J Cole - Neighbors [LYRICS HQ][Explicit] The song draws inspiration from an incident on March 18th, 2016 involving a house in North Carolina Cole had rented to use as a studio, called the “Sheltuh.” The house was raided by SWAT team members after complaints from neighbors who assumed the house w...
We’re losing the war against surveillance capitalism because we let Big Tech frame the debate It’s too late to "conserve" our digital privacy — but to preserve what's left, we must stop defining people as commodities
J. Cole – Neighbors “Neighbors” draws inspiration from an incident on March 18th, 2016 involving the house Cole rented (nicknamed “Sheltuh”) in North Carolina to use as a studio. The house was raided
dotnet/runtime .NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps. - dotnet/runtime
dotnet/runtime .NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps. - dotnet/runtime
ChesterYue/ohmyzsh-theme-passion An oh-my-zsh theme. Contribute to ChesterYue/ohmyzsh-theme-passion development by creating an account on GitHub.
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Remember, though, this chapter has covered the DRDA framework only. It has not discussed actual implementation in DB2. For this information, read Chapter 42 and Chapter 43, "DB2 Connect."


Bring more than enough water to fill the French press up to 90℃ (195℉). Later, you can play with the water temperature slightly to find your perfect cup!
Grind your coffee to a coarse, even grind (we use setting #32 on the Baratza Encore). For ~4 cups of coffee you'll want ~30 grams of beans.
Gently pour twice the amount of water than you have coffee onto the grounds. For example, if you have 35 grams of coffee, pour 70 grams of water.
Give the grounds a gentile stir and allow the coffee to bloom for 30 seconds. The bloom process releases gasses which have a large impact on the aromas and flavors.
Add the rest of the water and place the lid on the grounds. Let the coffee steep for four minutes. Use your timer - don't estimate!
Press down the French press plunger until all of the grounds are at the bottom. Serve the coffee immediately. Don't let it sit, as it will over-extract.


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Paragraph about C++ and what its used for as a programming language.
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Quick Start¶
Set-up¶
Install Hoki¶
To install the most recent stable release of hoki you can use:
sudo pip3 install --user hoki
If you are feeling adventurous and want the most recent (in development) version of hoki, you can clone our GitHub repository
Download the tutorials¶
They can be found in the side-bar to read, but you can dowload the jupyter notebooks so you can play with them later.
Download the BPASS models¶
BPASS models can be downloaded from the BPASS:
Download |
Hoki Compatible |
Release Date |
Reference |
|---|---|---|---|
Yes |
July 2018 |
||
untested |
October 2017 |
||
untested |
– |
– |
|
untested |
– |
– |
hoki
is dedicated to being an interface with the BPASS models, but given the substancial size of the entire set of models, they are not downloaded upon installation of hoki
, and you should download the models you want to work on.
Loading in Data¶
Tip
More details on the outputs mentioned below can be found in the BPASS manual.
Stellar Population Outputs¶
A stellar output file can be loaded in using the model_output()
function in the load
module.
1 2 3 | from hoki import load
output = load.model_output('path_to_output')
|
The function will figure out based on the file name what data is being loaded in and will return the appropriate data format: pandas.DataFrames
in most cases, apart from HR diagrams, which have their own HRDiagrams
class – because they’re quite a complex data structure.
Here we summarise the shape of the outputs (51 time bins) for a given metalicity and IMF.
Output |
File Name Root |
Shape |
|---|---|---|
Massive star type numbers |
numbers |
51 x 21 |
Supernova Rates |
supernova |
51 x 18 |
Energy and elemental yields |
yields |
51 x 9 |
Stellar mass remaining at the end |
starmass |
51 x 3 |
HR diagrams |
hrs |
51 x 100 x 100 x 3 x 3 |
Spectral Synthesis Outputs¶
The spectral synthesis output files can be also loaded in using the model_output()
function in the load
module.
All outputs are returned as pandas.DataFrames
with sensible column names.
1 2 3 | from hoki import load
output = load.model_output('path_to_output')
|
Output |
File Name Root |
Shape |
|---|---|---|
Spectra (SEDs) |
spectra |
52x10E5 |
Ionizing flux predictions |
ionizing |
51x5 |
Colours (broad-band) |
colours |
51x26 |
The full suite of stellar models is also available from the BPASS website (careful, the file is big ~50GB), and they are required to make colour magnitude diagram (CMD) such as in Figure 24 of BPASSv2.1. Creating these CMD and exploring these files is very tricky - in a future release hoki
will offer features to make this straight forward.
Stellar Models¶
In addition to the population and spectral synthesis outputs, the stellar models are also available as a BPASS data product. They can be downloaded from the google drive: the name of the folder containing the most recent models is bpass-v2.2-newmodels.
Warning
These folder is ~50 GB. Make sure you have a good internet connection and sufficient space.
The stellar models are required to make Colour-Magnitude Diagrams (see RECIPES) and other deeper searches within the models that are not summarised in the population or spectral synthesis outputs.
Note
This is where the files names model_input
become necesary. They are not human readable but are used within hoki
to apply the correct algorithm to the stellar models in order to collate them correctly. (So don’t panic if you open them and wonder what is going on).
Citing Hoki¶
Hoki has been reviewed and published by the Journal Of Open Source Software! Click the button below to access the online publication.
If you use hoki for your science, please include the following reference in your paper:
@ARTICLE{2020JOSS....5.1987S,
author = {{Stevance}, Heloise and {Eldridge}, J. and {Stanway}, Elizabeth},
title = "{Hoki: Making BPASS accessible through Python}",
journal = {The Journal of Open Source Software},
keywords = {Python, galaxies, Batchfile, SED, astronomy, binary stars, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
year = "2020",
month = "Jan",
volume = {5},
number = {45},
eid = {1987},
pages = {1987},
doi = {10.21105/joss.01987},
archivePrefix = {arXiv},
eprint = {2001.11069},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020JOSS....5.1987S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
See also
For dedicated tutorials about specific aspecs of hoki and BPASS, check our RECIPES section in the side bar!


Author: Nhi Chung Email: nhibchung at gmail.com Map Features:1 heatmap layer and 6 tweet marker layers; click marker to show candidate's picture, name, and tweet content; 1 custom basemap and 1 mapbox dark basemap (Note: turn off heat map to ensure marker features work) Description: - Map of tweets about candidates for the 2016 Elections - Simple election heatmap layer created from Twitter data collected over 4 weeks - Six layers of tweet markers with sentiment analysis(done using Stanford NLP API) for the top 6 candidates. Darker shade of marker color indicates negative tweet sentiment and lighter shade for non-negative.


| Path: | examples/README |
| Last Update: | Fri Jun 27 15:51:42 -0700 2008 |
This directory contains several examples that demonstrate use of the OpenID library. Make sure you have properly installed the library before running the examples. These examples are a great place to start in integrating OpenID into your application.
The rails_openid contains a fully functional OpenID server and relying party, and acts as a starting point for implementing your own production rails server. You‘ll need the latest version of Ruby on Rails installed, and then:
cd rails_openid ./script/server
Open a web browser to localhost:3000/ and follow the instructions.
The relevant code to work from when writing your Rails OpenID Relying Party is:
rails_openid/app/controllers/consumer_controller.rb
If you are working on an OpenID provider, check out
rails_openid/app/controllers/server_controller.rb
Since the library and examples are Apache-licensed, don‘t be shy about copy-and-paste.
For various reasons you may want or need to deploy your ruby openid consumer/server using an SQL based store. The active_record_openid_store is a plugin that makes using an SQL based store simple. Follow the README inside the plugin‘s dir for usage.


{% extends "layout.html" %} {% block title %}Home{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
Reduce the newsletters in your inbox. Create an email forwarder with below form and subscribe to RSS feed shown in the next page.
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Don't use this for websites that send sensitive information like password recovery links, 2FA confirmation codes etc.
In general don't use this for creating accounts because of the above risk. But if you don't mind, me neither.
You can setup auto forwarders from gmail etc. but keep above points in mind.


403 - Access is denied
Hello '[[${#httpServletRequest.remoteUser}]]', you do not have permission to access this page.


Earth Snake:
I am an element of this earth. The truths of the universe within the earth are within me.
So, you seek the Note of the Earth? What do you expect to do with it...? Do you plan to create absolute music?
Is that it...? And what then...?
"I will use it to bring the thousand year flower into bloom."


