From ac387dbc826387e775b9a93288028326a7e1671f Mon Sep 17 00:00:00 2001 From: wwhenxuan Date: Tue, 17 Feb 2026 13:13:53 +0800 Subject: [PATCH 1/5] whenxuan: update the version of s2generator --- s2generator/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/s2generator/__init__.py b/s2generator/__init__.py index c86ab9a..fa36c02 100644 --- a/s2generator/__init__.py +++ b/s2generator/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -__version__ = "0.0.5" +__version__ = "0.0.6" __all__ = [ "Node", diff --git a/setup.py b/setup.py index fa899de..1df471a 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ setuptools.setup( name="S2Generator", packages=setuptools.find_packages(), - version="0.0.5", + version="0.0.6", description="A series-symbol (S2) dual-modality data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic representations.", # 包的简短描述 url="https://github.com/wwhenxuan/S2Generator", author="whenxuan, johnfan12, changewam", From 93aa6b5612e58201d586f432b544c0e489c6ae61 Mon Sep 17 00:00:00 2001 From: wwhenxuan Date: Wed, 18 Feb 2026 14:01:35 +0800 Subject: [PATCH 2/5] whenxuan: create the wiener filter --- examples/20-kalman_filter_simulator.ipynb | 206 ++++++++++++++++ s2generator/simulator/wiener_filter.py | 272 ++++++++++++++++++++++ 2 files changed, 478 insertions(+) create mode 100644 examples/20-kalman_filter_simulator.ipynb diff --git a/examples/20-kalman_filter_simulator.ipynb b/examples/20-kalman_filter_simulator.ipynb new file mode 100644 index 0000000..d60ed89 --- /dev/null +++ b/examples/20-kalman_filter_simulator.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "67d8f57c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "原始AR(2)序列 ADF检验结果：\n", + "ADF统计量: -19.3357, p值: 0.0000\n", + "结论：平稳\n", + "\n", + "维纳滤波器系数（前10个）：\n", + "[ 0.59715128 -0.20360755 0.02363557 -0.06059091 0.05489398 0.00590325\n", + " -0.00102036 -0.04738855 0.06048262 -0.00675464]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ACF均方误差：0.027368（越小越相似）\n", + "PSD（dB）均方误差：139.129031（越小越相似）\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import signal\n", + "from scipy.linalg import toeplitz\n", + "from statsmodels.tsa.stattools import acf\n", + "from statsmodels.tsa.stattools import adfuller\n", + "\n", + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 显示中文\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 显示负号\n", + "\n", + "# ---------------------- 1. 生成原始平稳时间序列（AR(2)示例，更具代表性） ----------------------\n", + "np.random.seed(42)\n", + "n_obs = 1000 # 序列长度（长序列更利于ACF估计）\n", + "# AR(2)参数：phi1=0.6, phi2=-0.2（满足平稳性条件）\n", + "phi1, phi2 = 0.6, -0.2\n", + "sigma = 0.8 # 白噪声标准差\n", + "\n", + "# 生成AR(2)序列：y_t = 0.6*y_{t-1} -0.2*y_{t-2} + e_t\n", + "y = np.zeros(n_obs)\n", + "y[0] = np.random.normal(0, sigma)\n", + "y[1] = phi1 * y[0] + np.random.normal(0, sigma)\n", + "for t in range(2, n_obs):\n", + " y[t] = phi1 * y[t - 1] + phi2 * y[t - 2] + np.random.normal(0, sigma)\n", + "\n", + "\n", + "# 验证原始序列平稳性\n", + "def adf_test(series, name):\n", + " result = adfuller(series)\n", + " print(f\"{name} ADF检验结果：\")\n", + " print(f\"ADF统计量: {result[0]:.4f}, p值: {result[1]:.4f}\")\n", + " print(\"结论：\" + (\"平稳\" if result[1] < 0.05 else \"非平稳\") + \"\\n\")\n", + "\n", + "\n", + "adf_test(y, \"原始AR(2)序列\")\n", + "\n", + "# ---------------------- 2. 估计原始序列的自相关函数(ACF) ----------------------\n", + "# 计算ACF：lag_max设为滤波器阶数的2倍（保证估计精度）\n", + "filter_order = 50 # 维纳滤波器阶数（阶数越高，匹配度越好）\n", + "lag_max = filter_order * 2\n", + "acf_original = acf(y, nlags=lag_max, fft=True) # fft=True加速计算\n", + "\n", + "\n", + "# ---------------------- 3. 设计维纳滤波器（基于维纳-霍夫方程） ----------------------\n", + "def design_wiener_filter(acf_vals, filter_order):\n", + " \"\"\"\n", + " 设计维纳滤波器：求解维纳-霍夫方程 R*h = r\n", + " - R: 自相关矩阵（Toeplitz矩阵）\n", + " - h: 滤波器系数（待求）\n", + " - r: 自相关向量（前filter_order+1个ACF值）\n", + " \"\"\"\n", + " # 构造自相关矩阵R（Toeplitz矩阵，尺寸：filter_order x filter_order）\n", + " R = toeplitz(acf_vals[:filter_order])\n", + "\n", + " # 构造自相关向量r（尺寸：filter_order x 1）\n", + " r = acf_vals[1 : filter_order + 1].reshape(-1, 1)\n", + "\n", + " # 求解维纳-霍夫方程：R*h = r → h = R^{-1}*r\n", + " # 用伪逆避免矩阵奇异问题（更鲁棒）\n", + " h = np.linalg.pinv(R) @ r\n", + " return h.flatten() # 转为一维数组\n", + "\n", + "\n", + "# 设计维纳滤波器\n", + "wiener_coeffs = design_wiener_filter(acf_original, filter_order)\n", + "print(f\"维纳滤波器系数（前10个）：\\n{wiener_coeffs[:10]}\")\n", + "\n", + "# ---------------------- 4. 