metrics

问题 Can someone point me to a complete example that does all of the following? Fits batched (and pickled) data in a loop using train_on_batch() Sets aside data from each batch for validation purposes Sets aside test data for accuracy evaluation after all batches have been processed (see last line of my example below). I'm finding lots of 1 - 5 line code snippets on the internet illustrating how to call train_on_batch() or fit_generator() , but so far nothing that clearly illustrates how to

问题 Suppose there are 4 sets: s1={1,2,3,4}; s2={2,3,4}; s3={2,3,4,5}; s4={1,3,4,5}; Is there any standard metric to present the similarity degree of this group of 4 sets? Thank you for the suggestion of Jaccard method. However, it seems pairwise. How can I compute the similarity degree of the whole group of sets? 回答1: Pairwise, you can compute the Jaccard distance of two sets. It's simply the distance between two sets, if they were vectors of booleans in a space where {1, 2, 3…} are all unit

问题 I use @Timed annotation on String Boot rest controller and it works fine. Method from controller calls method from service which is also annotated with @Timed . However, this annotation on method in subsequent service bean doesn't work (I don't see results in /metrics ). Why is it happening? Could it be fixed? 回答1: As per Support for @Timed in any Spring-managed bean #361 you can get this behaviour by registering TimedAspect manually. @Configuration @EnableAspectJAutoProxy public class

问题 Related question - 1 I have a dataset like so: > head(training_data) year month channelGrouping visitStartTime visitNumber timeSinceLastVisit browser 1 2016 October Social 1477775021 1 0 Chrome 2 2016 September Social 1473037945 1 0 Safari 3 2017 July Organic Search 1500305542 1 0 Chrome 4 2017 July Organic Search 1500322111 2 16569 Chrome 5 2016 August Social 1471890172 1 0 Safari 6 2017 May Direct 1495146428 1 0 Chrome operatingSystem isMobile continent subContinent country source medium 1

问题 Related question - 1 I have a dataset like so: > head(training_data) year month channelGrouping visitStartTime visitNumber timeSinceLastVisit browser 1 2016 October Social 1477775021 1 0 Chrome 2 2016 September Social 1473037945 1 0 Safari 3 2017 July Organic Search 1500305542 1 0 Chrome 4 2017 July Organic Search 1500322111 2 16569 Chrome 5 2016 August Social 1471890172 1 0 Safari 6 2017 May Direct 1495146428 1 0 Chrome operatingSystem isMobile continent subContinent country source medium 1

问题 I have service implemented with spring-boot-starter-2.0.0.RELEASE . I have enabled actuator metrics for it, however I cannot understand what units are the metrics presented in. Specifically, I am interested in the http.server.requests . A sample output of the endpoint is: { "name": "http.server.requests", "measurements": [ { "statistic": "COUNT", "value": 2 }, { "statistic": "TOTAL_TIME", "value": 0.049653001 }, { "statistic": "MAX", "value": 0.040696019 } ], "availableTags": [ { "tag":

问题 I have seen people using IOU as the metric for detection tasks and Dice Coeff for segmentation tasks. The two metrics looks very much similar in terms of equation except that dice gives twice the weightage to the intersection part. If I am correct, then Dice: (2 x (A*B) / (A + B)) IOU : (A * B) / (A + B) Is there any particular reason for preferring dice for segmentation and IOU for detection? 回答1: This is not exactly right. The Dice coefficient (also known as the Sørensen–Dice coefficient

问题 I have seen people using IOU as the metric for detection tasks and Dice Coeff for segmentation tasks. The two metrics looks very much similar in terms of equation except that dice gives twice the weightage to the intersection part. If I am correct, then Dice: (2 x (A*B) / (A + B)) IOU : (A * B) / (A + B) Is there any particular reason for preferring dice for segmentation and IOU for detection? 回答1: This is not exactly right. The Dice coefficient (also known as the Sørensen–Dice coefficient

问题 (kinda related to How to create dynamic metric in Flink) I have a stream of events(someid:String, name:String) and for monitoring reasons, I need a counter per event ID. In all the Flink documentations and examples, I can see that the counter is , for instance, initialised with a name in the open of a map function. But in my case I can not initialise the counter as I will need one per eventId and I do not know the value in advance. Also, I understand how expensive it would be to create a new

问题 (kinda related to How to create dynamic metric in Flink) I have a stream of events(someid:String, name:String) and for monitoring reasons, I need a counter per event ID. In all the Flink documentations and examples, I can see that the counter is , for instance, initialised with a name in the open of a map function. But in my case I can not initialise the counter as I will need one per eventId and I do not know the value in advance. Also, I understand how expensive it would be to create a new