105 lines
4.9 KiB
Python
105 lines
4.9 KiB
Python
"""
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实验1: 基础降噪与动力学特性恢复实验 (核心实验)
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目标:
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验证 RC 降噪方法在降噪和动力学特性恢复方面的基本有效性，建立基准性能。
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实验设置:
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- 混沌系统: 例如 Lorenz 或 Rossler 系统。
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- 噪声类型: 高斯白噪声 (可扩展到其他噪声)。
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- 噪声水平: 不同 SNR 设置。
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- RC 模型: 使用 ESN 等 RC 模型，调优储备池大小、谱半径等参数。
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- 训练方法: 监督学习 (例如 Ridge 回归)。
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评估指标:
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- 降噪指标: SNR 提升、均方根误差 (RMSE)、相关系数、峰值信噪比。
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- 动力学特性恢复指标: 吸引子重构质量、Lyapunov 指数估计误差、分形维数估计误差、预测精度。
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结果展示:
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- 可视化时间序列、吸引子重构、PSD 对比图和表格展示指标结果。
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import reservoirpy as rpy
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import reservoirpy.nodes as rpn
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rpy.verbosity(0)
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rpy.set_seed(42)
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from tools import load_data, visualize_data_diff, visualize_psd_diff
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def build_esn_model(units, lr, sr, reg, output_dim, reservoir_node=None):
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if reservoir_node is None:
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reservoir_node = rpn.Reservoir(units=units, lr=lr, sr=sr)
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output_node = rpn.Ridge(output_dim=output_dim, ridge=reg)
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model = reservoir_node >> output_node
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return model, reservoir_node
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def train_model(model, clean_data, noisy_data, warmup=1000):
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model.fit(noisy_data, clean_data, warmup=warmup)
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prediction = model.run(noisy_data)
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return model, prediction
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def evaluate_results(model, clean_data, noisy_data, warmup=0, vis_data=True, vis_psd=False):
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# 预测
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prediction = model.run(noisy_data)
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# 计算各种指标
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snr = 10 * np.log10(np.mean(clean_data**2) / np.mean((clean_data - prediction)**2))
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rmse = np.sqrt(np.mean((clean_data - prediction)**2))
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psnr = 20 * np.log10(np.max(clean_data) / rmse)
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corr_coef = np.corrcoef(clean_data, prediction)[0, 1]
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# 3个维度分别绘制 noisy vs clean | prediction vs clean
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if vis_data:
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visualize_data_diff(clean_data, noisy_data, prediction, warmup=0)
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# PSD 对比
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if vis_psd:
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visualize_psd_diff(clean_data, noisy_data, prediction, warmup=0)
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return snr, rmse, psnr, corr_coef, prediction
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def run():
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# 数据加载与预处理
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clean_data, noisy_data = load_data(system='lorenz', noise='gaussian',
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intensity=0.5, init=[1, 1, 1],
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n_timesteps=40000, transient=10000, h=0.01)
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# 分为训练集和测试集 按百分比
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train_size = int(len(clean_data) * 0.8)
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train_clean_data = clean_data[:train_size]
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train_noisy_data = noisy_data[:train_size]
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test_clean_data = clean_data[train_size:]
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test_noisy_data = noisy_data[train_size:]
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# LV1
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model_lv1, reservoir_node = build_esn_model(units=1000, lr=0.1, sr=0.9, reg=1e-5, output_dim=3)
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model_lv1, lv1_train_pred = train_model(model_lv1, train_clean_data, train_noisy_data)
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snr, rmse, psnr, corr_coef, lv1_pred = evaluate_results(model_lv1, test_clean_data, test_noisy_data)
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print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
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# LV2
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model_lv2, _ = build_esn_model(units=1000, lr=0.2, sr=0.9, reg=1e-5, output_dim=3, reservoir_node=reservoir_node)
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model_lv2, lv2_train_pred = train_model(model_lv2, train_clean_data, lv1_train_pred)
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snr, rmse, psnr, corr_coef, lv2_pred = evaluate_results(model_lv2, test_clean_data, lv1_pred)
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print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
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# LV3
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model_lv3, _ = build_esn_model(units=1000, lr=0.3, sr=0.9, reg=1e-5, output_dim=3, reservoir_node=reservoir_node)
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model_lv3, lv3_train_pred = train_model(model_lv3, train_clean_data, lv2_train_pred)
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snr, rmse, psnr, corr_coef, lv3_pred = evaluate_results(model_lv3, test_clean_data, lv2_pred)
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print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
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# LV4
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model_lv4, _ = build_esn_model(units=1000, lr=0.4, sr=0.9, reg=1e-4, output_dim=3, reservoir_node=reservoir_node)
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model_lv4, lv4_train_pred = train_model(model_lv4, train_clean_data, lv3_train_pred)
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snr, rmse, psnr, corr_coef, lv4_pred = evaluate_results(model_lv4, test_clean_data, lv3_pred)
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print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
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# LV5
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model_lv5, _ = build_esn_model(units=1000, lr=0.5, sr=0.9, reg=1e-3, output_dim=3, reservoir_node=reservoir_node)
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model_lv5, lv5_train_pred = train_model(model_lv5, train_clean_data, lv4_train_pred)
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snr, rmse, psnr, corr_coef, lv5_pred = evaluate_results(model_lv5, test_clean_data, lv4_pred)
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print(f"SNR: {snr:.6f} RMSE: {rmse:.6f} PSNR: {psnr:.6f} CorrCoef: {corr_coef:.6f}")
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plt.show()
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if __name__ == "__main__":
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run() |