XML::Compile::WSDL11 understands WSDL version 1.1. An WSDL file defines a set of
messages to be send and received over (SOAP) connections. This involves encoding
of the message to be send into XML, sending the message to the server, collect
the answer, and finally decoding the XML to Perl.
As end-user, you do not have to worry about the complex details of the messages
and the way to exchange them: it's all simple Perl for you. Also, faults are
handled automatically. The only complication you have to worry about is to shape
a nested HASH structure to the sending message structure.
XML::Compile::Schema::template() may help you.
When the definitions are spread over multiple files you will need to use
addWSDL() (wsdl) or importDefinitions() (additional schema's) explicitly.
Usually, interreferences between those files are broken. Often they reference
over networks (you should never trust). So, on purpose you must explicitly load
the files you need from local disk! (of course, it is simple to find one-liners
as work-arounds, but I will to tell you how!)
WWW: http://search.cpan.org/dist/XML-Compile-WSDL11/
Fix build of mail/evolution on 9.x and 8.x after webkit updates [1]
PR: 196079 [1]
196706 [1]
Submitted by: truckman@, lawrence chen <beastie@tardisi.com>
gitinspector is a statistical analysis tool for git repositories. The
defaut analysis shows general statistics per author, which can be
complemented with a timeline analysis that shows the workload and
activity of each author. Under normal operation, it filters the
results to only show statistics about a number of given extensions and
by default only includes source files in the statistical analysis.
Differential Revision: https://reviews.freebsd.org/D1529
Reviewed by: bapt
Approved by: bapt
The Vowpal Wabbit (VW) project is a fast out-of-core learning system
sponsored by Microsoft Research and (previously) Yahoo! Research.
There are two ways to have a fast learning algorithm: (a) start with a slow
algorithm and speed it up, or (b) build an intrinsically fast learning
algorithm. This project is about approach (b), and it's reached a state
where it may be useful to others as a platform for research and experimentation.
There are several optimization algorithms available with the baseline
being sparse gradient descent (GD) on a loss function (several are available).
WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