There are several interfaces provided which allow users great flexibility in how they want to use Pools. Review the concepts document to get the basic understanding of how Pools work.
Object Usage is the method where each Pool is an object that may be created and destroyed. Destroying a Pool implicitly frees all chunks that have been allocated from it.
Singleton Usage is the method where each Pool is an object with static duration; that is, it will not be destroyed until program exit. Pool objects with Singleton Usage may be shared; thus, Singleton Usage implies thread-safety as well. System memory allocated by Pool objects with Singleton Usage may be freed through release_memory or purge_memory.
Some Pool interfaces throw exceptions when out-of-memory; others will return 0. In general, unless mandated by the Standard, Pool interfaces will always prefer to return 0 instead of throw an exception.
The pool interface is a simple Object Usage interface with Null Return.
Example:
void func() { boost::pool<> p(sizeof(int)); for (int i = 0; i < 10000; ++i) { int * const t = p.malloc(); ... // Do something with t; don't take the time to free() it } } // on function exit, p is destroyed, and all malloc()'ed ints are implicitly freed
The object_pool interface is an Object Usage interface with Null Return, but is aware of the type of the object for which it is allocating chunks. On destruction, any chunks that have been allocated from that object_pool will have their destructors called.
Example:
struct X { ... }; // has destructor with side-effects void func() { boost::object_pool<X> p; for (int i = 0; i < 10000; ++i) { X * const t = p.malloc(); ... // Do something with t; don't take the time to free() it } } // on function exit, p is destroyed, and all destructors for the X objects are called
The singleton_pool interface is a Singleton Usage interface with Null Return. It's just the same as the pool interface but with Singleton Usage instead.
Example:
struct MyPoolTag { }; typedef boost::singleton_pool<MyPoolTag, sizeof(int)> my_pool; void func() { for (int i = 0; i < 10000; ++i) { int * const t = my_pool::malloc(); ... // Do something with t; don't take the time to free() it } // Explicitly free all malloc()'ed int's my_pool::purge_memory(); }
The pool_alloc interface is a Singleton Usage interface with Exceptions. It is built on the singleton_pool interface, and provides a Standard Allocator-compliant class (for use in containers, etc.).
Example:
void func() { std::vector<int, boost::pool_allocator<int> > v; for (int i = 0; i < 10000; ++i) v.push_back(13); } // Exiting the function does NOT free the system memory allocated by the pool allocator // You must call // boost::singleton_pool<boost::pool_allocator_tag, sizeof(int)>::release_memory() // in order to force that
Another pool interface will be written: a base class for per-class pool allocation. This "pool_base" interface will be Singleton Usage with Exceptions, and built on the singleton_pool interface.
Revised 05 December, 2006
Copyright © 2000, 2001 Stephen Cleary (scleary AT jerviswebb DOT com)
Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)


Class UriScheme
A helper class for retrieving and comparing standard URI schemes.
Inheritance
Namespace: Cadru.Net.Http
Assembly: Cadru.Net.Http.dll
Syntax
public sealed class UriScheme : IEquatable<UriScheme>
Properties
| Improve this Doc View SourceFile
Represents a URI scheme for a resource which is a file on the local computer.
Declaration
public static UriScheme File { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is a file on the local computer. |
Ftp
Represents a URI scheme for a resource which is accessed through FTP.
Declaration
public static UriScheme Ftp { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is accessed through FTP. |
Gopher
Represents a URI scheme for a resource which is accessed through the Gopher protocol.
Declaration
public static UriScheme Gopher { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is accessed through the Gopher protocol. |
Http
Represents a URI scheme for a resource which is accessed through HTTP.
Declaration
public static UriScheme Http { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is accessed through HTTP. |
Https
Represents a URI scheme for a resource which is accessed through SSL-encrypted HTTP.
Declaration
public static UriScheme Https { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is accessed through SSL-encrypted HTTP. |
MailTo
Represents a URI scheme for a resource which is an e-mail address and is access through SMTP.
Declaration
public static UriScheme MailTo { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is an e-mail address and is accessed through SMTP. |
News
Represents a URI scheme for a resource which is accessed through NNTP.
Declaration
public static UriScheme News { get; }
Property Value
| Type | Description |
|---|---|
| UriScheme | A URI scheme for a resource which is accessed through NNTP. |
Methods
| Improve this Doc View SourceEquals(UriScheme)
Returns a value indicating whether the value of this instance is equal to the value of the specified UriScheme instance.
Declaration
public bool Equals(UriScheme other)
Parameters
| Type | Name | Description |
|---|---|---|
| UriScheme | other | The UriScheme to compare to this instance. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the |
Equals(Object)
Returns a value indicating whether the value of this instance is equal to the value of the specified UriScheme instance.
Declaration
public override bool Equals(object obj)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Object | obj | The object to compare to this instance. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the |
GetHashCode()
Returns the hash code for this instance.
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| System.Int32 | A 32-bit signed integer hash code. |
ToString()
Converts the value of the current UriScheme object to its equivalent string representation.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String | A string representation of the value of the current UriScheme object. |
Operators
| Improve this Doc View SourceEquality(UriScheme, UriScheme)
Determines whether two specified UriScheme objects represent the same scheme.
Declaration
public static bool operator ==(UriScheme left, UriScheme right)
Parameters
| Type | Name | Description |
|---|---|---|
| UriScheme | left | The first object to compare. |
| UriScheme | right | The second object to compare. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if both objects represent the same scheme; otherwise, false. |
Implicit(UriScheme to String)
Defines an implicit conversion from UriScheme to
Declaration
public static implicit operator string (UriScheme value)
Parameters
| Type | Name | Description |
|---|---|---|
| UriScheme | value | The object to convert. |
Returns
| Type | Description |
|---|---|
| System.String | The converted object. |
Inequality(UriScheme, UriScheme)
Determines whether two specified UriScheme objects represent different schemes.
Declaration
public static bool operator !=(UriScheme left, UriScheme right)
Parameters
| Type | Name | Description |
|---|---|---|
| UriScheme | left | The first object to compare. |
| UriScheme | right | The second object to compare. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if |


Lloyd Johnson
Product person, former creative agency strategist, and lapsed attorney. I help teams build great products that make meaningful connections with people.
Product person, former creative agency strategist, and lapsed attorney. I help teams build great products that make meaningful connections with people.


