what should be correct format of feature vectors matrix for feeding into neural networks?

In MATLAB, the correct format of feature vectors for feeding into neural networks is typically a matrix where each column represents a feature vector and each row represents a different observation (sample). Here’s a general guideline:

1. Matrix Dimensions:
   - For a classification or regression problem, your feature matrix should be of size `N x M`, where `N` is the number of features and `M` is the number of samples.
   - Ensure that each column corresponds to a single observation and each row corresponds to a different feature.

2. Example:
   
   % Example feature matrix with 5 features and 100 samples
   X = rand(5, 100);
   % Example target matrix for 3-class classification with 100 samples
   T = randi([0, 1], 3, 100);  % One-hot encoded targets
   

3. Feed into Neural Network:
   Use the appropriate functions to train the network with your feature matrix. For example, with a pattern recognition network:

   
   % Create a pattern recognition network with 10 hidden neurons
   net = patternnet(10);
   % Train the network
   net = train(net, X, T);
   

4. Additional Preprocessing:
   Ensure your data is preprocessed appropriately (e.g., normalized or standardized) to improve training performance.

By following these guidelines, you can properly format your feature vectors for neural networks in MATLAB.