>>> A = matrix('1.0 2.0; 3.0 4.0') >>> A [[ 1. 2.] [ 3. 4.]] >>> type(A) # file where class is defined >>> A.T # transpose [[ 1. 3.] [ 2. 4.]] >>> X = matrix('5.0 7.0') >>> Y = X.T >>> Y [[5.] [7.]] >>> print A*Y # matrix multiplica...
Element-wise multiplicationis where each pixel in the output matrix is formed by multiplying that pixel in matrix A by its corresponding entry in matrix B. The input matrices should be the same size, and the output will be the same size as well. This is achieved using themul()function: o...
matrix_multiplication_parallel(A, B) print('normal') %timeit matrix_multiplication(A, B) %timeit matrix_multiplication(A2, B2) %timeit matrix_multiplication(A3, B3) print('parallel') %timeit matrix_multiplication_parallel(A, B) %timeit matrix_multiplication_parallel(A2, B2) %timeit matrix_multi...
dtype=image.dtype) # Iterate over each pixel of the reduced image for i in range(new_height): for j in range(new_width): # Take every other pixel along each axis to reduce the image downsampled_image[i, j] = image[n*i, n*j] return downsampled_image #Try the function using n ...
Perform Matrix Multiplication in NumPy We use thenp.dot()function to perform multiplication between two matrices. Let's see an example. importnumpyasnp# create two matricesmatrix1 = np.array([[1,3], [5,7]]) matrix2 = np.array([[2,6], ...
defflip_image(image):# Takes all rows in image (:) and reverses it the order of columns (::-1)flip_image = image[:,::-1]returnflip_image#Try function using reduced imagedisplay(flip_image(reduced_M)) 3、垂直翻转 def rotate_image (image, n): ...
for j in range(new_width): # Take every other pixel along each axis to reduce the image downsampled_image[i, j] = image[n*i, n*j] return downsampled_image #Try the function using n = 2 reduced_M = reduce_image_size_by_n(M, 2) ...
Function Description abs, fabs Compute the absolute value element-wise for integer, floating-point, or complex values sqrt Compute the square root of each element (equivalent to arr ** 0.5) square Compute the square of each element (equivalent to arr ** 2) ...
函数function创建一个全是0的数组,函数ones创建一个全1的数组,函数empty创建一个内容随机并且依赖与内存状态的数组。默认创建的数组类型(dtype)都是float64。 >>> zeros( (3,4) ) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) ...
for j in range(new_width): # Take every other pixel along each axis to reduce the image downsampled_image[i, j] = image[n*i, n*j] return downsampled_image #Try the function using n = 2 reduced_M = reduce_image_size_by_n(M, 2) ...