INFO:tensorflow:Summary name conv5/weights:0/gradient is illegal; using conv5/weights_0/gradient instead. INFO:tensorflow:Summary name conv5/biases:0/gradient is illegal; using conv5/biases_0/gradient instead. INFO:tensorflow:Summary name fc_1/kernel:0/gradient is illegal; using fc_1/kernel_0/gradient instead. INFO:tensorflow:Summary name fc_1/bias:0/gradient is illegal; using fc_1/bias_0/gradient instead. INFO:tensorflow:Summary name fc_2/kernel:0/gradient is illegal; using fc_2/kernel_0/gradient instead. INFO:tensorflow:Summary name fc_2/bias:0/gradient is illegal; using fc_2/bias_0/gradient instead. INFO:tensorflow:Summary name fc_3/kernel:0/gradient is illegal; using fc_3/kernel_0/gradient instead. INFO:tensorflow:Summary name fc_3/bias:0/gradient is illegal; using fc_3/bias_0/gradient instead. INFO:tensorflow:Summary name output/kernel:0/gradient is illegal; using output/kernel_0/gradient instead. INFO:tensorflow:Summary name output/bias:0/gradient is illegal; using output/bias_0/gradient instead. INFO:tensorflow:Summary name conv5/weights:0 is illegal; using conv5/weights_0 instead. INFO:tensorflow:Summary name conv5/biases:0 is illegal; using conv5/biases_0 instead. INFO:tensorflow:Summary name fc_1/kernel:0 is illegal; using fc_1/kernel_0 instead. INFO:tensorflow:Summary name fc_1/bias:0 is illegal; using fc_1/bias_0 instead. INFO:tensorflow:Summary name fc_2/kernel:0 is illegal; using fc_2/kernel_0 instead. INFO:tensorflow:Summary name fc_2/bias:0 is illegal; using fc_2/bias_0 instead. INFO:tensorflow:Summary name fc_3/kernel:0 is illegal; using fc_3/kernel_0 instead. INFO:tensorflow:Summary name fc_3/bias:0 is illegal; using fc_3/bias_0 instead. INFO:tensorflow:Summary name output/kernel:0 is illegal; using output/kernel_0 instead. INFO:tensorflow:Summary name output/bias:0 is illegal; using output/bias_0 instead. Loaded 1200 training examples and 515 testing examples. Loading conv3 values... Loading conv2 values... Loading conv1 values... Loading conv5 values... Loading conv4 values... 2018-11-29 01:54:09.721773 Start training... 2018-11-29 01:54:09.722265 Open Tensorboard at --logdir tf_data/sample_model 0 iterations, 0 epochs, train accuracy = 0.625 , test accuracy = 0.40625 5 iterations, 0 epochs, train accuracy = 0.5 , test accuracy = 0.5625 10 iterations, 0 epochs, train accuracy = 0.71875 , test accuracy = 0.625 15 iterations, 0 epochs, train accuracy = 0.59375 , test accuracy = 0.4375 20 iterations, 0 epochs, train accuracy = 0.5625 , test accuracy = 0.59375 25 iterations, 0 epochs, train accuracy = 0.6875 , test accuracy = 0.71875 30 iterations, 0 epochs, train accuracy = 0.75 , test accuracy = 0.625 35 iterations, 0 epochs, train accuracy = 0.5 , test accuracy = 0.75 40 iterations, 1 epochs, train accuracy = 0.5 , test accuracy = 0.5 45 iterations, 1 epochs, train accuracy = 0.40625 , test accuracy = 0.4375 50 iterations, 1 epochs, train accuracy = 0.5 , test accuracy = 0.3125 55 iterations, 1 epochs, train accuracy = 0.46875 , test accuracy = 0.375 60 iterations, 1 epochs, train accuracy = 0.625 , test accuracy = 0.625 65 iterations, 1 epochs, train accuracy = 0.65625 , test accuracy = 0.78125 70 iterations, 1 epochs, train accuracy = 0.8125 , test accuracy = 0.75 75 iterations, 2 epochs, train accuracy = 0.78125 , test accuracy = 0.75 80 iterations, 2 epochs, train accuracy = 0.6875 , test accuracy = 0.78125 85 iterations, 2 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 90 iterations, 2 epochs, train accuracy = 0.65625 , test accuracy = 0.6875 95 iterations, 2 epochs, train accuracy = 0.625 , test accuracy = 0.5625 100 iterations, 2 epochs, train accuracy = 0.75 , test accuracy = 0.5625 2018-11-29 01:56:06.969672 Saving checkpoint of model... 2018-11-29 01:56:08.056048 Model checkpoint saved at tf_data/sample_model/model_epoch2.ckpt 105 iterations, 2 epochs, train accuracy = 0.65625 , test accuracy = 0.59375 110 iterations, 3 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 115 iterations, 3 epochs, train accuracy = 0.625 , test accuracy = 0.78125 120 iterations, 3 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 125 iterations, 3 epochs, train accuracy = 0.65625 , test accuracy = 0.75 130 iterations, 3 epochs, train accuracy = 0.75 , test accuracy = 0.8125 135 iterations, 3 epochs, train accuracy = 0.8125 , test accuracy = 0.46875 140 iterations, 3 epochs, train accuracy = 0.65625 , test accuracy = 0.78125 145 iterations, 3 epochs, train accuracy = 0.71875 , test accuracy = 0.78125 150 iterations, 4 epochs, train accuracy = 0.71875 , test accuracy = 0.65625 155 iterations, 4 epochs, train accuracy = 0.5625 , test accuracy = 0.8125 160 iterations, 4 epochs, train accuracy = 0.65625 , test accuracy = 0.625 165 iterations, 4 epochs, train accuracy = 0.78125 , test accuracy = 0.75 170 iterations, 4 epochs, train accuracy = 0.65625 , test accuracy = 0.75 175 iterations, 4 epochs, train accuracy = 0.78125 , test accuracy = 0.5625 180 iterations, 4 epochs, train accuracy = 0.59375 , test accuracy = 0.65625 185 iterations, 5 epochs, train accuracy = 0.6875 , test accuracy = 0.8125 190 iterations, 5 epochs, train accuracy = 0.65625 , test accuracy = 0.625 195 iterations, 5 epochs, train accuracy = 0.6875 , test accuracy = 0.59375 200 iterations, 5 epochs, train accuracy = 0.875 , test accuracy = 0.59375 2018-11-29 01:57:56.735423 Saving checkpoint of model... 2018-11-29 01:57:57.183288 Model checkpoint saved at tf_data/sample_model/model_epoch5.ckpt 205 iterations, 5 epochs, train accuracy = 0.6875 , test accuracy = 0.71875 210 iterations, 5 epochs, train accuracy = 0.78125 , test accuracy = 0.8125 215 iterations, 5 epochs, train accuracy = 0.71875 , test accuracy = 0.75 220 iterations, 5 epochs, train accuracy = 0.84375 , test accuracy = 0.71875 225 iterations, 6 epochs, train accuracy = 0.6875 , test accuracy = 0.78125 230 iterations, 6 epochs, train accuracy = 0.75 , test accuracy = 0.65625 235 iterations, 6 epochs, train accuracy = 0.875 , test accuracy = 0.65625 240 iterations, 6 epochs, train accuracy = 0.6875 , test accuracy = 0.625 245 iterations, 6 epochs, train accuracy = 0.5625 , test accuracy = 0.78125 250 iterations, 6 epochs, train accuracy = 0.71875 , test accuracy = 0.6875 255 iterations, 6 epochs, train accuracy = 0.75 , test accuracy = 0.6875 260 iterations, 7 epochs, train accuracy = 0.75 , test accuracy = 0.75 265 iterations, 7 epochs, train accuracy = 0.78125 , test accuracy = 0.53125 270 iterations, 7 epochs, train accuracy = 0.6875 , test accuracy = 0.6875 275 iterations, 7 epochs, train accuracy = 0.71875 , test accuracy = 0.59375 280 iterations, 7 epochs, train accuracy = 0.65625 , test accuracy = 0.75 285 iterations, 7 epochs, train accuracy = 0.6875 , test accuracy = 0.75 290 iterations, 7 epochs, train accuracy = 0.71875 , test accuracy = 0.71875 295 iterations, 8 epochs, train accuracy = 0.71875 , test accuracy = 0.71875 300 iterations, 8 epochs, train accuracy = 0.8125 , test accuracy = 0.78125 2018-11-29 01:59:45.746200 Saving checkpoint of model... 2018-11-29 01:59:46.083143 Model checkpoint saved at tf_data/sample_model/model_epoch8.ckpt 305 iterations, 8 epochs, train accuracy = 0.65625 , test accuracy = 0.65625 310 iterations, 8 epochs, train accuracy = 0.84375 , test accuracy = 0.8125 315 iterations, 8 epochs, train accuracy = 0.78125 , test accuracy = 0.6875 320 iterations, 8 epochs, train accuracy = 0.71875 , test accuracy = 0.5625 325 iterations, 8 epochs, train accuracy = 0.71875 , test accuracy = 0.75 330 iterations, 8 epochs, train accuracy = 0.8125 , test accuracy = 0.65625 335 iterations, 9 epochs, train accuracy = 0.6875 , test accuracy = 0.8125 340 iterations, 9 epochs, train accuracy = 0.625 , test accuracy = 0.75 345 iterations, 9 epochs, train accuracy = 0.75 , test accuracy = 0.75 350 iterations, 9 epochs, train accuracy = 0.75 , test accuracy = 0.625 355 iterations, 9 epochs, train accuracy = 0.625 , test accuracy = 0.65625 360 iterations, 9 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 365 iterations, 9 epochs, train accuracy = 0.65625 , test accuracy = 0.78125 370 iterations, 10 epochs, train accuracy = 0.78125 , test accuracy = 0.6875 375 iterations, 10 epochs, train accuracy = 0.71875 , test accuracy = 0.6875 380 iterations, 10 epochs, train accuracy = 0.71875 , test accuracy = 0.71875 385 iterations, 10 epochs, train accuracy = 0.875 , test accuracy = 0.5625 390 iterations, 10 epochs, train accuracy = 0.8125 , test accuracy = 0.6875 395 iterations, 10 epochs, train accuracy = 0.625 , test accuracy = 0.5625 400 iterations, 10 epochs, train accuracy = 0.71875 , test accuracy = 0.65625 2018-11-29 02:01:34.328906 Saving checkpoint of model... 