new msg
argument. The target
part on the
other hand is only used by the BiFlow
.
As you can see from train
, the training does not always stop when
the training node is reached. Instead it is possible to continue with
the execution to come back later. For example this is used in the
backpropagation example (in the MDP examples repository). There are also
the new stop_training
result options that start an execute phase.
This can be used to propagate results from the node training or to
prepare nodes for their upcoming training.
Some of these new options might be confusing at first. However, you can simply ignore those that you don’t need and concentrate on the features that are useful for your current project. For example you could use messages without ever worrying about targets.
There are also two more additions to the BiNode
API:
node_id
- This is a read-only property, which returns the node id
(which is
None
if it wasn’t specified). The__init__
method of aBiNode
generally accepts anode_id
keyword argument to set this value.
bi_reset
- This method is called by the
BiFlow
before and after training and execution (and after thestop_training
execution phase). You can be override the private_bi_reset
method to reset internal state variables (_bi_reset
is called bybi_reset
).
Inspection¶
Using jumps and messages can result in complex data flows. Therefore
BiMDP offers some convenient inspection capabilities to help with
debugging and analyzing what is going on. This functionality is based on
the static HTML view from the mdp.hinet
module. Instead of a static
view of the flow you get an animated slideshow of the flow training or
execution. An example is provided in
bimdp/test/demo_hinet_inspection.py
. You can simply call
bimdp.show_execution(flow, data)
instead of the normal
flow.execute(data)
. This will automatically perform the inspection
and open it in your webbrowser. Similar functionality is available for
training. Just call bimdp.show_execution(flow, data_iterables)
,
which will perform training as in flow.train(data_iterables)
. Have a
look at the docstrings to learn about additional options.
The BiMDP inspection is also useful to visualize the data processing
that is happening inside a flow. This is especially handy if you are
trying to build or understand new algorithms and want to know what is
going on. Therefore we made it very easy to customize the HTML views in
the inspection. One simple example is provided in
bimdp/test/demo_custom_inspection.py
, where we use matplotlib to
plot the data and present it inside the HTML view. Note that
bimdp.show_training
and bimdp.show_execution
are just helper
functions. If you need more flexibility you can directly access the
machinery below (but this is rather messy and hardly ever needed).
Browser Compatibility
The inspection works with all browser except Chrome.
This is due to a controversial chromium issue. Until
this is fixed by the Chrome developers the only workarounds
are to either start Chrome with the --allow-file-access-from-files
flag or to access the inspection via a webserver.
Extending BiNode and Message Handling¶
As in the Node
class any derived BiNode
classes should not
directly overwrite the public execute
or train
methods but
instead the private versions with an underscore in front (for training
you can of course also overwrite _get_train_seq
). In addition to the
dimensionality checks performed on x
by the Node
class this
enables a couple of message handling features.
The automatic message handling is a major feature in BiNode
and
relies on the dynamic nature of Python. In the FDABiNode
and
BiFlow
example we have already seen how a value from the message is
automatically passed to the _train
method, because the key of the
value is also the name of a keyword argument.
Public methods like execute
in BiNode
accept not only a data
array x
, but also a message dictionary msg
. When given a message
they perform introspection to determine the arguments for the
corresponding private methods (like _train
). If there is a matching
key for an argument in the message then the value is provided as a
keyword argument. It remains in the dictionary and can therefore be used
by other nodes in the flow as well.
A private method like _train
has the same return options as the
public train
method, so one can for example return a tuple (x,
msg)
. The msg
in the return value from _train
is then used by
train
to update the original msg
. Thereby _train
can
overwrite or add new values to the message. There are also some special
features (“magic”) to make handling messages more convenient:
- You can use message keys of the form
node_id->argument_key
to address parts of the message to a specific node. When the node with the corresponding id is reached then the value is not only provided as an argument, but the key is also deleted from the message. If theargument_key
is not an argument of the method then the whole key is simply erased. - If a private method like
_train
has a keyword argument calledmsg
then the complete message is provided. The message from the return value replaces the original message in this case. For example this makes it possible to delete parts of the message (instead of just updating them with new values). - The key
"method"
is treated in a special way. Instead of calling the standard private method like_train
(or_execute
, depending on the called public method) the"method"
value will be used as the method name, with an underscore in front. For example the message{"method": "classify"}
has the effect that a method_classify
will be called. Note that this feature can be combined with the extension mechanism, when methods are added at runtime. - The key
"target"
is treated in a special way. If the called private method does not return a target value (e.g., if it just returnedx
) then the"target"
value is used as target return value (e.g, instead ofx
the return value ofexecute
would then have the formx, None, target
). - If the key
"method"
