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Getting the updated examples from 'main' to 'workshop' #4

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Getting the updated examples from 'main' to 'workshop' #4

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c3b9f95
monkey reaching config.yaml
May 30, 2024
57ba249
monkey timing config.yaml
May 30, 2024
73f8b13
data path
May 30, 2024
0b144a8
Merge remote-tracking branch 'refs/remotes/origin/workshop' into main
Jun 3, 2024
5395047
prebuilt_models.py -> xfads/ssm_modules, use prebuilt model for monkey
Jun 4, 2024
55e144f
add small L filter (number of sample and rank agnostic)
Jun 6, 2024
389d482
update lds and vdp example to use smallL filter
Jun 6, 2024
a103021
edit readme
Jun 6, 2024
d79db0b
download and format monkeyreaching h5 data
Jun 10, 2024
f6f0942
single seed for example
Jun 10, 2024
99c91a8
noncausal_model.ckpt and post_train.py example loading model from lig…
Jun 10, 2024
cd9b53e
causal_model.ckpt and option to build causal/noncausal version of def…
Jun 11, 2024
f52843c
update causal_model.ckpt
Jun 11, 2024
d82e067
plot samples from each regime, post_train.py
Jun 12, 2024
dcbf780
Adding updated workshop folder to 'main' (already merged by matt)
mahmoud-elmakki Jun 12, 2024
6c9f8ac
Adding matt's changes
mahmoud-elmakki Jun 12, 2024
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update lds and vdp example to use smallL filter
  • Loading branch information
matthew-dowling committed Jun 6, 2024
commit 389d4823302398c57c86f2b7ba67a452e4fcff72
4 changes: 2 additions & 2 deletions examples/lds_example/inference_lightning.py
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Expand Up @@ -13,7 +13,7 @@
from xfads.smoothers.lightning_trainers import LightningNonlinearSSM
from xfads.ssm_modules.dynamics import DenseGaussianInitialCondition
from xfads.ssm_modules.encoders import LocalEncoderLRMvn, BackwardEncoderLRMvn
from xfads.smoothers.nonlinear_smoother import NonlinearFilter, LowRankNonlinearStateSpaceModel
from xfads.smoothers.nonlinear_smoother import NonlinearFilterSmallL, LowRankNonlinearStateSpaceModel


def main():
Expand Down Expand Up @@ -66,7 +66,7 @@ def main():
device=cfg.device)
local_encoder = LocalEncoderLRMvn(cfg.n_latents, n_neurons, cfg.n_hidden_local, cfg.n_latents, rank=cfg.rank_local,
device=cfg.device, dropout=cfg.p_local_dropout)
nl_filter = NonlinearFilter(dynamics_mod, initial_condition_pdf, device=cfg.device)
nl_filter = NonlinearFilterSmallL(dynamics_mod, initial_condition_pdf, device=cfg.device)

"""sequence vae"""
ssm = LowRankNonlinearStateSpaceModel(dynamics_mod, likelihood_pdf, initial_condition_pdf, backward_encoder,
Expand Down
13 changes: 2 additions & 11 deletions examples/vdp_example/inference_lightning.py
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@@ -1,9 +1,6 @@
import math
import torch
import torch.nn as nn
import xfads.utils as utils
import pytorch_lightning as lightning
import matplotlib.pyplot as plt

from hydra import compose, initialize
from pytorch_lightning.loggers import CSVLogger
Expand All @@ -13,13 +10,7 @@
from xfads.smoothers.lightning_trainers import LightningNonlinearSSM
from xfads.ssm_modules.dynamics import DenseGaussianInitialCondition
from xfads.ssm_modules.encoders import LocalEncoderLRMvn, BackwardEncoderLRMvn
# from dev.smoothers.nonlinear_smoother import NonlinearFilter, LowRankNonlinearStateSpaceModel
from xfads.smoothers.nonlinear_smoother_causal import NonlinearFilter, LowRankNonlinearStateSpaceModel

# from dev.smoothers.nonlinear_smoother_diagonal import NonlinearFilter
# from dev.ssm_modules.encoders import LocalEncoderDiagonal as LocalEncoderLRMvn
# from dev.ssm_modules.encoders import BackwardEncoderDiagonal as BackwardEncoderLRMvn
# from dev.smoothers.nonlinear_smoother_diagonal import DiagonalNonlinearStateSpaceModel as LowRankNonlinearStateSpaceModel
from xfads.smoothers.nonlinear_smoother import NonlinearFilterSmallL, LowRankNonlinearStateSpaceModel



Expand Down Expand Up @@ -72,7 +63,7 @@ def main():
device=cfg.device)
local_encoder = LocalEncoderLRMvn(cfg.n_latents, n_neurons, cfg.n_hidden_local, cfg.n_latents, rank=cfg.rank_local,
device=cfg.device, dropout=cfg.p_local_dropout)
nl_filter = NonlinearFilter(dynamics_mod, initial_condition_pdf, device=cfg.device)
nl_filter = NonlinearFilterSmallL(dynamics_mod, initial_condition_pdf, device=cfg.device)

"""sequence vae"""
ssm = LowRankNonlinearStateSpaceModel(dynamics_mod, likelihood_pdf, initial_condition_pdf, backward_encoder,
Expand Down
6 changes: 4 additions & 2 deletions xfads/smoothers/nonlinear_smoother.py
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Expand Up @@ -356,6 +356,8 @@ def forward(self,
k: torch.Tensor,
K: torch.Tensor,
n_samples: int,
get_v: bool=False,
get_kl: bool=False,
p_mask: float=0.0):

# mask data, 0: data available, 1: data missing
Expand All @@ -372,8 +374,8 @@ def forward(self,

for t in range(n_time_bins):
if t == 0:
m_0 = self.initial_c_pdf.m0
P_0_diag = Fn.softplus(self.initial_c_pdf.log_v0)
m_0 = self.initial_c_pdf.m_0
P_0_diag = Fn.softplus(self.initial_c_pdf.log_Q_0)
z_f_t, m_f_t, P_f_chol_t, P_p_chol_t = filter_step_0(m_0, k[:, 0], K[:, 0], P_0_diag, n_samples)
m_p.append(m_0 * torch.ones(n_trials, n_latents, device=k[:, 0].device))
else:
Expand Down