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 # Creation for weight matrices for initialising the first layer of a neural network
 
+## Implemented methods
+
+- PCA: Principal Component Analysis
+- ICA: Independent Component Analysis
+- SC: Sparse Coding (Dictionary Learning with positive code)
+- RG: Random Gabor filters (Gabor filters with randomly drawn parameters)
+- RP: Random Projections drawn from a Gaussian distribution
+
+All weight matrices are normalised such that the input vector (receptive field) of each unit in the hidden layer is normalised to L2-norm = 1.
+
 ## Dependencies & Environment
 
 - Conda needs to be installed
 - Setup the conda environment `weightmatrices` by running:
 
     ```
     bash setup_dependencies.sh
     ```
 
 ## Usage
 
 - To run the main script(s) that produce the weight matrices run:
 
     ```
     bash run.sh
     ```
 
     If you want to directly run `main.py` to use command line args (e.g. `python main.py --methods 'pca' --nhidden 10 25`), do not forget to activate the environment by running:
 
     ```
     conda activate weightmatrices
     ```
 
 ## Remarks
 
 By default, Datasets are downloaded and put into a /datasets directory.  
 To avoid this you can:
 1. Change the root variable in the torchvision data loader function.
 2. Create the /datasets folder and corresponding subfolder and put the datasets (or a link) in there.