diff --git a/exps/LFNA/basic-his.py b/exps/LFNA/basic-his.py
new file mode 100644
index 0000000..fca229f
--- /dev/null
+++ b/exps/LFNA/basic-his.py
@@ -0,0 +1,192 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/basic-his.py --srange 1-999
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from log_utils import time_string
+from log_utils import AverageMeter, convert_secs2time
+
+from utils import split_str2indexes
+
+from procedures.advanced_main import basic_train_fn, basic_eval_fn
+from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from datasets.synthetic_core import get_synthetic_env
+from models.xcore import get_model
+
+
+def subsample(historical_x, historical_y, maxn=10000):
+    total = historical_x.size(0)
+    if total <= maxn:
+        return historical_x, historical_y
+    else:
+        indexes = torch.randint(low=0, high=total, size=[maxn])
+        return historical_x[indexes], historical_y[indexes]
+
+
+def main(args):
+    prepare_seed(args.rand_seed)
+    logger = prepare_logger(args)
+
+    cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
+    if cache_path.exists():
+        env_info = torch.load(cache_path)
+    else:
+        env_info = dict()
+        dynamic_env = get_synthetic_env()
+        env_info["total"] = len(dynamic_env)
+        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
+            env_info["{:}-timestamp".format(idx)] = timestamp
+            env_info["{:}-x".format(idx)] = _allx
+            env_info["{:}-y".format(idx)] = _ally
+        env_info["dynamic_env"] = dynamic_env
+        torch.save(env_info, cache_path)
+
+    # check indexes to be evaluated
+    to_evaluate_indexes = split_str2indexes(args.srange, env_info["total"], None)
+    logger.log(
+        "Evaluate {:}, which has {:} timestamps in total.".format(
+            args.srange, len(to_evaluate_indexes)
+        )
+    )
+
+    per_timestamp_time, start_time = AverageMeter(), time.time()
+    for i, idx in enumerate(to_evaluate_indexes):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(
+                per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
+            )
+        )
+        logger.log(
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
+            + " "
+            + need_time
+        )
+        # train the same data
+        assert idx != 0
+        historical_x = env_info["{:}-x".format(idx)]
+        historical_y = env_info["{:}-y".format(idx)]
+        # build model
+        mean, std = historical_x.mean().item(), historical_x.std().item()
+        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
+        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+        # build optimizer
+        optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
+        criterion = torch.nn.MSELoss()
+        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer,
+            milestones=[
+                int(args.epochs * 0.25),
+                int(args.epochs * 0.5),
+                int(args.epochs * 0.75),
+            ],
+            gamma=0.3,
+        )
+        train_metric = MSEMetric()
+        best_loss, best_param = None, None
+        for _iepoch in range(args.epochs):
+            preds = model(historical_x)
+            optimizer.zero_grad()
+            loss = criterion(preds, historical_y)
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            # save best
+            if best_loss is None or best_loss > loss.item():
+                best_loss = loss.item()
+                best_param = copy.deepcopy(model.state_dict())
+        model.load_state_dict(best_param)
+        with torch.no_grad():
+            train_metric(preds, historical_y)
+        train_results = train_metric.get_info()
+
+        metric = ComposeMetric(MSEMetric(), SaveMetric())
+        eval_dataset = torch.utils.data.TensorDataset(
+            env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
+        )
+        eval_loader = torch.utils.data.DataLoader(
+            eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
+        )
+        results = basic_eval_fn(eval_loader, model, metric, logger)
+        log_str = (
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, env_info["total"])
+            + " train-mse: {:.5f}, eval-mse: {:.5f}".format(
+                train_results["mse"], results["mse"]
+            )
+        )
+        logger.log(log_str)
+
+        save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
+            idx, env_info["total"]
+        )
+        save_checkpoint(
+            {
+                "model": model.state_dict(),
+                "index": idx,
+                "timestamp": env_info["{:}-timestamp".format(idx)],
+            },
+            save_path,
+            logger,
+        )
+        logger.log("")
+
+        per_timestamp_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Use all the past data to train.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/use-same-timestamp",
+        help="The checkpoint directory.",
+    )
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=512,
+        help="The batch size",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=1000,
+        help="The total number of epochs.",
+    )
+    parser.add_argument(
+        "--srange", type=str, required=True, help="The range of models to be evaluated"
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=4,
+        help="The number of data loading workers (default: 4)",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+    args = parser.parse_args()
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "The save dir argument can not be None"
+    main(args)
diff --git a/exps/LFNA/basic-same.py b/exps/LFNA/basic-same.py
new file mode 100644
index 0000000..578c6f7
--- /dev/null
+++ b/exps/LFNA/basic-same.py
@@ -0,0 +1,196 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/basic-same.py --srange 1-999
