computing the magnitude from a single x, y, z reading.reading the x, y, z sensor data from the SensorManager.Other parts of the pipeline are as follows (include the WekaClassifier): Second, the classification or inference phase where we embed the WekaClassifier produced by WEKA into our MyRuns5 project in a file called WekaClassifier.java - this forms one component of the classification pipeline that runs as part of your application.Technically, this technique is called "supervised learning" in machine learning (ML) parlance. First, the training phase where we collect data using the MyRuns data collector (which you are given) and label the data (e.g., walking) and then feed the resulting sensor data into WEKA(Waikato Environment for Knowledge Analysis - a very cool machine learning tool) which, in turn produces a classifier (called the WekaClassifier java class in our MyRuns5) that we embedded in our MyRuns5 to form a component in what is called the activity recognition "classification pipeline. In this lecture, we will discuss the training phase of activity recognition the complete machine learning task of building a classification system that can automatically infer your activity (e.g., standing, walking, running) breaks down into two sequential phases:
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