生成白噪声激励信号 ----------------------\n", + "# 生成与原序列长度相同的白噪声（零均值、单位方差）\n", + "white_noise = np.random.normal(0, 1, size=n_obs)\n", + "# 调整白噪声方差，匹配原序列的功率水平\n", + "white_noise = white_noise * sigma\n", + "\n", + "# ---------------------- 5. 白噪声激励维纳滤波器，生成新序列 ----------------------\n", + "# 用线性滤波实现：新序列 = 白噪声 * 维纳滤波器（卷积）\n", + "# 使用filtfilt避免相位失真（可选，也可用lfilter）\n", + "simulated_y = signal.filtfilt(wiener_coeffs, 1.0, white_noise)\n", + "# 去除滤波后的边缘效应（前filter_order个点）\n", + "simulated_y = simulated_y[filter_order:]\n", + "original_y_trimmed = y[filter_order:] # 原序列同步截断\n", + "\n", + "# ---------------------- 6. 验证：对比原序列和模拟序列的特征 ----------------------\n", + "# 6.1 对比自相关函数(ACF)\n", + "acf_simulated = acf(simulated_y, nlags=lag_max, fft=True)\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "\n", + "# 子图1：原序列 vs 模拟序列\n", + "plt.subplot(3, 1, 1)\n", + "plt.plot(original_y_trimmed, label=\"原始AR(2)序列\", color=\"blue\", alpha=0.7)\n", + "plt.plot(simulated_y, label=\"维纳滤波器生成序列\", color=\"red\", alpha=0.7)\n", + "plt.title(\"原始序列 vs 维纳滤波器生成序列\")\n", + "plt.xlabel(\"时间步\")\n", + "plt.ylabel(\"值\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.3)\n", + "\n", + "# 子图2：ACF对比\n", + "plt.subplot(3, 1, 2)\n", + "lags = np.arange(lag_max + 1)\n", + "plt.stem(\n", + " lags, acf_original, linefmt=\"b-\", markerfmt=\"bo\", basefmt=\"k-\", label=\"原始序列ACF\"\n", + ")\n", + "plt.stem(\n", + " lags, acf_simulated, linefmt=\"r-\", markerfmt=\"ro\", basefmt=\"k-\", label=\"生成序列ACF\"\n", + ")\n", + "plt.title(\"自相关函数(ACF)对比\")\n", + "plt.xlabel(\"滞后阶数\")\n", + "plt.ylabel(\"ACF值\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.3)\n", + "\n", + "# 子图3：功率谱密度(PSD)对比\n", + "plt.subplot(3, 1, 3)\n", + "# 计算功率谱密度（使用Welch方法，更平滑）\n", + "f1, Pxx1 = signal.welch(original_y_trimmed, fs=1.0, nperseg=256, scaling=\"density\")\n", + "f2, Pxx2 = signal.welch(simulated_y, fs=1.0, nperseg=256, scaling=\"density\")\n", + "plt.plot(f1, 10 * np.log10(Pxx1), label=\"原始序列PSD\", color=\"blue\")\n", + "plt.plot(f2, 10 * np.log10(Pxx2), label=\"生成序列PSD\", color=\"red\", alpha=0.7)\n", + "plt.title(\"功率谱密度(PSD)对比（dB）\")\n", + "plt.xlabel(\"频率\")\n", + "plt.ylabel(\"功率谱密度 (dB/Hz)\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# 6.2 定量验证：计算ACF和PSD的相似度（均方误差）\n", + "acf_mse = np.mean((acf_original - acf_simulated) ** 2)\n", + "psd_mse = np.mean((10 * np.log10(Pxx1) - 10 * np.log10(Pxx2)) ** 2)\n", + "print(f\"ACF均方误差：{acf_mse:.6f}（越小越相似）\")\n", + "print(f\"PSD（dB）均方误差：{psd_mse:.6f}（越小越相似）\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84fa2991", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2generator", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/s2generator/simulator/wiener_filter.py b/s2generator/simulator/wiener_filter.py index e69de29..80dc0c2 100644 --- a/s2generator/simulator/wiener_filter.py +++ b/s2generator/simulator/wiener_filter.py @@ -0,0 +1,272 @@ +from typing import Optional, Union, Tuple + +import numpy as np +import matplotlib.pyplot as plt +from scipy import signal +from scipy.linalg import toeplitz +from statsmodels.tsa.stattools import acf + +from s2generator.utils import plot_simulator_statistics +from datasets import Dataset + + +def yule_walker(A: np.ndarray) -> Tuple[np.ndarray, Union[float, np.ndarray]]: + """ + Solving the Yule-Walker equations yields the parameters of the AR model. + + The Yule-Walker equations can be expressed in matrix form as:: + + r(0)*1 + r(1)*a[1] + r(2)*a[2] + ... + r(n)*a[n] = σ² + r(1)*1 + r(0)*a[1] + r(1)*a[2] + ... + r(n-1)*a[n] = 0 + r(2)*1 + r(1)*a[1] + r(0)*a[2] + ... + r(n-2)*a[n] = 0 + ... + r(n-1)*1 + r(n-2)*a[1] + r(n-3)*a[2] + ... + r(1)*a[n] = 0 + r(n)*1 + r(n-1)*a[1] + r(n-2)*a[2] + ... + r(0)*a[n] = 0 + + Where r(k) is the value of the autocorrelation function at lag k, + a[i] are the coefficients of the AR model, and σ² is the variance of the noise. + + This equation can be derived based on the orthogonality principle, + representing the relationship between the parameters of the AR model and the autocorrelation function of the sequence. + + :param A: The matrix representing the Yule-Walker equations, where the first row corresponds to the equation for σ² and the subsequent rows correspond to the equations for a1~an. + + :return: A