module Fiddle::Importer
A DSL that provides the means to dynamically load libraries and build modules around them including calling extern functions within the C library that has been loaded.
Example¶ ↑
require 'fiddle' require 'fiddle/import' module LibSum extend Fiddle::Importer dlload './libsum.so' extern 'double sum(double*, int)' extern 'double split(double)' end
Attributes
Public Instance Methods
Creates a global method from the given C signature
using the
given opts
as bind parameters with the given block.
# File ext/fiddle/lib/fiddle/import.rb, line 188 def bind(signature, *opts, &blk) name, ctype, argtype = parse_signature(signature, type_alias) h = parse_bind_options(opts) case h[:callback_type] when :bind, nil f = bind_function(name, ctype, argtype, h[:call_type], &blk) else raise(RuntimeError, "unknown callback type: #{h[:callback_type]}") end @func_map[name] = f #define_method(name){|*args,&block| f.call(*args,&block)} begin /^(.+?):(\d+)/ =~ caller.first file, line = $1, $2.to_i rescue file, line = __FILE__, __LINE__+3 end module_eval(" def #{name}(*args,&block) @func_map['#{name}'].call(*args,&block) end ", file, line) module_function(name) f end
Returns a new closure wrapper for the name
function.
-
ctype
is the return type of the function -
argtype
is an Array of arguments, passed to the callback function -
call_type
is the abi of the closure -
block
is passed to the callback
See Fiddle::Closure
# File ext/fiddle/lib/fiddle/import.rb, line 308 def bind_function(name, ctype, argtype, call_type = nil, &block) abi = CALL_TYPE_TO_ABI[call_type] closure = Class.new(Fiddle::Closure) { define_method(:call, block) }.new(ctype, argtype, abi) Function.new(closure, argtype, ctype, abi, name: name) end
Creates a class to wrap the C struct with the value ty
See also #struct
# File ext/fiddle/lib/fiddle/import.rb, line 239 def create_value(ty, val=nil) s = struct([ty + " value"]) ptr = s.malloc() if( val ) ptr.value = val end return ptr end
Creates an array of handlers for the given libs
, can be an
instance of Fiddle::Handle, Fiddle::Importer, or will create a new instance of
Fiddle::Handle using Fiddle#dlopen
Raises a DLError if the library cannot be loaded.
See Fiddle#dlopen
# File ext/fiddle/lib/fiddle/import.rb, line 75 def dlload(*libs) handles = libs.collect{|lib| case lib when nil nil when Handle lib when Importer lib.handlers else begin Fiddle.dlopen(lib) rescue DLError raise(DLError, "can't load #{lib}") end end }.flatten() @handler = CompositeHandler.new(handles) @func_map = {} @type_alias = {} end
Creates a global method from the given C signature
.
# File ext/fiddle/lib/fiddle/import.rb, line 164 def extern(signature, *opts) symname, ctype, argtype = parse_signature(signature, type_alias) opt = parse_bind_options(opts) f = import_function(symname, ctype, argtype, opt[:call_type]) name = symname.gsub(/@.+/,'') @func_map[name] = f # define_method(name){|*args,&block| f.call(*args,&block)} begin /^(.+?):(\d+)/ =~ caller.first file, line = $1, $2.to_i rescue file, line = __FILE__, __LINE__+3 end module_eval(" def #{name}(*args, &block) @func_map['#{name}'].call(*args,&block) end ", file, line) module_function(name) f end
The Fiddle::CompositeHandler instance
Will raise an error if no handlers are open.
# File ext/fiddle/lib/fiddle/import.rb, line 261 def handler (@handler ||= nil) or raise "call dlload before importing symbols and functions" end
Returns a new Fiddle::Function instance at the
memory address of the given name
function.
Raises a DLError if the name
doesn't exist.
-
argtype
is an Array of arguments, passed to thename
function. -
ctype
is the return type of the function -
call_type
is the ABI of the function
See also Fiddle:Function.new
See Fiddle::CompositeHandler#sym and Fiddle::Handler.sym
# File ext/fiddle/lib/fiddle/import.rb, line 291 def import_function(name, ctype, argtype, call_type = nil) addr = handler.sym(name) if( !addr ) raise(DLError, "cannot find the function: #{name}()") end Function.new(addr, argtype, ctype, CALL_TYPE_TO_ABI[call_type], name: name) end
Returns a new Fiddle::Pointer instance at the
memory address of the given name
symbol.
Raises a DLError if the name
doesn't exist.
See Fiddle::CompositeHandler#sym and Fiddle::Handle.sym
# File ext/fiddle/lib/fiddle/import.rb, line 271 def import_symbol(name) addr = handler.sym(name) if( !addr ) raise(DLError, "cannot find the symbol: #{name}") end Pointer.new(addr) end
Returns a new instance of the C struct with the value ty
at
the addr
address.
# File ext/fiddle/lib/fiddle/import.rb, line 251 def import_value(ty, addr) s = struct([ty + " value"]) ptr = s.new(addr) return ptr end
Returns the sizeof ty
, using Fiddle::CParser#parse_ctype to
determine the C type and the appropriate Fiddle constant.
# File ext/fiddle/lib/fiddle/import.rb, line 104 def sizeof(ty) case ty when String ty = parse_ctype(ty, type_alias).abs() case ty when TYPE_CHAR return SIZEOF_CHAR when TYPE_SHORT return SIZEOF_SHORT when TYPE_INT return SIZEOF_INT when TYPE_LONG return SIZEOF_LONG when TYPE_LONG_LONG return SIZEOF_LONG_LONG when TYPE_FLOAT return SIZEOF_FLOAT when TYPE_DOUBLE return SIZEOF_DOUBLE when TYPE_VOIDP return SIZEOF_VOIDP else raise(DLError, "unknown type: #{ty}") end when Class if( ty.instance_methods().include?(:to_ptr) ) return ty.size() end end return Pointer[ty].size() end
Creates a class to wrap the C struct described by signature
.
MyStruct = struct ['int i', 'char c']
# File ext/fiddle/lib/fiddle/import.rb, line 217 def struct(signature) tys, mems = parse_struct_signature(signature, type_alias) Fiddle::CStructBuilder.create(CStruct, tys, mems) end
Sets the type alias for alias_type
as orig_type
# File ext/fiddle/lib/fiddle/import.rb, line 98 def typealias(alias_type, orig_type) @type_alias[alias_type] = orig_type end
Creates a class to wrap the C union described by signature
.
MyUnion = union ['int i', 'char c']
# File ext/fiddle/lib/fiddle/import.rb, line 225 def union(signature) tys, mems = parse_struct_signature(signature, type_alias) Fiddle::CStructBuilder.create(CUnion, tys, mems) end
Private Instance Methods
# File ext/fiddle/lib/fiddle/import.rb, line 136 def parse_bind_options(opts) h = {} while( opt = opts.shift() ) case opt when :stdcall, :cdecl h[:call_type] = opt when :carried, :temp, :temporal, :bind h[:callback_type] = opt h[:carrier] = opts.shift() else h[opt] = true end end h end


