I created Cython code to perform matrix operations between a dense matrix and a sparse vector, as follows (since I am learning Cython, I'm not sure if this is good code, but this is the best I could think of so far):
import numpy as np cimport numpy as np ctypedef np.float64_t dtype_t ctypedef np.int32_t dtypei_t cimport cython @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) def cdenseXsparse(np.ndarray[dtype_t, ndim = 2] Y, np.ndarray[dtype_t, ndim = 1] V, np.ndarray[dtypei_t, ndim = 1] I, np.ndarray[dtype_t, ndim = 1] A = None): """ Computes A = Y * (V_I) """ if Y is None: raise ValueError("Input cannot be Null") A = np.zeros(Y.shape[1]) cdef Py_ssize_t i, indice cdef dtype_t s for i in range(A.shape[0]): s = 0 for indice in range(len(I)): s += Y[I[indice], i] * V[indice] A[i] = s return A
It works great. But when I change the third line from:
ctypedef np.float64_t dtype_t
in
ctypedef np.float32_t dtype_t
and compile the .pyx file and run the matrix operations again, I get an error:
"Buffer dtype mismatch, expected 'dtype_t' but got 'long'"
As an example, when compiling using np.float32_t and running the code:
In [3]: from numpy import random as rd, array, int32, float32 In [4]: y = array(rd.rand(10, 200), dtype = float32) In [5]: v = array([1, 2, 3], dtype = float32) In [6]: i = array([0, 1, 2], dtype = int32) In [7]: from cdenseXsparse import cdenseXsparse In [8]: r = cdenseXsparse(y, v, i) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-8-319f9c8c8d49> in <module>() ----> 1 r = cdenseXsparse(y, v, i) /home/will/workspace/s3_RecSys/SVD/cdenseXsparse.so in cdenseXsparse.cdenseXsparse (cdenseXsparse.c:1484)() ValueError: Buffer dtype mismatch, expected 'dtype_t' but got 'double'
Is there any other way to use float32 in Cython? Using float64 and float32 should not work the same?
For what I have investigated so far, it should work the same, but it wasn’t in this code.
Thanks in advance!