has the valueinverse
then, as expected, the_inverse
method is called. However, additionally the checks frominverse
are run on the data array. If_inverse
does not return a target value then the target -1 is returned. So with the message{"method": "inverse"}
one can execute aBiFlow
in inverse node (note that one also has to provide the last node in the flow as the initial target to the flow). - This is more of a
BiFlow
feature, but the target value specified inbimdp.EXIT_TARGET
(currently set to"exit"
) causesBiFlow
to terminate the execution and to return the last return value.
Of course all these features can be combined, or can be ignored when they are not needed.
HiNet in BiMDP¶
BiMDP is mostly compatible with the hierarchical networks introduced in
mdp.hinet
. For the full BiMDP functionality it is of
required to use the BiMDP versions of the the building blocks.
The bimdp.hinet
module provides a BiFlowNode
class, which is
offers the same functionality as a FlowNode
but with the added
capability of handling messages, targets, and all other BiMDP concepts.
There is also a new BiSwitchboard
base class, which is able to deal
with messages. Arrays present in the message are mapped with the
switchboard routing if the second axis matches the switchboard dimension
(this works for both execute and inverse).
Finally there is a CloneBiLayer
class, which is the BiMDP version of
the CloneLayer
class in mdp.hinet
. To support all the features
of BiMDP some significant functionality has been added to this class.
The most important new aspect is the use_copies
property. If it is
set to True
then multiple deep copies are used instead of just a
reference to the same node. This makes it possible to use internal
variables in a node that persist while the node is left and later
reentered. You can set this property as often as you like (note that
there is of course some overhead for the deep copying). You can also set
the use_copies
property via the message mechanism by simply adding a
"use_copies"
key with the required boolean value. The CloneBiLayer
class also looks for this key in outgoing messages (so it can be send
by nodes inside the layer). A CloneBiLayer
can also split arrays in the
message to feed them to the nodes (see the doctring for more details).
CloneBiLayer
is compatible with the target mechanism (e.g. if the
CloneBiLayer
contains a BiFlowNode
you can target an internal node).
Parallel in BiMDP¶
The parallelisation capabilites introduced in mdp.parallel
can be
used for BiMDP. The bimdp.parallel
module provides a
ParallelBiFlow
class which can be used like the normal
ParallelFlow
. No changes to schedulers are required.
Note that a ParallelBiFlow
uses a special callable class to handle
the message data. So if you want to use a custom callable you will have
to make a few modifications (compared to the standard callable class
used by ParallFlow
).
Coroutine Decorator¶
For complex flow control (like in the DBN example) one might need a node
that keeps track of the current status in the execution. The standard
pattern for this is to implement a state machine, which would require
some boilerplate code. Python on the other hand supports so called
continuations via coroutines. A coroutine is very similar to a
generator function, but the yield
statement can also return a value
(i.e., the coroutine is receiving a value). Coroutines might be
difficult to grasp, but they are well documented on the web. Most
importantly, coroutines can be a very elegant implementation of the
state machine pattern.
Using a couroutine in a BiNode to maintain a state would still require
some boilerplate code. Therefore BiMDP provides a special function
decorator to minimize the effort, making it extremely convenient to use
coroutines. This is demonstrated in the gradnewton
and binetdbn
examples. For example decorating the _execute
method can be done
like this:
>>> class SimpleCoroutineNode(bimdp.nodes.IdentityBiNode):
... # the arg ["b"] means that that the signature will be (x, b)
... @bimdp.binode_coroutine(["b"])
... def _execute(self, x, n_iterations):
... """Gather all the incomming b and return them finally."""
... bs = []
... for _ in range(n_iterations):
... x, b = yield x
... bs.append(b)
... raise StopIteration(x, {"all the b": bs})
>>> n_iterations = 3
>>> x = np.random.random((1,1))
>>> node = SimpleCoroutineNode()
>>> # during the first call the decorator creates the actual coroutine
>>> x, msg = node.execute(x, {"n_iterations": n_iterations})
>>> # the following calls go to the yield statement,
>>> # finally the bs are returned
>>> for i in range(n_iterations-1):
... x, msg = node.execute(x, {"b": i})
>>> x, msg = node.execute(x, {"b": n_iterations-1})
You can find the complete runable code in the bimdp_simple_coroutine.py
example.
Classifiers in BiMDP¶
BiMDP introduces a special BiClassifier
base class for the new
Classifier
nodes in MDP. This makes it possible to fully use
classifiers in a normal BiFlow
. Just like for normal nodes
the BiMDP versions of the classifier are available in bimdp.nodes
(the SVM classifiers are currently not available by default, but it is
possible to manually derive a BiClassifier
version of them).
The BiClassifier
class makes it possible to provide the training
labels via the message mechanism (simply store the labels with a
"labels"
key in the msg
dict). It is also possible to transport
the classification results in the outgoing message. The _execute
method of a
BiClassifier
has three keyword arguments called return_labels
,
return_ranks
, and return_probs
. These can be set via the message
mechanism. If for example return_labels
is set to True
then
execute
will call the label
method from the classifier node and
store the result in the outgoing message (under the key "labels"
). The
return_labels
argument (and the other two) can also be set to a
string value, which is then used as a prefix for the "labels"
key in
the outgoing message (e.g., to target this information at a specific
node in the flow).


