def fit_sudokuextract_classifier(classifier):
print("Fetch SudokuExtract data...")
X, y = get_sudokuextract_data()
print("Train classifier on SudokuExtract data...")
print("Label / N : {0}".format([(v, c) for v, c in zip(_range(10), np.bincount(y))]))
classifier.fit(X, y)
print("Completed training.")
return classifier
def fit_sudokuextract_classifier(classifier):
print("Fetch SudokuExtract data...")
X, y = get_sudokuextract_data()
print("Train classifier on SudokuExtract data...")
print("Label / N : {0}".format([
(v, c) for v, c in zip(_range(10), np.bincount(y))
]))
classifier.fit(X, y)
print("Completed training.")
return classifier
def fit_combined_classifier(classifier):
print("Fetch data...")
X1, y1 = get_sudokuextract_data()
X2, y2 = get_mnist_data()
X = np.concatenate([X1, X2], axis=0)
y = np.concatenate([y1, y2])
print("Train classifier on SudokuExtract and MNIST data...")
print("Label / N : {0}".format([(v, c) for v, c in zip(_range(10), np.bincount(y))]))
classifier.fit(X, y)
print("Completed training.")
return classifier
def fit_combined_classifier(classifier):
print("Fetch data...")
X1, y1 = get_sudokuextract_data()
X2, y2 = get_mnist_data()
X = np.concatenate([X1, X2], axis=0)
y = np.concatenate([y1, y2])
print("Train classifier on SudokuExtract and MNIST data...")
print("Label / N : {0}".format([
(v, c) for v, c in zip(_range(10), np.bincount(y))
]))
classifier.fit(X, y)
print("Completed training.")
return classifier