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from log_utils import time_string
+from log_utils import AverageMeter, convert_secs2time
+
+from utils import split_str2indexes
+
+from procedures.advanced_main import basic_train_fn, basic_eval_fn
+from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
+from datasets.synthetic_core import get_synthetic_env
+from models.xcore import get_model
+
+
+def subsample(historical_x, historical_y, maxn=10000):
+    total = historical_x.size(0)
+    if total <= maxn:
+        return historical_x, historical_y
+    else:
+        indexes = torch.randint(low=0, high=total, size=[maxn])
+        return historical_x[indexes], historical_y[indexes]
+
+
+def main(args):
+    prepare_seed(args.rand_seed)
+    logger = prepare_logger(args)
+
+    cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
+    if cache_path.exists():
+        env_info = torch.load(cache_path)
+    else:
+        env_info = dict()
+        dynamic_env = get_synthetic_env()
+        env_info["total"] = len(dynamic_env)
+        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
+            env_info["{:}-timestamp".format(idx)] = timestamp
+            env_info["{:}-x".format(idx)] = _allx
+            env_info["{:}-y".format(idx)] = _ally
+        env_info["dynamic_env"] = dynamic_env
+        torch.save(env_info, cache_path)
+
+    # check indexes to be evaluated
+    to_evaluate_indexes = split_str2indexes(args.srange, env_info["total"], None)
+    logger.log(
+        "Evaluate {:}, which has {:} timestamps in total.".format(
+            args.srange, len(to_evaluate_indexes)
+        )
+    )
+
+    per_timestamp_time, start_time = AverageMeter(), time.time()
+    for i, idx in enumerate(to_evaluate_indexes):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(
+                per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
+            )
+        )
+        logger.log(
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
+            + " "
+            + need_time
+        )
+        # train the same data
+        assert idx != 0
+        historical_x, historical_y = [], []
+        for past_i in range(idx):
+            historical_x.append(env_info["{:}-x".format(past_i)])
+            historical_y.append(env_info["{:}-y".format(past_i)])
+        historical_x, historical_y = torch.cat(historical_x), torch.cat(historical_y)
+        historical_x, historical_y = subsample(historical_x, historical_y)
+        # build model
+        mean, std = historical_x.mean().item(), historical_x.std().item()
+        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
+        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+        # build optimizer
+        optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
+        criterion = torch.nn.MSELoss()
+        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer,
+            milestones=[
+                int(args.epochs * 0.25),
+                int(args.epochs * 0.5),
+                int(args.epochs * 0.75),
+            ],
+            gamma=0.3,
+        )
+        train_metric = MSEMetric()
+        best_loss, best_param = None, None
+        for _iepoch in range(args.epochs):
+            preds = model(historical_x)
+            optimizer.zero_grad()
+            loss = criterion(preds, historical_y)
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            # save best
+            if best_loss is None or best_loss > loss.item():
+                best_loss = loss.item()
+                best_param = copy.deepcopy(model.state_dict())
+        model.load_state_dict(best_param)
+        with torch.no_grad():
+            train_metric(preds, historical_y)
+        train_results = train_metric.get_info()
+
+        metric = ComposeMetric(MSEMetric(), SaveMetric())
+        eval_dataset = torch.utils.data.TensorDataset(
+            env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
+        )
+        eval_loader = torch.utils.data.DataLoader(
+            eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
+        )
+        results = basic_eval_fn(eval_loader, model, metric, logger)
+        log_str = (
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, env_info["total"])
+            + " train-mse: {:.5f}, eval-mse: {:.5f}".format(
+                train_results["mse"], results["mse"]
+            )
+        )
+        logger.log(log_str)
+
+        save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
+            idx, env_info["total"]
+        )
+        save_checkpoint(
+            {
+                "model": model.state_dict(),
+                "index": idx,
+                "timestamp": env_info["{:}-timestamp".format(idx)],
+            },
+            save_path,
+            logger,
+        )
+        logger.log("")
+
+        per_timestamp_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Use data at the same timestamp.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/use-all-past-data",
+        help="The checkpoint directory.",
+    )
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=512,
+        help="The batch size",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=1000,
+        help="The total number of epochs.",
+    )
+    parser.add_argument(
+        "--srange", type=str, required=True, help="The range of models to be evaluated"
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=4,
+        help="The number of data loading workers (default: 4)",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+    args = parser.parse_args()
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "The save dir argument can not be None"
+    main(args)
diff --git a/exps/LFNA/basic.py b/exps/LFNA/basic.py
deleted file mode 100644
index 354b13e..0000000
--- a/exps/LFNA/basic.py
+++ /dev/null
