Free as in Cake
I love most forms of Cake and I've also been a bit of a build automation nut for nearly two decades now. So you can imagine my delight when I was able to combine these two passions with the CakeBuild.net project. At NDC Oslo today, Gary Ewan Park from the Cake project team announced that Cake was joining the .NET Foundation family. In this guest post, Gary explains more about the project. If it looks like it will be useful to you, I encourage you to give it a try and get involved in the growing community.
--Martin
What is Cake?
Currently, it supports running builds on:
- Windows
- Linux
- OS X
Why use C#? Aren't there already other build systems out there?
We firmly believe that creating a reliable and maintainable build automation script is best done in the same language as the application that you are building. For example, if you are working on a Powershell project, it might make sense to use something like
Although we agree that being a polyglot developer is definitely a good thing, using a build script as a mechanism to
- lack of adoption of that language across the team that you are working in
- only one person, the person who started it, being in charge of said build script
Rather, if the build script is written in the same primary language of the project, then everyone on the team can be immediately effective at altering/fixing that
Become immediately effective
On top of the fact that Cake allows you to create build scripts using a common language, out of the box, it has support for almost 30 of the most common build tools, including:
- MSBuild
- NUnit
- XUnit
- WiX
- GitVersion
- many others...
In addition, thanks to our growing and dedicated community members, we have almost 40 other build tools available via the
- Xamarin
- CMake
- Gulp
Npm - many others...
Ok, so how do I get started with Cake?
The best place to start with Cake would be the getting started guide. This will walk you through the process of using Cake to build an example project. From there, you can follow the setting up a new project guide.
More information
If you want to keep up to date with what is going on with Cake, be sure to subscribe to our blog feed and you can also follow us on twitter. In addition, if you have any questions or problems with Cake, you can join the Gitter chat room. There is almost always someone in the chat room, so feel free to ask any questions that you might have. Be sure to give
Thank You
Thank you very much for
Gary Ewan Park (@gep13), Project Maintainer, Cake (on behalf on the Cake maintainers and committers)


+S Traveler
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Information Awareness OfficeIf you're going to create an "Information Awareness Office," it's probably a good idea not to tell anyone about it (see below). I mean, aside from the old-fashioned notion that spying works best when it's secret, you really have to wonder about people who are so out of touch with American sensibilities that they think the whole idea would be just dandy.We were going to give you a good, meaty conspiracy-theory-type rundown of crackpot ideas about the IAO, whose logo was originally modeled on the creepiest Illuminati-Masonic imagery imaginable, but we thought you should probably allow yourself to be alarmed over the reality of the agency first. First off, the IAO is meant to be a central repository for information for fighting terrorism. This is called the "Total Information Awareness" program (TIA), which is not at all scary and Orwellian. The basic idea does make sense, considering that the government had ample information buried in its files that would have allowed it to prepare for the 9/11 attacks had anyone actually read the files. The problem, of course, is that terrorists pose as ordinary people like you and me, therefore in order to fight terrorism effectively, the idea is that information on people like you and me should be in the database. OK, now you're probably getting a little squeamish, especially since that information could potentially include your computer's Web browser history and credit card records. Actually, there's no "potentially" about it. IAO is meant to integrate spying on "designated targets" in the areas of Scalable Social Network Analysis (i.e., who your friends are), Biometrics (your medical records and physical characteristics), Financial (what you buy), and predicting "the behavior of specific terrorists by examining their behavior in the broader context of their political, cultural and ideological environment" (what you think, feel and believe). Wow. That is all pretty alarming. Time for some reassurance, courtesy of the IAO itself. According to the Office's FAQ:
No efforts are being performed or constructed under DARPA's (Defense Advanced Research Projects Agency) TIA program to identify U.S. citizens as terrorists. TIA research efforts are just beginning to examine processes for identifying individuals as potential or suspected international terrorists.Wow, what a relief! So you see, they haven't figured out how to identify U.S. citizens as terrorists YET, so there's nothing to worry about! Not feeling reassured yet? OK, it goes on:
The automated processing of data still requires innovative research, and this is part of the multiyear research effort. Even here, it is doubtful that any automated system will identify terrorists. However, the automated technologies will format data such that analysts from different agencies can collaborate on the outputs and make determinations whether a pattern is suspicious enough to warrant further analysis or data collection.OK, we're going to have to parse this statement for maximum clarity.
Former Navy admiral and drinking buddy of Oliver North, Poindexter dodged a felony conviction in the Iran-Contra scandal only because of the possibility that the prosecution might have used testimony given to Congress under an immunity agreement. (He was actually convicted but it was overturned on appeal.) Explaining his actions during that time, Poindexter once said "I made a very deliberate decision not to tell the president so that I could insulate him from the decision and provide some future deniability for the president if it ever leaked out." So who better to head up a project of dicey legality and even dicier ethicality for the current administration? In order to make sure you never catch on to what the government is up to, Total Awareness has crafted a Web site to help you fail to understand what the Information Awareness Office is all about. It's chock full of weird jargon, creepy code names and incomprehensible charts, some of which are featured here in this article. Rotten.com makes no assurances that visiting the IAO Web site won't get you listed somewhere as a terrorist. Click at your own risk. If you want to keep up on the whole issue without exposing your computer to keystroke logging, try The John Poindexter Awareness Office instead. If you do go to IAO's Web site, don't bother looking for the creepy logo, however. It was pulled after a public uproar. According to IAO: For the record, the IAO logo was designed to convey the mission of that office, i.e., to imagine, develop, apply, integrate, demonstrate and transition information technologies, components, and prototype, closed-loop information systems that will counter asymetric threats by achieving total information awareness useful for preemption, national security warning, and national security decision making.In other words, it meant everything you were afraid it meant, and more. In the Defense Department budget submitted in September 2003, the Information Awareness office had mysteriously disappeared. As if belatedly realizing that it's better not to tell people when you're preparing an oppressive evil computer system designed to crush their individual privacy, the office's functions were shuffled into an unknown number of "black bag" budget items — intelligence appropriations which do not have to be explained to the public or justified to legislators.  The next time you see him, he'll be kicking in your door. |


Top classic video games from the early years of gaming. This list contains the best games released on any system (arcade, home consoles, portable devices) from the 1960s through the year 2000 (which for this list would be considered the start of the "modern era" of gaming.) This includes all the essential games from the era of the arcade, as well as the original 8-bit consoles and their 16-bit follow-ups. Many of the retro games on this list have remained extremely popular to this present day, and are often still played by passionate fans on preserved consoles or on computers via emulator software. Some of these games are part of ongoing franchises that have remained vital to this day, such as the classic "Mario Bros." games, "Legend of Zelda" titles and fighting games like "Mortal Kombat." Experimental video games have been around since the 1950s, but it was two game breakthroughs in the early 1970s that really brought games more mainstream attention. "Spacewar!," released in 1961, became popular enough to generate about 1500 individual arcade machines, but was so complicated to learn that it failed to really develop mass appeal. The same was not true of 1972's "Pong," from the same creators, that recreated table tennis for arcades and eventually sold nearly 20,000 machines. By the late 1970s, video game consoles for the home were becoming more commonplace, with both the Intellivision and ColecoVision competing in toy stores and introducing many of the titles we're still familiar with today (including the original "Donkey Kong" and "Space Invaders.") 1978 is traditionally considered the start of the Golden Age of video games, particularly in terms of arcade games, with the introduction of numerous beloved retro titles (including "Frogger" and "Galaxian.") Finally, in 1985, Nintendo introduced the first Nintendo Entertainment System to stores, bundled with the game "Super Mario Bros.," a landmark release in console history.
$ 120,00
$ 80,00
$ 95,00
$ 180,00
$ 220,00
$ 350,00


GradientView
public final class GradientView: UIView
A gradient view that acts as the background of any ColorSlider
.
This class draws colors based on the orientation
passed to the initializer
and determines the output color of ColorSlider
after a touch event.
Customize the appearance of ColorSlider by setting layer properties on
this class, including borderWidth
, borderColor
, and cornerRadius
.
-
Whether the gradient should adjust its corner radius based on its bounds. When
true
, the layer’s corner radius is set tomin(bounds.width, bounds.height) / 2.0
inlayoutSubviews
.Declaration
Swift
public var automaticallyAdjustsCornerRadius: Bool = true
-
The saturation of all colors in the view. Defaults to
1
.Declaration
Swift
public var saturation: CGFloat = 1
-
The percent of space at the beginning (top for orientation
.vertical
and left for orientation.horizontal
) end of the slider reserved for the color white. Defaults to0.15
.Declaration
Swift
public var whiteInset: CGFloat = 0.15
-
The percent of space at the end (bottom for orientation
.vertical
and right for orientation.horizontal
) end of the slider reserved for the color black. Defaults to0.15
.Declaration
Swift
public var blackInset: CGFloat = 0.15
-
Declaration
Swift
required public init(orientation: Orientation)
Parameters
orientation
The orientation of the gradient view.


Please pick your algorithm and choose/create a graph
Algorithm Choice:
Dijkstra's algorithm Original
Dijkstra's partitioning algorithm
A* Search
Start Node:
Goal Node (can be null):
Save graph
Load Graph (please choose from the 'load graphs from here' folder):
Randomise a graph with N nodes. N=
Press to Start!


Do you care? Drag this button to your bookmark bar:
Then, visit your Facebook Wall, click the button you just added to your browser, and watch the top-right corner of your screen for a purple box.
WARNING: this thing seems to kinda break Facebook until you re-load the page. So after you've figured out what's hot and what's not, hit the refresh button or press F5.
After a short delay, you will notice some counters off in the top-right corner.
The code is BSD licensed, and patches welcome. Seriously, fork me
.