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QuantConnect provides an environment for you to perform exploratory research and brainstorm new ideas for your algorithms. We simply call this the research environment. Here you can run Python CLI commands and explore the data directly from a Jupyter notebook installed in QuantConnect.
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A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes.[1] Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1..
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Logistic regression sounds similar to linear regression but is actually focused on problems involving categorization instead of quantitative forecasting. Here the output variable values are discrete and finite rather than continuous and with infinite values as with linear regression. The goal of logistic regression is to categorize whether an instance of an input variable either fits within a category or not. The output of logistic regression is a value between 0 and 1. Results closer to 1 indicate that the input variable more clearly fits within the category. Results closer to 0 indicate that the input variable likely does not fit within the category. Logistic regression is often used to answer clearly defined yes or no questions. Will a customer buy again? Is a buyer credit worthy? Will the prospect become a customer? Predicting the answer to these questions can spawn a series of actions within the business process which can help drive future revenue.
Classification and regression trees use a decision to categorize data. Each decision is based on a question related to one of the input variables. With each question and corresponding response, the instance of data gets moved closer to being categorized in a specific way. This set of questions and responses and subsequent divisions of data create a tree-like structure. At the end of each line of questions is a category. This is called the leaf node of the classification tree. These classification trees can become quite large and complex. One method of controlling the complexity is through pruning the tree or intentionally removing levels of questioning to balance between exact fit and abstraction. A model that works well with all instances of input values, both those that are known in training and those that are not, is paramount. Preventing overfitting of this model requires a delicate balance between exact fit and abstraction. A variant of classification and regression trees is called random forests. Instead of constructing a single tree with many branches of logic, a random forest is a culmination of many small and simple trees that each evaluate the instances of data and determine a categorization. Once all of these simple trees complete their data evaluation, the process merges the individual results to create a final prediction of the category based on the composite of the smaller categorizations. This is commonly referred to as an ensemble method. These random forests often do well at balancing exact fit and abstraction and have been implemented successfully in many business cases. In contrast to logistic regression, which focuses on a yes or no categorization, classification and regression trees can be used to predict multivalue categorizations. They are also easier to visualize and see the definitive path that guided the algorithm to a specific categorization.
K-means clustering focuses on creating groups of related attributes. These groups are referred to as clusters. Once these clusters are created, other instances can be evaluated against them to see where they best fit. This technique is often used as part of data exploration. To start, the analyst specifies the number of clusters. The K-means cluster process breaks the data into that number of clusters based on finding data points with similarities around a common hub, called the centroid. These clusters are not the same as categories because initially they do not have business meaning. They are just closely related instances of input variables. Once these clusters are identified and analyzed, they can be converted to categories and provided a name that has business meaning. K-means clustering is often used because it is simple to use and explain and because it is fast. One area to note is that k-means clustering is extremely sensitive to outliers. These outliers can significantly shift the nature and definition of these clusters and ultimately the results of analysis.
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trait CapsuleManagerTrait
Methods
| void |
setAsGlobal()
Make this capsule instance available globally. |
|
| Container |
getContainer()
Get the IoC container instance. |
|
| void |
setContainer(Container $container)
Set the IoC container instance. |
Details
at line 43
void
setAsGlobal()
Make this capsule instance available globally.
at line 53
Container
getContainer()
Get the IoC container instance.
at line 64
void
setContainer(Container $container)
Set the IoC container instance.