@@ -1,165 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
-#####################################################
-# python exps/LFNA/basic.py
-#####################################################
-import sys, time, torch, random, argparse
-from copy import deepcopy
-from pathlib import Path
-
-lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
-if str(lib_dir) not in sys.path:
-    sys.path.insert(0, str(lib_dir))
-from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
-from log_utils import time_string
-
-from procedures.advanced_main import basic_train_fn, basic_eval_fn
-from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
-from datasets.synthetic_core import get_synthetic_env
-from models.xcore import get_model
-
-
-def main(args):
-    torch.set_num_threads(args.workers)
-    prepare_seed(args.rand_seed)
-    logger = prepare_logger(args)
-
-    dynamic_env = get_synthetic_env()
-    historical_x, historical_y = None, None
-    for idx, (timestamp, (allx, ally)) in enumerate(dynamic_env):
-
-        if historical_x is not None:
-            mean, std = historical_x.mean().item(), historical_x.std().item()
-        else:
-            mean, std = 0, 1
-        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
-        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
-
-        # create the current data loader
-        if historical_x is not None:
-            train_dataset = torch.utils.data.TensorDataset(historical_x, historical_y)
-            train_loader = torch.utils.data.DataLoader(
-                train_dataset,
-                batch_size=args.batch_size,
-                shuffle=True,
-                num_workers=args.workers,
-            )
-            optimizer = torch.optim.Adam(
-                model.parameters(), lr=args.init_lr, amsgrad=True
-            )
-            criterion = torch.nn.MSELoss()
-            lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
-                optimizer,
-                milestones=[
-                    int(args.epochs * 0.25),
-                    int(args.epochs * 0.5),
-                    int(args.epochs * 0.75),
-                ],
-                gamma=0.3,
-            )
-            for _iepoch in range(args.epochs):
-                results = basic_train_fn(
-                    train_loader, model, criterion, optimizer, MSEMetric(), logger
-                )
-                lr_scheduler.step()
-                if _iepoch % args.log_per_epoch == 0:
-                    log_str = (
-                        "[{:}]".format(time_string())
-                        + " [{:04d}/{:04d}][{:04d}/{:04d}]".format(
-                            idx, len(dynamic_env), _iepoch, args.epochs
-                        )
-                        + " mse: {:.5f}, lr: {:.4f}".format(
-                            results["mse"], min(lr_scheduler.get_last_lr())
-                        )
-                    )
-                    logger.log(log_str)
-            results = basic_eval_fn(train_loader, model, MSEMetric(), logger)
-            logger.log(
-                "[{:}] [{:04d}/{:04d}] train-mse: {:.5f}".format(
-                    time_string(), idx, len(dynamic_env), results["mse"]
-                )
-            )
-
-        metric = ComposeMetric(MSEMetric(), SaveMetric())
-        eval_dataset = torch.utils.data.TensorDataset(allx, ally)
-        eval_loader = torch.utils.data.DataLoader(
-            eval_dataset,
-            batch_size=args.batch_size,
-            shuffle=False,
-            num_workers=args.workers,
-        )
-        results = basic_eval_fn(eval_loader, model, metric, logger)
-        log_str = (
-            "[{:}]".format(time_string())
-            + " [{:04d}/{:04d}]".format(idx, len(dynamic_env))
-            + " eval-mse: {:.5f}".format(results["mse"])
-        )
-        logger.log(log_str)
-
-        save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
-            idx, len(dynamic_env)
-        )
-        save_checkpoint(
-            {"model": model.state_dict(), "index": idx, "timestamp": timestamp},
-            save_path,
-            logger,
-        )
-
-        # Update historical data
-        if historical_x is None:
-            historical_x, historical_y = allx, ally
-        else:
-            historical_x, historical_y = torch.cat((historical_x, allx)), torch.cat(
-                (historical_y, ally)
-            )
-        logger.log("")
-
-    logger.log("-" * 200 + "\n")
-    logger.close()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser("Use all the past data to train.")
-    parser.add_argument(
-        "--save_dir",
-        type=str,
-        default="./outputs/lfna-synthetic/use-all-past-data",
-        help="The checkpoint directory.",
-    )
-    parser.add_argument(
-        "--init_lr",
-        type=float,
-        default=0.1,
-        help="The initial learning rate for the optimizer (default is Adam)",
-    )
-    parser.add_argument(
-        "--batch_size",
-        type=int,
-        default=256,
-        help="The batch size",
-    )
-    parser.add_argument(
-        "--epochs",
-        type=int,
-        default=2000,
-        help="The total number of epochs.",
-    )
-    parser.add_argument(
-        "--log_per_epoch",
-        type=int,
-        default=200,
-        help="Log the training information per __ epochs.",
-    )
-    parser.add_argument(
-        "--workers",
-        type=int,
-        default=4,
-        help="The number of data loading workers (default: 4)",
-    )
-    # Random Seed
-    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
-    args = parser.parse_args()
-    if args.rand_seed is None or args.rand_seed < 0:
-        args.rand_seed = random.randint(1, 100000)
-    assert args.save_dir is not None, "The save dir argument can not be None"
-    main(args)