Subtracting Row Means in NumPy

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 between 0 and 9.
  • It then calculates the mean of each row using np.mean() with axis=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 and 10 using the NumPy random.randint() function.
  • Then, it subtracts the mean of each row of the matrix using the apply_along_axis() function with a lambda 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.