tuple containing the vector of AR coefficients (a1~an) and the variance of the noise (σ²). + """ + # Check if A is a ndarray and has the correct shape + if not isinstance(A, np.ndarray): + raise ValueError("Input A must be a numpy array.") + if A.shape[0] < 2 or A.shape[1] < 2: + raise ValueError("Input A must have at least 2 rows and 2 columns to solve for a1~an and σ².") + + # Check if the matrix A is square + if A.shape[0] != A.shape[1]: + raise ValueError("Input A must be a square matrix.") + + # Solve for the coefficients using the equations from the second line onwards. + B = A[1:, 1:] # Extracting the coefficient matrix + c = -A[1:, 0] # Extract the constant term and take its negative. + + try: + # Solve for a1~an + a = np.linalg.solve(B, c) + + except np.linalg.LinAlgError as e: + print(f"Solution failed: {e}") + print("Reason: Matrix B is not invertible, and therefore cannot uniquely determine a1~an.") + + # If B is not invertible, we can use least squares to find an approximate solution. + a = np.linalg.lstsq(B, c, rcond=None)[0] + + + # Solve for σ² using the first line of the equations + x = np.hstack([1, a]) # Construct the x vector + sigma_sq = np.dot(A[0], x) + + return x, sigma_sq + + +class WienerFilterSimulator(object): + """ + Simulate new time series using a Wiener optimal filter fitted to the input series. + + Any stationary signal can be viewed as the response obtained by exciting a linear system with white noise. + + Inspired by parametric spectral estimation methods, this module constructs a learnable Wiener filter for modeling time series. + It mainly learns a convolution kernel that transforms a white noise sequence into a sequence with statistical properties similar to the input sequence.Meanwhile, + it ensures that the autocorrelation and power spectral density of the output exhibit shapes analogous to those of the input sequence. + + Suppose the convolution kernel is a = [1, a1, a2, ..., an], the input white noise sequence is w[n], and the output analog sequence is y[n]. + The filter function is implemented as :: + + y[n] = a[0]*w[n] + a[1]*w[n-1] + ... + a[n]*w[n-n] + + That is, it is generated by exciting a linear system with a white noise sequence. + + We can solve for the filter coefficients a1~an and the noise variance σ² using the Yule-Walker equation. + + This equation is somewhat similar to the spectral estimation of the AR model, + and its basic principle is as follows:: + + 1 + Y(z) = -------------------------------- X(z) + -1 -N + a[0] + a[1]z + ... + a[N] z + + From the perspective of modern signal processing, this module is more akin to the parametric spectral estimation method of the AR model than to the Wiener filter. + In fact, this module can be regarded purely as the first part of the causal Wiener filter, + namely the whitening process.We only learn the whitening filter and do not learn the de-whitening filter.However, + in current deep learning-based time series analysis,naming the module as a Wiener filter is more understandable than referring to the spectral estimation theory of the AR model. + """ + + def __init__(self, filter_order: int = 6, revin: Optional[bool] = True, random_state: Optional[int] = 42) -> None: + """ + :param filter_order: The order of the Wiener filter (including a0=1). + :param revin: Whether to perform reversible normalization (keep the variance of the input sequence unchanged). + :param random_state: The random seed for reproducibility. + + :return: None + """ + # Filter order (including a0=1) + self.filter_order = filter_order + self.lag_max = filter_order * 2 + + # Record the autocorrelation function value of the input sequence + self.acf_vals = None + + # Record the autocorrelation matrix R + # Toeplitz matrix [filter_order x filter_order] + self.R = None + + # Noise variance σ² + self.sigma_sq = None + + # Wiener filter coefficients [filter_order, ] + self._coeffs = None + + # Should we perform normalization + # (keeping the variance of the input sequence constant)? + self.revin = revin + self.mean, self.std = None, None + + # Record the currently used random seed and create a random number generator. + self.random_state = random_state + self.rng = np.random.RandomState(seed=random_state) + + # Record the original time series of input + self.time_series = None + # The time series