有一些类似的问题,但没有一个解决我的问题,所以我在这里发布一个新的问题。
当我尝试给一个函数一个三维numpy数组作为输入时,Cython会给我一个错误,告诉我:"ValueError: Buffer has wrong number of dimensions (expected 2, got 3)"。但是当我给它一个二维数组时,它会崩溃(Python停止响应,我认为这是因为我正在尝试对二维数组进行三维矩阵运算)。
然后,我尝试将输入设置为三维数组,但函数仍然期望一个二维数组。我认为我的代码可能有问题,但当我摆脱Cython变量声明并将其作为Python文件运行时,一切都很好。
这是函数声明:
def isfc(np.ndarray[double, ndim=3] multi_activations, int gaussian_variance):
#cython variable declaration
cdef int time_len, activations_len, subj_num, timepoint, subj
cdef np.ndarray[double, ndim=2] correlations_vector, normalized_activations, coefficients,normalized_sum_activations
cdef np.ndarray[double, ndim=3] c_activations, activations_sum, correlations_mean
cdef np.ndarray[double, ndim=4] correlations
cdef np.ndarray gaussian_array, coefficients_sum, coefficient, sigma_activations, sigma_activations_sum
#assign initial parameters
**subj_num, activations_len, time_len= multi_activations.shape[0],multi_activations.shape[1],multi_activations.shape[2]**
coefficients_sum = np.zeros(time_len)
correlations= np.zeros([subj_num, time_len,activations_len,activations_len])
correlations_vector = np.zeros([time_len,(activations_len * (activations_len-1) / 2)])
coefficients = np.zeros([time_len, activations_len,time_len])
gaussian_array = np.array([exp(-timepoint**2/2/gaussian_variance)/sqrt(2*pi*gaussian_variance) for timepoint in range(-time_len+1,time_len)])
**c_activations = np.array(multi_activations)**
被讨论的输入是multi_activations,在**标记线上使用,然后复制到一个三维cython缓冲区中。我已经缩小错误范围,发现问题出在函数调用时,具体来说就是当我将一个三维数组作为multi_activations输入传递给该函数时。我在函数调用时遇到了错误,而不是在函数内部。它只是一个输入参数的缓冲区大小不匹配问题。希望能得到任何帮助。