--- layout: post esbma_id: 3025 title: Enterprise mobile will have a lot of SQL going on date: 2013-03-21 12:00:00 ---
A hearty "amen" to Annie Bourne of Kinvey on her blog entry today entitled The Top 4 Mobile Challenges for Enterprises, from which I quote:
"Enterprises have invested billions in certain databases and development frameworks and APIs. They also have invested in people who are trained for these systems."
This is exactly why we are so excited about the future here at Zumero.
In the enterprise arena, the words "data" and "SQL" are nearly synonymous. Sure, new stuff like "big data" and "NoSQL" are gaining traction, but they have a huge amount of ground to cover before they catch Oracle, IBM and Microsoft.
Some people believe the biggest competitor for SQL is SQL. The old, established SQL vendors I just mentioned are facing new challenges from the so-called "NewSQL" vendors like VoltDB, NuoDB, and Google Spanner.
The business world loves SQL. If you think the previous statement is BS, you're spending too much time with the "early adopter" part of the bell curve.
Broadly speaking, many enterprise companies have not really started developing for mobile devices. Some have done pilot projects. Few if any would say that they are at full steam on anything "mobile".
But this transition is happening. And it is accelerating. And as it does, we will see more and more developers asking how they can build a mobile solution which (1) integrates with their SQL database, and (2) leverages the SQL skills of their team. SQL is what they have. SQL is what they know.
We think that Zumero is going to have a great story for the customers in this wave:
On the device, SQLite. Fast, robust, and familiar. Already pre-installed on a billion phones and tablets.
On the backend, Big SQL. In the flavor of your choice.
Zumero handles the synchronization between the two.
Your developers don't have to worry about networking. Your app will work offline or online.
Your app needs the network only for sync, so your end-users never have to wait.
Familiar APIs on both sides
Server on-premises or in the cloud, whichever you prefer
Our vision for this is large. The incarnation of Zumero available today is just the beginning.


Euclid's Elements Visualized by Grant Steiner
An ongoing effort to generate easy-to-understand Visualizations of the propositions contained in Euclid's Elements.
An ongoing effort to generate easy-to-understand Visualizations of the propositions contained in Euclid's Elements.


A plan will detail the expected outputs during a specific time and there are long-term (5-10 years, aggregate capacity plan), medium-term (2-5 years, making resources available to meet long-term plan, resources are mostly inflexible), short-term (1-2 years, priorities need changing due to unexpected things)
<!– more –>
Operations planning = acquire and manage resources needed for the future Operations control = implementing plans and adjusting work to achieve plan
Organisational strategy, influenced by marketing and financial strategy, influences the operations but is not under its control. Resource and demand planning influence operations planning -> operations scheduling -> operations control (planning to control).
Planning is very important, especially in big organisations, because a bad plan will lead to higher costs due to too high stocks, poor customer service,…Plans make implications of decisions visible (marketing says higher stock and finance says less cost) and a schedule makes implications of actions clear (e.g. filling beds in a hospital)
The main planning and control responsibilities are managing capacity which means: - understanding and measure your capacity to output - understand and measure the demand - try to match those two
In capacity measurement it is important to measure the right thing.
Once you have found the level of capacity needed you will need to find out where to put your facilities which is where the economies of scale come in. These work, until a set amount when the complexity of the operation will be too high and the cost will rise again. You will also take into account your closeness to your markets, transportation of your goods, resources and more.
Forecasting demand involves looking at you environment (PEST, STEEP) and the product life-cycle needs to be taken into account (Introduction, take-off, slow-down, maturity, decline; book 16, page 11).
Then you need to match capacity and demand both long-term but also now which will see a lot of fluctuations. There are several strategies: - Level capacity: keeping it constant despite fluctuations, might be maximum level like emergency service; for manufacturing its ok to store often time - Chase demand: adjusting to meet demand; e.g. tourist attraction hiring more people in the summer - Demand management: change demand to match capacity (sometimes possible): e.g. by varying the price; reservation of capacity by clients; complementary goods
The next important thing is scheduling, which is about matching the flow of inputs with the outputs in the short term. 1. Production scheduling can be both push and pull scheduling. Push would focus on the output target and adapt everything else to achieve that, which might lead to high stocks of components if you have a problem along the line. Pull is the basis for the Japanese just-in-time concept, which means that if no input is needed then the previous step will not create an output. This might put everything to halt at problems but you have little unsold stock.
- Managing the customer order cycle involves thinking about what can be done before an order by a customer:
- Resource-to-order: resources bought at order, e.g. catering or major projects like a dam
- Make-to-order: no work done until order received; e.g. customer bikes
Make-to-stock: everything is done beforehand; e.g. stereo system, pre made sandwiches
Queuing is used with service operations as the product cannot be produced in advance, which results in creating a "stock" of customers waiting to be served. With this you need to think about:
- What capacity level do I need for adequate service?
- Queue management for least negative impact on customer satisfaction
- How to physically manage the queue E.g. one queue/ two cashiers or two cashiers splitting up task.
Other option: entertain, ads, productive use (forms at a doctor)
Sequencing is about assigning priorities: first come, first served; shortest processing time; customer priority; easiest due date


The XML_TREE
signature defines a tree representation of XML files.
Synopsis
signature XML_TREE
Interface
structure Schema : XML_SCHEMA
datatype doctype = DOCTYPE of string * external_id option
and external_id
= SYSTEM of string
| PUBLIC of string * string
datatype content
= TEXT of string
| CDATA of string
| ELEMENT of {
name : Schema.element,
attrs : Schema.attribute list,
content : content list
}
type tree = {
xmlDecl : Schema.attribute list option,
doctype : doctype option,
content : content
}
Description
structure Schema : XML_SCHEMA
-
This substructure defines the representation of elements and attributes.
datatype doctype = DOCTYPE of string * external_id option
-
This datatype represents the contents of the optional
DOCTYPE
element found the beginning of the file (following the optional XML declaration). We currently only support external DTDs. datatype external_id
-
This datatype represents an external DTD specification; its constructors are defined as follows:
SYSTEM of url
-
specifies a "private" external DTD, where the string
url
specifies the DTD’s location. PUBLIC(name, url)
-
specifies a "public" external DTD, where the string
name
is the name of the DTD andurl
specifies the DTD’s location.
datatype content
-
This datatype is used to represent the content of an XML file as a tree. The constructors have the following meanings:
TEXT s
-
represents the text described by the string
s
. When parsing, entities in the source (e.g.,<
) are replaced by their definition and, when printing, special characters (e.g.,<
) are replaced by their entities. CDATA s
-
represents the literal text described by the string
s
. ELEMENT{name, attrs, content}
-
represents a subtree enclosed by "`<element>` …
</element>" tags, where `name
is the name of the element,attrs
is a list of attributes in the start tag, andcontent
is the stuff between the tags.
type tree
-
An XML tree, which is a record type with the following fields:
xmlDecl : Schema.attribute list option
-
This field represents the optional XML declaration at the beginning of a file, where a value of
SOME attrs
means that there was an XML declaration present with the list of attributesattrs
. doctype : doctype option
-
This field represents the optional
DOCTYPE
element that follows the XML declaration. content : content
-
This field is the root of the content and will always be an
ELEMENT
.


We have the pleasure of inviting you to submit your technical research works for PEPQA 2017. The 3rd International Workshop on Power Electronics & Power Quality Applications will be held on May 31 June 2, 2017 at Universidad de los Andes, Bogotá, Colombia. A large number of participants will be participating in three days of keynotes, tutorials, handson, papers, paper presentations and forums among new activities designed to improve the collaboration between industry professionals and the academic community
This biannual workshop provides a space for students, academics, and industry professionals to collaborate on meeting the ever-increasing power electronics and power quality challenges in the Latin America region. The workshop covers a range of topics, including power electronics (PE) converters, adjustable speed drives, PE in transmission and distribution, power quality (PQ) analysis, PQ design and modeling, and simulation & education in PE/PQ.


rules
is a “parsnip
-adjacent” packages with model definitions for different rule-based models, including:
Th package is not yet on CRAN and can be installed via:
# install.packages("devtools")
devtools::install_github("tidymodels/rules")
Please note that the rules project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.


Reference Contents
Menu Commands
CIX Forums
Create Forum
Edit Topics
Edit File List
Participants
Settings
Moderate
Message
Withdraw


