CS231n Hinge Loss SVM Snippet - Vectorized

hingeloss.py

import numpy as np

W = np.array([(0.01,-0.05,0.1,0.05),(0.7,0.2,0.05,0.16),(0.0,-0.45,-0.2, 0.03)]) #Weights
xi = np.array([-15,22,-44,56]) #Input
b = np.array([0.0,0.2,-0.3]) #Bias

delta = 1

y = np.sum((np.dot(W,xi),b), axis=0)

#comp = (sj - syi)
# sj = y[:y.size-1]
# syi = y[y.size-1]
comp = np.sum((y[:y.size-1],-y[y.size-1]), axis=0)

# li = summation(max(0, sj-syi+delta))
li = np.sum(np.max((np.zeros((y.size-1,)), np.sum((comp,delta), axis=0)), axis=0))

# li = 1.5800000000000018