| ID | Title | Duration (ms) |
|---|---|---|
| 1 | ## actions test suite ## Should successfuly migrate N files Err should be undefined | 1 |
| 2 | ## actions test suite ## Should successfuly migrate N files Should call GetAll migrations | 1 |
| 3 | ## actions test suite ## Should successfuly migrate N files Should call insertMigration | 1 |
| 4 | ## actions test suite ## Should successfuly migrate N files Should call utils.require with 12347_file3.js | 0 |
| 5 | ## actions test suite ## Should successfuly migrate 1 file Err should be undefined | 4 |
| 6 | ## actions test suite ## Should successfuly migrate 1 file Should call GetAll migrations | 1 |
| 7 | ## actions test suite ## Should successfuly migrate 1 file Should call insertMigration once | 0 |
| 8 | ## actions test suite ## Should successfuly migrate 1 file Should call utils.require once | 1 |
| 9 | ## actions test suite ## Should successfuly migrate 0 files Err should be undefined | 0 |
| 10 | ## actions test suite ## Should successfuly migrate 0 files Should call GetAll migrations | 1 |
| 11 | ## actions test suite ## Should successfuly migrate 0 files Should NOT call insertMigrations | 0 |
| 12 | ## actions test suite ## Should successfuly migrate 0 files Should NOT call utils.require | 1 |
| 13 | ## actions test suite ## Should throw error and stop migration Err should not be undefined | 1 |
| 14 | ## actions test suite ## Should throw error and stop migration Should call GetAll migrations | 1 |
| 15 | ## actions test suite ## Should throw error and stop migration Should NOT call insertMigrations | 0 |
| 16 | ## actions test suite ## Should throw error and stop migration Should call utils.require | 0 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const chalk = require('chalk') | |
| 2 | 1 | const log = console.log | |
| 3 | 1 | const utils = require('../utils') | |
| 4 | |||
| 5 | 1 | const action = async (kong,migrations,db) => { | |
| 6 | |||
| 7 | let executedMigrations = await db.getAll() | ||
| 8 | |||
| 9 | 3 | executedMigrations = executedMigrations.map((el)=> { | |
| 10 | return el.dataValues.file | ||
| 11 | }) | ||
| 12 | |||
| 13 | 3 | migrations = migrations.filter( (m) => { | |
| 14 | return !executedMigrations.includes(m) | ||
| 15 | }) | ||
| 16 | |||
| 17 | 3 | const steps = migrations.length | |
| 18 | |||
| 19 | 4 | if (steps === 0) log(chalk.green('Migrations are up-to-date. Nothing to do here.')) | |
| 20 | |||
| 21 | 3 | migrations.forEach(async (migration,index)=>{ | |
| 22 | // check if file was already migrated. | ||
| 23 | log(chalk.green(`Running Migration [${index+1}/${steps}] > ${migration}`)) | ||
| 24 | // if not, up it. | ||
| 25 | 4 | const m = utils.require(`${process.cwd()}/kong-migrations/${migration}`) | |
| 26 | |||
| 27 | if ( m.up ) { |
||
| 28 | |||
| 29 | 4 | try { | |
| 30 | 4 | await m.up(kong) | |
| 31 | } catch (error) { | ||
| 32 | log(chalk.red(error.message)) | ||
| 33 | throw new Error(`An error as occured migrating file ${migration}`) | ||
| 34 | } | ||
| 35 | |||
| 36 | 4 | try { | |
| 37 | 4 | await db.insertMigration({file: migration}) | |
| 38 | } catch (error) { | ||
| 39 | if (error.parent.code !== '23505') { | ||
| 40 | log(chalk.red(error.message)) | ||
| 41 | return | ||
| 42 | } | ||
| 43 | |||
| 44 | log(chalk.red('Service already exists.')) | ||
| 45 | } | ||
| 46 | } | ||
| 47 | }) | ||
| 48 | |||
| 49 | } | ||
| 50 | |||
| 51 | |||
| 52 | 1 | module.exports = action | |
| 53 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | |||
| 2 | class PostgresDb { | ||
| 3 | constructor(options) { | ||
| 4 | |||
| 5 | const Migration = require('./models/migration')(options) | ||
| 6 | |||
| 7 | 1 | this.Migration = Migration | |
| 8 | } | ||
| 9 | |||
| 10 | async insertMigration(migration) { | ||
| 11 | |||
| 12 | await this.Migration.sync() |
||
| 13 | return await this.Migration.create({file:migration.file}) | ||
| 14 | } | ||
| 15 | async removeMigration(migration) { | ||
| 16 | |||
| 17 | await this.Migration.sync() |
||
| 18 | return await this.Migration.destroy({ | ||
| 19 | where: { | ||
| 20 | file: migration.file, | ||
| 21 | }, | ||
| 22 | }) | ||
| 23 | } | ||
| 24 | |||
| 25 | async getAll() { | ||
| 26 | return await this.Migration.findAll() |
||
| 27 | } | ||
| 28 | |||
| 29 | async getLatest() { | ||
| 30 | return await this.Migration.findAll({limit: 1, order: [['file','DESC']]}) |
||
| 31 | } | ||
| 32 | |||
| 33 | } | ||
| 34 | |||
| 35 | 1 | module.exports = PostgresDb | |
| 36 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const Sequelize = require('../sequelize') | |
| 2 | |||
| 3 | 1 | module.exports = (options) => { | |
| 4 | |||
| 5 | const sequelize = Sequelize(options) | ||
| 6 | |||
| 7 | 1 | const Migration = sequelize.Instance.define('kong_entity_migrations',{ | |
| 8 | file: { | ||
| 9 | type: sequelize.Types.STRING, | ||
| 10 | unique: true, | ||
| 11 | }, | ||
| 12 | }) | ||
| 13 | |||
| 14 | 1 | return Migration | |
| 15 | } | ||
| 16 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const Sequelize = require('sequelize') | |
| 2 | |||
| 3 | 1 | module.exports = (options) => { | |
| 4 | |||
| 5 | Sequelize.addHook('afterInit', (sequelize) => { | ||
| 6 | sequelize.options.handleDisconnects = false | ||
| 7 | |||
| 8 | 1 | sequelize.connectionManager.pool.clear() | |
| 9 | 1 | sequelize.connectionManager.pool = null | |
| 10 | 1 | sequelize.connectionManager.getConnection = function getConnection() { | |
| 11 | return this._connect(sequelize.config) |
||
| 12 | } | ||
| 13 | 1 | sequelize.connectionManager.releaseConnection = function releaseConnection(connection) { | |
| 14 | return this._disconnect(connection); |
||
| 15 | } | ||
| 16 | }) | ||
| 17 | |||
| 18 | |||
| 19 | 1 | return { | |
| 20 | Types: Sequelize, | ||
| 21 | Instance: new Sequelize({ | ||
| 22 | dialect: 'postgres', | ||
| 23 | operatorsAliases: false, | ||
| 24 | pool: { | ||
| 25 | max: 5, | ||
| 26 | min: 0, | ||
| 27 | acquire: 30000, | ||
| 28 | idle: 10000, | ||
| 29 | }, | ||
| 30 | ...options, | ||
| 31 | logging: false, | ||
| 32 | }) | ||
| 33 | } | ||
| 34 | |||
| 35 | } | ||
| 36 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const service = require('./service') | |
| 2 | 1 | const route = require('./route') | |
| 3 | |||
| 4 | 1 | exports = module.exports = { } | |
| 5 | |||
| 6 | |||
| 7 | class Kong { | ||
| 8 | |||
| 9 | constructor(options) { | ||
| 10 | this.createService = service.createService.bind(this, options) | ||
| 11 | 4 | this.removeService = service.removeService.bind(this, options) | |
| 12 | 4 | this.createRoute = route.createRoute.bind(this, options) | |
| 13 | 4 | this.removeRoute = route.removeRoute.bind(this, options) | |
| 14 | } | ||
| 15 | |||
| 16 | } | ||
| 17 | |||
| 18 | |||
| 19 | 1 | exports.Kong = Kong | |
| 20 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const http = require('axios') | |
| 2 | 1 | const log = console.log | |
| 3 | 1 | const chalk = require('chalk') | |
| 4 | |||
| 5 | 1 | exports = module.exports = {} | |
| 6 | |||
| 7 | |||
| 8 | 1 | const createRoute = async (options,service, ...route) => { | |
| 9 | |||
| 10 | route.forEach( async(route)=>{ |
||
| 11 | |||
| 12 | route.service = { |
||
| 13 | id: service.id, | ||
| 14 | } |
||
| 15 | |||
| 16 | try { | ||
| 17 | await http.post(`${options.kongAdminUrl}/routes`, route) | ||
| 18 | } catch (error) { | ||
| 19 | log(chalk.red('Route creation failed with message: ' + error.message)) | ||
| 20 | } | ||
| 21 | |||
| 22 | }) |
||
| 23 | } | ||
| 24 | |||
| 25 | 1 | const removeRoute = async (options, route) => { | |
| 26 | try { |
||
| 27 | |||
| 28 | await http.delete(`${options.kongAdminUrl}/routes/${route}`) | ||
| 29 | |||
| 30 | } catch (error) { | ||
| 31 | log(chalk.red('Route removal failed with message: ', error.message)) | ||
| 32 | } |
||
| 33 | } | ||
| 34 | |||
| 35 | |||
| 36 | 1 | exports.createRoute = createRoute | |
| 37 | 1 | exports.removeRoute = removeRoute | |
| 38 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const http = require('axios') | |
| 2 | 1 | const log = console.log | |
| 3 | 1 | const chalk = require('chalk') | |
| 4 | |||
| 5 | 1 | exports = module.exports = { } | |
| 6 | |||
| 7 | 1 | const createService = (options, service, cb) => { | |
| 8 | http.post(`${options.kongAdminUrl}/services`, service) |
||
| 9 | .then((response)=> { | ||
| 10 | return cb(null, response.data) |
||
| 11 | }) | ||
| 12 | .catch((e)=> cb(e, null)) |
||
| 13 | } | ||
| 14 | |||
| 15 | 1 | const removeService = async(options, service) => { | |
| 16 | try { |
||
| 17 | let response = await http.get(`${options.kongAdminUrl}/services/${service}/routes`) | ||
| 18 | if (response.data) { | ||
| 19 | response.data.data.forEach(async (route)=> { | ||
| 20 | await http.delete(`${options.kongAdminUrl}/routes/${route.id}`) |
||
| 21 | }) | ||
| 22 | } | ||
| 23 | |||
| 24 | |||
| 25 | response = await http.get(`${options.kongAdminUrl}/services/${service}/plugins`) | ||
| 26 | if (response.data) { | ||
| 27 | response.data.data.forEach(async (plugin)=> { | ||
| 28 | await http.delete(`${options.kongAdminUrl}/plugins/${plugin.id}`) |
||
| 29 | }) | ||
| 30 | } | ||
| 31 | |||
| 32 | await http.delete(`${options.kongAdminUrl}/services/${service}`) | ||
| 33 | |||
| 34 | } catch (error) { | ||
| 35 | log(chalk.red('Service removal failed with message: ', error)) | ||
| 36 | } |
||
| 37 | |||
| 38 | return this | ||
| 39 | } | ||
| 40 | |||
| 41 | |||
| 42 | 1 | exports.createService = createService | |
| 43 | 1 | exports.removeService = removeService | |
| 44 |
| Line | Lint | Hits | Source |
|---|---|---|---|
| 1 | 1 | const fs = require('fs') | |
| 2 | 1 | const cwd = process.cwd() | |
| 3 | |||
| 4 | 1 | exports = module.exports = {} | |
| 5 | |||
| 6 | |||
| 7 | function getDokikongfile() { | ||
| 8 | const files = fs.readdirSync(cwd) | ||
| 9 | const result = files.find((file)=> { | ||
| 10 | return file === 'Dokikongfile.js' |
||
| 11 | }) | ||
| 12 | |||
| 13 | if (!result || result.length === 0) { | ||
| 14 | throw new Error('Could not find Dokikongfile in the current directory') | ||
| 15 | } | ||
| 16 | |||
| 17 | return require(`${process.cwd()}/${result}`) | ||
| 18 | } | ||
| 19 | |||
| 20 | function getMigrationFiles() { | ||
| 21 | return fs.readdirSync(`${cwd}/kong-migrations/`).sort() | ||
| 22 | } | ||
| 23 | |||
| 24 | function r(path) { | ||
| 25 | return require(path) | ||
| 26 | } | ||
| 27 | |||
| 28 | 1 | exports.getDokikongfile = getDokikongfile | |
| 29 | 1 | exports.getMigrationFiles = getMigrationFiles | |
| 30 | 1 | exports.require = r | |
| 31 |


