Only tested on Mac El Captain OS Python 3

To run, you will need the following:

• Python3
• GPy
• Numpy
• Matpolotlib
• Scipy

And if you would like to run on a GPU pycuda *Note: Only works with RBF (SE) Kernels. Functionality may be disabled

Note2: The comments in the code may not be up-to-date with what is actually happening in the script. Will clear up later.

To add more kernels from the GPy framework, go to dataprocessing.py , edit kernel_list add additional kernels to this list or remove them>

Note:

• If using an IDE with tab completion typing Gpy.kern.<press_tab> will show all available kernels
• I've only tested with RBF, StdPeriodic, Matern32, Matern52 and MLP.
• Some kernels may not work with the current framework as is, as some kernels do not have a lengthscale.

To add more datasets, first add your desired dataset to the outer data folder:

• Then go to either importdata.py or import2d.py (if using 2D starting input) and add an "else if option == <you_define_an_option_name> and add the following:

  • data = <import_YOUR_data_function>
  • data = data[:,None] % transposes data to a column array
  • size_data = size_data(data.shape[0])
  • time = np.linspace(0,1,size_data)

• For import2d.py add something like this:

  • elif option == 'stock2d':
  • stock_no = 13
  • data,input2 = sd.get_stock(stock_no,if_2d = True)
  • data = data[0:1000][:,None]
  • size_data = data.shape[0]
  • inputs1 = np.linspace(0,1,size_data)
  • inputs2 = input2[0:1000][:,None]
  • inputs = np.column_stack((inputs1,inputs2))

• Go to main.py or mainNd.py and add to the Options list the name that you defined.

• Typically all observed inputs are times, where times is a linearly spaced array between [0,1] and is spaced depending on the size of your data set.

• All things are put into to column vector form so that they can be used throughout the script

To run:

• Go to main.py or mainNd.py
• Go to the main function at the bottom of the script
• Edit how many runs you want (no of layers) , the split of your training to test data and edit the options[<insert_Option_list_number>] and Models[<insert_0_or_1>] depending on whehther you want the augmented or single input model

The scrip automatically generates plots for each layer and saves them in ../Plots/<choosen_model>/ it also saves all data that has been gathered and save .csv files for each layer ../Data/<choosen_model>/data_layer_/<choosen_option>