# Find the number of non black unique colours
black = solver_utils.get_colour_code('black')
colours = grid_in_np[grid_in_np != black]
n = len(np.unique(colours))
n_rows, n_cols = grid_in_np.shape # This is always 3 x 3
grid_out_np = np.zeros((n_rows * n, n_cols * n), dtype='int64')
for i in range(n_rows):
for j in range(n_cols):
colour = grid_in_np[i, j] # Colour of pixel (i, j)
grid_out_np[i * n:(i * n) + n, j * n:(j * n) + n] = colour
# Convert back to list of lists
grid_out = grid_out_np.tolist()
return grid_out
if __name__ == '__main__':
# Get the data for the associated JSON file
data = solver_utils.parse_json_file()
# Iterate through training grids and test grids
for data_train in data['train']:
solver_utils.solve_wrapper(data_train['input'], solve)
for data_test in data['test']:
solver_utils.solve_wrapper(data_test['input'], solve)
The solution requires the rows to be ordered such that the row with m coloured squares is row n, the row
with m-1 coloured squares is row n-1, etc. Another stipulation is that the coloured squares start from the
right such that in row n-1, column 0 is coloured black and in row n-2, column 0 and 1 are coloured black, etc.
Input: input_grid - A python list of lists containing the unsolved grid
Output: out_grid - A python list of lists containing the solved grid
"""
# Change grid to NumPy array
np_grid = np.array(input_grid)
# Sort rows relative to eachother based on how many zeros they contain
# Source: https://stackoverflow.com/questions/28518568/numpy-sort-matrix-rows-by-number-of-non-zero-entries
# Accessed 23/11/2019
np_grid = np_grid[(np_grid != 0).sum(axis=1).argsort()]
# Sort entries in each row
np_grid.sort(axis=1)
return np_grid.tolist()
if __name__ == "__main__":
data = solver_utils.parse_json_file()
for training in data['train']:
solver_utils.solve_wrapper(training['input'], solve)
for testing in data['test']:
solver_utils.solve_wrapper(testing['input'], solve)