tmlib.datasets.base¶
This module includes some base classes and base functions which support for working with data input (corpus)
Contents
- tmlib.datasets.base
- class tmlib.datasets.base.DataFormat
- class tmlib.datasets.base.Corpus
- class tmlib.datasets.base.DataIterator
- Function base.get_data_home
- Function base.clear_data_home
- Function base.check_input_format
- Function base.load_batch_raw_text
- Function base.pre_process
- Function base.load_batch_formatted_from_file
- Function base.reformat_file_to_term_sequence
- Function base.reformat_file_to_term_frequency
- Function base.convert_corpus_format
- Function base.load_mini_batch_term_sequence_from_sequence_file
- Function base.load_mini_batch_term_sequence_from_term_frequency_file
- Function base.load_mini_batch_term_frequency_from_sequence_file
- Function base.load_mini_batch_term_frequency_from_term_frequency_file
- Function shuffle_formatted_data_file
- Function base.compute_sparsity
- Function base.write_topic_mixtures
- Function base.read_vocab
- Function base.parse_doc_list
class tmlib.datasets.base.DataFormat¶
This is class which contains 3 data-format types of library is: raw text, term_sequence, term-frequency
Static Attributes¶
- RAW_TEXT: string, value is ‘txt’
- TERM_FREQUENCY: string, value is ‘tf’
- TERM_SEQUENCE: string, value is ‘sq’
Example¶
This example allows checking data format for: corpus examples/ap/ap_train_raw.txt
from tmlib.datasets.base import DataFormat, check_input_format
input_format = check_input_format('examples/ap/ap_train_raw.txt')
print(input_format)
if input_format == DataFormat.RAW_TEXT:
print('Corpus is raw text')
elif input_format == DataFormat.TERM_SEQUENCE:
print('Corpus is term-sequence format')
else:
print(Corpus is term-frequency format')
Output:
txt
Corpus is raw text
class tmlib.datasets.base.Corpus¶
tmlib.datasets.base. Corpus (format_type)
This class is used to store the corpus with 2 formats: term-frequency and term-sequence
Parameters¶
- format_type: DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCY
Attributes¶
format_type: format of corpus
word_ids_tks: list of list,
Each element in this list is a list which include the words of a document in corpus (words is unique terms if format is term-frequency and is list of tokens if format is term-sequence)
cts_lens: list
if format is term-frequency, each element in list is a list frequency of unique terms in respectly document of corpus. If format is term-sequence, each element in list is the number of tokens in document (number of tokens in each doc).
Methods¶
append_doc (ids_tks, cts_lens)
Add a document to corpus. If format of this document is term-frequency, this method will append list of unique terms to word_ids_tks and append list of frequency to cts_lens. If format is term-sequence, the list of tokens and number of tokens will be appended respectly
Parameters: ids_tks and cts_lens is format (tf or sq) of added document
ids_tks: list of unique terms (term-frequency format) or list of tokens (term-sequence format) cts_lens: list of frequency of unique terms (term-frequency format) or number tokens in document (term-sequence format)
class tmlib.datasets.base.DataIterator¶
tmlib.datasets.base.DataIterator()
Abstract class is used for working with data input in learning LDA. Inherited by 2 class: tmlib.datasets.dataset.Dataset and tmlib.datasets.wiki_stream.WikiStream
Attributes¶
mini_batch_no: int,
Numerical order of mini-batch which used for learning at the present
end_of_data: boolean,
To check the mini-batch is the last mini-batch or not
Function base.get_data_home¶
tmlib.datasets.base. get_data_home (data_home=None)
This folder is used by some large dataset loaders to avoid downloading the data several times.
By default the data dir is set to a folder named ‘tmlib_data’ in the user home folder. We can change it by change value of data_home parameter The ‘~’ symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
- Return: path of the tmlib data dir.
>>> from tmlib.datasets import base
>>> print base.get_data_home()
/home/kde/tmlib_data
Function base.clear_data_home¶
tmlib.datasets.base. clear_data_home (data_home=None)
Delete all the content of the data home cache.
Function base.check_input_format¶
tmlib.datasets.base.check_input_format(file_path)
Check format of input file(text formatted or raw text)
Parameters: file_path (string)
Path of file input
Return: format of input (DataFormat.RAW_TEXT, DataFormat.TERM_FREQUENCY or DataFormat.TERM_SEQUENCE)
>>> from tmlib.datasets import base
>>> file_path = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> print base.check_input_format(file_path)
tf
>>> file_path = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train_raw.txt'
>>> print base.check_input_format(file_path)
txt
Function base.load_batch_raw_text¶
tmlib.datasets.base.load_batch_raw_text(file_raw_text_path)
load all of documents and store as a list. Each element in this list is a document with raw text format (string)
Parameters: file_raw_text_path (string)
Path of file input
Return: list, each element in list is string type and also is text of a document
>>> from tmlib.datasets import base
>>> path_file_raw_text = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_infer_raw.txt'
>>> list_docs = base.load_batch_raw_text(path_file_raw_text)
>>> print 'number of documents: ', len(list_docs)
number of documents: 50
>>> print list_docs[8]
Here is a summary of developments in forest and brush fires in Western states:
Function base.pre_process¶
tmlib.datasets.base.pre_process(file_path)
Preprocessing for file input if format of data is raw text
Paremeter: file_path (string)
Path of file input
Return: list which respectly includes path of vocabulary file, term-frequency file, term-sequence file after preprocessing
>>> from tmlib.datasets import base
>>> path_file = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train_raw.txt'
>>> path_vocab, path_tf, path_sq = base.pre_process(path_file)
Waiting...
>>> print 'path to file vocabulary extracted: ', path_vocab
path to file vocabulary extracted: /home/kde/tmlib_data/ap_train_raw/vocab.txt
>>> print 'path to file with term-frequency format: ', path_tf
path to file with term-frequency format: /home/kde/tmlib_data/ap_train_raw/ap_train_raw.tf
>>> print 'path to file with term-sequence format: ', path_sq
path to file with term-sequence format: /home/kde/tmlib_data/ap_train_raw/ap_train_raw.sq
Function base.load_batch_formatted_from_file¶
tmlib.datasets.base.load_batch_formatted_from_file(data_path, output_format=DataFormat.TERM_FREQUENCY)
- load all of documents in file which is formatted as term-frequency format or term-sequence format and return a corpus with format is output_format
- Parameters:
- data_path: path of file data input which is formatted
- output_format: format data of output, default: term-frequence format
- Return: object corpus which is the data input for learning
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> corpus_tf = base.load_batch_formatted_from_file(path_file_tf)
>>> print 'Unique terms in the 9th documents: ', corpus_tf.word_ids_tks[8]
Unique terms in the 9th documents: [5829 4040 2891 14 1783 381 2693]
>>> print 'Frequency of unique terms in the 9th documents: ', corpus_tf.cts_lens[8]
Frequency of unique terms in the 9th documents: [1 1 1 1 1 1 1]
>>> corpus_sq = base.load_batch_formatted_from_file(path_file_tf, output_format=base.DataFormat.TERM_SEQUENCE)
>>> print 'List of tokens in the 9th documents: ', corpus_sq.word_ids_tks[8]
List of tokens in the 9th documents: [5829 4040 2891 14 1783 381 2693]
>>> print 'Number of tokens in the 9th document: ', corpus_sq.cts_lens[8]
Number of tokens in the 9th document: 7
Function base.reformat_file_to_term_sequence¶
tmlib.datasets.base.reformat_file_to_term_sequence(file_path)
convert the formatted file input (tf or sq) to file with format term-sequence
Parameter: file_path (string)
Path of file input
Return: path of file which is formatted to term-sequence
>>> from tmlib.datasets import base
>>> path_file_tf = tmlib
>>> path_file_sq = base.reformat_file_to_term_sequence(path_file_tf)
>>> print 'path to file term-sequence: ', path_file_sq
path to file term-sequence: /home/kde/tmlib_data/ap_train/ap_train.sq
Function base.reformat_file_to_term_frequency¶
tmlib.datasets.base.reformat_file_to_term_sequence(file_path)
convert the formatted file input (tf or sq) to file with format term-frequency
Parameter: file_path (string)
Path of file input
Return: path of file which is formatted to term-frequency
>>> from tmlib.datasets import base
>>> path_file = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> path_file_tf = base.reformat_file_to_term_sequence(path_file)
>>> print 'path to file term-frequency: ', path_file_tf
path to file term-frequency: /home/kde/tmlib_data/ap_train/ap_train.tf
Function base.convert_corpus_format¶
tmlib.datasets.base.convert_corpus_format(corpus, data_format)
- convert corpus (object of class Corpus) to desired format
- Parameters:
- corpus: object of class Corpus,
- data_format: format type desired (DataFormat.TERM_SEQUENCE or DataFormat.TERM_FREQUENCY)
- Return: object corpus with desired format
>>> from tmlib.datasets import base
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> corpus = base.load_batch_formatted_from_file(path_file_tf)
>>> corpus_sq = base.convert_corpus_format(corpus, base.DataFormat.TERM_SEQUENCE)
>>> print 'Unique terms in the 22th documents: ', corpus.word_ids_tks[21]
Unique terms in the 22th documents: [ 32 396 246 87 824 3259 316 285]
>>> print 'Frequency of unique terms in the 22th documents: ', corpus.cts_lens[21]
Frequency of unique terms in the 22th documents: [1 1 1 2 1 1 2 1]
>>> print 'List of tokens in the 22th documents: ', corpus_sq.word_ids_tks[21]
List of tokens in the 22th documents: [32, 396, 246, 87, 87, 824, 3259, 316, 316, 285]
>>> print 'Number of tokens in the 22th document: ', corpus_sq.cts_lens[21]
Number of tokens in the 22th document: 10
Function base.load_mini_batch_term_sequence_from_sequence_file¶
tmlib.datasets.base.load_mini_batch_term_sequence_from_sequence_file(fp, batch_size)
- loading a mini-batch with size: batch_size from a file which has the file pointer fp. This file includes the documents with term-sequence format and the loaded mini-batch is also term-sequence format
- Parameter:
- fp: file pointer of file term-sequence format
- batch_size: int, size of mini-batch
- Return: (minibatch, end_file). minibatch is object of class Corpus with term-sequence format and end_file is boolean variable which check that file pointer is at end of file or not
>>> from tmlib.datasets import base
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> path_file_sq = base.reformat_file_to_term_sequence(path_file_tf)
>>> fp = open(path_file_sq)
>>> mini_batch, end_file = base.load_mini_batch_term_sequence_from_sequence_file(fp, 1500)
>>> print 'Format of mini_batch: ', mini_batch.format_type
Format of mini_batch: sq
>>> print 'List of tokens in the first document of mini_batch: \n', mini_batch.word_ids_tks[0]
List of tokens in the first document of mini_batch:
[ 0 6144 3586 3586 3 4 1541 8 10 3927 12 12
12 12 12 12 12 4621 527 9232 1112 1112 20 2587
6172 10269 10269 37 42 3117 1582 1585 1585 1585 435 9268
9268 9268 571 571 60 61 63 63 64 64 5185 11
4683<PHONE_NUMBER> 1103 592 5718 1623 1623 1624 1624 1624
1624 89 89 6234 8802 1638<PHONE_NUMBER> 106 3691 720
2672 113 2165 5751 123<PHONE_NUMBER><PHONE_NUMBER> 1670
4231 1167 144 147 149 149 149 149 149<PHONE_NUMBER>
3735 5272 5272 1732<PHONE_NUMBER> 27 1700 9893 9893 166
167<PHONE_NUMBER> 2248 372<PHONE_NUMBER> 4284 4284 3450
3450 117<PHONE_NUMBER> 5320 201 4215 9932 9932 207 207
208 208 208<PHONE_NUMBER> 7898<PHONE_NUMBER> 1744 744
234 1259 1259 4287 7254 249 8311 5884 5884 298 254 767
767 2304 4876 270 557 786<PHONE_NUMBER> 287 5409 290
5923 2854 1834 1834 1834 303 3888 3888 3888 3888 817 817
9523 334 1333<PHONE_NUMBER> 1417 325 1870 1870 1870 1870
1870 1870 1870 1361 1362 6995 342 343<PHONE_NUMBER> 5469
351 351 351<PHONE_NUMBER> 2402 487 884<PHONE_NUMBER>
3455 1410 5099 4489 395 2570<PHONE_NUMBER> 1430 3992 416
3491 2033 3499 429 3502 5040<PHONE_NUMBER> 1971 1971 1971
437 9667 9667 322 7119 8656 1102<PHONE_NUMBER> 2529 997
2022 2022 4071 2536 10219 1517 1009 221 3059 500 511]
>>> print 'Number of tokens in the first document of mini_batch: \n', mini_batch.cts_lens[0]
Number of tokens in the first document of mini_batch:
263
Function base.load_mini_batch_term_sequence_from_term_frequency_file¶
tmlib.datasets.base.load_mini_batch_term_sequence_from_term_frequency_file(fp, batch_size)
- loading a mini-batch with size: batch_size from a file which has the file pointer fp. This file includes the documents with term-frequency format and the returned mini-batch is term-sequence format
- Parameter:
- fp: file pointer of file term-frequency format
- batch_size: int, size of mini-batch
- Return: (minibatch, end_file). minibatch is object of class Corpus with term-sequence format and end_file is boolean variable which check that file pointer is at end of file or not
>>> from tmlib.datasets import base
>>> path_file_tf = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> fp = open(path_file_tf)
>>> mini_batch1, end_file = base.load_mini_batch_term_sequence_from_term_frequency_file(fp, 1500)
>>> print 'Format of mini_batch1: ', mini_batch1.format_type
Format of mini_batch1: sq
>>> print 'Size of mini_batch1: ', len(mini_batch1.word_ids_tks)
Size of mini_batch1: 1500
>>> print 'End file: ', end_file
End file: False
>>> mini_batch2, end_file = base.load_mini_batch_term_sequence_from_term_frequency_file(fp, 1500)
>>> print 'Size of mini_batch2: ', len(mini_batch2.word_ids_tks)
Size of mini_batch2: 700
>>> print 'End file: ', end_file
End file: True
Function base.load_mini_batch_term_frequency_from_sequence_file¶
tmlib.datasets.base.load_mini_batch_term_frequency_from_sequence_file(fp, batch_size)
- loading a mini-batch with size: batch_size from a file which has the file pointer fp.   Because the learning methods are stochastic, so this fuction help sample randomly mini-batch from corpus. And after shuffling, the documents with new position will be written to a new file.
- Parameter:
- data_path: file input which is formatted (tf or sq)
- batch_size: the necessary parameter for the shuffling algorithm designed by us
- Return: path of new file which is shuffled
>>> from tmlib.datasets import base
>>> path_file = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt'
>>> path_file_shuffled = base.shuffle_formatted_data_file(path_file, 500)
>>> print 'Path to file shuffled: ', path_file_shuffled
Path to file shuffled: /home/kde/Desktop/topicmodel-lib/examples/ap/ap_train.txt.shuffled
Function base.compute_sparsity¶
tmlib.datasets.base.compute_sparsity(doc_tp, num_docs, num_topics, _type)
- Compute document sparsity.
- Parameters:
- doc_tp: numpy.array, 2-dimention, the estimated topic mixtures of all documents in corpus
- num_docs: int, the number of documents in corpus
- num_topics: int, is the number of requested latent topics to be extracted from the training corpus.
- _type: string, if the value is ‘z’, the topic mixtures is estimated by the sampling method as CGS or CVB0, so we have the individual caculation for this. Otherwise, if the value of it isn’t ‘z’, this is for the methods as: VB, OPE or FW
- Return: float, sparsity of documents
>>> import numpy as np
>>> from tmlib.datasets import base
>>> theta = np.array([[0.1, 0.3, 0.2, 0.2, 0.1, 0.1], [0.02, 0.05, 0.03, 0.5, 0.2, 0.2]], dtype='float32')
>>> base.compute_sparsity(theta, theta.shape[0], theta.shape[1], _type='t')
1.0
Function base.write_topic_mixtures¶
tmlib.datasets.base.write_topic_mixtures(theta, file_name)
- save topic mixtures (theta) to a file
- Parameters:
- theta: numpy.array, 2-dimention
- file_name: name (path) of file which is written
Function base.read_vocab¶
tmlib.datasets.base. read_vocab (path_vocab)
- Read file vocabulary and store it with dictionary type of python (for example: the word ‘hello’ is the 2nd word (index = 2) in file vocabulary, this function return a object named dict, we have: dict[‘hello’] = 2), it’s used as input parameter for function parse_doc_list
- Parameters:
- path_vocab: path of file vocabulary
>>> from tmlib.datasets import base
>>> path_vocab = '/home/kde/Desktop/topicmodel-lib/examples/ap/vocab.txt'
>>> list_unique_terms = open(path_vocab).readlines()
>>> list_unique_terms[10].strip()
'years'
>>> dict_vocab = base.read_vocab(path_vocab)
>>> dict_vocab['years']
10
>>> list_unique_terms[1021].strip()
'laws'
>>> dict_vocab['laws']
1021
Function base.parse_doc_list¶
tmlib.datasets.base. parse_doc_list (docs, vocab_dict)
- Parameters:
- docs: list of document. Each document is represented as a string of words (same as raw text)
- vocab_dict: vocabulary is represented as dictionary format described above
- Return: object of class Corpus
>>> path_vocab = '/home/kde/Desktop/topicmodel-lib/examples/ap/vocab.txt'
>>> dict_vocab = base.read_vocab(path_vocab)
>>> path_file_raw_text = '/home/kde/Desktop/topicmodel-lib/examples/ap/ap_infer_raw.txt'
>>> list_docs = base.load_batch_raw_text(path_file_raw_text)
>>> print 'The 9th document has the content: "%s"' %list_docs[8]
The 9th document has the content: " Here is a summary of developments in forest and brush fires in Western states:"
>>> corpus_tf = base.parse_doc_list(list_docs, dict_vocab)
>>> list_unique_terms = open(path_vocab).readlines()
>>> list_terms_in_doc_9th = list()
>>> for index in corpus_tf.word_ids_tks[8]: \
... list_terms_in_doc_9th.append(list_unique_terms[index].strip())
...
>>> print 'List of unique terms in the 9th document: ', '\nindexs: ', corpus_tf.word_ids_tks[8], '\nwords: ', list_terms_in_doc_9th
List of unique terms in the 9th document:
indexs: [5829, 4040, 2891, 14, 1783, 381, 2693]
words: ['summary', 'brush', 'fires', 'states', 'forest', 'western', 'developments']
>>> print 'Frequency of unique terms: ', corpus_tf.cts_lens[8]
Frequency of unique terms: [1, 1, 1, 1, 1, 1, 1]


