modified: Figure_1.png

modified:   checkpoints/forward/best_tcn_model.pt
	modified:   checkpoints/forward/training_history.csv
	new file:   checkpoints_mlp/task1_feature_mlp/best_feature_mlp.pt
	new file:   checkpoints_mlp/task1_feature_mlp/training_history.csv
	new file:   checkpoints_rms/forward_rms/best_rms_model.pt
	new file:   checkpoints_rms/forward_rms/training_history.csv
	modified:   evaluation_outputs/forward/evaluation_forward_test_b0_s0.png
	new file:   evaluation_outputs/forward/evaluation_forward_val_b0_s0.png
	new file:   evaluation_outputs/forward/evaluation_forward_val_b3_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_test_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_test_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_val_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_val_s0_waveform.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_train_curve.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_curve.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_s0.png
	new file:   evaluation_outputs/task1_feature_mlp/harmonic_5mm_0.75Hz_prediction.png
	new file:   evaluation_outputs/task1_feature_mlp/harmonic_5mm_1.55Hz_prediction.png
	new file:   scripts/__pycache__/config.cpython-310.pyc
	new file:   scripts/__pycache__/dataset.cpython-310.pyc
	new file:   scripts/__pycache__/model.cpython-310.pyc
	new file:   scripts/config.py
	new file:   scripts/dataset.py
	new file:   scripts/evaluate.py
	new file:   scripts/model.py
	new file:   scripts/predict_single.py
	new file:   scripts/train.py
	modified:   src/__pycache__/config.cpython-310.pyc
	modified:   src/__pycache__/dataset.cpython-310.pyc
	modified:   src/__pycache__/model.cpython-310.pyc
	modified:   src/config.py
	modified:   src/dataset.py
	modified:   src/model.py
	new file:   src_new/__pycache__/config.cpython-310.pyc
	new file:   src_new/__pycache__/dataset.cpython-310.pyc
	new file:   src_new/__pycache__/evaluate.cpython-310.pyc
	new file:   src_new/__pycache__/model.cpython-310.pyc
	new file:   src_new/__pycache__/train.cpython-310.pyc
	new file:   src_new/config.py
	new file:   src_new/dataset.py
	new file:   src_new/evaluate.py
	new file:   src_new/model.py
	new file:   src_new/train.py

scripts/config.py

@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+CORE_FEATURE_NAMES: tuple[str, ...] = (
+    "dominant_frequency_hz",
+    "frequency_squared",
+    "inverse_frequency_hz",
+    "log_frequency_hz",
+    "input_rms",
+    "input_peak_abs",
+    "input_peak_to_peak",
+    "crest_factor",
+    "middle_length_ratio",
+    "dominant_amplitude",
+    "dominant_energy_ratio",
+    "harmonic_fit_amplitude",
+    "harmonic_fit_residual_ratio",
+    "spectral_peak_prominence",
+    "half_power_bandwidth_hz",
+    "spectral_centroid_hz",
+    "signal_mean",
+)
+
+
+@dataclass
+class DataConfig:
+    project_root: Path = field(default_factory=lambda: Path(__file__).resolve().parents[1])
+    scenario: str = "Non_TMD"
+    train_split_name: str = "train"
+    val_split_name: str = "val"
+    test_split_name: str = "test"
+    csv_pattern: str = "*.csv"
+
+    code_column: str = "code"
+    time_column: str = "time"
+    base_sensor_code: str = "WSMS00012"
+    base_axis: str = "value1"
+    response_sensor_code: str = "WSMS00007"
+    response_axis: str = "value3"
+
+    middle_segment_start_ratio: float = 0.20
+    middle_segment_end_ratio: float = 0.80
+    min_segment_length: int = 512
+    steady_window_ratio: float = 0.25
+    steady_window_stride_ratio: float = 0.05
+    stability_subwindow_count: int = 4
+    interpolation_method: str = "linear"
+    normalization_eps: float = 1e-6
+
+    downloads_dir: Path = field(init=False)
+    scenario_dir: Path = field(init=False)
+    train_dir: Path = field(init=False)
+    val_dir: Path = field(init=False)
+    test_dir: Path = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.project_root = Path(self.project_root).resolve()
+        self.downloads_dir = self.project_root / "downloads"
+        self.scenario_dir = self.downloads_dir / self.scenario
+        self.train_dir = self.scenario_dir / self.train_split_name
+        self.val_dir = self.scenario_dir / self.val_split_name
+        self.test_dir = self.scenario_dir / self.test_split_name
+
+
+@dataclass
+class ModelConfig:
+    input_dim: int = len(CORE_FEATURE_NAMES)
+    hidden_dims: tuple[int, ...] = (96, 64, 32)
+    dropout: float = 0.08
+
+
+@dataclass
+class TrainConfig:
+    epochs: int = 400
+    batch_size: int = 16
+    learning_rate: float = 1e-3
+    weight_decay: float = 1e-4
+    seed: int = 42
+    device: str = "cuda"
+    grad_clip_norm: float = 1.0
+    lr_scheduler_patience: int = 20
+    lr_scheduler_factor: float = 0.5
+    min_learning_rate: float = 1e-6
+    early_stop_patience: int = 50
+    checkpoint_dir: str = "checkpoints_mlp"
+    history_name: str = "training_history.csv"
+    best_model_name: str = "best_feature_mlp.pt"
+
+
+@dataclass
+class LossConfig:
+    relative_rms_weight: float = 1.0
+    log_rms_huber_weight: float = 0.75
+    mae_weight: float = 0.15
+
+
+@dataclass
+class ExperimentConfig:
+    data: DataConfig = field(default_factory=DataConfig)
+    model: ModelConfig = field(default_factory=ModelConfig)
+    train: TrainConfig = field(default_factory=TrainConfig)
+    loss: LossConfig = field(default_factory=LossConfig)
+
+
+def make_experiment_config() -> ExperimentConfig:
+    return ExperimentConfig()