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ErrorKind
public enum ErrorKind
Kinds of Error in SocketError
- socketErrorFCNTL: failure in BSD fcntl function
- socketErrorDefault: default error
- socketErrorIOCTL: failure in BSD ioctl function
- socketErrorWrongAddress: the given address is not correct
- socketErrorAlreadyConnected: the current socket is already connected
- socketErrorIncorrectSocketStatus: conflicts with current socket status
- socketErrorConnecting: failure in connecting to destination
- socketErrorReadSpecific: found error when reading data from sock (return from
read
) - socketErrorWriteSpecific: found error when writing data to sock (return from
write
) - socketErrorSetSockOpt: failure in BSD setsockopt function
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind
-
Undocumented
Declaration
Swift
public enum ErrorKind


An autoencoder, is an artificial neural network used for learning efficient codings.
The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
Based on https://blog.keras.io/building-autoencoders-in-keras.html
The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers.
All the Functional API relies on the fact that each keras.Layer
object is a callable object!
See 8.2 Multi-Modal Networks for further details.
from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist
import numpy as np
# this is the size of our encoded representations
encoding_dim = 32 # 32 floats -> compression of factor 24.5, assuming the input is 784 floats
# this is our input placeholder
input_img = Input(shape=(784,))
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim, activation='relu')(input_img)
# "decoded" is the lossy reconstruction of the input
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)
# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(encoding_dim,))
# retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]
# create the decoder model
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
#note: x_train, x_train :)
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
from matplotlib import pyplot as plt
%matplotlib inline
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
encoded_imgs = np.random.rand(10,32)
decoded_imgs = decoder.predict(encoded_imgs)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# generation
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
Since our inputs are images, it makes sense to use convolutional neural networks (convnets
) as encoders and decoders.
In practical settings, autoencoders applied to images are always convolutional autoencoders --they simply perform much better.
The encoder will consist in a stack of Conv2D
and MaxPooling2D
layers (max pooling being used for spatial down-sampling), while the decoder will consist in a stack of Conv2D
and UpSampling2D
layers.
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
conv_autoencoder = Model(input_img, decoded)
conv_autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
from keras import backend as K
if K.image_data_format() == 'channels_last':
shape_ord = (28, 28, 1)
else:
shape_ord = (1, 28, 28)
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, ((x_train.shape[0],) + shape_ord))
x_test = np.reshape(x_test, ((x_test.shape[0],) + shape_ord))
x_train.shape
from keras.callbacks import TensorBoard
batch_size=128
steps_per_epoch = np.int(np.floor(x_train.shape[0] / batch_size))
conv_autoencoder.fit(x_train, x_train, epochs=50, batch_size=128,
shuffle=True, validation_data=(x_test, x_test),
callbacks=[TensorBoard(log_dir='./tf_autoencoder_logs')])
decoded_imgs = conv_autoencoder.predict(x_test)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
We coudl also have a look at the 128-
dimensional encoded middle representation
conv_encoder = Model(input_img, encoded)
encoded_imgs = conv_encoder.predict(x_test)
n = 10
plt.figure(figsize=(20, 8))
for i in range(n):
ax = plt.subplot(1, n, i+1)
plt.imshow(encoded_imgs[i].reshape(4, 4 * 8).T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
One of the powerful tools of auto-encoders is using the encoder to generate meaningful representation from the feature vectors.
# Use the encoder to pretrain a classifier
Let's put our convolutional autoencoder to work on an image denoising problem. It's simple: we will train the autoencoder to map noisy digits images to clean digits images.
Here's how we will generate synthetic noisy digits: we just apply a gaussian noise matrix and clip the images between 0 and 1.
from keras.datasets import mnist
import numpy as np
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
Here's how the noisy digits look like:
n = 10
plt.figure(figsize=(20, 2))
for i in range(n):
ax = plt.subplot(1, n, i+1)
plt.imshow(x_test_noisy[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
If you squint you can still recognize them, but barely.
Can our autoencoder learn to recover the original digits? Let's find out.
Compared to the previous convolutional autoencoder, in order to improve the quality of the reconstructed, we'll use a slightly different model with more filters per layer:
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.callbacks import TensorBoard
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# at this point the representation is (7, 7, 32)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
Let's train the AutoEncoder for 100
epochs
autoencoder.fit(x_train_noisy, x_train,
epochs=100,
batch_size=128,
shuffle=True,
validation_data=(x_test_noisy, x_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder_denoise',
histogram_freq=0, write_graph=False)])
decoded_imgs = autoencoder.predict(x_test_noisy)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
(Reference https://blog.keras.io/building-autoencoders-in-keras.html)
Variational autoencoders are a slightly more modern and interesting take on autoencoding.
It's a type of autoencoder with added constraints on the encoded representations being learned.
More precisely, it is an autoencoder that learns a latent variable model for its input data.
So instead of letting your neural network learn an arbitrary function, you are learning the parameters of a probability distribution modeling your data.
If you sample points from this distribution, you can generate new input data samples: a VAE is a "generative model".
First, an encoder network turns the input samples $x$ into two parameters in a latent space, which we will note $z_{\mu}$ and $z_{log_{\sigma}}$.
Then, we randomly sample similar points $z$ from the latent normal distribution that is assumed to generate the data, via $z = z_{\mu} + \exp(z_{log_{\sigma}}) * \epsilon$, where $\epsilon$ is a random normal tensor.
Finally, a decoder network maps these latent space points back to the original input data.
The parameters of the model are trained via two loss functions:
You could actually get rid of this latter term entirely, although it does help in learning well-formed latent spaces and reducing overfitting to the training data.
batch_size = 100
original_dim = 784
latent_dim = 2
intermediate_dim = 256
epochs = 50
epsilon_std = 1.0
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_sigma = Dense(latent_dim)(h)
We can use these parameters to sample new similar points from the latent space:
from keras.layers.core import Lambda
from keras import backend as K
def sampling(args):
z_mean, z_log_sigma = args
epsilon = K.random_normal(shape=(batch_size, latent_dim),
mean=0., stddev=epsilon_std)
return z_mean + K.exp(z_log_sigma) * epsilon
# note that "output_shape" isn't necessary with the TensorFlow backend
# so you could write `Lambda(sampling)([z_mean, z_log_sigma])`
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])
Finally, we can map these sampled latent points back to reconstructed inputs:
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
What we've done so far allows us to instantiate 3 models:
# end-to-end autoencoder
vae = Model(x, x_decoded_mean)
# encoder, from inputs to latent space
encoder = Model(x, z_mean)
# generator, from latent space to reconstructed inputs
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(model_to_dot(vae).create(prog='dot', format='svg'))
## Exercise: Let's Do the Same for `encoder` and `generator` Model(s)
We train the model using the end-to-end model, with a custom loss function: the sum of a reconstruction term, and the KL divergence regularization term.
from keras.objectives import binary_crossentropy
def vae_loss(x, x_decoded_mean):
xent_loss = binary_crossentropy(x, x_decoded_mean)
kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)
return xent_loss + kl_loss
vae.compile(optimizer='rmsprop', loss=vae_loss)
Traing on MNIST Digits
from keras.datasets import mnist
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
vae.fit(x_train, x_train,
shuffle=True,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, x_test))
Because our latent space is two-dimensional, there are a few cool visualizations that can be done at this point.
One is to look at the neighborhoods of different classes on the latent 2D plane:
x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
plt.figure(figsize=(6, 6))
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
plt.colorbar()
plt.show()
Each of these colored clusters is a type of digit. Close clusters are digits that are structurally similar (i.e. digits that share information in the latent space).
Because the VAE is a generative model, we can also use it to generate new digits! Here we will scan the latent plane, sampling latent points at regular intervals, and generating the corresponding digit for each of these points. This gives us a visualization of the latent manifold that "generates" the MNIST digits.
# display a 2D manifold of the digits
n = 15 # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# we will sample n points within [-15, 15] standard deviations
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample = np.array([[xi, yi]]) * epsilon_std
x_decoded = generator.predict(z_sample)
digit = x_decoded[0].reshape(digit_size, digit_size)
figure[i * digit_size: (i + 1) * digit_size,
j * digit_size: (j + 1) * digit_size] = digit
plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()


