{"ref":"master","ref_type":"branch","master_branch":"master","description":"Build a classifier that classifies the given files into the appropriate activity ('Use_telephone', 'Standup_chair', 'Walk', 'Climb_stairs', 'Sitdown_chair', 'Brush_teeth', 'Comb_hair', 'Eat_soup', 'Pour_water', 'Descend_stairs', 'Eat_meat', 'Drink_glass', 'Getup_bed', 'Liedown_bed').  The data items in this case are the files themselves; the classifier you learn will be able to take an activity file of arbitrary length and classify it with one of the activity labels. You might notice that each file is of a different length, which is why we will use vector quantization to turn each file into a fixed-length feature vector.  For your classifier's features, you should use vector quantization, creating a histogram of cluster centers for each data item. You should use k-means in order to construct the pattern vocabulary. You may use whichever multi-class classifier you wish.","pusher_type":"user"}
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