AWS::ApplicationAutoScaling::ScalingPolicy
The AWS::ApplicationAutoScaling::ScalingPolicy
resource defines a scaling
policy that Application Auto Scaling uses to adjust your application resources.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" :
String
, "PolicyType" :String
, "ResourceId" :String
, "ScalableDimension" :String
, "ScalingTargetId" :String
, "ServiceNamespace" :String
, "StepScalingPolicyConfiguration" : StepScalingPolicyConfiguration, "TargetTrackingScalingPolicyConfiguration" : TargetTrackingScalingPolicyConfiguration } }
YAML
Type : "AWS::ApplicationAutoScaling::ScalingPolicy" Properties: PolicyName:
String
PolicyType:String
ResourceId:String
ScalableDimension:String
ScalingTargetId:String
ServiceNamespace:String
StepScalingPolicyConfiguration: StepScalingPolicyConfiguration TargetTrackingScalingPolicyConfiguration: TargetTrackingScalingPolicyConfiguration
Properties
PolicyName
-
A name for the scaling policy.
Required: Yes
Type: String
Update requires: Replacement
PolicyType
-
An Application Auto Scaling policy type.
Note
For DynamoDB, only
TargetTrackingScaling
is supported. For Amazon ECS, Spot Fleet, and Amazon RDS, bothStepScaling
andTargetTrackingScaling
are supported. For any other service, onlyStepScaling
is supported.Required: Yes
Type: String
Update requires: No interruption
ResourceId
-
The unique resource identifier for the scalable target that this scaling policy applies to. For more information, see the
ResourceId
parameter for the PutScalingPolicy action in the Application Auto Scaling API Reference.Required: Conditional. You must specify either the
ScalingTargetId
property or theResourceId
,ScalableDimension
, andServiceNamespace
properties. If you specify theResourceId
,ScalableDimension
, andServiceNamespace
properties, don't specify theScalingTargetId
property.Type: String
Update requires: Replacement
ScalableDimension
-
The scalable dimension of the scalable target that this scaling policy applies to. The scalable dimension contains the service namespace, resource type, and scaling property, such as ecs:service:DesiredCount for the desired task count of an Amazon ECS service.
Required: Conditional. You must specify either the
ScalingTargetId
property or theResourceId
,ScalableDimension
, andServiceNamespace
properties. If you specify theResourceId
,ScalableDimension
, andServiceNamespace
properties, don't specify theScalingTargetId
property.Type: String
Update requires: Replacement
ServiceNamespace
-
The AWS service namespace of the scalable target that this scaling policy applies to. For a list of service namespaces, see AWS Service Namespaces in the AWS General Reference.
Required: Conditional. You must specify either the
ScalingTargetId
property or theResourceId
,ScalableDimension
, andServiceNamespace
properties. If you specify theResourceId
,ScalableDimension
, andServiceNamespace
properties, don't specify theScalingTargetId
property.Type: String
Update requires: Replacement
ScalingTargetId
-
The AWS CloudFormation-generated ID of an Application Auto Scaling scalable target. For more information about the ID, see the Return Value section of the AWS::ApplicationAutoScaling::ScalableTarget resource.
Required: Conditional. You must specify either the
ScalingTargetId
property or theResourceId
,ScalableDimension
, andServiceNamespace
properties. If you specify this property, don't specify theResourceId
,ScalableDimension
, andServiceNamespace
properties.Type: String
Update requires: Replacement
StepScalingPolicyConfiguration
-
A step policy that configures when Application Auto Scaling scales resources up or down, and by how much.
Required: No
Type: Application Auto Scaling ScalingPolicy StepScalingPolicyConfiguration
Update requires: No interruption
TargetTrackingScalingPolicyConfiguration
-
Configures a target tracking scaling policy.
This parameter is required if you are creating a new policy and the policy type is
TargetTrackingScaling
.Required: No
Type: Application Auto Scaling ScalingPolicy TargetTrackingScalingPolicyConfiguration
Update requires: No interruption
Return Value
Ref
When the logical ID of this resource is provided to the Ref
intrinsic function, Ref
returns the Application Auto Scaling scaling policy Amazon Resource Name (ARN),
such as arn:aws:autoscaling:
us-east-2
:123456789012:scalingPolicy:12ab3c4d-56789-0ef1-2345-6ghi7jk8lm90:resource/ecs/service/ecsStack-MyECSCluster-AB12CDE3F4GH/ecsStack-MyECSService-AB12CDE3F4GH:policyName/MyStepPolicy
.
For more information about using the Ref
function, see Ref.
Examples
Application Auto Scaling Scaling Policy with a Step Policy Configuration
The following example creates an Application Auto Scaling scaling policy with a step policy configuration. When an associated alarm is triggered, the policy increases the desired count of the scalable target by 200%, with a cooldown period of 60 seconds.
JSON
"scalingPolicy" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : "AStepPolicy", "PolicyType" : "StepScaling", "ScalingTargetId" : {"Ref": "scalableTarget"}, "StepScalingPolicyConfiguration" : { "AdjustmentType" : "PercentChangeInCapacity", "Cooldown" : 60, "MetricAggregationType" : "Average", "StepAdjustments" : [{ "MetricIntervalLowerBound" : 0, "ScalingAdjustment" : 200 }] } } }
YAML
scalingPolicy: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: AStepPolicy PolicyType: StepScaling ScalingTargetId: Ref: scalableTarget StepScalingPolicyConfiguration: AdjustmentType: PercentChangeInCapacity Cooldown: 60 MetricAggregationType: Average StepAdjustments: - MetricIntervalLowerBound: 0 ScalingAdjustment: 200
Application Auto Scaling Scaling Policy with an Amazon DynamoDB Table
This example sets up Application Auto Scaling for a AWS::DynamoDB::Table
resource.
The template defines a TargetTrackingScaling
scaling policy that scales up the WriteCapacityUnits
throughput for the table.
JSON
{ "Resources": { "DDBTable": { "Type": "AWS::DynamoDB::Table", "Properties": { "AttributeDefinitions": [ { "AttributeName": "ArtistId", "AttributeType": "S" }, { "AttributeName": "Concert", "AttributeType": "S" }, { "AttributeName": "TicketSales", "AttributeType": "S" } ], "KeySchema": [ { "AttributeName": "ArtistId", "KeyType": "HASH" }, { "AttributeName": "Concert", "KeyType": "RANGE" } ], "GlobalSecondaryIndexes": [ { "IndexName": "GSI", "KeySchema": [ { "AttributeName": "TicketSales", "KeyType": "HASH" } ], "Projection": { "ProjectionType": "KEYS_ONLY" }, "ProvisionedThroughput": { "ReadCapacityUnits": 5, "WriteCapacityUnits": 5 } } ], "ProvisionedThroughput": { "ReadCapacityUnits": 5, "WriteCapacityUnits": 5 } } }, "WriteCapacityScalableTarget": { "Type": "AWS::ApplicationAutoScaling::ScalableTarget", "Properties": { "MaxCapacity": 15, "MinCapacity": 5, "ResourceId": { "Fn::Join": [ "/", [ "table", { "Ref": "DDBTable" } ] ] }, "RoleARN": { "Fn::GetAtt": ["ScalingRole", "Arn"] }, "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "ServiceNamespace": "dynamodb" } }, "ScalingRole": { "Type": "AWS::IAM::Role", "Properties": { "AssumeRolePolicyDocument": { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": [ "application-autoscaling.amazonaws.com" ] }, "Action": [ "sts:AssumeRole" ] } ] }, "Path": "/", "Policies": [ { "PolicyName": "root", "PolicyDocument": { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "dynamodb:DescribeTable", "dynamodb:UpdateTable", "cloudwatch:PutMetricAlarm", "cloudwatch:DescribeAlarms", "cloudwatch:GetMetricStatistics", "cloudwatch:SetAlarmState", "cloudwatch:DeleteAlarms" ], "Resource": "*" } ] } } ] } }, "WriteScalingPolicy": { "Type": "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties": { "PolicyName": "WriteAutoScalingPolicy", "PolicyType": "TargetTrackingScaling", "ScalingTargetId": { "Ref": "WriteCapacityScalableTarget" }, "TargetTrackingScalingPolicyConfiguration": { "TargetValue": 50.0, "ScaleInCooldown": 60, "ScaleOutCooldown": 60, "PredefinedMetricSpecification": { "PredefinedMetricType": "DynamoDBWriteCapacityUtilization" } } } } } }
YAML
Resources: DDBTable: Type: AWS::DynamoDB::Table Properties: AttributeDefinitions: - AttributeName: "ArtistId" AttributeType: "S" - AttributeName: "Concert" AttributeType: "S" - AttributeName: "TicketSales" AttributeType: "S" KeySchema: - AttributeName: "ArtistId" KeyType: "HASH" - AttributeName: "Concert" KeyType: "RANGE" GlobalSecondaryIndexes: - IndexName: "GSI" KeySchema: - AttributeName: "TicketSales" KeyType: "HASH" Projection: ProjectionType: "KEYS_ONLY" ProvisionedThroughput: ReadCapacityUnits: 5 WriteCapacityUnits: 5 ProvisionedThroughput: ReadCapacityUnits: 5 WriteCapacityUnits: 5 WriteCapacityScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 15 MinCapacity: 5 ResourceId: !Join - / - - table - !Ref DDBTable RoleARN: !GetAtt ScalingRole.Arn ScalableDimension: dynamodb:table:WriteCapacityUnits ServiceNamespace: dynamodb ScalingRole: Type: AWS::IAM::Role Properties: AssumeRolePolicyDocument: Version: "2012-10-17" Statement: - Effect: "Allow" Principal: Service: - application-autoscaling.amazonaws.com Action: - "sts:AssumeRole" Path: "/" Policies: - PolicyName: "root" PolicyDocument: Version: "2012-10-17" Statement: - Effect: "Allow" Action: - "dynamodb:DescribeTable" - "dynamodb:UpdateTable" - "cloudwatch:PutMetricAlarm" - "cloudwatch:DescribeAlarms" - "cloudwatch:GetMetricStatistics" - "cloudwatch:SetAlarmState" - "cloudwatch:DeleteAlarms" Resource: "*" WriteScalingPolicy: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: WriteAutoScalingPolicy PolicyType: TargetTrackingScaling ScalingTargetId: !Ref WriteCapacityScalableTarget TargetTrackingScalingPolicyConfiguration: TargetValue: 50.0 ScaleInCooldown: 60 ScaleOutCooldown: 60 PredefinedMetricSpecification: PredefinedMetricType: DynamoDBWriteCapacityUtilization