By default, Postfix relays mail:
- from trusted clients whose IP address matches
$mynetworks,
- from trusted clients matching $relay_domains or subdomains thereof,
- from untrusted clients to destinations that match $relay_domains
or subdomains thereof, except addresses with sender-specified routing.
The default relay_domains value is
$mydestination.
In addition to the above, the Postfix SMTP server by default accepts mail
that Postfix is final destination for:


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | 26x 26x 26x 26x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 5x 5x 5x 5x 5x 5x 1x 1x 4x 4x 4x 4x 5x 5x 5x 5x 5x 5x 1x 1x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x | export class Human { constructor(age, sex, smoke, exercise) { this.age = age; this.sex = sex; this.smoke = smoke; this.exercise = exercise; } mercuryCalc() { let earthAge = parseInt(this.age); let mercuryAge = Math.round(earthAge / .24); return mercuryAge; } venusCalc() { let earthAge = parseInt(this.age); let venusAge = Math.round(earthAge / .62); return venusAge; } marsCalc() { let earthAge = parseInt(this.age); let marsAge = Math.round(earthAge / 1.88); return marsAge; } jupiterCalc() { let earthAge = parseInt(this.age); let jupiterAge = Math.round(earthAge / 11.86); return jupiterAge; } earthExpectCalc() { let lifeExpec = 90; let age = this.age; let sex = this.sex; let smoke = this.smoke; let exercise = this.exercise; if (age > 90) { let newLife = age - lifeExpec; return newLife } else { sex == "male" ? lifeExpec -= 6 : lifeExpec; smoke == "smoker" ? lifeExpec -= 9 : lifeExpec; exercise == "don't workout" ? lifeExpec -= 5 : lifeExpec; return lifeExpec; } } mercuryExpectCalc() { let lifeExpec = 375; let age = Math.round(this.age / .24); let sex = this.sex; let smoke = this.smoke; let exercise = this.exercise; if (age > 375) { let newLife = age - lifeExpec; return newLife } else { sex == "male" ? lifeExpec -= 25 : lifeExpec; smoke == "smoker" ? lifeExpec -= 38 : lifeExpec; exercise == "don't workout" ? lifeExpec -= 21 : lifeExpec; return lifeExpec; } } venusExpectCalc() { let lifeExpec = 145; let sex = this.sex; let smoke = this.smoke; let exercise = this.exercise; sex == "male" ? lifeExpec -= 10 : lifeExpec; smoke == "smoker" ? lifeExpec -= 15 : lifeExpec; exercise == "don't workout" ? lifeExpec -= 8 : lifeExpec; return lifeExpec; } marsExpectCalc() { let lifeExpec = 47; let sex = this.sex; let smoke = this.smoke; let exercise = this.exercise; sex == "male" ? lifeExpec -= 3 : lifeExpec; smoke == "smoker" ? lifeExpec -= 5 : lifeExpec; exercise == "don't workout" ? lifeExpec -= 3 : lifeExpec; return lifeExpec; } jupiterExpectCalc() { let lifeExpec = 7; let sex = this.sex; let smoke = this.smoke; let exercise = this.exercise; sex == "male" ? lifeExpec -= 0.5 : lifeExpec; smoke == "smoker" ? lifeExpec -= 0.7 : lifeExpec; exercise == "don't workout" ? lifeExpec -= 0.4 : lifeExpec; return lifeExpec; } } |


|
MoveToCode
|
Public Member Functions | |
| bool | TryGetJointTransform (TrackedHandJoint joint, out Transform jointTransform) |
| Get a game object following the hand joint. More... | |
| void | OnHandMeshUpdated (InputEventData< HandMeshInfo > eventData) |
Protected Attributes | |
| readonly Dictionary< TrackedHandJoint, Transform > | joints = new Dictionary<TrackedHandJoint, Transform>() |
| MeshFilter | handMeshFilter |
Properties | |
| virtual Handedness | Handedness [get, set] |
| GameObject | GameObjectProxy [get] |
| IMixedRealityController | Controller [get, set] |
| Properties inherited from Microsoft.MixedReality.Toolkit.Input.IMixedRealityControllerVisualizer | |
| GameObject | GameObjectProxy [get] |
| The GameObjectreference for this controller. More... | |
| IMixedRealityController | Controller [get, set] |
| The current controller reference. More... | |
| void Microsoft.MixedReality.Toolkit.Input.BaseHandVisualizer.OnHandMeshUpdated | ( | InputEventData< HandMeshInfo > | eventData | ) |
| bool Microsoft.MixedReality.Toolkit.Input.BaseHandVisualizer.TryGetJointTransform | ( | TrackedHandJoint | joint, |
| out Transform | jointTransform | ||
| ) |
Get a game object following the hand joint.
Implements Microsoft.MixedReality.Toolkit.Input.IMixedRealityHandVisualizer.
|
protected |
|
protected |
|
getset |
|
get |
|
getset |


