arr = np.array([
[0.15, 0.71, 0.26, -0.11, -2.03],
[0.13, 0.44, -0.11, -0.23, 0.19],
[-1.44, 0.51, 0.42, 2.58, -0.88],
[-1.18, 2.73, 2.35, 0.21, -0.29],
[-1.64, -0.37, 0.27, 0.57, 1.82]
])

arr_filter = np.array([
[0.08, 0.27, -0.24],
[-0.25, -0.11, -0.18],
[0.44, 1.87, 1.18]
])

import numpy as np

rng = np.random.default_rng(123)
arr = rng.normal(size=(33,33,3)).round(2)


print(arr)

# [

#     [[-0.99 -0.37  1.29]
#      [ 0.19  0.92  0.58]
#      [-0.64  0.54 -0.32]
#      ...
#      [ 1.23  0.15  0.48]
#      [-0.15  1.32 -1.22]

#      [-0.3  -1.17  0.83]]
# 
#     [[ 0.85 -0.52  1.66]
#      [-0.3  -1.38 -0.28]
#      [ 0.36 -0.23  2.27]
#      ...

#      [ 1.52  0.49  0.7 ]
#      [ 0.85 -0.91  0.12]
#      [ 0.15 -0.16 -1.09]]
# 
#     ...
# 

#     [[-0.38 -0.45  1.  ]
#      [-0.58  2.18  0.36]
#      [-0.44 -2.77  0.82]
#      ...
#      [-1.58 -0.5  -0.52]
#      [-0.86 -0.54 -0.26]
#      [ 0.23  0.81 -0.4 ]]
# 
#     [[-1.98 -0.22 -0.79]
#      [-0.01 -1.47 -0.83]
#      [-0.87  0.3  -0.82]
#      ...
#      [ 1.42  0.7  -0.75]
#      [-0.81  1.68 -0.82]
#      [-0.93  0.28 -1.61]]
# ]

filter = rng.normal(size=(5,5,3)).round(2)

print(filter)
#[

#      [[-1.47  0.74  0.58]

#      [ 0.46  2.37  0.79]
#      [ 1.15 -1.11 -0.29]
#      [-0.98  0.29  0.44]
#      [-0.44 -0.03  0.69]]
# 
#     [[ 0.34  0.67 -0.11]
#      [-0.71 -1.   -0.88]
#      [ 0.61  0.49 -0.27]
#      [ 0.12 -1.56  0.11]
#      [ 0.32 -0.98  0.46]]
#
#     [[-1.03  0.58  0.08]
#      [ 0.89  0.86  1.49]
#      [-0.4   0.86 -0.29]
#      [ 0.07 -0.09 -0.87]
#      [ 0.2   1.22 -0.27]]
# 
#     [[ 1.1  -2.61  1.64]
#      [-1.15  0.47  1.44]
#      [-1.45  0.39  1.37]
#      [ 0.13 -0.1   0.04]
#      [ 0.27  0.57  0.57]]
# 
#     [[-0.61 -0.41  0.93]
#      [ 1.47 -0.07 -0.29]
#      [ 0.49  1.02  0.2 ]
#      [ 0.16  0.95  0.52]
#      [ 1.11  0.13 -0.17]]
# ]

mport numpy as np

def convolution_3d(arr, arr_filter, stride=2):

arr_height, arr_width, arr_depth = arr.shape
arr_filter_height, arr_filter_width, _ = arr_filter.shape

output_height = (arr_height - arr_filter_height) // stride + 1
output_width = (arr_width - arr_filter_width) // stride + 1
output = np.zeros((output_height, output_width))

for i in range(output_height):
for j in range(output_width):
h_start = i * stride
h_end = h_start + arr_filter_height
w_start = j * stride
w_end = w_start + arr_filter_width

arr_slice = arr[h_start:h_end, w_start:w_end, :]
output[i, j] = np.sum(arr_slice * arr_filter)

return output

rng = np.random.default_rng(123)
arr = rng.normal(size=(33,33,3)).round(2)
arr_filter = rng.normal(size=(5,5,3)).round(2)

result = convolution_3d(arr, arr_filter, stride=2)
print(f"Результат свертки: \n{result}") 
            

import numpy as np

rng = np.random.default_rng(123)
arr = rng.normal(size=(33,33,3)).round(2)
arr_filter = rng.normal(size=(5,5,3)).round(2)
stride = 2
result_size = 15
result = np.zeros((result_size, result_size))

for i in range(result_size):
for j in range(result_size):
x_start = i * stride
y_start = j * stride
x_end = x_start + arr_filter.shape[0]
y_end = y_start + arr_filter.shape[1]

sub_array = arr[x_start:x_end, y_start:y_end, :]
result[i, j] = np.sum(sub_array * arr_filter)

print(f"Результат свертки: \n{result}")