Can someone please explain what this code does? It's from the book "Introduction to Machine Learning with Python" on Bernoulli Naive Bayes classifier:
counts = {}
for label in np.unique(y):
# iterate over each class
# count (sum) entries of 1 per feature
counts[label] = X[y == label].sum(axis=0)
print("Feature counts:\n", counts)
I don't understand what happens on line5.
Let's use an example.
import numpy as np
X=np.array([[1,10],[2,20], [3,30], [4,40], [5,50], [6,60]])
y=np.array([11,22,33,11,22,11])
np.unique(y) is [11,22,33]
So label will successively be those.
When label is 11
y==label is [11,22,33,11,22,11]==11 which is [True,False,False,True,False,True]
so X[y==label] is X[[True,False,False,True,False,True]] so it is a selection of rows 0, 3 and 5 of X. So [[1,10],[4,40],[6,60]]
sum(axis=0) sum that along axis 0, so X[y==label].sum(axis=0) is [1+4+6,10+40,60] = [11,110]
so counts[11]=[11,110]
Likewise, when label is 22, y==label is [False,True,False,False,True,False], so X[y==label] is [[2,20],[5,50]] so X[y==label].sum(axis=0) is [7,70], which is affected to counts[22].
And when label is 33, y==label is just [False,False,True,False,False,False], so X[y==label] is [[3,30]] so X[y==label].sum(axis=0) is [3,30] which is affected to counts[33].
So at the end, if your X data are a list of k values, and y data a list of k classes, chosen among n possibilities, counts are, for each n possible classes, the k sums of the values of data matching that class.