Container Scopes
Before we start looking at scoped objects, we should first look at how Rezolver's IContainerScope interface, and its implementations, are used to implement lifetime scoping.
What's the purpose of a scope?
A scope is a disposable object whose main purpose, as mentioned in the introduction to this section, is to track and dispose
any IDisposable
objects produced by the container through that scope.
This is necessary, or at least desirable, because the IOC container takes control of both how and when your objects are created and, more importantly, they are more often than not created purely to be passed as dependencies into other objects via dependency injection (via, for example, constructor parameters). This means that the .Net developer's preferred pattern of working with disposable objects (at least in C#), namely:
using(var disposable = new MyDisposable())
{
//use disposable
}
is no longer possible. Not only that, but there's the issue of who is responsible for disposing of a given object, especially if it has been used as a dependency of more than one other object.
The scope offers a way to simplify this problem by taking ownership of those disposables for you. All you need to do is to create the scope, and then dispose of it when you no longer need the objects it produced.
Creating a Container Scope
If you've used other IOC containers, then you'll probably already have guessed how to create a new scope.
Formally, the IScopeFactory interface is how you create a scope - through its CreateScope method.
The interface is implemented by:
- IContainer
- IContainerScope (because scopes are hierarchical)
- IResolveContext
In the example code, therefore, you will see a lot of this:
var container = new Container();
// perform registrations
using(var scope = container.CreateScope())
{
}
Scope Behaviours
Different targets (see the Rezolver.Targets namespace) exhibit different scoping behaviours - that is, how the object produced by the target should be tracked within a scope when resolved. At present*, this is determined by the ScopeBehaviour enumeration, which has three values:
- Implicit - The object should be passively tracked within an enclosing scope (if present) when it is created if,
and only if, it is
IDisposable
. Each and every instance will be tracked, and none of those instances will be reused. When the scope is disposed, all objects that were tracked will also be disposed. - Explicit - The object requires an enclosing scope to be present when created, and only one instance of the
object should be created per scope. If the object is
IDisposable
then it, too, will be disposed when the scope is disposed. - None - The object does not interact with a scope and, if it's
IDisposable
, then it must be explicitly disposed by application code.
* In the future, the enumeration might be replaced with a type and static instances whose names are the same as those listed here, so that scoping behaviour can be abstracted and, therefore, made more extensible
The behaviour of an ITarget can be read through its ScopeBehaviour property - which is read only and is, for most targets, defaulted to Implicit.
Indeed, most implementations of the interface do not allow you to control this behaviour. One exception that does, however, is the
ObjectTarget - which defaults to None because you supply the instance
when you create the target, therefore it's assumed you will also take responsibility for disposing it, if it's IDisposable
. If you want
Rezolver to dispose of it for you, then you can pass a different behaviour when you create the target.
Note
The only other scope behaviour that you should pass to an ObjectTarget is Explicit, because
there's only ever one instance. If you use Implicit, then a scope will end up tracking the same instance multiple
times, creating unnecessary entries in its internal tracking arrays, and the object's Dispose
method will end up being called multiple
times.
Using Explicit
There is only one target whose ScopeBehaviour is set to Explicit - the ScopedTarget.
This target is used in the same way that we use the SingletonTarget (as described in the singletons documentation in this section) - you wrap another target with it, and it then ensures that only one instance per-scope is ever produced from the target.
Explicitly scoped objects are covered exclusively in the next topic in this section but before we head on over to that topic, we will go through a few examples which involve the Implicit behaviour - i.e, the default for most types of registration, to see how 'normal' objects interact with scopes.
Examples
For these examples, we're using this type:
public class DisposableType : IDisposable
{
public bool Disposed { get; private set; }
public void Dispose()
{
if (Disposed) throw new ObjectDisposedException(nameof(DisposableType));
Disposed = true;
}
}
Note that the type throws an exception if attempts are made to dispose of it multiple times - this is a feature of the tests which drive the examples,
as we want to prevent unnecessary Dispose()
calls as much as possible.
We're also using this trivial type for examples where we resolve the disposable as a dependency:
public class RequiresDisposableType
{
public DisposableType Disposable { get; }
public RequiresDisposableType(DisposableType disposable)
{
Disposable = disposable;
}
}
Implicitly scoped transient disposable
First we look at the simplest case - we create a Container from which we obtain a new scope through which we then resolve an instance of a disposable object. When we dispose the scope, the object should also be disposed:
var container = new Container();
container.RegisterType<DisposableType>();
DisposableType result;
using(var scope = container.CreateScope())
{
result = scope.Resolve<DisposableType>();
}
Assert.True(result.Disposed);
Nested child scopes
This time, the container is building the same type, but we're using child scopes - checking that each child scope only disposes of the objects resolved within it:
var container = new Container();
container.RegisterType<DisposableType>();
DisposableType rootResult;
using(var rootScope = container.CreateScope())
{
rootResult = rootScope.Resolve<DisposableType>();
DisposableType childResult;
using (var childScope = rootScope.CreateScope())
{
childResult = childScope.Resolve<DisposableType>();
DisposableType grandChildResult;
using(var grandChildScope = childScope.CreateScope())
{
grandChildResult = grandChildScope.Resolve<DisposableType>();
}
Assert.True(grandChildResult.Disposed);
Assert.False(childResult.Disposed);
Assert.False(rootResult.Disposed);
}
Assert.True(childResult.Disposed);
Assert.False(rootResult.Disposed);
}
Assert.True(rootResult.Disposed);
Disposable Dependencies
All the examples so far demonstrate disposable objects being resolved directly via a scope. What if a disposable object is instead created as a dependency of another?
var container = new Container();
container.RegisterType<RequiresDisposableType>();
container.RegisterType<DisposableType>();
RequiresDisposableType result;
using(var scope = container.CreateScope())
{
result = scope.Resolve<RequiresDisposableType>();
}
Assert.True(result.Disposable.Disposed);
Tip
Remember - the scope tracks all IDisposable
objects it creates - so it doesn't matter whether an object is created directly for a Resolve
operation or purely as a dependency for another object - it will be disposed by the scope that owns it.
Disposable Singletons
On the subject of ownership - a singleton has special rules regarding the scope it wants to belong to.
The singleton could be constructed at any time, inside any scope, but as we know from the singletons documentation, the object itself must remain available as long as the container itself is available. As a result, singletons must ensure that they are disposed by the root-most scope (i.e. one which does not have a parent):
// In this example we use the disposable ScopedContainer, which
// supports all the same functionality as 'Container' except it
// also has its own scope, and is therefore disposable.
DisposableType result;
using (var container = new ScopedContainer())
{
container.RegisterSingleton<DisposableType>();
using (var scope = container.CreateScope())
{
// singletons force tracking in the
// rootmost scope of a scope hierarchy
result = scope.Resolve<DisposableType>();
}
Assert.False(result.Disposed);
}
Assert.True(result.Disposed);
Important
As the example shows, if you are using disposable singletons, then you should always use ScopedContainer as your application's container instead of Container.
It supports exactly the same functionality shown in all the examples on this site, but it also has it's own scope which will automatically become the default root for your application, thus ensuring that any singletons will be disposed only when you dispose of the ScopedContainer.
Mixing lifetimes and scopes
The previous example throws up a slight problem: what happens when a singleton depends on a non-singleton disposable; and vice versa?
Transient depending on a Singleton
When a transient object depends on a singleton disposable, then (regardless of whether the transient is Disposable too) nothing changes, the singleton is still tracked in the root-most scope, and is not disposed until that scope is disposed - there is no difference to the singleton example shown above.
Singleton depending on a Transient
Note
This didn't work properly prior to v1.2. If you are still using v1.1, then consider upgrading to the latest package
If the roles are reversed and a singleton takes a dependency on a transient IDisposable
, then that transient (and any other dependencies)
will also be tracked in the root scope, so that it remains usable for the lifetime of the singleton:
var container = new Container();
container.RegisterSingleton<RequiresDisposableType>();
container.RegisterType<DisposableType>();
RequiresDisposableType result;
using (var scope = container.CreateScope())
{
using (var childScope = scope.CreateScope())
{
result = childScope.Resolve<RequiresDisposableType>();
}
Assert.False(result.Disposable.Disposed);
}
Assert.True(result.Disposable.Disposed);
In short - an object's lifetime is determined by the longest lifetime of any object that 'owns' that object.
Next steps
- Learn how to create per-scope singletons with scoped objects.


The_One
The One pits several psychics together by participating in trials which challenge their abilities in psychic activities such as clairvoyance, telepathy and mediumship to determine who is Australia's top psychic.


