# Generalized normal sweep across mixings (main-text figure)
# python run.py --config configs/gennorm.yaml --run spiral_lejepa --alpha 2.0 --seed 1337

experiment: gennorm
out: results/gennorm

N: 2
source_dist: gennorm   # alpha provided per-run via CLI
num_eval: 10000

steps: 20000
lr: 3.0e-3
batch_size: 256
rho: 0.95
log_every: 500

runs:
  spiral_lejepa:    {mixing: spiral,   encoder: mlp,     hidden: 256, mode: lejepa,  lamb: 1.0e-3}
  spiral_whiten:    {mixing: spiral,   encoder: mlp,     hidden: 256, mode: whiten,  lamb: 0.5}
  spiral_infonce:   {mixing: spiral,   encoder: mlp,     hidden: 256, mode: infonce, sigma: 1.0}
  banana_lejepa:    {mixing: banana,   encoder: mlp,     hidden: 256, mode: lejepa,  lamb: 1.0e-3}
  banana_whiten:    {mixing: banana,   encoder: mlp,     hidden: 256, mode: whiten,  lamb: 0.5}
  banana_infonce:   {mixing: banana,   encoder: mlp,     hidden: 256, mode: infonce, sigma: 1.0}
  sinusoid_lejepa:  {mixing: sinusoid, encoder: mlp,     hidden: 256, mode: lejepa,  lamb: 1.0e-3}
  sinusoid_whiten:  {mixing: sinusoid, encoder: mlp,     hidden: 256, mode: whiten,  lamb: 0.5}
  sinusoid_infonce: {mixing: sinusoid, encoder: mlp,     hidden: 256, mode: infonce, sigma: 1.0}
  nvp_lejepa:       {mixing: nvp,      encoder: matched, n_layers: 8, mode: lejepa,  lamb: 1.0e-3}
  nvp_whiten:       {mixing: nvp,      encoder: matched, n_layers: 8, mode: whiten,  lamb: 0.5}
  nvp_infonce:      {mixing: nvp,      encoder: matched, n_layers: 8, mode: infonce, sigma: 1.0}

alphas: [0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0]
seeds: [1337, 1338, 1339]