Adversarial thoughts
Posted: 2017-04-16 , Modified: 2017-04-16
Tags: neural nets, uncertainty, aaml
Posted: 2017-04-16 , Modified: 2017-04-16
Tags: neural nets, uncertainty, aaml
See also
Some explanations for adversarial examples.
Firstly, I think adversarial examples aren’t very surprising: we shouldn’t expect neural nets to do well against an adversary if we didn’t train against it. Neural nets will do the “laziest” thing, which does not involve the “broader” conceptual class we want them to learn (ex. everything close to a ‘5’ is also a ‘5’); vanilla training doesn’t communicate to them this broader class.
That said, why can’t neural nets do well after adversarial training?
Let \(A_a\) be a set of “adversarial” modifications to inputs \(x\). We say that an algorithm adversarially-PAC learns \((x,y)\sim D\) if after poly samples and time, it produces \(f\in F\) such that \[ \Pj_{x,y} (\max_a L(f(A_a(x)), y)\ne y) \le \ep \] in the realizable case, or \[ \Pj_{x,y} (\max_a L(f(A_a(x)), y)\ne y) \le \min_{f\in F} \Pj_{x,y} (\max_a L(f(A_a(x)), y)\ne y) + \ep \] in the agnostic case, where \(L\) is the loss function (ex. 0-1).
We probably also want to assume access to an oracle, which given \(f\) and \(x\), produces some \(a\) that maximizes \(L(f(A_a(x)),y)\).
Some work has been done here: see Yishay Mansour, Robust learning and inference. Cf. also boosting vs. game theory.
Example: \(A_a(x) = x+a\), restricted to \(\ve{a}\le \ep\).
Now there are settings where the adversarial setting isn’t harder - for example, for SVM, in the above example, if there is still a margin \(\ga\) between positive and negative examples even after adversarial perturbation, then we’ll make at most \(\rc{\ga^2}\) mistakes by perceptron analysis, which doesn’t care about the distribution.
But this is not the case for neural nets. (The fact that we can get training error to 0 suggests that there may be some “margin” at play (cf. Telgarsky). However, we can’t get to 0 training error if we include adversarial examples!)
Can we study a toy example here like dictionary learning? Generate \(Ah = x\), there is a SVM \(\sign(w^Th)=y\). Make it robust to \(+a\).
(This doesn’t seem to capture what’s going on though in \(L^2\) - here the adversary’s best bet is to change in the direction \(a_i\) where \(i=\amax_i |w_i|\), which would in fact change the sign of \(w^Th\).)
It’s not surprising that adversarial examples exist. A better question is why we can’t “train them away” by normal training methods, and what can we do to fix this.
I think there are 2 possibilities.
The “margin” analysis above is in favor of (2). (1) could still be true if there are e.g. exponentially more choices of parameters that would do well on normal examples than choices that also do well on the adversarial examples. But this isn’t enough to explain it, because adversarial training stalls eventually.
We can talk about \(L^\iy\) or \(L^2\) adversarial examples. Also whatever threshold we choose, there shouldn’t be human-adversarial examples below that threshold. (I don’t think this is so much of an issue now.)
Just take a hodgepodge of things that reduce adversarial examples, including things that detect and reject adversarial examples. For example,
However, this would be an arms race: likely, for many of these, you can find adversarial examples that pass the test (ex. restrict search to top singular vectors). If this were not the case, that would be very interesting.
Are there things like clipping, normalizing, that you do on input before feeding into the network that could help? (Ex. can damping the smaller singular vectors help?)
These are interesting questions but not as satisfying an approach.
This doesn’t seem to help.
One question: after doing adversarial training for a while, does the NN still do well against the original adversarial examples? If not, then it’s catastrophically forgetting. If it is forgetting, this should be easy to fix.
Train a bunch of NN’s on the real examples and then use majority vote.
Problem: this doesn’t work.
Are we somehow not exploring the space of NN’s which do well on the real examples? Can we use Fisher information, Langevin to sample better?
Probably we can’t do much better. Also the existence of a universal perturbation and the fact that adversarial examples from a “linear” model transfer suggest that most NN’s which do well on real examples suffer from the same adversarial examples. (cf. there being exponentially more NN’s which are weak against these adversarial examples, then good.)
The objective/regularization is wrong. Ex. we should be encouraging sparsity, using exponentiated gradient/multiplicative weights, etc.
Perhaps the Lipschitz constant is just really large. Can we train a NN to have small Lipschitz constant in the correct norm? (Note this can be challenging because for example the \(\iy\to 2\) norm is difficult to compute; even approximating it takes a linear program, which is too computationally intensive to do at every step.)
Also if we can’t find a NN with small Lipschitz constant why not? Does it not exist or is there something wrong with the geometry of the optimization problem?
Path norm, batch normalization seem attractive here.
ReLUs seem like a very bad idea - the adversary can just keep increasing. But is the problem more for
The first suggests capping - having \(y=ReLU(x)\) grow unlimitedly is a bad idea. (We still have the same problem for sigmoids.) The second suggests that we are getting past thresholds by adding a lot of correlated noise, so we should zero out small ReLU(x)’s.
In the brain we don’t have thresholds exceeded by contributions from hundreds of neurons - there’s some kind of attenuation or normalization (of activations because of limited energy?) that prevents this. What if we just take the top \(k\) activations? (During training or testing?)
What about quantization/binarization? For MNIST, binarizing all pixels to be 0 or 1 is fine, and helps against adversarial examples (because small norm changes don’t have much effect - although you should consider other attacks here!). But this is in some sense cheating because MNIST is basically black-white. Can we binarize/quantize intermediate features?
I.e. are we just screwed after the first layer because it somehow destroyed the input?
Ex. consider the toy problem of dictionary learning. If the layers of a NN were really doing sparse coding, would this be a problem? I.e. does sparse coding suffer from the same problem? Or are NN really not doing sparse coding? While we sort-of say it is, I don’t think it is at all! For the convolutional NN, the convolution isn’t computing \(h\) such that \(Ah=x\). In fact, the overlaps between different patches might make things badly conditioned.
If we actually did DL on the first layer, then solved the sparse recovery problem for every input, would we still have the same problem?
Have the network be able to “abstain” (like a confidence score). This should be similar in principle to training detection between real and adversarial examples.
What if we use RBF’s on the first layer? Even something like pretraining to find the right RBF’s, and keeping them fixed. The idea is that RBF’s are very conservative. Maybe we’ll need the \(L^\iy\) analogue if we’re protecting against that…
A vital thing that’s missing in current NN’s is back-and-forth between higher and lower layers. Humans can reinterpret lower-level data when they see a higher-level pattern.
Also humans have some kind of idea of a “prototype” of a digit - another instance must have its curves match up with that prototype in some fashion. There are these ideas of strokes, etc. Something hierarchical, probabilistic model? cf. Tenenbaum.
It may be that there needs to be something fundamentally new in the architecture - something more principled. Anything from neuroscience?