Explore some samples. For any questions, comments, requests, or feedback please join the Pretty Diff mailing list or chat on Gitter. Find Pretty Diff on GitHub.
This tool was originally created to compare minified code by attaching a beautifier and minifier to a file comparison tool. Over the years it has grown into custom language parsers capable of performing a variety of language analysis. This application is 100% vanilla JavaScript and is API independent.
@source http://prettydiff.com/prettydiff.js
@documentation - English http://prettydiff.com/documentation.xhtml
@licstart The following is the entire license notice for Pretty Diff.
This code may not be used or redistributed unless the following conditions are met:
If each and all these conditions are met use, extension, alteration, and redistribution of Pretty Diff and its required assets is unlimited and free without author permission.
@licend The above is the entire license notice for Pretty Diff.
Install the prettydiff package locally
npm install prettydiff
Add this following code to your application
var prettydiff = require("prettydiff"),
args = {
source: "asdf",
diff : "asdd",
lang : "text",
},
output = prettydiff.api(args);
Install the prettydiff package globally
npm install prettydiff -g
Example of a command line instruction
prettydiff source:"c:\mydirectory\myfile.js" readmethod:"file"
diff:"c:\myotherfile.js"
cscript prettydiff.wsf /source:"myFile.xml" /mode:"beautify"
var args = {
source: "asdf",
diff : "asdd",
lang : "text",
},
output = prettydiff(args);
The parameters are optional and are provided solely for portability. The parameters may occur in any order. Examples:
Pretty Diff is an application written entirely in JavaScript and expressed as a single function named 'prettydiff()'. This application was originally written as a means to algorithmically difference between two similar pieces of code regardless of minification and other white space differences. The result is a fast difference engine offering many options that allows access to the world's most advanced markup beautification algorithm.
While the Pretty Diff application is expressed as a single function it contains a few libraries. These libraries are a diff engine and a few language parsers for providing beautification and minification. The libraries can be used independently of Pretty Diff. Browse the libraries in the local lib directory.
The Pretty Diff application is completely environment agnostic. It can run on the command line, web browser, or any other environment. All that is required is an appropriate API. Feel free to write your own or use the ones provided in the local API directory.
There is one exception in the Pretty Diff application to complete environmental isolation. At this time the charDecoder.js code is entirely reliant upon DOM access. This library transforms character entity references into literal characters and vise versa. Pretty Diff does not contain a Unicode character map, so this library is reliant upon execution in a web browser, but it does degrade gracefully in other environments.
The Pretty Diff function receives input from a single argument. This argument is a really big object literal that specifies the code to process and options on how to process it. Read about the various options in the Pretty Diff API section of this document.

into

.Pretty Diff hopes to encourage conventions of efficiency, but not at cost to recursion, regression testing, or altered functionality. For example consider the situation of minifying markup. In a typical scenario the practice of code minifcation is to remove all code comments and all white space characters not absolutely necessary for syntax interpretation. If the most aggressive form of minification is exercised upon markup the functionality of the code is certainly changed. Consider these two examples:

This is a paragraph with a text field. <input
type="text"/>

This is a paragraph with a text field.<input
type="text"/>

The difference between the two examples above is the difference of a single space character between the period and the input tag. In markup white space characters are tokenized when the code is parsed to output by default, and rarely is this default challenged. Tokenized white space means sequential white space characters are converted to a single space character and then sequential space characters are converted to a single space character. This means the presence of some white space characters are completely trivial, while others are not. A single space character separating words of content is not trivial if it is an isolated space. In the above sample the difference of a space separating the input tag from the content is also not trivial since it alters how tokenized content is interpreted.
If markup code were fully minified then all white space characters outside of syntax containers, such as tags, would be removed, thereby making the content illegible. Markup can still be correctly minified, but only when rendering of tokenized white space is fully considered. The opposite of this problem is accidental addition of white space characters from flawed beautification schemes. Consider the following two examples:

This is a statement with a hyperlink.

This is a statement with a

hyperlink

.

