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| from darknet import * | |
| def predict_tactic(net, s): | |
| prob = 0 | |
| d = c_array(c_float, [0.0]*256) | |
| tac = '' | |
| if not len(s): | |
| s = '\n' | |
| for c in s[:-1]: | |
| d[ord(c)] = 1 | |
| pred = predict(net, d) | |
| d[ord(c)] = 0 | |
| c = s[-1] | |
| while 1: | |
| d[ord(c)] = 1 | |
| pred = predict(net, d) | |
| d[ord(c)] = 0 | |
| pred = [pred[i] for i in range(256)] | |
| ind = sample(pred) | |
| c = chr(ind) | |
| prob += math.log(pred[ind]) | |
| if len(tac) and tac[-1] == '.': | |
| break | |
| tac = tac + c | |
| return (tac, prob) | |
| def predict_tactics(net, s, n): | |
| tacs = [] | |
| for i in range(n): | |
| reset_rnn(net) | |
| tacs.append(predict_tactic(net, s)) | |
| tacs = sorted(tacs, key=lambda x: -x[1]) | |
| return tacs | |
| net = load_net("cfg/coq.test.cfg", "/home/pjreddie/backup/coq.backup", 0) | |
| t = predict_tactics(net, "+++++\n", 10) | |
| print t | |