1. Can you access Cookie using javascript?
2. Which of the following is the correct syntax to redirect a url using JavaScript?
3. Which built-in method removes the last element from an array and returns that element?
4. Which built-in method returns the characters in a string beginning at the specified location?
5. Which of the following function of Boolean object returns the primitive value of the Boolean object?
6. Which of the following function of String object executes the search for a match between a regular expression and a specified string?
7. Which of the following function of String object creates a string to be displayed in a big font as if it were in a big tag?
8. Which of the following function of String object causes a string to be displayed as struck-out text, as if it were in a strike tag?
9. Which of the following function of Array object creates a new array with the results of calling a provided function on every element in this array?
10. Which of the following function of Array object sorts the elements of an array?
GET RESULT IN PERCENTAGE:


Suarez already sees Liverpool as Premier League champions!
by BeSoccerFC Barcelona striker and former Liverpool player, Luis Suarez, this Friday said that for him it is "difficult" to imagine Liverpool letting the Premier League title escape them.
"Winning the Premier League is difficult because the season is very long. But at this time, they have a good advantage," the 33-year-old Uruguayan commented to 'ESPN FC' during a presention of branded boots that he sponsors.
"But we all know what football is like. Last season was a similar advantage at one point and, in the end, Manchester City won the title (in the last game)," he continued.
Featured links
- Mourinho replaces Pochettino at Tottenham
- The latest football transfers news and rumours
- Wales, golf, Madrid... in that order
- Luis Enrique returns to Spain national team
Suarez, who played for Liverpool for three and a half seasons (he arrived in the winter market from Ajax for 26.5 million euros), was very close to getting Liverpool their first league title since 1990, a little before signing for Barcelona.
"We almost won the Premier League with young players. We almost did so without the budget that they have now. considering everything that Liverpool is at the moment, winning then would have been more of a merit," the top scorer for Uruguay stated (59 goals).


| Meeting | Slack Channel | Meeting Minutes | Meeting Link | Meeting Recordings |
|---|---|---|---|---|
| Layer5 Community Meeting | #general | Minutes | Zoom | YouTube |
| Websites Meeting | #websites | Minutes | Zoom | YouTube |
| Meshery Development Meeting | #meshery | Minutes | Zoom | YouTube |
| MeshMap Meeting | #meshery-ci | Minutes | Zoom | YouTube |
| Newcomers' Meeting | #newcomers | Minutes | Zoom | YouTube |
| Meshery CI WG Meeting | #meshery-ci | Minutes | Zoom | YouTube |
| CNCF - SMI Community Meeting | #smi | Minutes | Zoom | N/A |
| CNCF - SIG Network Meeting | #sig-network | Minutes | Zoom | YouTube |


A Canadian solution to nurse scheduling and management for healthcare organizations.
Designed to:
Increase Productivity: Save tremendous time for nurse
managers / administration and
nurses
themselves through quick, seamless and real time scheduling changes.
Increase Transparency: Allows everyone to equitably view
the schedule and any
changes with
electronic documentation to ensure no errors.
Increase Team Cohesiveness: Working to see which gaps to
fill and
helping each
other
out to successfully fill the schedule. Increases a sense of 'ownership' over one's
schedule.
Increase Organizational Efficiency: Metrics gleaned from
software used to predict
peak times
for
scheduling based on absences and other contingencies.


