Update README.md

Updated comment above weights in README to provide more detail about the requirements for the weights parameter.

Author: sm00thix

README.md

@@ -50,8 +50,10 @@ The `cvmatrix` software package now also features **weigthed matrix produts** $\
 > Y = np.random.uniform(size=(N, M)) # Random Y data
 > folds = np.arange(100) % 5 # 5-fold cross-validation
 >
-> # Weights must be non-negative and the sum of weights for any training partition must
-> # be greater than zero.
+> # Weights must be non-negative. If centering or scaling is used, the sum of weights
+> # for any training partition must be greater than zero. If scaling is used, the
+> # number of non-negative weights for any training partition must be greater than
+> # the ddof provided in the constructor.
 > weights = np.random.uniform(size=(N,)) + 0.1
 >
 > # Instantiate CVMatrix
@@ -115,4 +117,4 @@ Guidelines](https://github.com/Sm00thix/CVMatrix/blob/main/CONTRIBUTING.md).
 
 ## Funding
 - Up until May 31st 2025, this work has been carried out as part of an industrial Ph. D. project receiving funding from [FOSS Analytical A/S](https://www.fossanalytics.com/) and [The Innovation Fund Denmark](https://innovationsfonden.dk/en). Grant number 1044-00108B.
-- From June 1st 2025 and onward, this work is sponsored by [FOSS Analytical A/S](https://www.fossanalytics.com/).
+- From June 1st 2025 and onward, this work is sponsored by [FOSS Analytical A/S](https://www.fossanalytics.com/).