import torch

from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader

from torch import autograd

import time
import os
import fire
import random

import numpy as np
import matplotlib.pyplot as plt

# from datasets import BatteryDataset
from processed_datasets import BatteryDataset

# import multiprocessing

import aesmc.train as train
import aesmc
from models import SSM, Proposal
from aesmc.statistics import sample_from_prior


class LGSSMTester:
    def run(self,
            run_dir: str = './runs/',
            state_dim: int = 5,
            # action_dim: int = 3,
            obs_dim: int = 13,
            sequence_length: int = 50,
            batch_size: int = 32,
            device_name: str = "cuda" if torch.cuda.is_available() else "cpu",
            seed: int = 100):
        # np.random.seed(seed)
        # random.seed(seed)
        # torch.manual_seed(seed)
        # torch.cuda.manual_seed(seed)
        # torch.cuda.manual_seed_all(seed)

        device = torch.device(device_name)
        model = SSM(state_dim, obs_dim, 5, 1, 5, 1)

        model.to(device)

        os.makedirs(run_dir, exist_ok=True)
        checkpoint_path = os.path.join(run_dir, 'checkpoint.pt')
        log_dir = None
        if os.path.exists(checkpoint_path):
            checkpoint = torch.load(checkpoint_path, map_location=device)
            model.load_state_dict(checkpoint['model'])
            log_dir = checkpoint['log_dir']

        model.double()
        model.eval()
        observations = sample_from_prior(model.prior, model.transition, model.observation, sequence_length, batch_size)[1]
        for batch in range(batch_size):
            plt.plot(range(len(observations)), [obs[batch, -1].item() for obs in observations])
        plt.gcf().savefig("runs/prior_sample.jpg")
        plt.close()

if __name__ == '__main__':
    fire.Fire(LGSSMTester)

2737e0cda43d5d6a6c9616b6f4c46809d0a5db4b
<mask token>


class LGSSMTester:
    <mask token>


<mask token>

<mask token>


class LGSSMTester:

    def run(self, run_dir: str='./runs/', state_dim: int=5, obs_dim: int=13,
        sequence_length: int=50, batch_size: int=32, device_name: str=
        'cuda' if torch.cuda.is_available() else 'cpu', seed: int=100):
        device = torch.device(device_name)
        model = SSM(state_dim, obs_dim, 5, 1, 5, 1)
        model.to(device)
        os.makedirs(run_dir, exist_ok=True)
        checkpoint_path = os.path.join(run_dir, 'checkpoint.pt')
        log_dir = None
        if os.path.exists(checkpoint_path):
            checkpoint = torch.load(checkpoint_path, map_location=device)
            model.load_state_dict(checkpoint['model'])
            log_dir = checkpoint['log_dir']
        model.double()
        model.eval()
        observations = sample_from_prior(model.prior, model.transition,
            model.observation, sequence_length, batch_size)[1]
        for batch in range(batch_size):
            plt.plot(range(len(observations)), [obs[batch, -1].item() for
                obs in observations])
        plt.gcf().savefig('runs/prior_sample.jpg')
        plt.close()


<mask token>

<mask token>


class LGSSMTester:

    def run(self, run_dir: str='./runs/', state_dim: int=5, obs_dim: int=13,
        sequence_length: int=50, batch_size: int=32, device_name: str=
        'cuda' if torch.cuda.is_available() else 'cpu', seed: int=100):
        device = torch.device(device_name)
        model = SSM(state_dim, obs_dim, 5, 1, 5, 1)
        model.to(device)
        os.makedirs(run_dir, exist_ok=True)
        checkpoint_path = os.path.join(run_dir, 'checkpoint.pt')
        log_dir = None
        if os.path.exists(checkpoint_path):
            checkpoint = torch.load(checkpoint_path, map_location=device)
            model.load_state_dict(checkpoint['model'])
            log_dir = checkpoint['log_dir']
        model.double()
        model.eval()
        observations = sample_from_prior(model.prior, model.transition,
            model.observation, sequence_length, batch_size)[1]
        for batch in range(batch_size):
            plt.plot(range(len(observations)), [obs[batch, -1].item() for
                obs in observations])
        plt.gcf().savefig('runs/prior_sample.jpg')
        plt.close()


if __name__ == '__main__':
    fire.Fire(LGSSMTester)

import torch
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from torch import autograd
import time
import os
import fire
import random
import numpy as np
import matplotlib.pyplot as plt
from processed_datasets import BatteryDataset
import aesmc.train as train
import aesmc
from models import SSM, Proposal
from aesmc.statistics import sample_from_prior


class LGSSMTester:

    def run(self, run_dir: str='./runs/', state_dim: int=5, obs_dim: int=13,
        sequence_length: int=50, batch_size: int=32, device_name: str=
        'cuda' if torch.cuda.is_available() else 'cpu', seed: int=100):
        device = torch.device(device_name)
        model = SSM(state_dim, obs_dim, 5, 1, 5, 1)
        model.to(device)
        os.makedirs(run_dir, exist_ok=True)
        checkpoint_path = os.path.join(run_dir, 'checkpoint.pt')
        log_dir = None
        if os.path.exists(checkpoint_path):
            checkpoint = torch.load(checkpoint_path, map_location=device)
            model.load_state_dict(checkpoint['model'])
            log_dir = checkpoint['log_dir']
        model.double()
        model.eval()
        observations = sample_from_prior(model.prior, model.transition,
            model.observation, sequence_length, batch_size)[1]
        for batch in range(batch_size):
            plt.plot(range(len(observations)), [obs[batch, -1].item() for
                obs in observations])
        plt.gcf().savefig('runs/prior_sample.jpg')
        plt.close()


if __name__ == '__main__':
    fire.Fire(LGSSMTester)

import torch

from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader

from torch import autograd

import time
import os
import fire
import random

import numpy as np
import matplotlib.pyplot as plt

# from datasets import BatteryDataset
from processed_datasets import BatteryDataset

# import multiprocessing

import aesmc.train as train
import aesmc
from models import SSM, Proposal
from aesmc.statistics import sample_from_prior


class LGSSMTester:
    def run(self,
            run_dir: str = './runs/',
            state_dim: int = 5,
            # action_dim: int = 3,
            obs_dim: int = 13,
            sequence_length: int = 50,
            batch_size: int = 32,
            device_name: str = "cuda" if torch.cuda.is_available() else "cpu",
            seed: int = 100):
        # np.random.seed(seed)
        # random.seed(seed)
        # torch.manual_seed(seed)
        # torch.cuda.manual_seed(seed)
        # torch.cuda.manual_seed_all(seed)

        device = torch.device(device_name)
        model = SSM(state_dim, obs_dim, 5, 1, 5, 1)

        model.to(device)

        os.makedirs(run_dir, exist_ok=True)
        checkpoint_path = os.path.join(run_dir, 'checkpoint.pt')
        log_dir = None
        if os.path.exists(checkpoint_path):
            checkpoint = torch.load(checkpoint_path, map_location=device)
            model.load_state_dict(checkpoint['model'])
            log_dir = checkpoint['log_dir']

        model.double()
        model.eval()
        observations = sample_from_prior(model.prior, model.transition, model.observation, sequence_length, batch_size)[1]
        for batch in range(batch_size):
            plt.plot(range(len(observations)), [obs[batch, -1].item() for obs in observations])
        plt.gcf().savefig("runs/prior_sample.jpg")
        plt.close()

if __name__ == '__main__':
    fire.Fire(LGSSMTester)
