Deep Learning Workshop

@@ -86,18 +86,17 @@ if __name__ == "__main__":
     print("Max exon length: {}".format(max_exon_length))
     print("Max utr length: {}".format(max_utr_length))
 
-    quit()
-
     # Fill sequence with X char to get max length
     # One-hot-encoding of sequences
     for sequence, label in zip(sequences, labels):
         if label == "exon":
             if len(sequence) < max_exon_length:
-                sequence.ljust(max_exon_length + len(sequence), 'X')
+                sequence_adjust = sequence.ljust(max_exon_length + len(sequence), 'X')
         elif label == "utr":
             if len(sequence) < max_utr_length:
-                sequence.ljust(max_utr_length + len(sequence), 'X')
-        integer_encoded = integer_encoder.fit_transform(list(sequence))
+                sequence_adjust = sequence.ljust(max_utr_length + len(sequence), 'X')
+        print("Length sequence_adjust: {}".format(len(sequence_adjust)))
+        integer_encoded = integer_encoder.fit_transform(list(sequence_adjust))
         integer_encoded = np.array(integer_encoded).reshape(-1, 1)
         one_hot_encoded = one_hot_encoder.fit_transform(integer_encoded)
         input_features.append(one_hot_encoded.toarray())