pacemaker recall

 The following is a brief list of common recommended system metrics: 1. Accuracy rate and Recall rate (Precision Recall) Accuracy and recall rates are two measures widely used in the field of information retrieval and statistical classification to evaluate the quality of the results. The accuracy is to retrieve the number of related documents and the total nu

yu Code Comments Machine learning (ML), Natural language Processing (NLP), Information Retrieval (IR) and other fields, evaluation (Evaluation) is a necessary work, and its evaluation indicators tend to have the following points: accuracy (accuracy), accuracy (Precision), Recall (Recall) and F1-measure. (Note: In contrast, the IR ground truth is often a Ordered List, not a Bool type of Unordered Collection

Berkeley Computer Vision PagePerformance Evaluation Classification performance metrics for machine learning: ROC curve, AUC value, accuracy rate, recall rate True Positives, TP: Predicted as a positive sample, actually also a positive sample of the characteristics of the numberFalse Positives, FP: Predicted as positive sample, actual negative sample characteristic numberTrue negatives, TN: Predicted as negative sample, actual also negative sample char

of the benefits of using a real number as an evaluation measure is that it can help us quickly decide if we need to make some improvements to the algorithm. Increase the accuracy from 99.2% to 99.5%, but does our improvement really work, or do we just replace the code with something like always predicting y=0, so if you have a skew class, using categorical accuracy is not a good way to measure an algorithm, Because you may get a high accuracy, or very low error rate, but we do not know whether

Original link http://bookshadow.com/weblog/2014/06/10/precision-recall-f-measure/Below is a brief list of several commonly used recommended system evaluation indicators:1. Accuracy rate and Recall rate (Precision Recall) accuracy and recall rates are widely used in the field of information retrieval and statistica

Summary In the process of training YOLO V2, the system will show some values of evaluation training effect, such as Recall,iou and so on. In order to be afraid of forgetting later, now put their own understanding of these several measures of the way to record.This article first assumes a test set, and then around the test set to introduce the methods of calculation of these measures. the wild Goose and the plane Assuming there is a test set, the imag

1. Accuracy rate and Recall rate (Precision Recall) Accuracy and recall rates are two measures widely used in the field of information retrieval and statistical classification to evaluate the quality of the results. The accuracy is to retrieve the number of related documents and the total number of documents retrieved, to measure the precision of the retrieval s

In order to better evaluate the performance of IR system, IR has a complete evaluation system, through which the evaluation system can understand the merits and demerits of different information systems, the characteristics of different retrieval models, the influence of different factors on information retrieval, and further optimize the information retrieval.Since the objective of IR is to return more comprehensive and accurate information in a relatively short period of time, the evaluation i

0-1 Predictions for test sets Accuracy: The forecast pair/total forecast, including 0 of the forecast pair also includes 1 of the forecast pair, usefulness: represents the overall alignment of the model, the higher the model the more accurate Accuracy: predicted to be 1 accuracy, usefulness: represents 1 of the degree of alignment Recall: The predicted 1 accounted for the true 1 percentage, use: Represents the forecast 1 coverage Example: Now to

In general, the relationship between recall and precision is as follows:1, if the need for a high degree of confidence, the precision will be very high, the corresponding recall rate is very low, 2, if the need to avoid false negative, the recall rate will be very high, the precision will be very low. on the right, the relationship between

Recall (recall rate); Precision (accuracy rate); F1-meature (Comprehensive evaluation index); These parameters are often used in information retrieval (such as search engines), natural language processing, and detection classifications.Precision: The percentage of information that is detected that is correct or relevant ( that is, what you want)(Number of positive samples predicted as a proportion of the to

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Corosync + Pacemaker build highly available Clusters I. Overview:1.1 Introduction to AIS and OpenAISAIS application interface specification is a set of open specifications used to define application interface (API). These applications provide an open and highly portable application interface for application services as middleware. It is urgently needed to implement high-availability applications. The Service Availability Forum (SA Forum) is an open Fo

NOTE: Mobile QQ message recall must be in the QQ5.9 version of this function and is the mobile version of the previous version does not have this function oh. Mobile QQ Message withdrawal How to use? 1, open mobile phone QQ, we find a test chat good, and then enter a message to click "Send" out of a piece of information, as shown in the following picture: 2, then we long press the message then the system will appear options, simply select "

How do I get the message back from the micro-letter? Method detailed If we send a voice now to recall, we just have to press a dialog box that pops up and there's a recall button here, and then "the other side recalls a message." Micro-letter Recall message time limit Know that the micro-letter can be withdrawn, it is not very magical, but also n

First, the concept of pacemaker(1) Pacemaker (pacemaker), is a highly available cluster resource manager. It achieves maximum availability of resource management for node and resource level fault detection and recovery by using the message and member capabilities provided by the preferred cluster infrastructure (Corosync or heartbeat). It monitors and recovers no

[Root@host1 mysql]# MySQL9. Uninstall DRBD file System at node Node1[Root@node1 ~]# UMOUNT/VAR/LIB/MYSQL_DRBD[Root@node1 ~]# Drbdadm Secondary DbclusterMount the DRBD File system node Node2[Root@node2 ~]# Drbdadm Primary Dbcluster[Root@node2 ~]# mount/dev/drbd0/mysql/Configure MySQL on node Node2 and test[Root@node1 ~]# SCP node2:/etc/my.cnf/etc/my.cnf[Root@node2 ~]# chown mysql/etc/my.cnf[Root@node2 ~]# chmod 644/etc/my.cnfMySQL login test on Node2[Root@node2 ~]# MySQL11. Uninstall the DRBD fil

Linux Corosync + Pacemaker Complete HA structure: Install and configure a high-availability cluster:1. node name: the names of each node in the cluster must be resolved to each other./Etc/hostsIn hosts, the forward and reverse resolution results of the host name must be consistent with those of "uname-n;2. Time must be synchronizedUse Network Time Server synchronization time3. Not required: each node can communicate with each other through ssh key au

The version of the Pacemaker cluster configuration. About the version of the Pacemaker cluster configuration, CIB in Pacemaker has a version composed of admin_epoch, epoch, and num_updates. when a node is added to the cluster, the version number is used, obtain the version of the Pacemaker cluster. In

) = joinedRuntime.totem.pg.mrp.srp.members.167772173.config_version (U64) = 0Runtime.totem.pg.mrp.srp.members.167772173.ip (str) = R (0) IP (10.0.0.13)Runtime.totem.pg.mrp.srp.members.167772173.join_count (U32) = 2Runtime.totem.pg.mrp.srp.members.167772173.status (str) = joined167772172 is the member ID, its IP is 10.0.0.12, the state is joined;167772173 is the member ID, its IP is 10.0.0.13, the state is joined;Corosync Service status is correct.Start the P