2018-11-29 02:01:34.682806 Model checkpoint saved at tf_data/sample_model/model_epoch10.ckpt 405 iterations, 10 epochs, train accuracy = 0.6875 , test accuracy = 0.84375 410 iterations, 11 epochs, train accuracy = 0.625 , test accuracy = 0.75 415 iterations, 11 epochs, train accuracy = 0.6875 , test accuracy = 0.71875 420 iterations, 11 epochs, train accuracy = 0.8125 , test accuracy = 0.6875 425 iterations, 11 epochs, train accuracy = 0.71875 , test accuracy = 0.71875 430 iterations, 11 epochs, train accuracy = 0.875 , test accuracy = 0.75 435 iterations, 11 epochs, train accuracy = 0.8125 , test accuracy = 0.84375 440 iterations, 11 epochs, train accuracy = 0.96875 , test accuracy = 0.875 445 iterations, 12 epochs, train accuracy = 0.84375 , test accuracy = 0.875 450 iterations, 12 epochs, train accuracy = 0.90625 , test accuracy = 0.875 455 iterations, 12 epochs, train accuracy = 0.9375 , test accuracy = 0.9375 460 iterations, 12 epochs, train accuracy = 0.875 , test accuracy = 0.84375 465 iterations, 12 epochs, train accuracy = 0.8125 , test accuracy = 0.75 470 iterations, 12 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 475 iterations, 12 epochs, train accuracy = 0.84375 , test accuracy = 0.8125 480 iterations, 13 epochs, train accuracy = 0.9375 , test accuracy = 0.8125 485 iterations, 13 epochs, train accuracy = 0.75 , test accuracy = 0.84375 490 iterations, 13 epochs, train accuracy = 0.6875 , test accuracy = 0.625 495 iterations, 13 epochs, train accuracy = 0.75 , test accuracy = 0.4375 500 iterations, 13 epochs, train accuracy = 0.59375 , test accuracy = 0.5 2018-11-29 02:03:23.666717 Saving checkpoint of model... 2018-11-29 02:03:24.002205 Model checkpoint saved at tf_data/sample_model/model_epoch13.ckpt 505 iterations, 13 epochs, train accuracy = 0.71875 , test accuracy = 0.5625 510 iterations, 13 epochs, train accuracy = 0.40625 , test accuracy = 0.375 515 iterations, 13 epochs, train accuracy = 0.4375 , test accuracy = 0.375 520 iterations, 14 epochs, train accuracy = 0.46875 , test accuracy = 0.375 525 iterations, 14 epochs, train accuracy = 0.40625 , test accuracy = 0.34375 530 iterations, 14 epochs, train accuracy = 0.4375 , test accuracy = 0.34375 535 iterations, 14 epochs, train accuracy = 0.40625 , test accuracy = 0.34375 540 iterations, 14 epochs, train accuracy = 0.34375 , test accuracy = 0.25 545 iterations, 14 epochs, train accuracy = 0.375 , test accuracy = 0.46875 550 iterations, 14 epochs, train accuracy = 0.25 , test accuracy = 0.3125 555 iterations, 15 epochs, train accuracy = 0.34375 , test accuracy = 0.25 560 iterations, 15 epochs, train accuracy = 0.46875 , test accuracy = 0.375 565 iterations, 15 epochs, train accuracy = 0.40625 , test accuracy = 0.3125 570 iterations, 15 epochs, train accuracy = 0.3125 , test accuracy = 0.3125 575 iterations, 15 epochs, train accuracy = 0.40625 , test accuracy = 0.375 580 iterations, 15 epochs, train accuracy = 0.375 , test accuracy = 0.3125 585 iterations, 15 epochs, train accuracy = 0.53125 , test accuracy = 0.53125 590 iterations, 15 epochs, train accuracy = 0.375 , test accuracy = 0.40625 595 iterations, 16 epochs, train accuracy = 0.375 , test accuracy = 0.1875 600 iterations, 16 epochs, train accuracy = 0.3125 , test accuracy = 0.25 2018-11-29 02:05:11.331099 Saving checkpoint of model... 2018-11-29 02:05:11.713551 Model checkpoint saved at tf_data/sample_model/model_epoch16.ckpt 605 iterations, 16 epochs, train accuracy = 0.34375 , test accuracy = 0.34375 610 iterations, 16 epochs, train accuracy = 0.40625 , test accuracy = 0.375 615 iterations, 16 epochs, train accuracy = 0.3125 , test accuracy = 0.25 620 iterations, 16 epochs, train accuracy = 0.40625 , test accuracy = 0.34375 625 iterations, 16 epochs, train accuracy = 0.40625 , test accuracy = 0.4375 630 iterations, 17 epochs, train accuracy = 0.34375 , test accuracy = 0.3125 635 iterations, 17 epochs, train accuracy = 0.34375 , test accuracy = 0.40625 640 iterations, 17 epochs, train accuracy = 0.3125 , test accuracy = 0.34375 645 iterations, 17 epochs, train accuracy = 0.5 , test accuracy = 0.21875 650 iterations, 17 epochs, train accuracy = 0.5 , test accuracy = 0.40625 655 iterations, 17 epochs, train accuracy = 0.28125 , test accuracy = 0.34375 660 iterations, 17 epochs, train accuracy = 0.15625 , test accuracy = 0.53125 665 iterations, 18 epochs, train accuracy = 0.25 , test accuracy = 0.375 670 iterations, 18 epochs, train accuracy = 0.21875 , test accuracy = 0.34375 675 iterations, 18 epochs, train accuracy = 0.3125 , test accuracy = 0.34375 680 iterations, 18 epochs, train accuracy = 0.34375 , test accuracy = 0.3125 685 iterations, 18 epochs, train accuracy = 0.28125 , test accuracy = 0.3125 690 iterations, 18 epochs, train accuracy = 0.34375 , test accuracy = 0.375 695 iterations, 18 epochs, train accuracy = 0.375 , test accuracy = 0.34375 700 iterations, 18 epochs, train accuracy = 0.375 , test accuracy = 0.3125 2018-11-29 02:06:58.156088 Saving checkpoint of model... 2018-11-29 02:06:58.535315 Model checkpoint saved at tf_data/sample_model/model_epoch18.ckpt 705 iterations, 19 epochs, train accuracy = 0.34375 , test accuracy = 0.34375 710 iterations, 19 epochs, train accuracy = 0.5 , test accuracy = 0.5 715 iterations, 19 epochs, train accuracy = 0.5 , test accuracy = 0.46875 720 iterations, 19 epochs, train accuracy = 0.375 , test accuracy = 0.46875 725 iterations, 19 epochs, train accuracy = 0.4375 , test accuracy = 0.46875 730 iterations, 19 epochs, train accuracy = 0.5 , test accuracy = 0.375 735 iterations, 19 epochs, train accuracy = 0.40625 , test accuracy = 0.28125 740 iterations, 20 epochs, train accuracy = 0.4375 , test accuracy = 0.40625 745 iterations, 20 epochs, train accuracy = 0.46875 , test accuracy = 0.46875 750 iterations, 20 epochs, train accuracy = 0.4375 , test accuracy = 0.53125 755 iterations, 20 epochs, train accuracy = 0.28125 , test accuracy = 0.46875 760 iterations, 20 epochs, train accuracy = 0.46875 , test accuracy = 0.4375 765 iterations, 20 epochs, train accuracy = 0.5 , test accuracy = 0.40625 770 iterations, 20 epochs, train accuracy = 0.375 , test accuracy = 0.25 775 iterations, 20 epochs, train accuracy = 0.5 , test accuracy = 0.46875 780 iterations, 21 epochs, train accuracy = 0.5 , test accuracy = 0.46875 785 iterations, 21 epochs, train accuracy = 0.34375 , test accuracy = 0.34375 790 iterations, 21 epochs, train accuracy = 0.375 , test accuracy = 0.46875 795 iterations, 21 epochs, train accuracy = 0.65625 , test accuracy = 0.34375 800 iterations, 21 epochs, train accuracy = 0.3125 , test accuracy = 0.5 2018-11-29 02:08:45.963212 Saving checkpoint of model... 2018-11-29 02:08:46.351568 Model checkpoint saved at tf_data/sample_model/model_epoch21.ckpt 805 iterations, 21 epochs, train accuracy = 0.5 , test accuracy = 0.34375 810 iterations, 21 epochs, train accuracy = 0.40625 , test accuracy = 0.46875 815 iterations, 22 epochs, train accuracy = 0.375 , test accuracy = 0.5625 820 iterations, 22 epochs, train accuracy = 0.40625 , test accuracy = 0.375 825 iterations, 22 epochs, train accuracy = 0.34375 , test accuracy = 0.3125 830 iterations, 22 epochs, train accuracy = 0.65625 , test accuracy = 0.53125 835 iterations, 22 epochs, train accuracy = 0.78125 , test accuracy = 0.8125 840 iterations, 22 epochs, train accuracy = 0.8125 , test accuracy = 0.5625 845 iterations, 22 epochs, train accuracy = 0.53125 , test accuracy = 0.28125 850 iterations, 23 epochs, train accuracy = 0.8125 , test accuracy = 0.84375 855 iterations, 23 epochs, train accuracy = 0.75 , test accuracy = 0.71875 860 iterations, 23 epochs, train accuracy = 0.46875 , test accuracy = 0.53125 865 iterations, 23 epochs, train accuracy = 0.65625 , test accuracy = 0.65625 870 iterations, 23 epochs, train accuracy = 0.5625 , test accuracy = 0.5625 875 iterations, 23 epochs, train accuracy = 0.5 , test accuracy = 0.6875 880 iterations, 23 epochs, train accuracy = 0.84375 , test accuracy = 0.59375 885 iterations, 23 epochs, train accuracy = 0.6875 , test accuracy = 0.5 890 iterations, 24 epochs, train accuracy = 0.84375 , test accuracy = 0.625 895 iterations, 24 epochs, train accuracy = 0.71875 , test accuracy = 0.6875 900 iterations, 24 epochs, train accuracy = 0.59375 , test accuracy = 0.625 2018-11-29 02:10:34.545305 Saving checkpoint of model... 2018-11-29 02:10:34.914588 Model checkpoint saved at tf_data/sample_model/model_epoch24.ckpt 905 iterations, 24 epochs, train accuracy = 0.75 , test accuracy = 0.65625 910 iterations, 24 epochs, train accuracy = 0.78125 , test accuracy = 0.6875 915 iterations, 24 epochs, train accuracy = 0.65625 , test accuracy = 0.84375 920 iterations, 24 epochs, train accuracy = 0.84375 , test accuracy = 0.78125 925 iterations, 25 epochs, train accuracy = 0.84375 , test accuracy = 0.84375 930 iterations, 25 epochs, train accuracy = 0.75 , test accuracy = 0.65625 935 iterations, 25 epochs, train accuracy = 0.84375 , test accuracy = 0.78125 940 iterations, 25 epochs, train accuracy = 0.8125 , test accuracy = 0.84375 945 iterations, 25 epochs, train accuracy = 0.84375 , test accuracy = 0.84375 950 iterations, 25 epochs, train accuracy = 0.6875 , test accuracy = 0.59375 955 iterations, 25 epochs, train accuracy = 0.90625 , test accuracy = 0.875 960 iterations, 25 epochs, train accuracy = 0.8125 , test accuracy = 0.84375 965 iterations, 26 epochs, train accuracy = 0.875 , test accuracy = 0.84375 970 iterations, 26 epochs, train accuracy = 0.96875 , test accuracy = 0.875 975 iterations, 26 epochs, train accuracy = 0.90625 , test accuracy = 0.9375 980 iterations, 26 epochs, train accuracy = 0.8125 , test accuracy = 0.625 985 iterations, 26 epochs, train accuracy = 0.875 , test accuracy = 0.84375 990 iterations, 26 epochs, train accuracy = 0.90625 , test accuracy = 0.875 995 iterations, 26 epochs, train accuracy = 0.9375 , test accuracy = 0.625 1000 iterations, 27 epochs, train accuracy = 0.875 , test accuracy = 0.75 2018-11-29 02:12:23.269959 Saving checkpoint of model... 