The differences between the two prior examples is that the second example introduces white space tokens where they do not exist in the first example. In the first example there are no characters between the opening
 tag and the text or the opening  and the text while this is not true of the second example. Therefore these two statements are not similar enough for a logical comparison. A well crafted beautifier, or pretty printer, will take these differences into account so far as to alter the entirety of a code base for easier reading but not at the cost of manipulating how the code is parsed by a given interpreter.
Code must never be minified if it cannot be automatically recovered into an easily readable form and must never be beautified if such beautification changes how the code is parsed. This is the importance of regression. The most extreme form of minification is referred to as obfuscation. Obfuscation removes all code comments and all white space characters not absolutely required for syntax compliance, but goes one step further and changes all variable and command names to the fewest available character length. Pretty Diff considers the practice of obfuscation to be harmful as its practice eliminates the possibility of regression. Without the possibility for regression recursive practices are improbable.
An instantiation of a pattern where the pattern's presence is available in the given instance without regard for multiplication is said to be idempotent. A recursive practice is the ability replicate an action where the replication does not harm the potential of further replication, or the idempotent nature of a pattern, upon or resulting from that action. In the case of Pretty Diff code that begins unminified should be capable of being minified, beautified, minified again, and so on without harm or difference to the functional integrity of the supplied code. Any process that prevents such recursive practices, such as obfuscation, are harmful and must be avoided.
Comments are the regression exception. There is no way to efficiently reduce code while retaining comments and documentation. Pretty Diff strongly recommends that documentation be separated from production code into either a redundant development version or into a separated documentation archive so that it can be preserved apart from the production code.
There is one extremely limited exception to functional interference observed by Pretty Diff. The Cascading Style Sheets language provides a syntax and vocabulary that are limited and fully known. Therefore functional changes can, and are, supplied to CSS code during minification because in this one narrow instance there is no harm to regression. Superior minification can be performed by supplying minor functional changes to the code which be easily and intelligently reversed without error or prior knowledge of the code sample.
The Pretty Diff option comment is similar in convention to the JSLint option comment. In the case where multiple Pretty Diff option comments are present in a document only the first will be processed. If in diff mode and an option comment is present in the diff code but not the source code then this option comment will be processed. In order for the option comment to be recognized it must start with /*prettydiff.com and end with */. The options are listed in this comment separated by commas as a colon separated name value pair. The options match the exact value definition for the Pretty Diff application properties above and options that allow abstract values must have their values enclosed in either single or double quotes. The options can be listed in any order. The option comment should be separated from other comments to prevent any possibility of corrupted interpretation. These are examples of appropriate option strings:
The function outputs an array of two indexes. The first array index is always the processed data and the second array index contains some meta data. In the case of the "beautify" and "minify" operations the first index of the output array is the processed source code as text and the second array index is the code report, as seen generated on the client side tool, formatted as HTML. The output from the diff operation returns an HTML table of the actual diff output as the first array index and a some minor meta data about the number of errors in the second array index with both indexes formatted as HTML and neither comprising a complete HTML document. To form the diff output of the prettydiff function into a single HTML document I supply the following extra code from the various files in the api directory:
Node.js is a command line run time. Node.js execution could be the result of a one-time execution or part of a scripted automation process. To execute the prettydiff function with Node.js the following code needs to be added after the prettydiff function. Nothing else needs to occur for compatibility or integration with Node.js.
if(typeof exports!=="string"){exports.api=function(x){"use strict";return
prettydiff(x);};}
Some Node.js API files are supplied as part of the Pretty Diff project. An actively maintained API for use on the local file system is supplied as api/node-local.js.
Windows Script Host allows for a JavaScript run time in Windows environments from a command line with output directly returned to the command line or in a debugger window. To execute JavaScript with WSH a file is needed to supply the JavaScript function call, pass arguments from command line into a JavaScript compatible format, and to request dependencies.
An actively maintained API is provided at api/prettydiff.wsf. A WSF file follows basic XML syntax and may allow multiple operations of different languages to execute in tandem so long as each operation is confined to a job tag. The named elements in the example file are used to intercept arguments supplied via command line.
The example file would be operated for HTML compatibility using the following
command:
cscript prettydiff.wsf /source:"my_source_file.js" /html:true /mode:"beautify"
Writing the output of a WSH task into a file would require an additional ActiveX instruction in the wsh.wsf file or would require the automation of script execution in the context of the PowerShell language.
JavaScript beautification uses the jspretty library. JavaScript is beautified in a manner that conforms to the rules of JSHint and JSLint. jspretty counts unnecessary use of the new keyword in its report summary, which is any use except immediately preceeding one of these global objects:
"ActiveXObject", "ArrayBuffer", "AudioContext", "Canvas", "CustomAnimation", "DOMParser", "DataView", "Date", "Error", "EvalError", "FadeAnimation", "FileReader", "Flash", "Float32Array", "Float64Array", "FormField", "Frame", "Generator", "HotKey", "Image", "Iterator", "Intl", "Int16Array", "Int32Array", "Int8Array", "InternalError", "Loader", "Map", "MenuItem", "MoveAnimation", "Notification", "ParallelArray", "Point", "Promise", "Proxy", "RangeError", "Rectangle", "ReferenceError", "Reflect", "RegExp", "ResizeAnimation", "RotateAnimation", "Set", "SQLite", "ScrollBar", "Set", "Shadow", "StopIteration", "Symbol", "SyntaxError", "Text", "TextArea", "Timer", "TypeError", "URL", "Uint16Array", "Uint32Array", "Uint8Array", "Uint8ClampedArray", "URIError", "WeakMap", "WeakSet", "Web", "Window", "XMLHttpRequest"
An unassigned anonymous function creates the summary report and assigns its output to a variable named summary. Variable summary is not declared within jspretty. It is used as a closure from a higher scope so that it may be externally available and yet still access all the internals of jspretty.
CSS beautification uses csspretty library. A minimalist parser that is capable of quick extension and deep analysis. csspretty is written to supply extended support for the conventions of SCSS and LESS grammars.
CSV typically stands for comma separated values, but in this tool it stands for character separated values. The csvbeauty library takes a sequence of characters and splits the input upon that supplied sequence onto new lines. Prior existing line breaks, if they were quoted, are converted to a space contained by braces: { }. Unquote line breaks are converted into two simultaneous line breaks. If the final character(s) match the user supplied character sequence, after charDecoder processing, then those characters are converted into {|} so that csvmin will know a character sequence must exist at the extreme end of input. Escaped double quote characters, escaped using the formal CSV method by immediately preceeding the characters with an extra double quote character, are converted in a single double quote character to improve legibility.
CSV beautification uses charDecoder to decode Unicode character entities. The charDecode library accepts any combination of HTML decimal Unicode entities and Unicode hexadecimal entities. HTML decimal entities must begin with an ampersand and pound character '&#', be immediately followed with between one and six decimals, and be immediately terminated by a semicolon ';'. Examples of accepted HTML entities are:
The Unicode hexadecimal entities must begin with a lowercase u and plus character 'u+', be immediately followed by a four or five digit hexadecimal value, and be immediately terminated by a plus character. Hexadecimal values smaller than four digits must be padded with 0 characters necessary to achieve four digits. Examples of accepted Unicode entities are:
Please be aware that charDecode is reliant upon the interpreting application's HTML character rendering engine to map entity values to character maps, which means if the browser does not support the entity supplied the browser will return a generic character marker instead of the intended character. The content will then be separated in accordance to the rendered sequence value, which means a generic character marker will be used in the separation instead of the character referenced by the supplied entity. In summary, if your browser has limited support for Unicode characters you must expect equally limited results when using entity references.
Markup beautification uses the markuppretty library. The code is formally parsed. The parsed code is then analyzed for indentation. The beautify mode of markuppretty also provides a summary with some analytics and accessibility analysis on the way.
The markuppretty function contains, at its end, an unassigned anonymous function that generates summary. Variable summary is not provided a scope by markuppretty, because it is meant to be supplied as a closure to markuppretty. This summary variable must be provided a scope by some external declaration.
The markup_summary creates a report of the number of parts comprising the markup, the weight of each of those parts, and a score using a math formula to compute a performance rating that reinforces reliance upon structure and
The custom built csspretty library is used to analyze and minify JavaScript code. It is also capable of providing some minor auto-correction, such as inserting missing semicolons and curly braces.
The custom built csspretty library is used to analyze and minify CSS code.
CSV typically stands for comma separated values, but in this tool it stands for character separated values. The csvmin library reverts all changes inflicted by the csvbeauty library.
CSV beautification uses charDecoder to decode Unicode character entities. The charDecode library accepts any combination of HTML decimal Unicode entities and Unicode hexadecimal entities. HTML decimal entities must begin with an ampersand and pound character '&#', be immediately followed with between one and six decimals, and be immediately terminated by a semicolon ';'. Examples of accepted HTML entities are:
The Unicode hexadecimal entities must begin with a lowercase u and plus character 'u+', be immediately followed by a four or five digit hexadecimal value, and be immediately terminated by a plus character. Hexadecimal values smaller than four digits must be padded with 0 characters necessary to achieve four digits. Examples of accepted Unicode entities are:
Please be aware that charDecoder is reliant upon the interpreting application's HTML character rendering engine to map entity values to character maps, which means if the browser does not support the entity supplied the browser will return a generic character marker instead of the intended character. The content will then be separated in accordance to the rendered sequence value, which means a generic character marker will be used in the separation instead of the character referenced by the supplied entity. In summary, if your browser has limited support for Unicode characters you must expect equally limited results when using entity references. csvmin does not revert any changes supplied by the charDecoder library.
Markup is minified using markuppretty. This library does little more than tokenize a run of whitespace characters into a single space character and scrubbing of comments. It does, however, preserve whitespace inside ASP and PHP tags and preserve SSI tags. It will also assume the contents of a script tag are JavaScript and minify them according, and also assumes the contents of style tags are CSS and minifies them as such.
The diff engine uses diffview. Originally by Snowtide Informatics Systems. diffview is almost entirely rewritten from scratch so that JavaScript arrays are used to store the dynamic output instead of DOM objects. This change has result in a faster and more extensible application. charcomp is the function used to highlight per character differences.
JavaScript code is beautified with the jspretty library and then compared. CSV is first minified with csvmin and then beautified with csvbeauty. CSS is beautified with the csspretty library and then compared. Markup is beautified using the markuppretty library. Plain text is compared without any minification or beautification. If code that needs to be compared is not compatible with the other processes then use the plain text mode.
Most parsers generate an Abstract Syntax Tree, instead the libraries used in Pretty Diff generate parallel arrays. This means multiple separate arrays are generated with a one to one relation of values at any given indexes between the arrays.
The parse data is exposed if the mode option has a value of parse. The language CSV is not currently supported in parse mode.
The parse data is always returned as object with two or three properties. This is handy if Pretty Diff is embedded directly in some application and the parse data needs to be immediately available. In other instances the generated object will need to be converted to a string using the JSON.stringify method.
For JavaScript and similar languages, such as React JSX, the jspretty library returns an object containing two arrays associated with the property names: token and types. The token array contains the actual parsed code into atomic fragments called tokens. The types array contains a categorical label that identifies each token into a grouped term.
The jspretty creates some tokens that are not present in the code source. In the case of missing semicolons and missing curly braces the jspretty library will create these tokens in the proper location. The created tokens are pseudo tokens in this form: "x;", "x{", and "x}". If the correct option is used the pseudo tokens are converted to actual code tokens. When the correct option is not used the pseudo tokens will be present in the parsed output, but are removed from the output when the mode option is not "parse".
Here are the supported types:
The csspretty library parses CSS and similar languages, such as: SCSS (Sass) and Less. In the parse mode an object containing two parallel arrays are generated ans assigned to properties: token and types.
The types generated by csspretty are:
All markup type languages are parsed with the markuppretty library and returns an object containing two parallel arrays: token and types.
The HTML5 specification allows for list item elements to contain text content without an end tag. A start tag without a matching end tag would disrupt beautification, so Pretty Diff attempts to identify these instances by use of a pseudo tag: </prettydiffli>. If the option correct the pseudo tags are instantly supplied as
 tags otherwise the Pretty Diff pseudo tag is removed immediately prior to generating output. The pseudo token logic is only evaluated if the input is HTML.
Types available in types array:
| Component | Author(s) | Summary | Revised |
|---|---|---|---|
| charDecoder.js | Austin Cheney | The function that decodes Unicode character entities for csvbeauty.js and csvmin.js. | |
| ace | Ace Code Editor | Ace Code Editor. | |
| csspretty.js | Austin Cheney | CSS parser. | |
| csvbeauty.js | Austin Cheney | The function that beautifies character sequence values. | |
| csvmin.js | Austin Cheney | The function that minifies character sequence values. | |
| diffview.css | Austin Cheney | The CSS that powers everything to do with the form, diff output, and this documentation. | |
| diffview.js | Chas Emerick - Original Austin Cheney - Major Revision | Builds the HTML diff output. | |
| documentation.xhtml | Austin Cheney Maria Ramos - Español | This documentation page. | |
| dom.js | Austin Cheney | A supplemental JavaScript file providing DOM access and interaction with the web tool. | |
| jspretty.js | Austin Cheney Harry Whitfield - QA support | Beautifies JavaScript code. | |
| markuppretty.js | Austin Cheney | Minifies markup code. | |
| node-local.js | Austin Cheney | An API for processing the local command line JavaScript with Node.js | |
| prettydiff.com.xhtml | Austin Cheney | Actual Pretty Diff tool HTML file. | |
| prettydiff.js | Austin Cheney | Actual Pretty Diff application code. | |
| prettydiff.wsf | Austin Cheney | Pretty Diff API for Windows Script Host. |
Please send comments, feedback, and requests to info@prettydiff.com.


Determine the absolute value of the following complex number:
REPRESENTATION
The absolute value of any number is its distance from zero. As complex numbers can be visualized as points on the complex plane, absolute values of complex numbers can be determined using the distance formula.
REPRESENTATION
is plotted as a blue circle above.
The absolute value we need is the length of the orange line segment.
The orange line segment is the hypotenuse of a right triangle.
Its two legs (shown in blue) have lengths abs( REAL )
and abs( IMAG )
, which corresponds
to the absolute values of the real and imaginary parts of the complex number REPRESENTATION
.
Substituting into the Pythagorean theorem:
\qquad |REPRESENTATION|^2 = abs( REAL )^2 + abs( IMAG )^2
, so
\qquad |REPRESENTATION| = \sqrt{abs( REAL )^2 + abs( IMAG )^2}
.
\qquad \sqrt{abs(REAL)^2 + abs(IMAG)^2} = \sqrt{REAL * REAL + IMAG * IMAG} = \sqrt{ABS_SQUARE}
Simplifying the radical gives formattedSquareRootOf( ABS_SQUARE )
. That is the absolute value of REPRESENTATION
.
The radical cannot be simplified. The absolute value of REPRESENTATION
is \sqrt{ABS_SQUARE}
.