Copyright © 2000 by George Williams
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
The name of the author may not be used to endorse or promote products derived from this software without specific prior written permission.


Christopher Stanfill
About
Projects/Assignments
Music
Contact
Project: Learn-Something-New
Website
Github Repo
Project: Raspberry-Jam-Session
Website
Github Repo
Assignment: Eat-Da-Burger
Website
Github Repo
Assignment: Weather Dashboard
Website
Github Repo


A Responsive web/app responds to its viewing environment.
Screen fragmentation
Scalable Vector Graphics
Build a responsive registration form using HTML5 and CSS3.
Codepen.io is encouraged.
email<EMAIL_ADDRESS>website: www.castillo.io


| networkD3-package | Tools for Creating D3 Network Graphs from R |
| as.radialNetwork | Convert an R hclust or dendrogram object into a radialNetwork list. |
| chordNetwork | Create Reingold-Tilford Tree network diagrams. |
| chordNetworkOutput | Shiny bindings for networkD3 widgets |
| dendroNetwork | Create hierarchical cluster network diagrams. |
| dendroNetworkOutput | Shiny bindings for networkD3 widgets |
| diagonalNetwork | Create Reingold-Tilford Tree network diagrams. |
| diagonalNetworkOutput | Shiny bindings for networkD3 widgets |
| forceNetwork | Create a D3 JavaScript force directed network graph. |
| forceNetworkOutput | Shiny bindings for networkD3 widgets |
| igraph_to_networkD3 | Function to convert igraph graph to a list suitable for networkD3 |
| JS | Create character strings that will be evaluated as JavaScript |
| MisLinks | Les Miserables character links |
| MisNodes | Les Miserables character nodes |
| networkD3 | Tools for Creating D3 Network Graphs from R |
| networkD3-shiny | Shiny bindings for networkD3 widgets |
| radialNetwork | Create Reingold-Tilford Tree network diagrams. |
| radialNetworkOutput | Shiny bindings for networkD3 widgets |
| renderchordNetwork | Shiny bindings for networkD3 widgets |
| renderDendroNetwork | Shiny bindings for networkD3 widgets |
| renderDiagonalNetwork | Shiny bindings for networkD3 widgets |
| renderForceNetwork | Shiny bindings for networkD3 widgets |
| renderRadialNetwork | Shiny bindings for networkD3 widgets |
| renderSankeyNetwork | Shiny bindings for networkD3 widgets |
| renderSimpleNetwork | Shiny bindings for networkD3 widgets |
| sankeyNetwork | Create a D3 JavaScript Sankey diagram |
| sankeyNetworkOutput | Shiny bindings for networkD3 widgets |
| saveNetwork | Save a network graph to an HTML file |
| SchoolsJournals | Edge list of REF (2014) journal submissions for Politics and International Relations |
| simpleNetwork | Function for creating simple D3 JavaScript force directed network graphs. |
| simpleNetworkOutput | Shiny bindings for networkD3 widgets |


Compute a confidence interval around a summary statistic. Both
simulation-based and theoretical methods are supported, though only
type = "se"
is supported for theoretical methods.
Learn more in vignette("infer")
.
get_confidence_interval(x, level = 0.95, type = NULL, point_estimate = NULL)
get_ci(x, level = 0.95, type = NULL, point_estimate = NULL)
| x | A distribution. For simulation-based inference, a data frame
containing a distribution of |
|---|---|
| level | A numerical value between 0 and 1 giving the confidence level. Default value is 0.95. |
| type | A string giving which method should be used for creating the
confidence interval. The default is |
| point_estimate | A data frame containing the observed statistic (in a
|
A tibble containing the following columns:
term
: The explanatory variable (or intercept) in question. Only
supplied if the input had been previously passed to fit()
.
lower_ci
, upper_ci
: The lower and upper bounds of the confidence
interval, respectively.
A null hypothesis is not required to compute a confidence interval. However,
including hypothesize()
in a pipeline leading to get_confidence_interval()
will not break anything. This can be useful when computing a confidence
interval using the same distribution used to compute a p-value.
Theoretical confidence intervals (i.e. calculated by supplying the output
of assume()
to the x
argument) require that the point estimate lies on
the scale of the data. The distribution defined in assume()
will be
recentered and rescaled to align with the point estimate, as can be shown
in the output of visualize()
when paired with shade_confidence_interval()
.
Confidence intervals are implemented for the following distributions and
point estimates:
distribution = "t"
: point_estimate
should be the output of
calculate()
with stat = "mean"
or stat = "diff in means"
distribution = "z"
: point_estimate
should be the output of
calculate()
with stat = "prop"
or stat = "diff in props"
get_ci()
is an alias of get_confidence_interval()
.
conf_int()
is a deprecated alias of get_confidence_interval()
.
Other auxillary functions:
get_p_value()
boot_dist <- gss %>%
# We're interested in the number of hours worked per week
specify(response = hours) %>%
# Generate bootstrap samples
generate(reps = 1000, type = "bootstrap") %>%
# Calculate mean of each bootstrap sample
calculate(stat = "mean")
boot_dist %>%
# Calculate the confidence interval around the point estimate
get_confidence_interval(
# At the 95% confidence level; percentile method
level = 0.95
)
#> # A tibble: 1 × 2
#> lower_ci upper_ci
#> <dbl> <dbl>
#> 1 40.1 42.7
# for type = "se" or type = "bias-corrected" we need a point estimate
sample_mean <- gss %>%
specify(response = hours) %>%
calculate(stat = "mean")
boot_dist %>%
get_confidence_interval(
point_estimate = sample_mean,
# At the 95% confidence level
level = 0.95,
# Using the standard error method
type = "se"
)
#> # A tibble: 1 × 2
#> lower_ci upper_ci
#> <dbl> <dbl>
#> 1 40.1 42.7
# using a theoretical distribution -----------------------------------
# define a sampling distribution
sampling_dist <- gss %>%
specify(response = hours) %>%
assume("t")
# get the confidence interval---note that the
# point estimate is required here
get_confidence_interval(
sampling_dist,
level = .95,
point_estimate = sample_mean
)
#> # A tibble: 1 × 2
#> lower_ci upper_ci
#> <dbl> <dbl>
#> 1 40.1 42.7
# using a model fitting workflow -----------------------
# fit a linear model predicting number of hours worked per
# week using respondent age and degree status.
observed_fit <- gss %>%
specify(hours ~ age + college) %>%
fit()
observed_fit
#> # A tibble: 3 × 2
#> term estimate
#> <chr> <dbl>
#> 1 intercept 40.6
#> 2 age 0.00596
#> 3 collegedegree 1.53
# fit 100 models to resamples of the gss dataset, where the response
# `hours` is permuted in each. note that this code is the same as
# the above except for the addition of the `generate` step.
null_fits <- gss %>%
specify(hours ~ age + college) %>%
hypothesize(null = "independence") %>%
generate(reps = 100, type = "permute") %>%
fit()
null_fits
#> # A tibble: 300 × 3
#> # Groups: replicate [100]
#> replicate term estimate
#> <int> <chr> <dbl>
#> 1 1 intercept 44.5
#> 2 1 age -0.0785
#> 3 1 collegedegree 0.228
#> 4 2 intercept 42.3
#> 5 2 age -0.0260
#> 6 2 collegedegree 0.452
#> 7 3 intercept 41.1
#> 8 3 age 0.00370
#> 9 3 collegedegree 0.468
#> 10 4 intercept 42.1
#> # … with 290 more rows
get_confidence_interval(
null_fits,
point_estimate = observed_fit,
level = .95
)
#> # A tibble: 3 × 3
#> term lower_ci upper_ci
#> <chr> <dbl> <dbl>
#> 1 age -0.115 0.0819
#> 2 collegedegree -2.18 2.82
#> 3 intercept 38.3 45.9
# more in-depth explanation of how to use the infer package
if (FALSE) {
vignette("infer")
}


Hi, I am Clara, a junior in Western Reserve Academy. I come from Shenzheng China. I am in cross-country, rifelry and track & Field teams.
I also attended Model United Nation trips several times and I realy enjoy doing it.
I have been in Dance and Choir for my freshman and sophomore year. They all are really interesting courses and bring me so much valuable experience.
I am a kind of person who likes to try new stuffs everything. As such, this year I choose to take digital fabrication which seems like a really fun course so far.
Besides trying something new, the other reason that I take digital fabrication is to learn how to use those magical machines to make what I want.
I saw many of my friend use 3D printer to print stamps and models. I am really looking forward to learn different new skills in this class!!!