after truncation of the original sequence + # (removing the first filter_order points to avoid edge effects). + self.time_series_trimmed = None + + # Record the fitting residuals of the model + self.residuals = None + + # Record the time series generated by the simulation + self.simulated_series = None + + + def fit(self, time_series: np.ndarray) -> None: + """拟合Wiener滤波器，计算滤波器系数""" + + # 检查输入时间序列的格式和长度 + time_series = self.check_inputs(time_series) + # 记录原始输入序列 + self.time_series = time_series + + # 是否要进行可你归一化 + if self.revin: + self.mean = np.mean(time_series) + self.std = np.std(time_series) + time_series = (time_series - self.mean) / self.std + + self.acf_vals = self.acf(time_series=time_series) + + # 构建自相关矩阵 + self.R = toeplitz(self.acf_vals[: self.filter_order]) + + # 通过求解Yule-Walker方程得到滤波器系数和噪声方差 + self._coeffs, self.sigma_sq = yule_walker(A=self.R) + + # Initialize noise and calculate the fitted residuals. + # Note that increasing the filter order is necessary to avoid edge effects. + white_noise = self.rng.normal(0, scale=np.sqrt(self.sigma_sq), size=len(time_series) + self.filter_order) + self.residuals = time_series - self.invoke(white_noise=white_noise) + + def transform( + self, num_samples: int, seq_len: int, random_state: Optional[int] = None + ) -> np.ndarray: + + # 如果提供了随机种子，则使用该种子生成新的随机数生成器 + if random_state is not None: + rng = np.random.RandomState(seed=random_state) + else: + rng = self.rng # 否则使用类中已有的随机数生成器 + + # 生成与原序列长度相同的白噪声（零均值、单位方差） + white_noise = rng.normal(0, scale=np.sqrt(self.sigma_sq), size=(num_samples, seq_len + self.filter_order)) # 注意增加滤波器阶数以避免边缘效应 + + # 利用白噪声激励维纳最优滤波器使其生成全新的序列 + simulated_series = np.zeros(shape=(num_samples, seq_len)) + + for i in range(num_samples): + # 用线性滤波实现：新序列 = 白噪声 * 维纳滤波器（卷积） + simulated_series[i, :] = self.invoke(white_noise=white_noise[i, :]) + + # 去除滤波后的边缘效应（前filter_order个点） + simulated_series = simulated_series[:, self.filter_order:] + + # 如果之前进行了可你归一化，现在需要反归一化以保持与原序列相同的均值和方差 + if self.revin: + self.simulated_series = simulated_series * self.std + self.mean + else: + self.simulated_series = simulated_series + + return self.simulated_series + + def invoke(self, white_noise: np.ndarray) -> np.ndarray: + """ + Generate directly by calling the filter + (without generating random noise). + + :param white_noise: The input white noise sequence to excite the filter, with shape 1D [seq_len + filter_order, ]. + + :return: The generated time series with shape 1D [seq_len, ]. + """ + if self._coeffs is None: + raise ValueError("The filter coefficients have not been calculated yet; please call the `fit` method first.") + + # Implemented using linear filtering: + # New sequence = White noise * Wiener filter (convolution) + simulated_series = signal.lfilter(a=self._coeffs, b=1.0, x=white_noise) + + # Remove edge effects after filtering (first filter_order points) + return simulated_series[self.filter_order:] + + def acf(self, time_series: np.ndarray, lag_max: Optional[int] = None, fft: Optional[bool] = True) -> np.ndarray: + # 使用FFT来加速计算 + return acf(time_series, nlags=self.lag_max if lag_max is None else lag_max, fft=fft) + + def check_inputs(self, time_series: np.ndarray) -> np.ndarray: + if not isinstance(time_series, np.ndarray): + raise ValueError("输入时间序列必须是numpy数组") + + if len(time_series.shape) > 2: + raise ValueError( + "The input time series must be 1D with [seq_len, ] or 2D with [num_samples, seq_len], but got shape: {}".format( + time_series.shape + ) + ) + + if len(time_series.shape) == 2: + time_series = time_series.flatten() # 将2D数组展平为1D + + if len(time_series) < 2 * self.filter_order: + raise ValueError( + f"输入时间序列长度必须至少为{2 * self.filter_order}以确保ACF估计的准确性" + ) + + return time_series + + @property + def coeffs(self) -> np.ndarray: + """Get the Wiener filter coefficients after fitting the model.""" + if self._coeffs is None: + raise ValueError("The filter coefficients have not been calculated yet; please call the `fit` method first.") + return self._coeffs + + +if __name__ == "__main__": + ds = Dataset.load_from_disk(r"data\train\spectrum\Madrid") + ds.set_format(type="numpy") + target = ds[0]["target"] + + + simulator = WienerFilterSimulator(filter_order=6, revin=True, random_state=42) + simulator.fit(target) + simulated_data = simulator.transform(num_samples=5, seq_len=target.shape[0]) + + fig = plot_simulator_statistics( + original_series=target, + generated_series=simulated_data[2, :], + residuals=simulator.residuals,) \ No newline at end of file From 1f0320ab245f26d1606d5c5338fae772768e6f1e Mon Sep 17 00:00:00 2001 From: wwhenxuan Date: Wed, 18 Feb 2026 14:13:51 +0800 Subject: [PATCH 3/5] whenxuan: update the yule-walker --- s2generator/simulator/wiener_filter.py | 81 +++++++------------------- s2generator/utils/__init__.py | 4 ++ s2generator/utils/_tools.py | 57 ++++++++++++++++++ 3 files changed, 82 insertions(+), 60 deletions(-) diff --git a/s2generator/simulator/wiener_filter.py b/s2generator/simulator/wiener_filter.py index 80dc0c2..69da386 100644 --- a/s2generator/simulator/wiener_filter.py +++ b/s2generator/simulator/wiener_filter.py @@ -7,64 +7,8 @@ from statsmodels.tsa.stattools import acf from s2generator.utils import plot_simulator_statistics -from datasets import Dataset -def yule_walker(A: np.ndarray) -> Tuple[np.ndarray, Union[float, np.ndarray]]: - """ - Solving the Yule-Walker equations yields the parameters of the AR model. - - The Yule-Walker equations can be expressed in matrix form as:: - - r(0)*1 + r(1)*a[1] + r(2)*a[2] + ... + r(n)*a[n] = σ² - r(1)*1 + r(0)*a[1] + r(1)*a[2] + ... + r(n-1)*a[n] = 0 - r(2)*1 + r(1)*a[1] + r(0)*a[2] + ... + r(n-2)*a[n] = 0 - ... - r(n-1)*1 + r(n-2)*a[1] + r(n-3)*a[2] + ... + r(1)*a[n] = 0 - r(n)*1 + r(n-1)*a[1] + r(n-2)*a[2] + ... + r(0)*a[n] = 0 - - Where r(k) is the value of the autocorrelation function at lag k, - a[i] are the coefficients of the AR model, and σ² is the variance of the noise. - - This equation can be derived based on the orthogonality principle, - representing the relationship between the parameters of the AR model and the autocorrelation function of the sequence. - - :param A: The matrix representing the Yule-Walker equations, where the first row corresponds to the equation for σ² and the subsequent rows correspond to the equations for a1~an. - - :return: A tuple containing the vector of AR coefficients (a1~an) and the variance of the noise (σ²). - """ - # Check if A is a ndarray and has the correct shape - if not isinstance(A, np.ndarray): - raise ValueError("Input A must be a numpy array.") - if A.shape[0] < 2 or A.shape[1] < 2: - raise ValueError("Input A must have at least 2 rows and 2 columns to solve for a1~an and σ².") - - # Check if the matrix A is square - if A.shape[0] != A.shape[1]: - raise ValueError("Input A must be a square matrix.") - - # Solve for the coefficients using the equations from the second line onwards. - B = A[1:, 1:] # Extracting the coefficient matrix - c = -A[1:, 0] # Extract the constant term and take its negative. - - try: - # Solve for a1~an - a = np.linalg.solve(B, c) - - except np.linalg.LinAlgError as e: - print(f"Solution failed: {e}") - print("Reason: Matrix B is not invertible, and therefore cannot uniquely determine a1~an.") - - # If B is not invertible, we can use least squares to find an approximate solution. - a = np.linalg.lstsq(B, c, rcond=None)[0] - - - # Solve for σ² using the first line of the equations - x = np.hstack([1, a]) # Construct the x vector - sigma_sq = np.dot(A[0], x) - - return x, sigma_sq - class WienerFilterSimulator(object): """ @@ -147,7 +91,15 @@ def __init__(self, filter_order: int = 6, revin: Optional[bool] = True, random_s def fit(self, time_series: np.ndarray) -> None: - """拟合Wiener滤波器，计算滤波器系数""" + """ + Fit a Wiener filter and calculate the filter coefficients. + + The specific parameters will be obtained using the Yule-Walker method. + + :param time_series: The input time series to fit the Wiener filter, with shape 1D [seq_len, ] or 2D [num_samples, seq_len]. + + :return: None + """ # 检查输入时间序列的格式和长度 time_series = self.check_inputs(time_series) @@ -224,8 +176,16 @@ def invoke(self, white_noise: np.ndarray) -> np.ndarray: return simulated_series[self.filter_order:] def acf(self, time_series: np.ndarray, lag_max: Optional[int] = None, fft: Optional[bool] = True) -> np.ndarray: - # 使用FFT来加速计算 - return acf(time_series, nlags=self.lag_max if lag_max is None else lag_max, fft=fft) + """ + Calculate the autocorrelation function (ACF) of the input time series. + + :param time_series: The input time series for which to calculate the ACF, with shape 1D [seq_len, ]. + :param lag_max: The maximum