2018-11-29 02:12:23.663039 Model checkpoint saved at tf_data/sample_model/model_epoch27.ckpt 1005 iterations, 27 epochs, train accuracy = 0.75 , test accuracy = 0.71875 1010 iterations, 27 epochs, train accuracy = 0.9375 , test accuracy = 0.875 1015 iterations, 27 epochs, train accuracy = 0.875 , test accuracy = 0.84375 1020 iterations, 27 epochs, train accuracy = 1.0 , test accuracy = 0.90625 1025 iterations, 27 epochs, train accuracy = 0.90625 , test accuracy = 0.75 1030 iterations, 27 epochs, train accuracy = 0.90625 , test accuracy = 0.90625 1035 iterations, 28 epochs, train accuracy = 0.84375 , test accuracy = 0.90625 1040 iterations, 28 epochs, train accuracy = 0.90625 , test accuracy = 0.9375 1045 iterations, 28 epochs, train accuracy = 0.9375 , test accuracy = 0.96875 1050 iterations, 28 epochs, train accuracy = 0.96875 , test accuracy = 0.8125 1055 iterations, 28 epochs, train accuracy = 0.96875 , test accuracy = 0.90625 1060 iterations, 28 epochs, train accuracy = 0.96875 , test accuracy = 0.84375 1065 iterations, 28 epochs, train accuracy = 0.96875 , test accuracy = 0.9375 1070 iterations, 28 epochs, train accuracy = 0.8125 , test accuracy = 0.71875 1075 iterations, 29 epochs, train accuracy = 0.90625 , test accuracy = 0.84375 1080 iterations, 29 epochs, train accuracy = 0.84375 , test accuracy = 0.9375 1085 iterations, 29 epochs, train accuracy = 0.90625 , test accuracy = 0.8125 1090 iterations, 29 epochs, train accuracy = 0.875 , test accuracy = 0.875 1095 iterations, 29 epochs, train accuracy = 0.90625 , test accuracy = 0.90625 1100 iterations, 29 epochs, train accuracy = 0.875 , test accuracy = 0.71875 2018-11-29 02:14:12.323689 Saving checkpoint of model... 2018-11-29 02:14:12.716245 Model checkpoint saved at tf_data/sample_model/model_epoch29.ckpt 1105 iterations, 29 epochs, train accuracy = 0.84375 , test accuracy = 0.8125 1110 iterations, 30 epochs, train accuracy = 0.96875 , test accuracy = 0.90625 1115 iterations, 30 epochs, train accuracy = 0.8125 , test accuracy = 0.84375 1120 iterations, 30 epochs, train accuracy = 0.96875 , test accuracy = 0.9375 1125 iterations, 30 epochs, train accuracy = 0.90625 , test accuracy = 0.875 1130 iterations, 30 epochs, train accuracy = 0.84375 , test accuracy = 0.9375 1135 iterations, 30 epochs, train accuracy = 0.90625 , test accuracy = 0.875 1140 iterations, 30 epochs, train accuracy = 0.78125 , test accuracy = 0.8125 1145 iterations, 30 epochs, train accuracy = 1.0 , test accuracy = 0.875 1150 iterations, 31 epochs, train accuracy = 0.9375 , test accuracy = 0.90625 1155 iterations, 31 epochs, train accuracy = 0.90625 , test accuracy = 0.75 1160 iterations, 31 epochs, train accuracy = 1.0 , test accuracy = 0.90625 1165 iterations, 31 epochs, train accuracy = 0.96875 , test accuracy = 0.9375 1170 iterations, 31 epochs, train accuracy = 0.9375 , test accuracy = 0.90625 1175 iterations, 31 epochs, train accuracy = 0.96875 , test accuracy = 0.90625 1180 iterations, 31 epochs, train accuracy = 0.78125 , test accuracy = 0.78125 1185 iterations, 32 epochs, train accuracy = 0.96875 , test accuracy = 0.6875 1190 iterations, 32 epochs, train accuracy = 0.71875 , test accuracy = 0.6875 1195 iterations, 32 epochs, train accuracy = 0.875 , test accuracy = 0.75 1200 iterations, 32 epochs, train accuracy = 0.625 , test accuracy = 0.53125 2018-11-29 02:16:01.447847 Saving checkpoint of model... 2018-11-29 02:16:01.839257 Model checkpoint saved at tf_data/sample_model/model_epoch32.ckpt 1205 iterations, 32 epochs, train accuracy = 0.59375 , test accuracy = 0.59375 1210 iterations, 32 epochs, train accuracy = 0.71875 , test accuracy = 0.59375 1215 iterations, 32 epochs, train accuracy = 0.71875 , test accuracy = 0.625 1220 iterations, 33 epochs, train accuracy = 0.625 , test accuracy = 0.6875 1225 iterations, 33 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 1230 iterations, 33 epochs, train accuracy = 0.65625 , test accuracy = 0.5 1235 iterations, 33 epochs, train accuracy = 0.5 , test accuracy = 0.5625 1240 iterations, 33 epochs, train accuracy = 0.8125 , test accuracy = 0.65625 1245 iterations, 33 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 1250 iterations, 33 epochs, train accuracy = 0.78125 , test accuracy = 0.625 1255 iterations, 33 epochs, train accuracy = 0.9375 , test accuracy = 0.90625 1260 iterations, 34 epochs, train accuracy = 0.8125 , test accuracy = 0.90625 1265 iterations, 34 epochs, train accuracy = 0.84375 , test accuracy = 0.84375 1270 iterations, 34 epochs, train accuracy = 0.96875 , test accuracy = 0.90625 1275 iterations, 34 epochs, train accuracy = 0.96875 , test accuracy = 0.84375 1280 iterations, 34 epochs, train accuracy = 0.96875 , test accuracy = 0.9375 1285 iterations, 34 epochs, train accuracy = 0.71875 , test accuracy = 0.75 1290 iterations, 34 epochs, train accuracy = 0.59375 , test accuracy = 0.4375 1295 iterations, 35 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 1300 iterations, 35 epochs, train accuracy = 0.84375 , test accuracy = 0.8125 2018-11-29 02:17:49.873870 Saving checkpoint of model... 2018-11-29 02:17:50.253172 Model checkpoint saved at tf_data/sample_model/model_epoch35.ckpt 1305 iterations, 35 epochs, train accuracy = 0.8125 , test accuracy = 0.6875 1310 iterations, 35 epochs, train accuracy = 0.84375 , test accuracy = 0.6875 1315 iterations, 35 epochs, train accuracy = 0.96875 , test accuracy = 0.8125 1320 iterations, 35 epochs, train accuracy = 0.90625 , test accuracy = 0.84375 1325 iterations, 35 epochs, train accuracy = 0.875 , test accuracy = 0.71875 1330 iterations, 35 epochs, train accuracy = 0.71875 , test accuracy = 0.65625 1335 iterations, 36 epochs, train accuracy = 0.8125 , test accuracy = 0.71875 1340 iterations, 36 epochs, train accuracy = 0.90625 , test accuracy = 0.90625 1345 iterations, 36 epochs, train accuracy = 1.0 , test accuracy = 0.9375 1350 iterations, 36 epochs, train accuracy = 0.8125 , test accuracy = 0.65625 1355 iterations, 36 epochs, train accuracy = 0.78125 , test accuracy = 0.65625 1360 iterations, 36 epochs, train accuracy = 0.625 , test accuracy = 0.6875 1365 iterations, 36 epochs, train accuracy = 0.8125 , test accuracy = 0.75 1370 iterations, 37 epochs, train accuracy = 0.90625 , test accuracy = 0.8125 1375 iterations, 37 epochs, train accuracy = 0.9375 , test accuracy = 0.71875 1380 iterations, 37 epochs, train accuracy = 0.90625 , test accuracy = 0.78125 1385 iterations, 37 epochs, train accuracy = 0.8125 , test accuracy = 0.8125 1390 iterations, 37 epochs, train accuracy = 0.90625 , test accuracy = 0.90625 1395 iterations, 37 epochs, train accuracy = 0.875 , test accuracy = 0.875 1400 iterations, 37 epochs, train accuracy = 1.0 , test accuracy = 0.90625 2018-11-29 02:19:38.504014 Saving checkpoint of model... 2018-11-29 02:19:38.879232 Model checkpoint saved at tf_data/sample_model/model_epoch37.ckpt 1405 iterations, 38 epochs, train accuracy = 0.71875 , test accuracy = 0.625 1410 iterations, 38 epochs, train accuracy = 0.75 , test accuracy = 0.625 1415 iterations, 38 epochs, train accuracy = 0.6875 , test accuracy = 0.34375 1420 iterations, 38 epochs, train accuracy = 0.5625 , test accuracy = 0.625 1425 iterations, 38 epochs, train accuracy = 0.71875 , test accuracy = 0.65625 1430 iterations, 38 epochs, train accuracy = 0.8125 , test accuracy = 0.6875 1435 iterations, 38 epochs, train accuracy = 0.75 , test accuracy = 0.875 1440 iterations, 38 epochs, train accuracy = 0.90625 , test accuracy = 0.78125 1445 iterations, 39 epochs, train accuracy = 0.84375 , test accuracy = 0.875 1450 iterations, 39 epochs, train accuracy = 0.9375 , test accuracy = 0.6875 1455 iterations, 39 epochs, train accuracy = 0.875 , test accuracy = 0.96875 1460 iterations, 39 epochs, train accuracy = 0.875 , test accuracy = 0.90625 1465 iterations, 39 epochs, train accuracy = 0.90625 , test accuracy = 0.90625 1470 iterations, 39 epochs, train accuracy = 0.96875 , test accuracy = 0.84375 1475 iterations, 39 epochs, train accuracy = 0.96875 , test accuracy = 0.96875 1480 iterations, 40 epochs, train accuracy = 0.9375 , test accuracy = 0.90625 1485 iterations, 40 epochs, train accuracy = 0.96875 , test accuracy = 0.96875 1490 iterations, 40 epochs, train accuracy = 0.96875 , test accuracy = 0.8125 1495 iterations, 40 epochs, train accuracy = 0.8125 , test accuracy = 0.65625


