Search Results for "labels"

Automatically generate change log from your tags, issues, labels and pull requests on GitHub. Fully automated changelog generation - This gem generates a changelog file based on tags, issues and merged pull requests (and splits them into separate lists according to labels) from octocat: GitHub. What’s the point of a changelog? To make it easier for users and contributors to see precisely what notable changes have been made between each release (or version) of the project. ...

...Under the hood, it works with popular charting libraries such as Google Charts, Chart.js, and Highcharts, but hides their verbose JavaScript APIs behind a clean Ruby interface. It also handles things like automatic labels, legends, colors, and responsiveness without requiring you to manually configure chart options. Because of its integration with ActiveRecord, developers can quickly visualize trends in their application’s data with minimal transformation. Chartkick is especially valued in dashboards, reporting tools, and admin panels where quick, maintainable visualization is needed without heavy front-end development.

...Note that this applies to Rails-generated validation messages. HTML 5 client-side validation and Rails validation out of the box doesn't really work well together. Mark-up for Bootstrap horizontal forms (labels to the left of their fields, like a traditional desktop application), if that's what you want.

Ready to use log management solution for Kubernetes storing data in ClickHouse and providing web UI. Ready to use log management solution for Kubernetes. Efficiently store big amounts of your logs (in ClickHouse database), process them using a simple query language and monitor them online through web UI. Easy and quick to deploy in an already functioning Kubernetes cluster. The status is alpha. However, we (Flant) use it in our production Kubernetes deployments since September 2017. Data...

...It can train models from discrete, continuous, or mixed attribute data. Continuous features are evaluated across possible split points to build threshold-based binary branches. Discrete models classify unique labels and can be rendered for visual inspection. The library supports inconsistent datasets, multiple or symbolic outputs, and fallback values when no branch matches an input. Its Ruleset trainer converts a tree into pruned rules using a held-out portion of the training data. A bagging trainer builds ten rulesets and combines their predictions through voting.