cython / numpy type of an array

Question

I am trying to construct a matrix of python type int, a 64bit signed integer.

cdef matrix33():
    return np.zeros((3,3),dtype=int)

cdef do_stuf(np.ndarray[int, ndim=2] matrix):
    ...
    return some_value

def start():
    print do_stuf(matrix33())

It compiles right, but when I run it I keep getting this error:

ValueError: Buffer dtype mismatch, expected 'int' but got 'long'

I can not work with python long's, but I don't know how to properly convert to a 64 int.

UPDATE

Okey. I am quite sure that I used Cython correctly. The code that I wrote was for an minmax search in the game of capture go / atari go.

By far the most called functions are these:

cdef isThere_greedy_move(np.ndarray[np.int64_t, ndim=2]board, int player):
    cdef int i, j
    for i in xrange(len(board)):
        for j in xrange(len(board)):
            if board[i,j] == 0:
                board[i,j] = player
                if player in score(board):
                    board[i,j] = 0
                    return True
                board[i,j] = 0
    return False


# main function of the scoring system.
# returns list of players that eat a stone
cdef score(np.ndarray[np.int64_t, ndim=2] board):
    scores = []
    cdef int i,j
    cdef np.ndarray[np.int64_t, ndim = 2] checked
    checked = np.zeros((board.shape[0], board.shape[1]), dtype = int)
    for i in xrange(len(board)):
        for j in xrange(len(board)):
            if checked[i,j] == 0 and board[i,j] !=0:
                life, newly_checked = check_life(i,j,board,[])
                if not life:
                    if -board[i,j] not in scores:
                        scores.append(-board[i,j])
                        if len(scores) == 2:
                            return scores
                checked = update_checked(checked, newly_checked)
    return scores

# helper functions of score/1
cdef check_life(int i, int j, np.ndarray[np.int64_t, ndim=2] board, checked):
    checked.append((i,j))
    if liberty(i,j,board):
        return True, checked
    for pos in [[1,0],[0,1],[-1,0],[0,-1]]:
        pos = np.array([i,j]) + np.array(pos)
        if check_index(pos[0],pos[1],len(board)) and board[pos[0],pos[1]] == board[i,j] and (pos[0],pos[1]) not in checked:
            life, newly_checked = check_life(pos[0],pos[1],board,checked)
            if life:
                checked = checked + newly_checked             
                return life, checked
    return False, []    # [] is a dummy.

cdef liberty(int i,int j, np.ndarray[np.int64_t, ndim=2] board):
    for pos in [np.array([1,0]),np.array([0,1]),np.array([-1,0]),np.array([0,-1])]:
        pos = np.array([i,j]) - pos
        if check_index(pos[0],pos[1],len(board)) and board[pos[0],pos[1]] == 0:
            return True
    return False

I would really have thought that this would be a chance to shine for cython. To solve 3x3 capture go:

Python 2.7 does a consistent 2.28 seconds, with cython it is a consistent 2.03 Both were tested with the python time module and on an i7 processor of less than 60C°

Now the question for me is if I am going to switch to Haskell or C++ for this project...

Fred Foo · Accepted Answer · 2012-02-28 11:57:35Z

6

Cython's int type is the same as a C int, i.e. usually (but not necessarily) 32-bit. You should declare the dtype in matrix33 as np.int64 and in do_stuf as its C counterpart, np.int64_t:

cimport numpy as np
import numpy as np

cdef do_stuff(np.ndarray[np.int64_t, ndim=2] matrix):
    pass

cdef matrix33():
    return np.zeros((3,3), dtype=int)

def start():
    print do_stuff(matrix33())

answered Feb 28, 2012 at 11:57

Fred Foo

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4 Comments

Ihmahr Over a year ago

Thank you, the code now runs correct. But, it runs just as fast as it does when I declare everything long. I know the long object is not fast, as it can not overflow and is treated different (uses more cycles). I would like to use an int that can overflow and that is faster than the python type long.

Ihmahr Over a year ago

Also, the speed up over interpreted python is just under 10%, which makes me suspicious that I don't quite have the types right...

Fred Foo Over a year ago

@user1020753: the types are right. The speedup depends on what you're doing in your functions.

joshayers Over a year ago

@user1020753 Your functions are using Python data types - lists, tuples, etc - for most of their operations. For Cython to have a large effect, it needs to be rewritten to use native C data types. Also, it would probably speed things up if you typed your functions. See docs.cython.org/src/quickstart/cythonize.html#typing-functions.

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