lag for which to calculate the ACF. If None, it defaults to self.lag_max (which is filter_order * 2). + :param fft: Whether to use FFT to speed up the ACF calculation. Default is True. + + :return: The autocorrelation function values for lags from 0 to lag_max, with shape 1D [lag_max + 1, ]. + """ + return acf(time_series, nlags=self.lag_max if lag_max is None else lag_max, fft=fft) # Use FFT to speed up the calculation def check_inputs(self, time_series: np.ndarray) -> np.ndarray: if not isinstance(time_series, np.ndarray): @@ -257,11 +217,12 @@ def coeffs(self) -> np.ndarray: if __name__ == "__main__": + from datasets import Dataset + ds = Dataset.load_from_disk(r"data\train\spectrum\Madrid") ds.set_format(type="numpy") target = ds[0]["target"] - simulator = WienerFilterSimulator(filter_order=6, revin=True, random_state=42) simulator.fit(target) simulated_data = simulator.transform(num_samples=5, seq_len=target.shape[0]) diff --git a/s2generator/utils/__init__.py b/s2generator/utils/__init__.py index ee6713b..c777f41 100644 --- a/s2generator/utils/__init__.py +++ b/s2generator/utils/__init__.py @@ -21,6 +21,7 @@ "generate_arma_samples", "generate_nonstationary_sine", "eacf_rlike", + "yule_walker", "fft", "fftshift", "ifft", @@ -68,6 +69,9 @@ # The EACF function to determine the order of ARMA model from ._tools import eacf_rlike +# The Yule-Walker method to estimate the parameters of AR model +from ._tools import yule_walker + # Print the Generation Status from ._print_status import PrintStatus diff --git a/s2generator/utils/_tools.py b/s2generator/utils/_tools.py index d394460..37468ac 100644 --- a/s2generator/utils/_tools.py +++ b/s2generator/utils/_tools.py @@ -30,6 +30,7 @@ "generate_arma_samples", "generate_nonstationary_sine", "eacf_rlike", + "yule_walker", ] import os @@ -527,3 +528,59 @@ def eacf_rlike( ) return eacf_matrix, threshold, eacf_df + + +def yule_walker(A: np.ndarray) -> Tuple[np.ndarray, Union[float, np.ndarray]]: + """ + Solving the Yule-Walker equations yields the parameters of the AR model. + + The Yule-Walker equations can be expressed in matrix form as:: + + r(0)*1 + r(1)*a[1] + r(2)*a[2] + ... + r(n)*a[n] = σ² + r(1)*1 + r(0)*a[1] + r(1)*a[2] + ... + r(n-1)*a[n] = 0 + r(2)*1 + r(1)*a[1] + r(0)*a[2] + ... + r(n-2)*a[n] = 0 + ... + r(n-1)*1 + r(n-2)*a[1] + r(n-3)*a[2] + ... + r(1)*a[n] = 0 + r(n)*1 + r(n-1)*a[1] + r(n-2)*a[2] + ... + r(0)*a[n] = 0 + + Where r(k) is the value of the autocorrelation function at lag k, + a[i] are the coefficients of the AR model, and σ² is the variance of the noise. + + This equation can be derived based on the orthogonality principle, + representing the relationship between the parameters of the AR model and the autocorrelation function of the sequence. + + :param A: The matrix representing the Yule-Walker equations, where the first row corresponds to the equation for σ² and the subsequent rows correspond to the equations for a1~an. + + :return: A tuple containing the vector of AR coefficients (a1~an) and the variance of the noise (σ²). + """ + # Check if A is a ndarray and has the correct shape + if not isinstance(A, np.ndarray): + raise ValueError("Input A must be a numpy array.") + if A.shape[0] < 2 or A.shape[1] < 2: + raise ValueError("Input A must have at least 2 rows and 2 columns to solve for a1~an and σ².") + + # Check if the matrix A is square + if A.shape[0] != A.shape[1]: + raise ValueError("Input A must be a square matrix.") + + # Solve for the coefficients using the equations from the second line onwards. + B = A[1:, 1:] # Extracting the coefficient matrix + c = -A[1:, 0] # Extract the constant term and take its negative. + + try: + # Solve for a1~an + a = np.linalg.solve(B, c) + + except np.linalg.LinAlgError as e: + print(f"Solution failed: {e}") + print("Reason: Matrix B is not invertible, and therefore cannot uniquely determine a1~an.") + + # If B is not invertible, we can use least squares to find an approximate solution. + a = np.linalg.lstsq(B, c, rcond=None)[0] + + + # Solve for σ² using the first line of the equations + x = np.hstack([1, a]) # Construct the x vector + sigma_sq = np.dot(A[0], x) + + return x, sigma_sq \ No newline at end of file From a177eef673a681d6fb62a9bed42b59fa85a74f04 Mon Sep 17 00:00:00 2001 From: wwhenxuan Date: Wed, 18 Feb 2026 14:21:48 +0800 Subject: [PATCH 4/5] whenxuan: fix the wiener filter for simulate --- s2generator/simulator/wiener_filter.py | 209 ++++++++++++++----------- s2generator/utils/_tools.py | 39 ++--- 2 files changed, 141 insertions(+), 107 deletions(-) diff --git a/s2generator/simulator/wiener_filter.py b/s2generator/simulator/wiener_filter.py index 69da386..0c7887a 100644 --- a/s2generator/simulator/wiener_filter.py +++ b/s2generator/simulator/wiener_filter.py @@ -1,35 +1,33 @@ from typing import Optional, Union, Tuple import numpy as np -import matplotlib.pyplot as plt from scipy import signal from scipy.linalg import toeplitz from statsmodels.tsa.stattools import acf -from s2generator.utils import plot_simulator_statistics - +from s2generator.utils._tools import yule_walker class WienerFilterSimulator(object): """ Simulate new time series using a Wiener optimal filter fitted to the input series. - + Any stationary signal can be viewed as the response obtained by exciting a linear system with white noise. - + Inspired by parametric spectral estimation methods, this module constructs a learnable Wiener filter for modeling time series. - It mainly learns a convolution kernel that transforms a white noise sequence into a sequence with statistical properties similar to the input sequence.Meanwhile, + It mainly learns a convolution kernel that transforms a white noise sequence into a sequence with statistical properties similar to the input sequence.Meanwhile, it ensures that the autocorrelation and power spectral density of the output exhibit shapes analogous to those of the input sequence. - + Suppose the convolution kernel is a = [1, a1, a2, ..., an], the input white noise sequence is w[n], and the output analog sequence is y[n]. The filter function is implemented as :: - + y[n] = a[0]*w[n] + a[1]*w[n-1] + ... + a[n]*w[n-n] - + That is, it is generated by exciting a linear system with a white noise sequence. - + We can solve for the filter coefficients a1~an and the noise variance σ² using the Yule-Walker equation. - - This equation is somewhat similar to the spectral estimation of the AR model, + + This equation is somewhat similar to the spectral estimation of the AR model, and its basic principle is as follows:: 1 @@ -38,23 +36,28 @@ class WienerFilterSimulator(object): a[0] + a[1]z + ... + a[N] z From the perspective of modern signal processing, this module is more akin to the parametric spectral estimation method of the AR model than to the Wiener filter. - In fact, this module can be regarded purely as the first part of the causal Wiener filter, - namely the whitening process.We only learn the whitening filter and do not learn the de-whitening filter.However, + In fact, this module can be regarded purely as the first part of the causal Wiener filter, + namely the whitening process.We only learn the whitening filter and do not learn the de-whitening filter.However, in current deep learning-based time series analysis,naming the module as a Wiener filter is more understandable than referring to the spectral estimation theory of the AR model. """ - def __init__(self, filter_order: int = 6, revin: Optional[bool] = True, random_state: Optional[int] = 42) -> None: + def __init__( + self, + filter_order: int = 6, + revin: Optional[bool] = True, + random_state: Optional[int] = 42, + ) -> None: """ :param filter_order: The order of the Wiener filter (including a0=1). :param revin: Whether to perform reversible normalization (keep the variance of the input sequence unchanged). :param random_state: The random seed for reproducibility. - + :return: None """ # Filter order (including a0=1) - self.filter_order = filter_order + self.filter_order = filter_order self.lag_max = filter_order * 2 - + # Record the autocorrelation function value of the input sequence self.acf_vals = None @@ -64,133 +67,175 @@ def __init__(self, filter_order: int = 6, revin: Optional[bool] = True, random_s # Noise variance σ² self.sigma_sq = None - + # Wiener filter coefficients [filter_order, ] self._coeffs = None - - # Should we perform normalization + + # Should we perform normalization # (keeping the variance of the input sequence constant)? self.revin = revin self.mean, self.std = None, None - + # Record the currently used random seed and create a random number generator. self.random_state = random_state