blanket.js

blanket.js

Blanket.js
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About

GNU DDD is a graphical front-end for command-line debuggers such as GDB, DBX, WDB, Ladebug, JDB, XDB, the Perl debugger, the bash debugger bashdb, the GNU Make debugger remake or the Python debugger pydb. Besides usual front-end features such as viewing source texts. DDD has become famous through its interactive graphical data display, where data structures are displayed as graphs. You can support the principle of software freedom by buying stuff from the FSF shop. To run DDD, you need the GNU debugger (GDB), version 4.16 or later (or depending on the program to be debugged, possibly other command-line debuggers such as Ladebug, JDB, XDB, the Perl debugger, the bash debugger bashdb, the GNU Make debugger remake, or the Python debugger pydb).

About

A seamless JavaScript code coverage library. Blanket.js is a code coverage tool for JavaScript that aims to be easy to install, easy to use, and easy to understand. Blanket.js can be run seamlessly or can be customized for your needs. JavaScript code coverage compliments your existing JavaScript tests by adding code coverage statistics (which lines of your source code are covered by your tests). Parsing the code using Esprima and node-falafel, and instrumenting the file by adding code tracking lines. Connecting to hooks in the test runner to output the coverage details after the tests have been completed. A Grunt plugin has been created to allow you to use Blanket like a "traditional" code coverage tool (creating instrumented copies of physical files, as opposed to live-instrumenting). Runs the QUnit-based Blanket report headlessly using PhantomJS. Results are displayed on the console, and the task will cause Grunt to fail if any of your configured coverage thresholds are not met.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers looking for a Debugging solution

Audience

Developers seeking a solution to manage their code tracking processes and statistics

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

GNU
www.gnu.org/software/ddd/

Company Information

Blanket.js
github.com/alex-seville/blanket

Company Information

scikit-learn
United States
scikit-learn.org/stable/

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Replicate

Categories

Categories

Categories

Integrations

Axis LMS
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
JSON
JavaScript
Keepsake
MLJAR Studio
Matplotlib
Mocha
ModelOp
NumPy
Python
QUnit
Train in Data

Integrations

Axis LMS
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
JSON
JavaScript
Keepsake
MLJAR Studio
Matplotlib
Mocha
ModelOp
NumPy
Python
QUnit
Train in Data

Integrations

Axis LMS
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
JSON
JavaScript
Keepsake
MLJAR Studio
Matplotlib
Mocha
ModelOp
NumPy
Python
QUnit
Train in Data
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