We at vanillahub are creating an experince
thats all new to the current servers. A
highly anti-grief vanilla experince. We
also want the players to focus on an
community based operation, politics and
belifs to create debate and thought into
our servers.
our server operate only the highest quality hardware anywhere
our server loves to see an active community and active subreddit!
we always have giveaways, contests, screenshot of the week on the subreddit!
Always share your adventures with your friends!


Conference
Talk
When
Where
2014
1.
TouK Weekly Workshops 2014
Intellij IDEA Advanced Code Folding
28.03.2014
Warsaw/Poland


--- title: Wigner_D sidebar: function_reference_sidebar permalink: Wigner_D.html folder: function_reference toc: false ---
compute the Wigner d and Wigner D functions Note that for rot = pi/2 and high L the relative error of some matrix entries of the Wigner-d matrix increase rapidly. Moreover the matrix is no longer symmetric w.r.t. d(k,l)=d(-k,l).
| L | harmonic degree |
| rot | rotation |
| beta | second Euler angle |
| Dmn | Wigner D matrix D_L^(m,n) |
| dmn | Wigner d matrix d_L^(m,n) |


Raiders rookie Foster Moreau emerging as red zone threat
by Myles SimmonsOAKLAND, Calif. — Foster Moreau could see it coming. He knew he was going to score.
That’s at least what the rookie tight end told the media about his 2-yard touchdown reception in the second quarter that tied the Raiders and Bengals at seven.
“I knew from the moment it left Derek’s lips, to be honest with you,” Moreau said. “Walked up and ripped through the D-line, kept my eyes down, came out late, easy.”
Quarterback Derek Carr, however, didn’t quite see that third-and-1 play from the Cincinnati 2 the same way.
“Foster can see better into the future than I can,” Carr said with a laugh. “I knew there was a chance, but with him being that confident — that fires me up.”
Moreau has emerged as a strong red-zone threat in the Raiders offense, with Sunday’s touchdown his fourth of the season. He’s had scores of 18, 10, 3 and 2 yards — and three of the four have resulted in Oakland taking a lead.
Fellow tight end Darren Waller said Moreau’s work behind the scenes has allowed him to be productive.
“Just the work he’s putting, how much he’s improving as far as the timing of where he needs to be,” Waller said. “And getting in windows — being deceptive as far as how he’s running and setting things up.
”He’s just becoming a well-rounded football player.”
Through 10 games, Moreau has 16 receptions for 142 yards with four touchdowns. Raiders coach Jon Gruden said there’s a chance for Moreau’s role to continue to expand this season.
“I think you’re going to see more and more from Moreau down the stretch here as a receiver,” Gruden said. “He’s a guy that can do a lot of things.”
More Raiders: Follow at vegasnation.com and @VegasNation on Twitter.


This article summarized the famous sorting algorithms, including insertion sort
- Insertion sort - O(N) / O(N^2) [Best case / Worst case]
Insertion sort consists of n - 1 passes. For pass p = 2 through n, insertion sort ensures that the elements in positions 1 through p are in sorted order. Insertion sort makes use of the fact that elements in positions 1 through p - 1 are already known to be in sorted order.
void InsertionSort(ElementType A[], int N)
{
int j, P;
ElementType Temp;
for (P = 1; P < N; P++)
{
Temp = A [P];
for(j = P; j > 0 && A[j-1] > Temp; j--)
{
A[j] = A[j-1];
}
A[j] = Temp;
}
}
- Bubble sort - O(N) / O(N^2)
Starting from the beginning of the list, compare every adjacent pair, swap their position if they are not in the right order (the latter one is smaller than the former one). After each iteration, one less element (the last one) is needed to be compared until there are no more elements left to be compared.
void BubbleSort(ElementType A[], int N)
{
int i, j;
ElementType Temp;
for (i = 0; i < N-1; i++)
{
for(j = 0; j < N - 1 - i; j++)
{
if(A[j] > A [j+1])
{
Temp = A[j];
A[j] = A[j+1];
A[j+1] = Temp;
}
}
}
}
- Selection sort - O(N^2)
The algorithm divides the input list into two parts: the sublist of items already sorted, which is built up from left to right at the front (left) of the list, and the sublist of items remaining to be sorted that occupy the rest of the list. Initially, the sorted sublist is empty and the unsorted sublist is the entire input list. The algorithm proceeds by finding the smallest (or largest, depending on sorting order) element in the unsorted sublist, exchanging (swapping) it with the leftmost unsorted element (putting it in sorted order), and moving the sublist boundaries one element to the right.
void SelectionSort(ElementType A[], int N)
{
int i, j, Min;
ElementType Temp;
for(i = 0; i < N - 1 ; i++)
{
Min = i;
for(j = i + 1; j < N ; j++ )
{
if(A[Min] > A [j])
min = j;
}
Temp = A[Min];
A[Min] = A[i];
A[i] = Temp;
}
}
- Shell sort - O(N^2)
Shell sort is a generalization of insertion sort that allows the exchange of items that are far apart. The idea is to arrange the list of elements so that, starting anywhere, considering every h-th element gives a sorted list. Such a list is said to be h-sorted.
The first pass, 5-sorting, performs insertion sort on separate subarrays (a1, a6, a11), (a2, a7, a12), (a3, a8), (a4, a9), (a5, a10). For instance, it changes the subarray (a1, a6, a11) from (62, 17, 25) to (17, 25, 62). The next pass, 3-sorting, performs insertion sort on the subarrays (a1, a4, a7, a10), (a2, a5, a8, a11), (a3, a6, a9, a12). The last pass, 1-sorting, is an ordinary insertion sort of the entire array (a1,…, a12).
Shellsort is unstable: it may change the relative order of elements with equal values. It is an adaptive sorting algorithm in that it executes faster when the input is partially sorted.
void ShellSort(ElementType A[], int N)
{
int i,j,Increment;
ElementType Temp;
for(Increment = N/2; Increment > 0; Increment /= 2)
{
for(i = Increment; i < N; i++)
{
Temp = A[i];
for(j = i; j >= Increment; j -= Increment)
{
if(Temp < A[j - Increment])
A[j] = A[j - Increment];
else
break;
}
A[j] = Temp;
}
}
}
- Heap sort - O(NlogN)
The heapsort algorithm involves preparing the list by first turning it into a max heap. The algorithm then repeatedly swaps the first value of the list with the last value, decreasing the range of values considered in the heap operation by one, and sifting the new first value into its position in the heap. This repeats until the range of considered values is one value in length.
#define LeftChild(i) (2 * (i) + 1)
void PerDown(ElementType A[], int i, int N)
{
int Child;
ElementType Temp;
for(Temp = A[i]; LeftChild(i) < N; i = Child)
{
Child = LeftChild(i);
if(Child != N - 1 && A[Child + 1] > A[Child])
Child ++;
if(Temp < A[Child])
A[i] = A[Child];
else
break;
}
A[i] = Temp;
}
void HeapSort(ElementType A[], int N)
{
int i;
for(i = N / 2; i >= 0; i--) /* Build Heap */
PerDown(A, i, N);
for(i = N - 1; i > 0; i--) /* Delete Max */
{
Swap(&A[0], &A[i]);
PerDown(A, 0, i);
}
}
- Quick sort - O(NlogN) / O(N^2)
Quicksort is a divide and conquer algorithm. Quicksort first divides a large array into two smaller sub-arrays: the low elements and the high elements. Quicksort can then recursively sort the sub-arrays.
The steps are:
-
Pick an element, called a pivot, from the array.
-
Reorder the array so that all elements with values less than the pivot come before the pivot, while all elements with values greater than the pivot come after it (equal values can go either way). After this partitioning, the pivot is in its final position. This is called the partition operation.
-
Recursively apply the above steps to the sub-array of elements with smaller values and separately to the sub-array of elements with greater values.
The base case of the recursion is arrays of size zero or one, which never need to be sorted.
void QuickSort(ElementType A[], int N)
{
Qsort(A, 0, N - 1);
}
/* Return median of Left, Center and Right */
/* Order these and hide the pivot */
ElementType Median3(ElementType A[], int Left, int Right)
{
int Center = (Left + Right) / 2;
if(A[Left] > A[Center])
Swap(&A[Left], &A[Center]);
if(A[Left] > A[Right])
Swap(&A[Left], &A[Right]);
if(A[Center] > A[Right])
Swap(&A[Center], &A[Right]);
/* Invariant: A[Left] <= A[Center] <= A[Right] */
Swap(&A[Center], &A[Right - 1]); /* Hide pivot */
return A[Right - 1]; /* Return pivot */
}
#define Cutoff (3)
void QSort(ElementType A[], int Left, int Right)
{
int i, j;
ElementType Pivot;
if(Left + Cutoff <= Right)
{
Pivot = Median3(A, Left, Right);
i = Left; j = Right - 1;
for( ; ; )
{
while(A[++i] < Pivot) {}
while(A[--j] < Pivot) {}
if(i < j)
Swap(&A[i], &A[j]);
else
break;
}
Swap(&A[i], &A[Right - 1]); /* Restroe pivot */
QSort(A, Left, i - 1);
QSort(A, i + 1, Right);
}
else /* Do an insertion sort on the subarray */
InsertionSort(A + Left, Right - Left + 1);
}
P.S. Don’t write code like this, since there is an endless loop if A[i] = A[j] = Pivot.
i = Left + 1; j = Right - 2;
for( ; ; )
{
while(A[i] < Pivot) i++;
while(A[j] < Pivot) j--;
if(i < j)
Swap(&A[i], &A[j]);
else
break;
}
- Merge sort - O(NlogN)
Conceptually, a merge sort works as follows:
-
Divide the unsorted list into n sublists, each containing 1 element (a list of 1 element is considered sorted).
-
Repeatedly merge sublists to produce new sorted sublists until there is only 1 sublist remaining. This will be the sorted list.
void MSort(ElementType A[], ElementType TmpArray[], int Left, int Right)
{
int Center;
if(Left < Right)
{
Center = (Left + Right) / 2;
MSort(A, TmpArray, Left, Center);
MSort(A, TmpArray, Center + 1; Right);
Merge(A, TmpArray, Left, Center + 1, Right);
}
}
void MergeSort(ElementType A[], int N)
{
ElementType *TmpArray;
TmpArray = malloc(N * sizeof(ElementType));
if(TmpArray != NULL)
{
MSort(A, TmpArray, 0, N - 1);
free(TmpArray);
}
else
FataError("No space for tmp array!!!");
}
/* Lpos = start of left half, Rpos = start of right half */
void Merge(ElementType A[], ElementType TmpArray[], int Lpos, int Rpos, int RightEnd)
{
int i, LeftEnd, NumElements, TmpPos;
LeftEnd = Rpos - 1;
TmpPos = Lpos;
NumElements = RightEnd - Lpos + 1;
/* main loop */
while(Lpos <= LeftEnd && Rpos <= RightEnd)
if(A[Lpos] <= A[Rpos])
TmpArray[TmpPos++] = A[Lpos++];
else
TmpArray[TmpPos++] = A[Rpos++];
while(Lpos <= LeftEnd) /* Copy rest of first half */
TmpArray[TmpPos++] = A[Lpos++];
while(Rpos <= RightEnd) /* Copy rest of second half */
TmpArray[TmpPos++] = A[Rpos++];
/* Copy TmpArray back */
for(i = 0; i < NumElements; i++, RightEnd--)
A[RightEnd] = TmpArray[RightEnd];
}
Updated, Dec. 3rd, 2015.
This work is licensed under a CC A-S 4.0 International License.


