55     digits = split2d(digits_img, (SZ, SZ))
56     labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
57     return digits, labels
103 def evaluate_model(model, digits, samples, labels):  argument
116     for img, flag in zip(digits, resp == labels):
123 def preprocess_simple(digits):  argument
124     return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
126 def preprocess_hog(digits):  argument
128     for img in digits:
152     digits, labels = load_digits(DIGITS_FN)  variable
157     shuffle = rand.permutation(len(digits))
158     digits, labels = digits[shuffle], labels[shuffle]  variable
160     digits2 = map(deskew, digits)
164     cv2.imshow('test set', mosaic(25, digits[train_n:]))