metrics

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I want to fetch the Cloudmetrics data for my EC2 instance so that I can draw graphs using those data and display it on my android device. How do I do that? Is there any sample program or tutorial for the same? Thanks in advance. This is what I am doing: private static void findCloudWatchData() { AmazonCloudWatchClient cloudWatch = new AmazonCloudWatchClient(new BasicAWSCredentials(AccessKey, SecretKey)); cloudWatch.setEndpoint("monitoring.us-east-1.amazonaws.com"); long offsetInMilliseconds = 1000 * 60 * 60 * 24; Dimension instanceDimension

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: As stated in this question : The tensorflow documentation does not provide any example of how to perform a periodic evaluation of the model on an evaluation set The accepted answer suggested the use of Experiment (which is deprecated according to this README ). All I found on online points towards using the train_and_evaluate method. However, I still do not see how to switch between the two processes (train and evaluate). I have tried the following: estimator = tf.estimator.Estimator( model_fn=model_fn, params=hparams, model_dir=model_dir,

I'm trying to create an optical character recognition system with the dictionary. In fact I don't have an implemented dictionary yet=) I've heard that there are simple metrics based on Levenstein distance which take in account different distance between different symbols. E.g. 'N' and 'H' are very close to each other and d("THEATRE", "TNEATRE") should be less than d("THEATRE", "TOEATRE") which is impossible using basic Levenstein distance. Could you help me locating such metric, please. This might be what you are looking for: http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I try to use Kafka with version 0.9.0 with port 9092. If I use telnet, I successfully connect to this address, but I fail to connect to Kafka server with Java API Here is my Java example exactly use the official supplied documentation: Properties props = new Properties(); props.put("bootstrap.servers", "192.168.174.128:9092"); props.put("acks", "all"); props.put("retries", 0); props.put("batch.size", 16384); props.put("linger.ms", 1); props.put("buffer.memory", 33554432); props.put("key.serializer", "org.apache.kafka.common.serialization

is there a way in the NER model in spaCy to extract the metrics (precision, recall, f1 score) per entity type? Something that will look like this: precision recall f1-score support B-LOC 0.810 0.784 0.797 1084 I-LOC 0.690 0.637 0.662 325 B-MISC 0.731 0.569 0.640 339 I-MISC 0.699 0.589 0.639 557 B-ORG 0.807 0.832 0.820 1400 I-ORG 0.852 0.786 0.818 1104 B-PER 0.850 0.884 0.867 735 I-PER 0.893 0.943 0.917 634 avg / total 0.809 0.787 0.796 6178 taken from: http://www.davidsbatista.net/blog/2018/05/09/Named_Entity_Evaluation/ Thank you! Nice question. First, we should clarify that spaCy uses the

Making sure I am getting this right: If we use sklearn.metrics.log_loss standalone, i.e. log_loss(y_true,y_pred), it generates a positive score -- the smaller the score, the better the performance. However, if we use 'neg_log_loss' as a scoring scheme as in 'cross_val_score", the score is negative -- the bigger the score, the better the performance. And this is due to the scoring scheme is built to be consistent with other scoring schemes. Since generally, the higher the better, we negate usual log_loss to be consistent with the trend. And it is done so solely for that purpose. Is this

I want to allow embedding of HTML but avoid DoS due to deeply nested HTML documents that crash some browsers. I'd like to be able to accommodate 99.9% of documents, but reject those that nest too deeply. Two closely related question: What document depth limits are built into browsers? E.g. browser X fails to parse or does not build documents with depth > some limit. Are document depth statistics for documents available on the web? Is there a site with web statistics that explains that some percentage of real documents on the web have document depths less than some value. Document depth is