Exemple #1
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class ClassifierConfig():
  def __init__(self, dataset, L, numfolds=4):
    self.d = Dataset(dataset)
    self.e = Extractor()
    self.dense_codebook = self.e.get_codebook(self.d, 'dsift')
    self.sparse_codebook = self.e.get_codebook(self.d, 'sift')
    self.L = L
    self.numfolds = numfolds
    
  def kfold(self):
    train_idx, val_idx = KFold(len(len(self.d.images), self.numfolds))
    self.d.create_folds(self.numfolds)
Exemple #2
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class ClassifierConfig():
    def __init__(self, dataset, L, numfolds=4):
        self.d = Dataset(dataset)
        self.e = Extractor()
        self.dense_codebook = self.e.get_codebook(self.d, 'dsift')
        self.sparse_codebook = self.e.get_codebook(self.d, 'sift')
        self.L = L
        self.numfolds = numfolds

    def kfold(self):
        train_idx, val_idx = KFold(len(len(self.d.images), self.numfolds))
        self.d.create_folds(self.numfolds)
Exemple #3
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 def test_kfold(self):
     """
 'sizes' here are empirical values over the trainval set.
 """
     d = Dataset('full_pascal_trainval')
     numfolds = 4
     d.create_folds(numfolds)
     cls = 'dog'
     sizes = [314, 308, 321, 320]
     for i in range(len(d.folds)):
         d.next_folds()
         pos = d.get_pos_samples_for_fold_class(cls)
         neg = d.get_neg_samples_for_fold_class(cls, pos.shape[0])
         assert (pos.shape[0] == sizes[i])
         assert (neg.shape[0] == sizes[i])
Exemple #4
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 def test_kfold(self):
   """
   'sizes' here are empirical values over the trainval set.
   """
   d = Dataset('full_pascal_trainval')
   numfolds = 4
   d.create_folds(numfolds)
   cls = 'dog'
   sizes = [314, 308, 321, 320]
   for i in range(len(d.folds)):
     d.next_folds()
     pos = d.get_pos_samples_for_fold_class(cls)
     neg = d.get_neg_samples_for_fold_class(cls, pos.shape[0])
     assert(pos.shape[0] == sizes[i])
     assert(neg.shape[0] == sizes[i])