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Choose a directory to install the R tree (R is not just a binary, but has additional data sets, help files, font metrics etc). Let us call this place R_HOME . Untar the source code. This should create directories src , doc , and several more under a top-level directory: change to that top-level directory (At this point North American readers should consult Setting paper size .) Issue the following commands:

(See Using make if your make is not called ‘ make ’.) Users of Debian-based 64-bit systems 4 may need

Then check the built system works correctly by

Failures are not necessarily problems as they might be caused by missing functionality, but you should look carefully at any reported discrepancies. (Some non-fatal errors are expected in locales that do not support Latin-1, in particular in true C locales and non-UTF-8 non-Western-European locales.) A failure in tests/ok-errors.R may indicate inadequate resource limits (see Running R ).

More comprehensive testing can be done by

see file tests/README and Testing a Unix-alike Installation for the possibilities of doing this in parallel. Note that these checks are only run completely if the recommended packages are installed.

If the configure and make commands execute successfully, a shell-script front-end called R will be created and copied to R_HOME /bin . You can link or copy this script to a place where users can invoke it, for example to /usr/local/bin/R . You could also copy the man page R.1 to a place where your man reader finds it, such as /usr/local/man/man1 . If you want to install the complete R tree to, e.g., /usr/local/lib/R , see Installation . Note: you do not need to install R: you can run it from where it was built.

You do not necessarily have to build R in the top-level source directory (say, TOP_SRCDIR ). To build in BUILDDIR , run

and so on, as described further below. This has the advantage of always keeping your source tree clean and is particularly recommended when you work with a version of R from Subversion. (You may need make to allow this, and you will need no spaces in the path to the build directory. It is unlikely to work if the source directory has previously been used for a build.)

Now rehash if necessary, type R , and read the R manuals and the R (files FAQ or doc/manual/R-FAQ.html , or Saint Laurent Glitter boots 5OifO
which always has the version for the latest release of R).

The FormRequest class adds a new argument to the constructor. The remaining arguments are the same as for the Request class and are not documented here.

The FormRequest objects support the following class method in addition to the standard Request methods:

Returns a new FormRequest object with its form field values pre-populated with those found in the HTML <form> element contained in the given response. For an example see Using FormRequest.from_response() to simulate a user login .

The policy is to automatically simulate a click, by default, on any form control that looks clickable, like a <input type="submit"> . Even though this is quite convenient, and often the desired behaviour, sometimes it can cause problems which could be hard to debug. For example, when working with forms that are filled and/or submitted using javascript, the default from_response() behaviour may not be the most appropriate. To disable this behaviour you can set the dont_click argument to True . Also, if you want to change the control clicked (instead of disabling it) you can also use the clickdata argument.


Using this method with select elements which have leading or trailing whitespace in the option values will not work due to a Romika Womens Romika Cassie 24 Flat QS9uHXQG
, which should be fixed in lxml 3.8 and above.

The other parameters of this class method are passed directly to the FormRequest constructor.

New in version 0.10.3: The parameter.

New in version 0.17: The parameter.

New in version 1.1.0: The parameter.

New in version 1.1.0: The parameter.

If you want to simulate a HTML Form POST in your spider and send a couple of key-value fields, you can return a FormRequest object (from your spider) like this:

It is usual for web sites to provide pre-populated form fields through <input type="hidden"> elements, such as session related data or authentication tokens (for login pages). When scraping, you’ll want these fields to be automatically pre-populated and only override a couple of them, such as the user name and password. You can use the Gianvito Rossi Platform Sandals Suzie suede kxeMbo
method for this job. Here’s an example spider which uses it:

If you'd like to delete an application, you may do so at any time by visiting the application page and pressing the 'Delete application' button. You'll be asked to confirm your change. Please note that once you delete an application, we cannot recover it. You will also lose all images, concepts and models associated with that application. Proceed with caution .

Proceed with caution

The Clarifai API supports many languages in addition to English. When making a predict api request , you can pass in the language you would like the concepts returned in.

The only public model which supports languages other than English is the General model . If you make a predict request using a language other than English on a public model other than General, you will receive an error.

When you create a new Application, you must specify a default language. This will be the default language concepts are returned in when you do not explicitly set a language in an API request. You cannot change the default language. You can however change languages per request.

You can predict concepts in a language other then the Application's default, by explicitly passing in the language. Here is how you predict concepts in Chinese:

Show All

You can search for concepts in other languages even if the default language of your application is English. When you add inputs to your application, concepts are predicted for every language. Here is an example of searching for '人' which is simplified Chinese for 'people'.

You can also search for concepts in a different language:

Show All

Clarifai’s Apple SDK enables machine learning directly on your device, bypassing the traditional requirement of internet connectivity and extensive computing power. It makes it easy to use image and video recognition on device and in real-time.

The Apple SDK is currently available in Public Preview. You can get more information on installing the sdk here.

The SDK is built around a simple idea. You give inputs (images) to the library and it returns predictions (concepts). You need to add inputs to make predictions on it, and/or to train custom models using the inputs as training data.

The sections below will showcase how to add Inputs on your device and tag them with concepts in order to train a custom model.

All inputs are created from a DataAsset object in the Apple SDK. A Data Asset is a container for the asset in question, plus metadata related to it. You can create a DataAsset initialized with an Image on Device or from a URL as shown in the example below.

This page was last reviewed on July 13, 2018. Contact us with questions or comments .

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