Electron Date Planner
Bot for a Flyff private server
Object recognition by color using ROS, OpenNI, and OpenCV


We have trained A.I models to monitor your health and accurately know when you are in an unhealthy state. Our software will notify you whenever your health needs attention
Whenenever your health requires attention, you will be notified with the problem and the cause of the problem. We will also guide you in what steps you need to take to best keep you in optimal health
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum


Public Member Functions | |
| (OOArray< ChildRecord * >) | - children |
| (id) | - init [implementation] |
| (void) | - dealloc [implementation] |
Static Public Member Functions | |
| (NSString *) | + ooTableName [implementation] |
| (NSString *) | + ooTableKey [implementation] |
Public Attributes | |
| OOString | ID |
| char | c |
| short | s |
| int | i |
| float | f |
| double | d |
Definition at line 94 of file ObjCppAppDelegate.m.
| - (OOArray< ChildRecord * >) children |
Definition at line 121 of file ObjCppAppDelegate.m.
| - (void) dealloc | [implementation] |
Definition at line 124 of file ObjCppAppDelegate.m.
| - (id) init | [implementation] |
Definition at line 120 of file ObjCppAppDelegate.m.
| + (NSString *) ooTableKey | [implementation] |
Definition at line 118 of file ObjCppAppDelegate.m.
| + (NSString *) ooTableName | [implementation] |
Definition at line 117 of file ObjCppAppDelegate.m.
| - (char) c |
Definition at line 97 of file ObjCppAppDelegate.m.
| - (double) d |
Definition at line 101 of file ObjCppAppDelegate.m.
| - (float) f |
Definition at line 100 of file ObjCppAppDelegate.m.
| - (int) i |
Definition at line 99 of file ObjCppAppDelegate.m.
Definition at line 96 of file ObjCppAppDelegate.m.
| - (short) s |
Definition at line 98 of file ObjCppAppDelegate.m.


The fundamental principles of computer science have allowed us to master electronic systems with billions of components and software with millions of lines of code to do amazingly complex tasks.
Learn MoreOur long-term vision is to establish molecular programming as a subdiscipline of computer science—one that will enable a yet-to-be imagined array of applications from chemical circuitry for interacting with biological molecules to nanoscale computing and molecular robotics.


KWICK-E-MART
We are a small local mini-market where you can find all sorts of food and beverages from fresh to preprocessed. Our drive is customer satisfaction so you will have a fantastic experience when you get into our store.
Overall Score: 🥑🥑🥑
Max Power: 🥑
The food gave me stomach ache. It was old ans stale and the clerk had a bad attitude and not responsive at all
Apu N.: 🥑🥑🥑🥑🥑
The best store in the city. All the food is fresh and always find what I need. Great service!!!!


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Performs network communication to send GraphQL queries between Unity and a Shopify GraphQL endpoint.
More...
|
| | UnityLoader (string domain, string accessToken, string locale=null) |
| |
| override void | Load (string query, LoaderResponseHandler callback) |
| | Send a query to the Storefront API. More...
|
| |
| override void | SetHeader (string key, string value) |
| | Assigns a custom header field to the loader. More...
|
| |
| override string | SDKVariantName () |
| | Returns the loader's X-SDK-Variant header value. Required by a BaseLoader implementation to let the server know who is sending the request. More...
|
| |
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| BaseLoader (string domain, string accessToken, string locale=null) |
| |
Performs network communication to send GraphQL queries between Unity and a Shopify GraphQL endpoint.
◆ UnityLoader()
| Shopify.Unity.SDK.UnityLoader.UnityLoader |
( |
string |
domain, |
|
|
string |
accessToken, |
|
|
string |
locale = null |
|
) |
| |
|
inline |
- Parameters
-
| domain | Shopify store domain that you'd like to query on |
| accessToken | access token used to communicate with the Shopify Store |
◆ Load()
| override void Shopify.Unity.SDK.UnityLoader.Load |
( |
string |
query, |
|
|
LoaderResponseHandler |
callback |
|
) |
| |
|
inlinevirtual |
Send a query to the Storefront API.
- Parameters
-
| query | Query that will be sent to the Storefront API |
| callback | callback that receives a response |
Implements Shopify.Unity.SDK.BaseLoader.
◆ SDKVariantName()
| override string Shopify.Unity.SDK.UnityLoader.SDKVariantName |
( |
| ) |
|
|
inlinevirtual |
Returns the loader's X-SDK-Variant header value. Required by a BaseLoader implementation to let the server know who is sending the request.
- Returns
- Identifying name of the loader to be sent to the server using the X-SDK-Variant header.
Implements Shopify.Unity.SDK.BaseLoader.
◆ SetHeader()
| override void Shopify.Unity.SDK.UnityLoader.SetHeader |
( |
string |
key, |
|
|
string |
value |
|
) |
| |
|
inlinevirtual |
Assigns a custom header field to the loader.
- Parameters
-
| key | Header field name |
| value | Header field value |
Implements Shopify.Unity.SDK.BaseLoader.
The documentation for this class was generated from the following file:
- Assets/Shopify/Unity/SDK/UnityLoader.cs


