How To Install "libt-digest-java" Package on Ubuntu
Quick Install Instructions of libt-digest-java on Ubuntu Server. It’s Super Easy! simply click on Copy button to copy the command and paste into your command line terminal using built-in APT package manager.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
Quick Install Steps:
Step 1
sudo apt-get update -y
Step 2
sudo apt-get install -y libt-digest-java
Step 3
Check the system logs to confirm that there are no related errors.
You can use
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see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
Execute the commands above step by step.
You can simply hit the copy button to copy the command and paste into the command line interface.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
Data structure for quantiles and related rank statistics
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to product a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk. Data structure for quantiles and related rank statistics
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to product a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to product a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk. Data structure for quantiles and related rank statistics
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to product a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.
Detailed Instructions:
Step 1
Run update command to update package repositories and get latest package information.
sudo apt-get update -y
Step 2
Run the install command with -y flag to quickly install the packages and dependencies.
sudo apt-get install -y libt-digest-java
Step 3
Check the system logs to confirm that there are no related errors.
You can use
ZoomAdmin
to check the logs, manager servers, host multiple websites and apps on your servers and more. The apps run in docker containers, to learn more
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.