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	* Add validation for argmaxing in _ml.pyx
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				|  | @ -29,22 +29,25 @@ cdef int arg_max(const weight_t* scores, const int n_classes) nogil: | |||
| cdef int arg_max_if_true(const weight_t* scores, const int* is_valid, | ||||
|                          const int n_classes) nogil: | ||||
|     cdef int i | ||||
|     cdef int best = 0 | ||||
|     cdef weight_t mode = -900000 | ||||
|     cdef int best = -1 | ||||
|     cdef weight_t mode = 0 | ||||
|     for i in range(n_classes): | ||||
|         if is_valid[i] and scores[i] > mode: | ||||
|         if is_valid[i] and (best == -1 or scores[i] > mode): | ||||
|             mode = scores[i] | ||||
|             best = i | ||||
|     return best | ||||
| 
 | ||||
| 
 | ||||
| class ValidationError(Exception): | ||||
|     pass | ||||
| 
 | ||||
| cdef int arg_max_if_zero(const weight_t* scores, const int* costs, | ||||
|                          const int n_classes) nogil: | ||||
|     cdef int i | ||||
|     cdef int best = 0 | ||||
|     cdef weight_t mode = -900000 | ||||
|     cdef int best = -1 | ||||
|     cdef weight_t mode = 0 | ||||
|     for i in range(n_classes): | ||||
|         if costs[i] == 0 and scores[i] > mode: | ||||
|         if costs[i] == 0 and (best == -1 or scores[i] > mode): | ||||
|             mode = scores[i] | ||||
|             best = i | ||||
|     return best | ||||
|  | @ -63,13 +66,18 @@ cdef class Model: | |||
|             self._model.load(self.model_loc, freq_thresh=0) | ||||
| 
 | ||||
|     def predict(self, Example eg): | ||||
|         assert self.n_classes == eg.c.nr_class | ||||
|         memset(eg.c.scores, 0, sizeof(weight_t) * eg.c.nr_class) | ||||
|         self.set_scores(eg.c.scores, eg.c.atoms) | ||||
|         eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes) | ||||
|         if eg.c.guess == -1: | ||||
|             raise ValidationError("No valid classes during prediction") | ||||
| 
 | ||||
|     def train(self, Example eg): | ||||
|         self.predict(eg) | ||||
|         eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes) | ||||
|         if eg.c.best == -1: | ||||
|             raise ValidationError("No zero-cost classes during training.") | ||||
|         eg.c.cost = eg.c.costs[eg.c.guess] | ||||
|         self.update(eg.c.atoms, eg.c.guess, eg.c.best, eg.c.cost) | ||||
| 
 | ||||
|  |  | |||
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