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37 comments
Hi Marcus,
You are right, and I'm aware of the issue. :-(
I have a quick fix in the local branch, where the methods are overridden to just call delegate.getXxMetadata()
instead of super.getXxMetadata()
which always return null.
Unfortunately, the metadata from the delegate isn't necessary correct (ie: it never contains a APP2/ICC_PROFILE), because I filter out certain JPEG segments from the delegate, to prevent it from crashing. Now, this is probably OK for you, as you won't need the original CMYK color profile in the converted RGB image. But it's still an issue that I feel should be resolved, and relying on this "feature" might break things in the future.
I do have a couple of ideas for how to fix the issue, but they are all quite time consuming, as the com.sun.imageio.plugins.jpeg.JPEGMetadata
isn't extendable or mutable... So I need to recreate it all from scratch + extend it with the fixes that the new reader brings along. In addition, we probably want support for the EXIF flavor (in contrast to JFIF) that the current metadata just ignores.
A rough guesstimate would be a full weeks worth of work, just for the basics.
So... How badly do you need metadata? ;-)
Hi Harald,
thanks for explanation. I'm not sure if I need the metadata but hey...maybe my employer will! ;-)
Sadly your advice to call the delegate does not work:
javax.imageio.IIOException: Inconsistent metadata read from stream
at com.sun.imageio.plugins.jpeg.JPEGMetadata.(JPEGMetadata.java:343)
at com.sun.imageio.plugins.jpeg.JPEGImageReader.getImageMetadata(JPEGImageReader.java:998)
However, i'm not very familar with all this image/metadata stuff, but wouldn't it be better to not rely on that delegate with all the filtering stuff needed? What about to implement the fixed logic directly (by copying most of the OpenJDK implementation for example). Is it that what you have in mind? At least this is it what i would try to do if my employer insists this functionality. Maybe thats some kind naive and/or violates some licence terms. WDYT?
Regards
Too bad, we're out of luck for a quick fix then... (these exceptions are part of the reason I created the reader in the first place).
I agree, the best thing is not to rely on the delegate, and that is my goal. However, the metadata code for the JPEG format alone is about 4000+ lines, and requires quite extensive knowledge of the JPEG/JFIF format. The OpenJDK code is LGPL licensed, and I can't use it, but I have thought about looking at the code from JAI (jai-imageio), which is BSD licensed and thus more compatible (license-wise). That code should provide compatible metadata.
It's still several days work I'm afraid, just to shoe-horn one of these implementations onto my reader...
Yeah, i agree. It will be much work to implement it. After consulting my employer we decided to renounce the meta data functionality as we can live without it at the moment. However, could you shortly explain the licence compatibility issue with LGPL code? I did not found any licence terms in your code.
The project may be distributed under BSD license, every source file should include the license in the header. Most modules have a license.txt in them as well.
I'm not a lawyer, but from my understanding of the (L)GPL license, is that if I add parts from (L)GPL licensed projects (in contrast to just dynamic linking), my project has to become (L)GPL licensed as well. So, I try to avoid that, just in case.
While the research regarding the meta data topic i found
https://github.com/cescoffier/Commons-Image-IO
Would it be an option to handle the metadata stuff via Apache commons-imaging?
Hmmm.. Re. license: I wasn't even aware there were alternative spellings.. But yes, I use american english. ;-)
The twelvemonkeys library has decent support for reading (not writing at the moment) EXIF, IPTC, and XMP metadata (see the com.twelvemonkeys.imageio.medata
package).
Commons Image IO seems to have a nice API, but my guess is, it relies on ImageIO for the metadata, so you'll have the same problem with "Inconsistent metadata". Apache commons-imaging might help. I have no experience with either of these libraries though.
Might be worth giving it a try!
Hi Marcus!
Good news! :-)
I think I "accidentally" found a way to fix this issue, without having to re-invent all of the metadata-classes, as part of another issue I was working on. Do you have some more sample images that throws the "inconsistent metadata" exception you could add to this issue? Preferably not too large in size, but I'll take what you've got. :-)
This way, I can create a more robust test case/regression test, and get rid of this bug, hopefully.
Thanks for all your input so far!
Regards,
Harald K
Hi Harald,
sorry for the late answer but i was really busy last days... I will continue the work on the mentioned CMYK -> RGB topic soon. I have a bunch of CMYK test/example images here and probably i will find some proper images for the regression test then. So stay patient with me for 2-3 weeks please. However, have you already committed the fix or do you have it in a local branch only?
No stress! :-)
I checked in a small part of it, but I have much more comprehensive changes in the local branch. Unfortunately, I got stuck on some bugs in the com.sun...metadata classes when I started more heavy testing, but I might work them out. It's annoying, because if not, I'd have a solution by now... In any case, more test cases can only be good.
Regards,
Harald K
...while another image's metadata can be read. But when i try to write metadata back to a file via:
writer.write(null, new IIOImage(image, null, metadata),writer.getDefaultWriteParam());
i get
javax.imageio.IIOException: Metadata components != number of destination bands
at com.sun.imageio.plugins.jpeg.JPEGImageWriter.checkSOFBands(JPEGImageWriter.java:1324)
at com.sun.imageio.plugins.jpeg.JPEGImageWriter.writeOnThread(JPEGImageWriter.java:695)
at com.sun.imageio.plugins.jpeg.JPEGImageWriter.write(JPEGImageWriter.java:340)
at com.twelvemonkeys.imageio.plugins.jpeg.JPEGImageWriter.write(JPEGImageWriter.java:155)
Any idea?
I have to thank you Harald. I'm looking forward for a working solution.. ;) Having some other images here for both cases but they are rather big. But if you need more test files i will post it here...
The first image fails with latest 1.6.0_51 from Apple under Mountain Lion here. Don't know why this is not reproducable. Could you post your test code to see if there are any differences regarding our test conditions?
Beside this here are a few CMYK test images: https://www.dropbox.com/sh/avsx712khejcbyh/PwYm3cLDiK
Hi Harald,
just working on the CMYK -> RGB conversion topic and one question raised up. How could i detect that the source JPEG was CMYK encoded? Is there any possibility to check this?
Regards
Thanks for the detailed explanation. What a mess... things get even more complicated than i thought ;) So it would be really cool to have that functionality as a utility method. For me at the moment it seems enough to detect whether the source image is RGB and otherwise load and save as such. But maybe requirements changes sometimes...
Regards
Regarding the non-reproducable "javax.imageio.IIOException: Inconsistent metadata read from stream" while processing of the first image: I just found that i used accidentally an old version of TwelveMonkeys lib for the tests. So with the latest 3.0-SNAPSHOT it works as expected. No Exception at all then... Sorry for confusing you!
Looking at your ImageReader.getRawImageType
implementation i see that the method mostly returns null in case the delegate returns null. The only exception is made when CMYK is detected via metadata. Is this the expected behavior?
I mean if metadata says its RGB it returns null nevertheless.
...and back to your question: I read the images using read() method and get the metadata separately.
Sounds good! However I checked approx. 1800 JPEGs with the ImageReader.getRawImageType
method and in 1700 cases the method returns null. This seems strange to me. The test images are uploaded by our customers over the last 6 months. So RGB JPEGs are rare cases?
OK. Thanks.  ;-) Need to think about it. The only solution for me seems to extract ImageReader.getSourceCSType
as utility method... However, have a nice weekend!
Good news: A quick test shows that the Chroma node is correct most of the time. :-)
It sometimes reports RGB for YCbCr, but I guess that's less of a problem, as the image will be decoded to RGB in both cases.
I also found that getSourceCSType
reports wrong in a few cases where the Adobe transform segment is present (but the transform doesn't match the number of color components in the SOF, ie. YCCK but only 3 components). I'll fix that at some point. Chroma node was right in this case (but I think that was coincidence).
Yeah but parsing Chroma
node sometimes throws:
javax.imageio.metadata.IIOInvalidTreeException: Xdensity attribute out of range
at com.sun.imageio.plugins.jpeg.MarkerSegment.getAttributeValue(MarkerSegment.java:141)
at com.sun.imageio.plugins.jpeg.JFIFMarkerSegment.updateFromNativeNode(JFIFMarkerSegment.java:248)
at com.sun.imageio.plugins.jpeg.JFIFMarkerSegment.(JFIFMarkerSegment.java:133)
at com.sun.imageio.plugins.jpeg.JPEGMetadata.setFromNativeTree(JPEGMetadata.java:2184)
at com.sun.imageio.plugins.jpeg.JPEGMetadata.setFromTree(JPEGMetadata.java:2149)
at com.twelvemonkeys.imageio.plugins.jpeg.JPEGImage10MetadataCleaner.cleanMetadata(JPEGImage10MetadataCleaner.java:259)
at com.twelvemonkeys.imageio.plugins.jpeg.JPEGImageReader.getImageMetadata(JPEGImageReader.java:1043)
So for now i will use getSourceCSType
method probably. Therefore I switched it to public
so i can use it. Would it be possible to merge that into master? WDYT?
Progress: Just pushed a fix for the X density out of range issue. Your sample image reads fine now. It was unrelated to the Chroma node reading. It's just another example on how to break the JFIF spec.. sigh
I'm still cheering for the Chroma node. And no, I don't want the getSourceCSType
public in master. Sorry. :-)
.k
Great! Now that i can use the metadata approach there is no need for making getSourceCSType
public anymore. Thanks for your effort and have a nice weekend!
haraldk closed the issue
I'm currently trying to convert a CMYK jpeg to an RGB one while preserving its meta data. Then i recognized that JPEGImageReader.getImageMetadata(...) method always returns null.