France: Steven Clerima steps down as Head of Delegation
by Sanjay (Sergio) JiandaniThe French Head of Delegation at the Eurovision Song Contest, Steven Clerima will be leaving his post after working for four years with the Frech ESC Delegation (3 years as Assistant HoD and 1 year as HoD).
Steven Clerima joined the French Delegation at the Eurovision Song Contest in 2016 and served as the Assistant Head of Delegation in 2016, 2017, 2018. In 2019 he was appointed as the French Head of Delegation, thus replacing Edoardo Grassi.
Steven led the French Delegation at the 2019 Eurovision Song Contest in Tel Aviv. He also played a key role in bringing France back to the Junior Eurovision Song Contest in 2018.
France Televisions has announced that Steven Clerima will be replaced by Ms. Alexandra Redde- Amiel (Head of Entertainment France 2). She will be in charge of the French delegation at the forthcoming 2020 Eurovision Song Contest in Rotterdam.
France in Eurovision
France debuted at the Eurovision Song Contest in the very first contest in 1956 and is one of the founder members of the competition. The French nation has won the event 5 times: 1958, 1960, 1962, 1969, 1977. France has hosted the Eurovision the contest on 3 occasions : Cannes (1959,1961) and Paris (1978).
In 2019 the French selected their ESC hopeful and entry via Destination Eurovision, thus sending Bilal Hassani to Tel Aviv with his entry Roi. Bilal placed 16th in the Grand Final in Israel.


I’m a bit tired of reinventing the wheel every time I need to deploy a rails app.
Capistrano has made the good decision to use rake instead of his own DSL so I’ll follow that path.
I need a deploy.rake
file with some tasks inside.
I want to be able to perform ssh commands but I’d also like a good DSL for this so I ended up using the same tool Capistrano uses: sshkit.
It is a very lightweight gem with some good syntactic sugar for ssh commands.
It basically has four commands that wraps ssh and system commands.
We can see the use of run_locally and capture in the following task.
# run_locally runs a command on your local machine # useful if you need to add LOCAL machine pub key to REMOTE machine authorized_keys if not present run_locally do within '~' do remote_authorized_keys = capture("ssh<EMAIL_ADDRESS>'cat ~/.ssh/authorized_keys'") # capture as its name implies captures the stdout of the command and returns a String # you can check to see if the the keys include your key if !remote_authorized_keys.include?(capture("cat ~/.ssh/id_rsa.pub")) execute("cat ~/.ssh/id_rsa.pub | ssh<EMAIL_ADDRESS>'mkdir ~/.ssh; cat >> ~/.ssh/authorized_keys'") end end end
The other two commands test
and execute
works together in the following task:
# test returns true or false unless test "[ -d /var/rails ]" # execute runs the command # accepted syntax --> :sudo, :mkdir, :rails || "sudo mkdir rails" or :sudo, "mkdir #{MY.deploy_to}" execute :sudo, :mkdir, :rails end
Armed with sshkit and my understanding of what a simple rails deployment has to do I ended up with the following ideas about my deploy.rake.
It will be split into several files:
- Machine Preparation
- Db setup (with specific file for every supported db starting with PostgreSQL)
- Deploy with Git
I’m starting with the Deploy part. I’m assuming the following: use of rvm and Ubuntu 12.04. In any case migrating to different tools should not be too painful!
The Deploy script will have three basic tasks:
- deploy:setup
- deploy:update
- deploy:rollback
deploy:setup will
- clone the repo
- run bundle install
- run db:migrate and db:seed
- run assets:precompile
deploy:update will:
- fetch the last commit
- run bundle install if Gemfile is changed
- run db:migrate if database.yml or db/ folder changed
- run assets:precompile if assets/ folder changed
deploy:rollback will:
- check and store if Gemfile, db or assets changed
- rollback db if changed
- revert to previous commit (git back, git back … git back to where you once belonged)
- bundle install if Gemfile was different
- assets precompile if assets were different
It is not easy to be able to use a remote environment configuration (included a modified $PATH) in ssh (useful if you are in need to use a specifically installed ruby version).
This is because when you are deploying an app ssh won’t use an interactive shell but a non interactive one. This means that you basically lose all the .profile .bash_profile .bashrc
configurations.
To be able to add path and env options to your non interactive shell the only solution (please advice if you have another way to do it) seems to edit /etc/ssh/sshd_config
, and uncomment this line:
PermitUserEnvironment yes
Configure the environment you want to be loaded in $HOME/.ssh/environment
and restart sshd.
This works well but leaves a potential security hole in your machine. This is why I’m evaluating if it is possible to wrap the needing calls in two tasks that will change the sshd configuration and revert it back. For now I’ll leave it configured this way.
The first problem I need to face is how to share configuration parameters between rake files.
I want to be able to split rake files to have a single file for each concern: machine.rake deploy.rake database.rake
.
This means that I need to require all those files in my main rake file like this:
# Load all tasks from easy_peasy dir Dir['./easy_peasy/*.rake'].each{ |f| require f }
Requiring files means I can’t use local variables to store configuration data because they won’t be available.
So I ended up opting for an OpenStruct which enables options definition on the fly, instantiated in a constant available through files:
# Configuration data put in an OpenStruct constant # to make them available to all rake files MY = OpenStruct.new MY.machine = 'machine_ip_or_dns' MY.deploy_user = 'deployer' MY.deploy_host =<EMAIL_ADDRESS>MY.deploy_to = 'deploy_folder' MY.remote_path = "/var/rails/#{MY.deploy_to}" MY.git_repo = 'git@github.com:user/repo.git' MY.git_branch = 'branch'
With this code in place I will be able to call configuration in other files simply by calling:
MY.deploy_user
Now here is my bare deploy.rake
file with the three actions and comments to explain what’s going on:
namespace :deploy do desc "Easy Peasy Setup" task :setup do # Setup directory and permissions on remote host on MY.deploy_host do |host| unless test "[ -d #{MY.remote_path} ]" within "/var/rails" do execute :git, :clone, MY.git_repo execute :bundle, :install execute :rake, 'db:migrate' execute :rake, 'db:seed' execute :rake, 'assets:precompile' end end end end desc "Easy Peasy Update" task :update do on MY.deploy_host do |host| within MY.remote_path do execute :git, :fetch execute :git, :reset, "--hard origin/#{MY.git_branch}" execute :bundle, :install if test("git diff HEAD^ HEAD | grep Gemfile") execute :rake, 'db:migrate' if test("git diff HEAD^ HEAD | grep database.yml") || test("git diff HEAD^ HEAD -- db/") execute :rake, 'assets:precompile' if test("git diff HEAD^ HEAD -- assets/") end end end desc "Easy Peasy Rollback" task :rollback do on MY.deploy_host do |host| within MY.remote_path do # Before gitting back I need to rollback the db execute :rake, 'db:rollback' if test("git diff HEAD^ HEAD | grep database.yml") || test("git diff HEAD^ HEAD -- db/") # And store Gemfile and assets changes before gitting back gem_changes = test("git diff HEAD^ HEAD | grep Gemfile") assets_changes = test("git diff HEAD^ HEAD -- assets/") # I then git back execute :git, :reset, '--hard HEAD^' # And eventually perform bundle install and assets precompile # if there were previous changes execute :bundle, :install if gem_changes execute :rake, 'assets:precompile' if assets_changes end end end end
The next article will be about the Machine Preparation.