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Infopocalyse 2020
Building a Secure Chat App with AngularJS and Ionic
Josh Teague
Engaging Engineering Teams in Design
K. Devin McIntyre
Angular 1.x Performance Pitfalls
Keith J Grant
CSS from the Future
Ken Kousen
Advanced Android: Fragments & Services
Gradle Recipes for Android
Kent C. Dodds
Testing React
ES6 (and beyond) Workshop
Kevin Old
Deploying GraphQL in a Serverless Architecture
Kristin Marsicano
Activities in the Wild: Exploring the Android Activity Lifecycle
Kristin Ottofy
Break & Rasberry Pi Demo (IoT Lounge)
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Introduction to Microsoft Graph with Node.js
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Kyle Simpson
Advanced JavaScript: Foundations
Lance Gleason
Kotlin for the Busy Android Developer
Larry Garfield
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PHP 7: The New New PHP
Leif Wells
Electron: Building Desktop Apps
Marcus Smith
Swift Memory Management
Mark Mandel
Creating Interactive Multiplayer Experiences with Firebase
Marlene Jaeckel
Creating Connections with Core Location and Map Kit
Matthew Groves
I have a NoSQL toaster
Mike Gorgone
ImprovUX
Mike Hartington
Up and Running with Ionic v2
Ionic 2: A new way to do mobile
Mike Schinkel
Easy Local Development for PHP or WordPress
Minko Gechev
Angular 2 Performance Checklist
Angular 2 Toolset Support
Nick Larsen
Building non trivial things with WebAssembly
Nicole Saidy
Bridging the designer-developer gap
UX patterns for developers
Nic Rosental
Developing Bots for Slack
Parashuram
ReactNative - Under the hood
Patrick Childers
Optimizing app install and user on boarding costs
Patrick Seda
Your Code is Terrible, I Love It!
Paul M. Jones
Action-Domain-Responder: MVC for the Web
PSR-7 and Action-Domain-Responder
Pearl Latteier
Web Components & Polymer: Lego bricks of the web
Peter Pavlovich
Angular Architectures: A roadmap for the hearty traveller
Forming an opinion about Angular Forms in 1.X and 2.0
Rachita Joshi
Creating meaningful HTML and CSS
Raymond Camden
Introduction to LoopBack
Shauvik Roy Choudhary
Testing Android Apps with Software Robots
Shawn Johnson
Diving into Swift for iOS
Stephanie Brubaker
Engaging Engineering Teams in Design
Stephen A Thomas
Leave No One Behind: Building Accessible Web Applications
Stephen M. Carlson
Javascript Software Craftsmanship Part 1
Javascript Software Craftsmanship Part 2
Stephen Schwahn
Creating Robust Cloud Services using Node.js
Summers Pittman
Communicating with Android Devices
Taggart Bowen-Gaddy
React.js Hands-on workshop
Introduction to React.JS
Tamil Periasamy
Let's Get Animated!
Tara Walker
Developing IOT and Smart Devices at Scale with the Cloud
Tim Dorr
React Router 4.0: What's New
Tim Whitacre
Getting Started with Elm Lang
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Prototype to Production - Ember to the Rescue
Wesley Hales
Open Source Web Performance Tooling
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ExD: Designing for Exponential Change
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--- layout: post status: publish published: true title: Add/Remove VS2008 Components after SP1 author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 36 wordpress_url: http://winterdom.com/2008/10/addremovevs2008componentsaftersp1 date: '2008-10-15 19:09:11 +0000' date_gmt: '2008-10-15 19:09:11 +0000' categories: - ".NET" tags: [] comments: [] ---
One of my development Virtual Machines has Visual Studio 2008 installed with SP1. For several reasons, I had not done a complete installation originally, but only certain pieces of it, like Visual C# and Visual Web Developer and some of the Team Developer tools.
A couple days ago turned out that it would’ve been really nice to also have Visual C++ install as well. I figured I could probably install the missing pieces and then apply SP1 again.
When I tried to do this, however, ran into a snag: I fired up the Visual Studio 2008 setup again, went into Add/Remove components and then, when the setup was initializing, I got an error to the effect that some components could not be loaded, and installation would cancel.
After trying a few times I gave up, started an older copy of the VM I had without SP1 installed and so was able to work around it after all, but still, I’m a bit curious. Has anyone else seen this? Is setup just foobar’ed after the installation of SP1?


