def test(self, image, char=None): coms = connected_components(image) com = largest_component(coms) x = params_from_component(com, with_one=False) indexes, errors = self.kdtree.knn(x, self.k) scores = {lab: 0.0 for lab in self.label_set} for i in indexes[0]: scores[self.labels[i]] += 1.0 / self.k del scores[''] return KNNClassifications(scores)
def test(image, char=None): classifications = MLClassifications() coms = connected_components(image) com = largest_component(coms) xs = params_from_component(com, with_one=True) for c, ws in models.items(): s = dot(ws, Trainer.get_transformed_data(xs, polynomial_transform_order)) print c, s classifications.add(c, s) return classifications