New twist in double murder at Durban beach
by Algoa FMDurban police say last week's double murder on Suncoast Beach has been changed to a murder-suicide.
The bodies of the 24-year-old Kresan Chandiah and the 50-year-old Fatima Muhammad were found on the beach's dunes last Monday morning.
They were found with stab wounds to the chest by a man collecting plastic and paper in the area.
Police spokesperson, Jay Naicker the post mortems conducted changed their investigation to an inquest docket as well as a murder.
Muhammad's bag with her ID book was found at the scene.


2021-02-27
How do you become a more effective product manager (PM)? Many will say you need to learn to code. Some will point to design. Others will argue SQL is the key. Each is partially right, but missing the underlying point [1].
PMs don't need to code.
PMs don't need to design.
PMs don't need to write SQL.
PMs need to understand each enough to call BS.
What does it mean to call BS? It is the art of challenging constraints. And it can happen at any stage in the product development process.
If a sales rep tells you a feature is absolutely critical, can you access the data to see if it's true? Even better, can you ask the right question to determine what data to look for? This highlights the difference between being able to write SQL and knowing what kind of data you need in the first place.
If an engineer tells you something can't be done, can you walk through the system with them to understand why? Can you provide more context that changes the constraints of the problem? You aren't writing the actual code. You are equipping them with the information they need to solve the problem.
There are countless examples but you get the point
The best way to learn to call BS is through observation and iteration. First you need to observe how others think. When an engineer or designer makes a decision, ask them why. Don't stop at the surface-level answer. Dig for more but make it clear it's because you want to learn, not because you want to change their mind. Identify the assumptions they are making. Figure out which are valid, and which could be challenged. But don't try to change the past. You are in observation mode.
The next time a decision is being made, reflect on your newfound knowledge, and think of a question that can push others to challenge the assumptions that support their logic. If a certain feature is hard to build , ask how they would build it and where it would break. You may be able to provide additional context (e.g. the customer doesn't care about an edge case they are worried about). Or you may be swiftly shut down. That's okay! You will undoubtedly learn something new for the next time. Iterate.
Over time you will build the muscle that tells you when to push and how hard. At that point, it's your duty to start teaching others how you think, so they can call BS on you.
[1] To be clear, learning to code is valuable. Learning to design is valuable. Learning to write SQL is valuable. But none are required to be a product manager. And none of those skills (in isolation) is going to be the thing that sets you apart. That doesn't mean you shouldn't learn them.


The schema alignment skeleton below specifies how your grid-shaped data will be transformed into graph-shaped data in Freebase's schemas. Find out more ...


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/complementary.20sequences.html ---
Suppose I have a strictly monotonous f : nat → nat
. How hard is it to define g : nat → nat
that is the strictly monotonous sequence of numbers that don't occur in the sequence f
...
Can we define the Figure Figure sequences? https://en.wikipedia.org/wiki/Hofstadter_sequence#Hofstadter_Figure-Figure_sequences
You need additional assumptions on f
, right? You cannot define g
when f = id
.
I constructively proved at some point that every infinite subset of nat was denumerable.
It's in mathlib. It was part of the denumerability of Q proof.
The Figure Figure sequences is a bit tricky since it is a mutual definition. I would probably define them mutually, together with R_list
which is equal to (list.range n).map R
What's the point of this sequence? I skimmed through the wikipedia page without understand why it could be interesting
Just because it's in the OEIS doesn't mean it's interesting to someone who doesn't care about sequences. The OEIS contains huge collections of uninteresting sequences now, which to be honest is really really good, because whenever someone like me runs into a sequence it's now very likely it will be there. Someone like you is I guess far less likely to be running into sequences of positive integers in your day job.
"This is the lexicographically earliest sequence that together with its first differences contains every positive integer exactly once". If that floats your boat, then that's the point of the sequence.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


---
layout: simple_page
title: Vipin Sharma
description: ""
header-img: "img/ebook.jpg"
---
About Me
I help professional Java developers
to learn language features
so they can work on best Java projects.
Senior Software Engineer with 15+ years of software development experience.
Comprehensive knowledge of Java application development and JVM Internals.
Contributor to OpenJDK, a free and open source implementation of the Java Platform, Standard Edition (Java SE).
Creator of Open source project MSG (Microservices generator) https://github.com/Vipin-Sharma/MSG


Index module.
MIT. See LICENSE for full details.
These are common commands that apply to all widgets that have them injected.
Get the length of the text.
The length of the text.
Get the index where the insert cursor is.
An int of the index where the insert cursor is.
Get the start and end indexes of the text selection if there is one.
An array holding the start and end indexes of a text selection.