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- Model is: [name].Model
- You get the point!
- A view extends Common.Views.Complex
- To show a view call .render()


[dscrd-chips] <jorian> trying to fundchannel gives an error
26b49c7a33d08424ddfd9fab0f7187410ea chan #1: Opening channel: sent ERROR feerate_per_kw 2500 above maximum 1265
then i did ~/lightning$ cli/lightning-cli dev-setfees 2500 2500 2500
{ "immediate" : 2500, "normal" : 2500, "slow" : 2500 }
to make it 2500, and when i fundchannel again, it crashed. this was june 25th though


Solar eclipse: Sabarimala temple to be closed for four hours on Dec 26
The Travancore Devaswom Board (TDB), the apex temple body which manages the hill shrine, said here on Sunday the sanctum santorum would be closed between 7.30 a.m and 11.30 a.m on the day.
by PTIThe Lord Ayyappa temple in Sabarimala, where the two-month-long annual pilgrim season is underway will remain closed for four hours on December 26 due to solar eclipse.
The Travancore Devaswom Board (TDB), the apex temple body which manages the hill shrine, said here on Sunday the sanctum santorum would be closed between 7.30 a.m and 11.30 a.m on the day.
The solar eclipse is from 8.06 a.m to 11.13 a.m on December 26.
The temple would be closed after the customary daily rituals and poojas including 'Neyyabhishekam' and would be reopened after 'punyaham', the purification ritual post the eclipse, temple executive officer said here in a statement.
Sabarimala 'tantri' (head priest) Mahesh Mohanaru had informed the executive officer that the opening of the shrine during the eclipse hours was not appropriate, it said.
The other TDB-run temples including the Malikappuram and Pamba shrines would also be closed during the time of solar eclipse, the statement added.
Meanwhile, the Sabarimala shrine, where the annual Mandalam Makaravilakku pilgrimage began on November 17, is witnessing heavy rush of devotees.


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The equations of motion for the dynamic system can be written as: $$m \begin{bmatrix}1 & 0\\0 & 1\end{bmatrix} \begin{bmatrix} \ddot{r_1} \\ \ddot{r_2} \end{bmatrix} + \frac{2\sqrt{2}EA}{\ell} \begin{bmatrix}1 & 0\\0 & 1\end{bmatrix} \begin{bmatrix} r_1 \\ r_2 \end{bmatrix} = \begin{bmatrix}R_1\\R_2\end{bmatrix} $$
A vibration mode of an oscillating system is a pattern of motion in which all parts of the system move sinusoidally with the same frequency. In order to compute the vibration frequencies for the free motion described by two vibration modes of this dynamic system, the responses are assumed to be periodic. Thus, the displacement vector and acceleration vector can be expressed as $$\begin{bmatrix}r_1\\r_2\end{bmatrix} = \cos(\omega t) \begin{bmatrix}r_{10}\\r_{20}\end{bmatrix}$$ $$\begin{bmatrix} \ddot{r_1}\\ \ddot{r_2}\end{bmatrix} = -\omega^2 \cos(\omega t) \begin{bmatrix}r_{10}\\r_{20}\end{bmatrix}$$ where $r_{10}$ and $r_{20}$ are constants.
Replacing back to the equations of motion and setting the forces $(R_1, R_2)$ to zero for free vibrations: $$\omega^2 m \begin{bmatrix}1 & 0\\0 & 1\end{bmatrix} \begin{bmatrix}r_{10}\\r_{20}\end{bmatrix} = \frac{2\sqrt{2}EA}{\ell} \begin{bmatrix}1 & 0\\0 & 1\end{bmatrix} \begin{bmatrix}r_{10}\\r_{20}\end{bmatrix} $$ The two equations are uncoupled and can be solved for $\omega_1$ and $\omega_2$ independently. It immediatly means that $r_1$ and $r_2$ can vibrate independently and hence the mode shapes are $\phi_1=[1,0]$ and $\phi_2=[0,1]$. In any case the natural frequencies could also be determined solving an eigenvalue problem (even though this is not necessary in this case). In this eigenvalue problem, $\omega^2$ is the eigenvalue and $\begin{bmatrix}r_{10}\\r_{20}\end{bmatrix}$ the associated eigenvector.
Solving for eigenvalues and eigenvectors: $$\begin{bmatrix} \frac{2\sqrt{2}EA}{\ell} - \omega^2 m & 0\\0 & \frac{2\sqrt{2}EA}{\ell} - \omega^2 m\end{bmatrix} \begin{bmatrix}r_{10}\\r_{20}\end{bmatrix} = 0$$ Therefore, the natural vibration frequencies can be found by setting the determinant of the matrix to zero: $$ \det \begin{bmatrix} \frac{2\sqrt{2}EA}{\ell} - \omega^2 m & 0\\0 & \frac{2\sqrt{2}EA}{\ell} - \omega^2 m\end{bmatrix} = 0 $$ Finally, the square of the vibration frequencies for the two mode shapes are: $$ \omega_i^2 = \frac{2\sqrt{2}EA}{m\ell} $$ The two degrees of freedom are uncoupled. In addtion, it also happens that both natural frequencies are coincident, i.e. both natural frequencies are $ \omega_1=\omega_2 = \sqrt{\frac{2\sqrt{2}EA}{m\ell}} \space$.


{% extends "base.html" %} {# Copyright 2014 Google Inc. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. #} {% block title %}Error Reasons{% endblock %} {% block subheader %}Error Reasons{% endblock %} {% block html_block %} {% if tpl_errors|length > 0 %}
| Timestamp (UTC) | User Email (optional) | Reason | |
|---|---|---|---|
| {{ error_reason.error_datetime }} | {% if error_reason.user_email is not none %}{{ error_reason.user_email }} | {% else %}{% endif %} | {{ error_reason.error_reason }} |


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|---|---|---|---|---|---|---|---|---|---|
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| following.js | 81.82% | 45/55 | 94.29% | 33/35 | 52.94% | 9/17 | 43.75% | 7/16 | |
| followingActions.js | 35.71% | 15/42 | 20% | 4/20 | 6.67% | 1/15 | 26.67% | 8/30 | |
| headline.js | 58.82% | 10/17 | 50% | 4/8 | 16.67% | 1/6 | 50% | 6/12 | |
| headline.spec.js | 75% | 21/28 | 75% | 6/8 | 71.43% | 5/7 | 72% | 18/25 | |
| main.js | 94.12% | 16/17 | 100% | 4/4 | 50% | 1/2 | 90% | 9/10 | |
| mainActions.js | 64.71% | 11/17 | 100% | 4/4 | 42.86% | 3/7 | 50% | 6/12 | |
| mainReducer.js | 91.67% | 33/36 | 73.91% | 17/23 | 100% | 4/4 | 85% | 17/20 | |
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| nav.js | 62.5% | 10/16 | 66.67% | 4/6 | 16.67% | 1/6 | 58.33% | 7/12 |


Animals-How they survive themselves in nature and lifestyle of them.
Description
Nature-Protect nature and it will protect you from all the disasters.
Description
How humans should take safety measures to protect nature.
Save the Nature
Protect Nature
During our day to day routine we are using alot of natural resources and there is alot of harm
is done to the nature.But still the nature returnin us alot.T protect our environment our
foundation creates awareness among all the cities and other corporate organizations.
"SAVE NATURE IT WILL SAVE U."


Hello {{ name }} you are {{ age }} years old. { if age > 15 } You are over the age of 15! { endif } { for user in users } {{ user.name }} is {{ user.age }} years old. { endfor } {{ 'string' | md5 }}


import {MatRadioModule} from '@angular/material/radio';
MatRadioGroup
A group of radio buttons. May contain one or more <mat-radio-button>
elements.
Selector: mat-radio-group
Exported as: matRadioGroup| Name | Description |
|---|---|
@Input()
|
Theme color for all of the radio buttons in the group. |
@Input()
|
Whether the radio group is disabled |
@Input()
|
Whether the labels should appear after or before the radio-buttons. Defaults to 'after' |
@Input()
|
Name of the radio button group. All radio buttons inside this group will use this name. |
@Input()
|
Whether the radio group is required |
@Input()
|
The currently selected radio button. If set to a new radio button, the radio group value will be updated to match the new selected button. |
@Input()
|
Value for the radio-group. Should equal the value of the selected radio button if there is a corresponding radio button with a matching value. If there is not such a corresponding radio button, this value persists to be applied in case a new radio button is added with a matching value. |
@Output()
|
Event emitted when the group value changes.
Change events are only emitted when the value changes due to user interaction with
a radio button (the same behavior as |
MatRadioButton
Selector: mat-radio-button
Exported as: matRadioButton| Name | Description |
|---|---|
@Input('aria-describedby')
|
The 'aria-describedby' attribute is read after the element's label and field type. |
@Input('aria-label')
|
Used to set the 'aria-label' attribute on the underlying input element. |
@Input('aria-labelledby')
|
The 'aria-labelledby' attribute takes precedence as the element's text alternative. |
@Input()
|
Whether this radio button is checked. |
@Input()
|
Theme color of the radio button. |
@Input()
|
Whether ripples are disabled. |
@Input()
|
Whether the radio button is disabled. |
@Input()
|
The unique ID for the radio button. |
@Input()
|
Whether the label should appear after or before the radio button. Defaults to 'after' |
@Input()
|
Analog to HTML 'name' attribute used to group radios for unique selection. |
@Input()
|
Whether the radio button is required. |
@Input()
|
The value of this radio button. |
@Output()
|
Event emitted when the checked state of this radio button changes.
Change events are only emitted when the value changes due to user interaction with
the radio button (the same behavior as |
|
Tabindex to which to fall back to if no value is set. |
|
ID of the native input element inside |
|
The parent radio group. May or may not be present. |
| focus | |
|---|---|
|
Focuses the radio button. |
|
| Parameters | |
|
options? FocusOptions
|
|
|
origin? FocusOrigin
|
|
MatRadioChange
Change event object emitted by radio button and radio group.
| Name | Description |
|---|---|
|
The radio button that emits the change event. |
|
The value of the radio button. |
MatRadioDefaultOptions
| Name | Description |
|---|---|
|
| MAT_RADIO_DEFAULT_OPTIONS_FACTORY | |
|---|---|
| Returns | |
MatRadioDefaultOptions
|
|
MAT_RADIO_GROUP
Injection token that can be used to inject instances of MatRadioGroup
. It serves as
alternative token to the actual MatRadioGroup
class which could cause unnecessary
retention of the class and its component metadata.
const MAT_RADIO_GROUP: InjectionToken<MatRadioGroup>;
MAT_RADIO_DEFAULT_OPTIONS
const MAT_RADIO_DEFAULT_OPTIONS: InjectionToken<MatRadioDefaultOptions>;