I am implementing a stream learner for text classification. There are some
single-valued parameters in my implementation that needs to be updated as
new stream items arrive. For example, I want to change learning rate as the
new predictions are made. However, I doubt that there is a way to broadcas
I have the following code:
SparkConf conf = new
SparkConf().setAppName("streamer").setMaster("local[2]");
conf.set("spark.driver.allowMultipleContexts", "true");
JavaStreamingContext ssc = new JavaStreamingContext(conf, new
Duration(batch_interval));
Hi, I am working on a text mining project and I want to use
NaiveBayesClassifier of MLlib to classify some stream items. So, I have two
Spark contexts one of which is a streaming context. Everything looks fine if
I comment out train and predict methods, it works fine although doesn't
obviously do w