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THE GINGERBREAD MAN RATING: 7 / 10 --> Good movie
For more movie reviews and screensavers, visit http://www.joblo.com/
PLOT: Philandering hotshot Southern lawyer takes on a simple case of a father stalking/harassing his daughter (whom said lawyer is sleeping with), and succeeds in getting the man put away. Unfortunately for all involved, the nutty father escapes and begins to make the lives of his daughter, the lawyer and his family a living nightmare. His band of religious hillbilly followers help him along his twisted path.
CRITIQUE: Picking up steam like a snowball barreling down a mountainside, this film starts off looking like your basic Southern Grisham "lawyer flick", but slowly crafts itself into a tension-filled thriller, with interesting characters, some relevant and unique stylings via the watchful eye of veteran director Robert Altman, and many stormy scenes succinctly paralleling the film's own brewing tension. Understand that this film is not your everyday thriller, and does take some time to get going, but I personally found the setting, the players, and Bobby Duvall's nutty character interesting enough to keep me tuned into its two-hour stretch. I will admit that I saw the film's "twist" coming up 5th Avenue from frame one, but nonetheless, a couple of curve balls managed to keep me in the game. Downey Jr. also plays a pretty neat supporting jokester, and Branagh drops his British accent for a near-perfect Southern drawl. All in all, this film's got great atmosphere, strong performances, interesting characters, nice style, despite the predictable but enjoyable plot line. Check it out on video, and bring an umbrella...you'll need it.
Little Known Facts about this film and its stars: Word on the street is that director Robert Altman rewrote this film's screenplay so much, that the only recognizable John Grisham story elements left seem to be the loathsome Southern lawyer at the center of a crisis and a sleazy detective following his beat. Grisham only ended up with a "Story by" credit in this film, with the screenplay going to the pseudonym Al Hayes (really Robert Altman). Mary Jo Slater was the casting director of this film. She's actor Christian Slater's mom. Stephen Altman was the production designer of this film. He's director Robert Altman's son, and was also credited for the same on at least six other films by dad. Embeth Davidtz's real name is Gretta Milano (Don't ask me why she changed it?? Very odd choice).
Review Date: June 13, 1999 Director: Robert Altman Writer: Al Hayes (really Robert Altman) Producer: Jeremy Tannenbaum Actors: Kenneth Branagh as Rick Magruder Robert Duvall as Dixon Doss Embeth Davidtz as Mallory Doss Robert Downey Jr. as Clyde Pell Genre: Thriller Year of Release: 1998 --------------------------------------- JoBlo's Movie Emporium http://www.joblo.com --------------------------------------- (c) 1999 Berge Garabedian
The review above was posted to the
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Related links: index of all rec.arts.movies.reviews reviews


