# Grab sasnets repo, and sasmodels repo to generate data.
git clone https://github.com/scattering/sasnets
git clone https://github.com/pkienzle/sasmodels

# Create the conda environment.
# ... probably missing some packages here...
conda create -n sasnets python numpy scipy matplotlib scikit-learn pandas
conda activate sasnets
pip install columnize # ... for pretty output columns [optional]

# Add tensorflow or tensorflow-gpu, depending on whether you have nvidia
# hardware (and drivers for cuda 10.0 for tensorflow 2.x).
#conda install -y tensorflow-gpu
conda install -y tensorflow

# Need sasmodels for data generation. This requires pycuda, pyopencl
# or a C compiler to run.
conda install -y pybind11 appdirs decorator mako
pip install pyopencl
#pip install pycuda
#pip install tinycc  # small C compiler for windows, if you need it.

# Work from the sasnets directory
cd sasnets

# Generate the table "train" in sasnets.db.  In this case we are
# only selecting the different types of sphere models.
bin/sasgen --count=2000 --mono=true *sphere | tee generate.log
# Use 'all' for the full dataset (600 MB at 2000 items per model).

# Train on the dataset (you will want to use more epochs...)
bin/sasnet -v --epochs=10

# Show training results
bin/sasanal
# Show dendrogram
bin/sasanal -c