#### Pooling Functions

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# Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
# Importing the libraries
import numpy as np
from PIL import Image

# Maxpooling Function
def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform maxpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of maxpooled matrix
Sample Input Output:
>>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 6.,  8.],
[14., 16.]])
>>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[241., 180.],
[241., 157.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0

# compute the shape of the output matrix
maxpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
updated_arr = np.zeros((maxpool_shape, maxpool_shape))

while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1

# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1

# reset the column index to 0
j = 0
mat_j = 0

return updated_arr

# Averagepooling Function
def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
"""
This function is used to perform avgpooling on the input array of 2D matrix(image)
Args:
arr: numpy array
size: size of pooling matrix
stride: the number of pixels shifts over the input matrix
Returns:
numpy array of avgpooled matrix
Sample Input Output:
>>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
array([[ 3.,  5.],
[11., 13.]])
>>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
array([[161., 102.],
[114.,  69.]])
"""
arr = np.array(arr)
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix")
i = 0
j = 0
mat_i = 0
mat_j = 0

# compute the shape of the output matrix
avgpool_shape = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
updated_arr = np.zeros((avgpool_shape, avgpool_shape))

while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1

# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
j = 0
mat_j = 0

return updated_arr

# Main Function
if __name__ == "__main__":
from doctest import testmod

testmod(name="avgpooling", verbose=True)