Configuring Puppet Dashboard
This is a chapter of the Puppet Dashboard 1.2 manual.
Navigation
- Installing Dashboard
- Upgrading Dashboard
- Configuring Dashboard
- Maintaining Dashboard
- Using Dashboard
- Rake API
Overview
Now that you’ve installed Dashboard and prepared it for basic production-level use, you can configure it to:
- Enable advanced features
- Increase security
- Improve performance
- Install plugins
Note that all rake tasks should be performed from a shell in the directory that contains Dashboard’s code. Any relative paths mentioned below refer to locations within this directory. If you are running Dashboard in the recommended “production” environment, note that Rails does not consider production its default environment, and you must specify it manually with the RAILS_ENV
environment variable when running any rake tasks.
Advanced Features
By default, Dashboard only responds to requests from a user or a puppet master. However, if you allow it to pull data from your puppet master, you can enable two extra features: the inventory service, and the file viewer.
Generating Certs and Connecting to the Puppet Master
Puppet uses SSL certificates to control who can make requests to the puppet master, so Dashboard has to obtain a signed cert before asking for facts or files. To do this, edit config/settings.yml
to ensure that the ca_server
and ca_port
settings match the address and port of your puppet master, then run the following commands:
$ sudo -u puppet-dashboard rake cert:create_key_pair
$ sudo -u puppet-dashboard rake cert:request
You’ll need to sign the certificate request on the master by running puppet cert sign dashboard
. Then, from Dashboard’s directory again, run:
$ sudo -u puppet-dashboard rake cert:retrieve
Enabling Inventory Support
With inventory support, Dashboard can display a complete list of facts on each node’s detail page. It also adds a new “Inventory Search” page which can search your entire site for nodes matching a fact query.
Requirements: To use the inventory, you must be using Puppet 2.6.7 or later, configured to provide the inventory service. If you are running Puppet 2.7.12 or later, you have the option of using PuppetDB instead of the inventory_active_record
backend.
Once the puppet master is properly configured with a database-backed inventory, edit your puppet master’s auth.conf
file to grant Dashboard find and search access to /facts:
path /facts
auth yes
method find, search
allow dashboard
Then, edit Dashboard’s config/settings.yml
to set enable_inventory_service
to true
and point inventory_server
and inventory_port
to your puppet master. Restart Dashboard, and node pages should now contain lists of facts.
Enabling the Filebucket Viewer
With the filebucket viewer, Dashboard can display the contents of different file versions when you click on MD5 checksums in reports.
Requirements: To use the filebucket viewer, you must be using Puppet 2.6.5 or later and your agent nodes must be configured to back up all files to a remote filebucket; this is done in your puppet master’s site.pp
manifest, where you must define a filebucket resource named “main”…
filebucket { "main":
server => "{your puppet master}",
path => false,
}
…and set a global resource default of…
File { backup => "main" }
If you are using inspect reports for a compliance workflow, you must also set archive_files = true
in each agent’s puppet.conf
.
Once the site manifest has been properly configured, edit Dashboard’s config/settings.yml
to set use_file_bucket_diffs
to true
and point file_bucket_server
and file_bucket_port
to your puppet master. Restart Dashboard, and you should be able to view the contents of any file mentioned in a report by clicking on its MD5 checksum. Diffs are not currently enabled, but will appear in a future version of Dashboard.
Security
As Dashboard provides access to sensitive information and can make changes to your Puppet-managed infrastructure, you’ll need some way to restrict access to it. Dashboard does not yet provide authentication or authorization, so you’ll need to use external tools to secure it. Some options include:
- Host firewalling — The Dashboard server’s firewall (e.g.
iptables
) can be used to limit which hosts can access the port Dashboard runs on. stunnel
orssh
tunneling — You can use tunneling to provide an encrypted connection between hosts, e.g. if the Puppet Master and Puppet Dashboard are running on separate hosts. It can also allow you to access the web interface from a workstation once you’ve restricted access by IP.-
HTTP Basic Authentication — When serving Dashboard via Apache, you can require a username and password to access its URLs by setting authentication rules for
/
in Dashboard’s vhost configuration:<Location "/"> Order allow,deny Allow from <IP_ADDRESS> # your puppet master's IP Satisfy any AuthName "Puppet Dashboard" AuthType Basic AuthUserFile /etc/apache2/htpasswd Require valid-user </Location>
Notice that you need to leave an access exception for your puppet master(s). Although it’s possible to configure Puppet to use a password when connecting to Dashboard (by adding a username and password to Puppet’s
reporturl
and the URL used by theexternal_nodes
script), this currently requires patching Puppet’shttp
report handler; see issue 7173 for more details. -
HTTPS (SSL) Encryption — When serving Dashboard via Apache, you can encrypt traffic between Puppet and the Dashboard. Using this requires a set of signed certificates from the puppet master — see generating certs and connecting to the puppet master for how to obtain them. The example configuration in
ext/passenger/dashboard-vhost.conf
includes a commented-out vhost configured to use SSL. You may need to change the Apache directivesSSLCertificateFile
,SSLCertificateKeyFile
,SSLCACertificateFile
, andSSLCARevocationFile
to the paths of the files created by thecert
rake tasks.If you have Dashboard set up to use HTTPS, you’ll need to add an
https
prefix to theDASHBOARD_URL
in theexternal_node
script and potentially correct the port number (443, by default). You may also need to change theCERT_PATH
,PKEY_PATH
, andCA_PATH
variables if your puppet master’s hostname is notpuppet
or if your ssldir is not/etc/puppet/ssl
.In order for reporting to work correctly via SSL, you will have to be running puppet master via Passenger or some other app server/webserver combination that can handle SSL; reporting to an SSL Dashboard is not supported when running puppet master under WEBrick. You’ll also have to change the
reporturl
setting inpuppet.conf
to start with “https” instead of “http”.This information may be outdated, and is currently being checked for accuracy.
Performance
Puppet Dashboard slows down as it manages more data. Here are ways to make it run faster, from easiest to hardest:
- Run exactly one
delayed_job
worker per CPU core. - Make sure Dashboard is running in a production-quality web server, like Apache with Passenger.
- Make sure Dashboard is running in the production environment. Although Passenger runs Rails apps in production mode by default, other Rails tools may default to the much slower development environment.
- Optimize your database once a month; create a cron job that runs
rake RAILS_ENV=production db:raw:optimize
from your Puppet Dashboard directory. This will reorganize and reanalyze your database for faster queries. - Tune the number of processes Dashboard uses to handle more concurrent requests. If you’re using Apache with Phusion Passenger to serve Dashboard (as covered in the Installing chapter), you can modify the appropriate settings in Dashboard’s vhost file; in particular, pay attention to the
PassengerHighPerformance
,PassengerMaxPoolSize
,PassengerPoolIdleTime
,PassengerMaxRequests
, andPassengerStatThrottleRate
settings. - Regularly prune your old reports; see “cleaning old reports” in the maintenance chapter for more details.
- Run on a machine with a fast, local database.
- Run on a machine with enough processing power and memory.
- Run on a machine with fast backplane, controllers, and disks.
Installing Plugins
Puppet Labs plans to ship a variety of free and commercial plugins for Dashboard, which will add new features to support specific workflows. If you are installing a plugin, it probably came with official packages and its own installation instructions, but some general guidelines follow:
When installing a plugin from an official package, its files should be moved into the proper place with the proper ownership. However, you will probably have to run the db:migrate
rake task after the installation is complete.
To install a plugin from source, rather than a package, you’ll have to know the hardcoded internal name of the plugin. This should be listed in its documentation. Copy the plugin’s directory to vendor/plugins
, rename it to its proper internal name, and chown the directory and its files to the Dashboard user. Then, run the puppet:plugin:install
task, passing the environment you’re using1 and the name of the plugin as variables:
$ sudo -u puppet-dashboard rake puppet:plugin:install PLUGIN=name RAILS_ENV=production
After this, the plugin should be available and functioning. If you’ve been using Git to install and upgrade Dashboard, it should leave all plugin files untouched the next time you upgrade.
Uninstalling Plugins
This section will be filled in at a later date.
Navigation
- Installing Dashboard
- Upgrading Dashboard
- Configuring Dashboard
- Maintaining Dashboard
- Using Dashboard
- Rake API
-
Puppet:plugin:install
runsdb:migrate
at the end. If you run in multiple environments regularly, you’ll need to runrake db:migrate
again for each additional one. ↩


