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