Net neutrality is a major issue facing the tech world and world at large. The basic concept is up unto this point no information or site on the web has advantage of loading faster or getting their information to the user quicker than anyone else. It is a completely equal playing field for everyone. This is important because it maximizes growth and chances for the “little guy”. If you have a great site and people start visiting it they can load just as fast as huge corporate sites like espn. The problem is service providers are trying to charge companies to get their information to the end user. This will stagnate the internet making it so those with the most money can get their information out their and the internet will start to look like the special interests lobbyists that currently are entwined in our political system.


This is the Unify back-end api
Routes
Unprotected
- GET /news-categories
- GET /news-locations
- GET /news-sources
- GET /schijnwerper-articles
protected
These routes can only be accessed through the Unify front-end
Unify front-end
- POST /create-bookmark
- POST /delete-bookmark
- POST /get-bookmarks
- POST /create-new-feed
- POST /get-user-feeds


Home
Anchor Bay Tattoos
History |
Tattoo Artists |
Gallery |
Media |
Contact Us
Tattoos at Anchor Bay!
Modified as f#%k!


Arrays and Hashes
Arrays
Arrays are ordered collections which can contain any Object type including Integers, Strings, and even other Arrays or Hashses.
For example, x = [1, 2, 3, 4]
is an Array containing four elements.
Arrays are Integer Indexed starting at 0 so our four element Array has the following Indexes:
Elements from within the Array can be accessed using the #[]
Method so as an example: x[2] #=> 3
Once created, there are many Array Methods that can be used to:
- Obtain information about the Array
- Add elements to the Array
- Remove elements from the Array
- Iterate over the Array
- Select items from the Array
For more information on the various Array Methods available, check out the API on Arrays at http://ruby-doc.org/core-2.2.0/Array.html
Hashes
Like Arrays, Hashes are also a collection of Objects. The difference is in the storage format. Instead of using Indexed Positioning, Hashes use Keys to identify Values. Both Keys and Values can use any Object type. Hashes are often referred to as being dictionary-like because Values can be looked up using their Keys.
Here's a basic Hash:
concert_tshirt_quantity = { s: 14, m: 12, l: 28, xl: 17 }
Similar to to Arrays, information in our Hash can be accessed using the < code>#[] method. For example: concert_tshirt_quantity[:m] #=> 12
One of the advantages of using Hashes over Arrays is that they are not order dependent and that keys themselves can have meaning as opposed to Array indexing which only represents positioning.
Like Arrays, Hashes also have many methods available. For more information on the various Hash Methods available, check out the API on Hashes at http:// ruby-doc.org/core-2.2.3/Hash.html
Both Arrays and Hashes can utilize the Enumerable module to take advantage of methods. More information can be found at http://ruby-doc.org/core-2.2.3 /Enumerable.html


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Using the above results, the Vlasov equation in guiding-center coordinates is written
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addReproduciblePeakSet.Rd
This function will get insertions from coverage files, call peaks, and merge peaks to get a "Union Reproducible Peak Set".
addReproduciblePeakSet(ArchRProj = NULL, groupBy = "Clusters", reproducibility = "2", peaksPerCell = 500, maxPeaks = 150000, minCells = 25, excludeChr = c("chrM", "chrY"), pathToMacs2 = findMacs2(), genomeSize = NULL, shift = -75, extsize = 150, method = "q", cutOff = 0.1, additionalParams = "--nomodel --nolambda", extendSummits = 250, promoterDist = 500, genomeAnnotation = getGenomeAnnotation(ArchRProj), geneAnnotation = getGeneAnnotation(ArchRProj), threads = getArchRThreads(), parallelParam = NULL, force = FALSE, verboseHeader = TRUE, verboseAll = FALSE, ...)
| ArchRProj | An |
|---|---|
| groupBy | The name of the column in |
| reproducibility | A string that indicates how peak reproducibility should be handled. This string is dynamic and can be a function of |
| peaksPerCell | The upper limit of the number of peaks that can be identified per cell-grouping in |
| maxPeaks | A numeric threshold for the maximum peaks to retain per group from |
| minCells | The minimum allowable number of unique cells that was used to create the coverage files on which peaks are called. This is important to allow for exclusion of pseudo-bulk replicates derived from very low cell numbers. |
| excludeChr | A character vector containing the |
| pathToMacs2 | The full path to the MACS2 executable. |
| genomeSize | The genome size to be used for MACS2 peak calling (see MACS2 documentation). |
| shift | The number of basepairs to shift each Tn5 insertion. When combined with |
| extsize | The number of basepairs to extend the MACS2 fragment after |
| method | The method to use for significance testing in MACS2. Options are "p" for p-value and "q" for q-value. When combined with |
| cutOff | The numeric significance cutoff for the testing method indicated by |
| additionalParams | A string of additional parameters to pass to MACS2 (see MACS2 documentation). |
| extendSummits | The number of basepairs to extend peak summits (in both directions) to obtain final fixed-width peaks. For example, |
| promoterDist | The maximum distance in basepairs from the peak summit to the nearest transcriptional start site to allow for a peak to be annotated as a "promoter" peak. |
| genomeAnnotation | The genomeAnnotation (see |
| geneAnnotation | The geneAnnotation (see |
| threads | The number of threads to be used for parallel computing. |
| parallelParam | A list of parameters to be passed for biocparallel/batchtools parallel computing. |
| force | A boolean value indicating whether to force the reproducible peak set to be overwritten if it already exist in the given |
| verboseHeader | A boolean value that determines whether standard output includes verbose sections. |
| verboseAll | A boolean value that determines whether standard output includes verbose subsections. |
| ... | additional args |


In step 1... ...you will import the 3D model you produced in the scanning steps. Press the "Import" button to start.
Checklist:


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Designed and maintained by
Ajmal Hussain Hy
B.E / Computer Science
Chennai
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Thirukkural Web Application V2 - This application included along with English Translation, Transliteration and Explanation from Mu. Varadarajan, Prof. Salaman Paappayya, Kalaignar M. Karunanidhi. The Database is designed by Arjun Kumar.