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GRITS :
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GRITS is a cross-functional effort to create a boilerplate codebase to provide to game developers that are looking to get started using Google's technology stack.


---
layout: post
title: Hex clock
date: '2014-06-20T09:06:58-07:00'
tags:
- fromtheinternet
tumblr_url: http://beekley.xyz/post/89368319101/hex-clock
---
Hex clock
A precise hexadecimal color clock that goes the whole 24 hours color range, from #000000 to #235959.


Full featured example or looking for documentation?
1. Default example
2. Insert a custom button in EasyEditor
3. Insert a button and add youtube video in EasyEditor
4. Insert direct image URL in EasyEditor
5. Select a text and wrap it with a HTML tag
6. Select a text and wrap it HTML tag with custom style and class
7. Select a text and add html tag around it with a custom attribute
8. Select a text wrap a block element
9. Upload and insert image into EasyEditor
10. Use fontawesome icon in button html
11. Overwrite default button settings
12. Implementation in form
13. Multiple easy editor in one page / jQuery like calling and chaining
14. Theming easyeditor
15. Dropdown in easy editor (Select a text and change its font family)
16. Destroy easyeditor


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Book trip!


Three Iraqis killed in front of Iranian consulate in Kerbala: sources
BAGHDAD (Reuters) - Iraqi security forces killed three protesters overnight when they opened fire on a crowd that gathered in front of the Iranian consulate in the Shi’ite holy city of Kerbala, security and medical sources said on Monday.
The crowd had tried to set fire to the consulate, the sources said. At least seven people, including six members of the security forces, were injured, the sources added.


C_worked_example01.Rmd
This is an example of how to create a new dataset folder and populate it with equilibrium starting data for a range of baseline malaria level values. The R code used here can also be found in the create_dataset.R file in the vectorpower/inst/ folder.
First load the package (install it first if necessary as shown in the Installation article:
library(vectorpower)
To create a new data set, it is necessary to specify the files to be used to load the parameter values governing the malaria model. Three files are used:
-param_file, which contains the main set of model parameters along with the parameters used to calculate rainfall as a function of time (which controls the seasonality of malaria) -age_file, which specifies the age intervals used for the human population -het_file, which specifies the parameter values controlling biting heterogeneity
In this example, the three parameter files are located in the vectorpower repository itself and their locations input using the system.file() commaand.
param_file=system.file("extdata/", "/param_examples/model_parameters_Mali.txt", package = 'vectorpower', mustWork = TRUE)
age_file=system.file("extdata/", "/param_examples/age_data_145.txt", package = 'vectorpower', mustWork = TRUE)
het_file=system.file("extdata/", "/param_examples/het_data_9.txt", package = 'vectorpower', mustWork = TRUE)
To specify online file locations, use url() as shown:
param_file=url("https://raw.githubusercontent.com/KeithJF82/vectorpower/master/inst/extdata/param_examples/model_parameters_const.txt")
age_file=url("https://raw.githubusercontent.com/KeithJF82/vectorpower/master/inst/extdata/param_examples/age_data_145.txt")
het_file=url("https://raw.githubusercontent.com/KeithJF82/vectorpower/master/inst/extdata/param_examples/het_data_9.txt")
To load from a location on the computer where vectorpower is being run, give the file names and location in the hierarchy of data folders as shown below:
input_folder="C:/Users/YourName/Documents/GitHub/vectorpower/inst/extdata/param_examples"
param_file=paste(input_folder,"model_parameters_Mali.txt",sep="/")
age_file=paste(input_folder,"age_data_145.txt",sep="/")
het_file=paste(input_folder,"het_data_9.txt",sep="/")
The location of the folder where the new data set is to be created must also be specified. If a folder with the same already exists and contains an existing dataset, the dataset files in that folder will be overwritten. In this example, the output location is placed in the inst/extdata sub-folder in the vectorpower package folder (and will overwrite an existing, identical dataset).
output_location = system.file("extdata",package="vectorpower")
dataset_location = "DemoFolder1"
dataset_folder=paste(output_location,dataset_location,sep="/")
To change the output, change output_location to another existing folder, e.g.:
output_location = "C:/Users/YourName/Documents/VPDatasets"
dataset_location = "Demo Folder 1"
dataset_folder=paste(output_location,dataset_location,sep="/")
Ensure that folder names are separated by a “/”. Alternatively, substitute the entire folder name for dataset_folder, e.g.:
dataset_folder="C:/Users/YourName/Documents/VPDatasets/DemoFolder1"
The baseline malaria levels represented by the input data to go in the dataset are set by specifying a vector of values of steady-state annual entomological inoculation rate (EIR). These values will not exactly match the year-round EIR values represented by the data once the time-dependent model has been run; the final dataset will include a file specifying the year-round EIR at equilibrium under the time-dependent model.
In this example, the EIR values are between 50 and 100 infectious bites per person per year:
EIR_values=c(50.0,60.0,70.0,80.0,90.0,100.0)
Next, the integer number of years (from 1-20) for which the time-dependent model should be run to reach equilibrium must be specified. The more years for which it is run, the more stable the equilibrium; if the equilibrium is less stable, the malaria level will vary more significantly from year to year in calculations if the model is run for multiple years. For most purposes, running for at least 5 years is sufficient. In this example, nyears is set to 1 for speed.
nyears=10
With all the parameters specified, the dataset_create function is run. This will first set initial conditions using steady-state calculations, then run the time-dependent model for the specified number of years to reach equilibrium. The results will be output as the dataset’s starting data. The year-round data output from the starting data (assuming no new interventions) will then be calculated and output as an aid to users of the dataset.
data1 <- create_data_folder(dataset_folder=dataset_folder,param_file=param_file,age_file=age_file,het_file=het_file,EIR_values=EIR_values,nyears=nyears)
[Code block above is not currently evaluated.]


