(x, w)
| 48 | # OUTPUT: |
| 49 | # probability of p(y = 1 | x; w) |
| 50 | def get_p(x, w): |
| 51 | wTx = 0. |
| 52 | for i in x: # do wTx |
| 53 | wTx += w[i] * 1. # w[i] * x[i], but if i in x we got x[i] = 1. |
| 54 | return 1. / (1. + exp(-max(min(wTx, 20.), -20.))) # bounded sigmoid |
| 55 | |
| 56 | |
| 57 | # D. Update given model |