Subtracting the mean of each row of a matrix is a common data preprocessing technique used in various machine learning applications. This technique involves subtracting the mean of each row from each element of the corresponding row in a matrix. The resulting matrix will have a zero mean for each row, which can be useful for normalization or standardization purposes. Here are a few ways through which you can do this:
1. Using "NumPy vectorization":
- The code first creates a 3x3 matrix called
arr
with random integer values between0
and9
. - It then calculates the mean of each row using
np.mean()
withaxis=1
to sum over each row. - After which, it subtracts the row means from each element in the row using NumPy vectorization by adding a new axis to
row_means
with[:, np.newaxis]
. - The resulting array is printed to the console.
2. Using "np.apply_along_axis()" function:
- This code generates a 3x3 matrix of random integers between
0
and10
using the NumPyrandom.randint()
function. - Then, it subtracts the mean of each row of the matrix using the
apply_along_axis()
function with alambda
function that calculates the mean of each row and subtracts it from the elements of that row. - Finally, it prints the resulting matrix with the mean of each row subtracted.
-
In terms of efficiency, the first method is likely to be more efficient because it uses NumPy’s built-in broadcasting to subtract the row means from each element in the array, which is a vectorized operation that can be performed quickly.
-
The second method involves applying the
lambda
function to each row individually, which can be slower for larger arrays. -
However, the advantage of the second method is that it is more concise and potentially easier to read, especially for people who are familiar with the
apply_along_axis()
function. -
Additionally, the second method can be more flexible than the first method because it allows for more complex functions to be applied to each row, not just the mean subtraction.