davy wybiral

The other day I post ed about numericjs and provided a simple PCA implementation using it. Today I rewrote my logistic regression classifer using numericjs. Below is the commented sourcecode and demo in jsFiddle. For the demo, you click on the large square (in the "Result" panel) to add a point. The dropdown box labeled "group" allows you to select the color of point to add. You can change the learning rate (alpha) and the regularization parameter (lambda) to see how it changes the classification. In case you're using a feed reader and can't see the box below, click here . ​

I've been playing around with various classification algorithms lately, so I wrote a really simplified discrete naive bayes classifier in Python. No emphasis on sample correction, simplicity was key here, but it still works quite well. from operator import itemgetter from collections import defaultdict class BayesClassifier: def __init__(self): self.total_count = 0 # Observations of individual attributes self.class_count = defaultdict(int) # Observations of cls self.attrs_count = defaultdict(int) # Observations of (cls, attrs) self.correction = 0.0001 # Prevent multiplication by 0.0 def train(self, cls, attrs): ''' Add observation of 'attrs' as being an instance of 'cls' ''' self.class_count[cls] += 1 for attr in attrs: self.attrs_count[(cls, attr)] += 1 self.total_count += 1 def rate(self, cls, attrs): ''' Return probability rating of 'attrs' bei...