Use the search bar to find your favourite lunch spots or check out one of these places:
{{mealSuggestion.name}} {{mealSuggestion.numPeople}} attending at {{mealSuggestion.time}} (tomorrow)
{{pick[3]}}
Help
Use the search box to find restaurants.
Click a pin or marker on the map.
Create or join a meal.
Press "Links" tab to add friends.
Links
Please Log In with Facebook Find and Add Links!
Links
Meet someone new at lunch? Add them as a Link!
Use 'Find Facebook Friends' to see if your friends are using Linksupp.
Add a New Link!
Your ID is: {{UID}}
Give your ID to a friend so they can add you!
{{mealBuddy[0].name}}
I am a {{mealBuddy[0].description}} with a profession in
{{mealBuddy[0].profession}} and I am between
{{mealBuddy[0].ageRange}}.
Requests
{{mealBuddyRequest[0].name}}
I am a {{mealBuddyRequest[0].description}} with a profession in
{{mealBuddyRequest[0].profession}} and I am between
{{mealBuddyRequest[0].ageRange}}.
Suggested Links
{{mealBuddySuggestion[0].name}}
I am a {{mealBuddySuggestion[0].description}} with a profession in
{{mealBuddySuggestion[0].profession}} and I am between
{{mealBuddySuggestion[0].ageRange}}.
{{currentPin.name}}
{{currentPin.name}}
We Suggest
Pick a time below to see who's going:
Meal Time
Attendees
Info
{{meal.attendees.length}}
{{meal.time}} {{meal.tomorrow}}
- This is You! -
- {{ attendee.name }} -
I am a {{attendee.description}} with a profession in
{{attendee.profession}} and I am between
{{attendee.ageRange}}.


My passion for programming started with a simple "Hello world." I've always had creative ideas but I never knew how to execute them. After long nights of studying, drinking coffee and building simple applications. I realize that programming is a modern art where everything is possible. Now I create powerful websites with passion and clarity so that the user experience is simple, beautiful and powerful.
The idea of recursion is not a commonly used word. So, it seems a bit confusing to the novice programmer trying to understand the idea behind it. I am learning to program and to my understanding Recursion is the idea that a function calls itself. Pretty simple, right? Well not really. Recursion is used to take a big problem and start breaking it down into smaller and smaller pieces ("Divide and Conquer") and continuing to feed their solutions back into the original function until some sort of answer is achieved and the whole chain unwinds. I am a visual learner so I hope that the following example gives you a slight understanding of what recursion does.


Find out how TADA can make your life easier!
Scan the barcode of a product and get the anti-doping information back instantly!
The helpful auto-complete feature helps you find the product you are looking for!
If the status of any previously queried product changes, you’ll get alerts directly to your phone!
When you are not searching, keep up to date with news article involving doping around the world.


The only place where you can easily and collaboratively plan your trips,
share them, rate them, explore what others have done, and get inspired to join in on the fun!
Plan your trip.
Do it alone, with friends or even ask unknown people for opinions.
Get inspired by what others have done.
Explore trips others have done and share your trips.
Help others by rating trips and places.
Pleasantly surprised in one of your trips? Let us know!
Like something you've seen?
Why don't you ask the trip owner if you can join them?
A team of travel enthusiasts who couldn't find the proper tool to plan their trips, so we decided to make our own.
Travelling is more fun with friends, and even more when you get to know interesting people from all over the place. With Trabel, we want to make this as simple and easy as it can be.
The guys who made all this possible


John McGarvey Biography
- Demographic Information
- Skills and Professional Background>
- Hobbies
I was born in Kalispell, MT in 1955. That means I am OLD.
I have worked as a machinist, a high school teacher, and for 33 years, a system developer.
I am the author or co-author of 19 patents, as listed here: My PatentsThis was expected of me during my career.
I coded in C, C++, Java, and some JavaScript, but never Ruby and I never really did web development. I was more of a system programmer.
| Hobbies | Notes |
| Fishing | I like to do flyfishing for trout. |
| Running | I have run two marathons but I am slow. |
Here is an image of my favorite place.


Gamers want to have fun, they want the kick that got them to play games in the first place. More importantly, they want the games they’re playing to be virtual worlds, complete and comprehensive in themselves.
here can be no thought of finishing for ‘aiming for the stars.’ Both figuratively and literally, it is a task to occupy the generations. And no matter how much progress one makes, there is always the thrill of just beginning.
Many games, like Fortnite and League of Legends, have built themselves on top of paid transactions for characters, rare aesthetic in-game items and currency. Yet the details of how these purchases interact are often unclear. What should players own? How should goods be traded? Who should dictate pricing? The answers depend on the game you play and can vary depending on the developers, sowing distrust and confusion. Players want consistency, flexibility, transparency, and privacy and they’re not getting it.
A Gamers’ usual cry :
- I can’t get my items from one account to another
- I wish I could sell my stuff
- I wish I could sell my stuff
- I wish I could sell my stuff
These restrictions are imposed by digital distribution intermediary platforms like Steam or Google Play and are the present day status quo.


Every organization is unique in way they run their business - there is no silver bullet for success on an ERP implementation. As such, we partner closely with you to understand your unique business environment and help bring about a solution that is tailored to achieve your goals while taking budget into consideration. With a 'One Team' approach, we comfortably work alongside your project team as well as with other existing VAR's / ISV's.
We've got your back. Our subject matter experts act as your trusted advisers and provide advice on best practices across various stages of implementation. You will save a lot of time and expense by doing it right the first time. Our involvement from the early stages of a project can reduce TCO and protect your technology investments.
Our team will work alongside yours to act as the catalyst in delivering first-class solutions for complex and critical path items. You'll get to your destination faster with our deep expertise in Dynamics AX and Business Intelligence working to keep the project on track.
Once your initial ERP platform is in place, it's time for stabilization, business process re-engineering, and continuous improvement. Our experience in industry best practices, problem solving and helping customers build solid technology roadmaps will help you transform your business and enhance your bottom line.
Yogesh brings over 15 years of experience in ERP Consulting and Audits to the team. He has played Solution Architect & Project Lead role on many enterprise engagements including global Microsoft Dynamics AX Order to Cash & Financials implementation at Microsoft Business Solutions. He has Played Partner advisory role with Microsoft Product team. Global customer experience: USA, Canada, UK, Ireland, Japan, India, Singapore. Key Implementations: Zones, Asurion, DriveTime, Microsoft and others.
In his past role he was VP – Dynamics AX delivery at Ignify and oversaw deployments in North America region. Many AX projects have benefited from his writing - "Microsoft Dynamics AX Implementation Guide" book. He holds Bachelor's degree in Computer Science from University of Pune, India and is currently based out of Southern California.
Over 15 years of experience leading and producing innovative Business Intelligence and Data Analytics solutions for firms ranging in size from Fortune 500 to small business, with a particular focus on the Microsoft Business Intelligence stack (PowerBI, SSAS, SSIS, SSRS) and Microsoft Dynamics. Also deep experience with tools that represent the broader BI vendor landscape, like Tableau and MicroStrategy.
Focused on providing best-in-class and actionable analytical capabilities for sales, finance, supply chain, marketing, HR, and customer management, while integrating well with ERP business applications. Based in Southern California.
Saket Brings over 12 years of experience in Information technology which includes over 5 years of Microsoft Dynamics AX and over 10 years of Microsoft technology stack experience. He is Microsoft certified Technical consultant for Microsoft Dynamics AX 2012, .Net and SQL Server. He was Technical Architect for many global Microsoft Dynamics AX and .Net implementation. His responsibility includes Technical Architecture, Integration, Data Migration, EDI, Solution Design and Development, Performance optimization, System Administration of Dynamics AX.
He has also worked extensively on various Microsoft Dynamics AX Modules like Accounts Payable, Accounts Receivable, Inventory, Product Information Management, General Ledger, Project Accounting and HR. He has worked with organization like Living Spaces, Zones Inc, Harris Corporation, Maritz, OneWest Bank (Formerly IndyMac Bank), J P Morgan Chase. He holds a Master degree in Computer Software from Birla Institute of Technology and Science(BITS), Pilani in India and is based out of Southern California.
Rahul brings 16 years of expertise in ERP consulting, implementation and pre-sales focusing on Microsoft Dynamics 365 for Operations (AX). He has diverse and rich experience working with customers and partners globally, enabling them to realize the full value of their future Dynamics platform. He strives to work as a trusted advisor, playing diverse roles, all while working extensively in both the functional and technical domains within full lifecycle implementations.
Rahul's experience spans across multiple geographies (viz. Americas, Europe, Asia, India) and multiple domains (viz. Financials, Supply Chain, Distribution, Manufacturing, Warehousing, Retail, Professional Services and more). Rahul is also a worldwide trainer for Dynamics 365 for Operations and worked at Microsoft for more than 8 years. He is currently based from India.
Sandeep brings over 14 years of consulting experience working on ERP systems and implementations. He has helped direct and manage successful implementations of Microsoft Dynamics AX\365 for multiple customers and has experience working in multiple domains including Multi-Channel Retail, Supply Chain, Distribution, Manufacturing and Warehousing.
During this time, he has played key roles including Project Manager, Solution\Technical Architect, Technical Lead and Developer, all of which allow him to be well versed in requirement gathering, integration to 3rd party applications, creating detailed functional and technical design documents, data migration, software development, go-live planning\execution, post go-live support and end user training.
He holds bachelor’s degree in Computer Science and is based out of San Antonio, Texas.
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