Text input element 1
This example shows how a user can enter text into an input element. Three events are demonstrated here:
- An event is triggered whenever the user hits a key.
- An event is triggered whenever the user hits enter.
- An event is triggered whenever the user clicks the "submit" button.


Here you can store HTTP traffic data in the http-types format using a UUID key as shown below:
UUID=`uuidgen`
curl --data-binary @recordings.jsonl https://meeshkan.io/http-storage/$UUID
Then visit the same URL in the browser to claim the data.
See the meeshkan-hosted-http-storage repository for how this service is built.


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A function called when the property value has changed.
This will be invoked when the property value is changed and the comparator indicates that the old value is not equal to the new value.
This will not be called for the initial default value.
A function used to coerce a supplied value into the final value.
This will be called whenever the property value is changed, or when the property is explicitly coerced. The return value will be used as the final value of the property.
This will not be called for the initial default value.
A function used to compare two values for equality.
This is called to determine if the property value has changed.
It should return true
if the given values are equivalent, or
false
if they are different.
If this is not provided, it defaults to the ===
operator.
A factory function used to create the default property value.
This will be called whenever the property value is required, but has not yet been set for a given owner.
The human readable name for the property.
By convention, this should be the same as the name used to define the public accessor for the property value.
This does not have an effect on the property lookup behavior. Multiple properties may share the same name without conflict.
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The options object used to initialize an attached property.


My First Tooltip Plugin
My first paragraph.
Click Here!! LEFT
Click Here!! TOP
Click Here!! RIGHT
Click Here!! BOTTOM


Two companies of 105 Battalion RAF deployed at Sabarimala
by Staff ReporterTwo companies of the 105 Battalion of Rapid Action Force (RAF) based at Mahalingapuram in Coimbatore have been deployed at Sabarimala to strengthen security cover at the hill shrine.
The RAF companies were deployed at Sabarimala on November 15 and they will continue security duty at the temple up to January 20.
Crowd control
A statement issued by the RAF said that approximately 240 personnel would take part in crowd control initiatives from Marakootam to Sannidhanam. They would also involve in activities to ensure cleanliness in and around Pamba and Sannidhanam.
The garbage elimination initiative is implemented in Sannidhanam and surroundings under the ‘Punyam Poongavanam’ project that was inaugurated by Kadakampalli Surendran, Minister for Devasom, Government of Kerala, in the presence of RAF Deputy Commandant G. Dinesh on Sunday.
Under this project, officials of different government departments and Ayyappa devotees together would work between 9 a.m. and 10 a.m. every day to clean the surroundings of Sannidhanam, after which conservancy workers would take over the work.


To get a fuller picture of the performance of the house there are two extra things I'd like to take into account: the amount of sunshine and the amount of wind. Both are significant because we have a lot of south-facing glass (and a lot of draughts!)
Since the house is a complex shape and shaded by the adjecent property by a differing amount seasonally, I know there's no point going for anything more than a crude estimate of insolation. I've been wondering whether I could get a useful approximation from two small (5.5v) PV panels, mounted on the same plane as the windows on either side of the house.
Could I connect these across an analogue pin and read directly to give a sunlight proxy?
I'm less sure about wind infiltration. I've currently got a pair of D18B20 sensors midway through the loft insulation above the same room, but one is next to the eaves (which are vented) and one is further towards the middle of the house. They show a variable temperature difference and I'm hoping to see the readings diverge when there's a lot of wind (and if I perfect the airtightness detail, will no longer diverge when it's windy).
 The other thing I've considered is fitting a PC case fan somewhere so that it gets spun up by the wind entering the house at which point the motor might act as a dynamo and generate a voltage I could measure?
Any thoughts very welcome!
Re: Isolation and wind infiltration monitoring
Here's an example of passive solar in action: you can see the green line (bedroom 1) jumps up for some afternoon sun then drops off again before the heating kicks in at 16:30
I'd like to measure this directly (so I can differentiate the sun from other stuff (bedroom 2 faces north-east... who knows why the temperature suddenly spikes?)