self.rng = np.random.RandomState(seed=random_state) - + # Record the original time series of input self.time_series = None - # The time series after truncation of the original sequence + # The time series after truncation of the original sequence # (removing the first filter_order points to avoid edge effects). self.time_series_trimmed = None - + # Record the fitting residuals of the model self.residuals = None - + # Record the time series generated by the simulation self.simulated_series = None - def fit(self, time_series: np.ndarray) -> None: """ Fit a Wiener filter and calculate the filter coefficients. - + The specific parameters will be obtained using the Yule-Walker method. - + :param time_series: The input time series to fit the Wiener filter, with shape 1D [seq_len, ] or 2D [num_samples, seq_len]. - + :return: None """ - # 检查输入时间序列的格式和长度 + # Check the format and length of the input time series. time_series = self.check_inputs(time_series) - # 记录原始输入序列 - self.time_series = time_series - - # 是否要进行可你归一化 + # Record the original input sequence + self.time_series = time_series + + # Should we perform normalization? if self.revin: self.mean = np.mean(time_series) self.std = np.std(time_series) time_series = (time_series - self.mean) / self.std - + self.acf_vals = self.acf(time_series=time_series) - # 构建自相关矩阵 + # Constructing the autocorrelation matrix self.R = toeplitz(self.acf_vals[: self.filter_order]) - # 通过求解Yule-Walker方程得到滤波器系数和噪声方差 + # The filter coefficients and noise variance are obtained by solving the Yule-Walker equation. self._coeffs, self.sigma_sq = yule_walker(A=self.R) - + # Initialize noise and calculate the fitted residuals. # Note that increasing the filter order is necessary to avoid edge effects. - white_noise = self.rng.normal(0, scale=np.sqrt(self.sigma_sq), size=len(time_series) + self.filter_order) + white_noise = self.rng.normal( + 0, scale=np.sqrt(self.sigma_sq), size=len(time_series) + self.filter_order + ) self.residuals = time_series - self.invoke(white_noise=white_noise) def transform( self, num_samples: int, seq_len: int, random_state: Optional[int] = None ) -> np.ndarray: - - # 如果提供了随机种子，则使用该种子生成新的随机数生成器 + """ + Generate new time series by exciting the fitted Wiener filter with white noise. + + :param num_samples: The number of new time series to generate. + :param seq_len: The length of each generated time series. + :param random_state: The random seed for reproducibility. If None, it will use the random state of the class instance. + + :return: The generated time series with shape [num_samples, seq_len]. + """ + + # Check if the filter coefficients have been calculated; if not, raise an error. + if self._coeffs is None: + raise ValueError( + "The filter coefficients have not been calculated yet; please call the `fit` method first." + ) + + # If a random seed is provided, a new random number generator will be used to generate the random number. if random_state is not None: rng = np.random.RandomState(seed=random_state) else: - rng = self.rng # 否则使用类中已有的随机数生成器 - - # 生成与原序列长度相同的白噪声（零均值、单位方差） - white_noise = rng.normal(0, scale=np.sqrt(self.sigma_sq), size=(num_samples, seq_len + self.filter_order)) # 注意增加滤波器阶数以避免边缘效应 - - # 利用白噪声激励维纳最优滤波器使其生成全新的序列 + # Otherwise, use the existing random number generator in the class. + rng = self.rng + + # Generate white noise (zero mean, unit variance) with the same length as the original sequence. + white_noise = rng.normal( + 0, + scale=np.sqrt(self.sigma_sq), + size=(num_samples, seq_len + self.filter_order), + ) # Note that increasing the filter order is necessary to avoid edge effects. + + # Using white noise to excite the Wiener optimal filter to generate entirely new sequences simulated_series = np.zeros(shape=(num_samples, seq_len)) - + for i in range(num_samples): - # 用线性滤波实现：新序列 = 白噪声 * 维纳滤波器（卷积） + # Implemented using linear filtering: + # New sequence = White noise * Wiener filter (convolution) simulated_series[i, :] = self.invoke(white_noise=white_noise[i, :]) - # 去除滤波后的边缘效应（前filter_order个点） - simulated_series = simulated_series[:, self.filter_order:] - - # 如果之前进行了可你归一化，现在需要反归一化以保持与原序列相同的均值和方差 + # Remove edge effects after filtering (first filter_order points) + simulated_series = simulated_series[:, self.filter_order :] + + # If normalization was performed previously, + # denormalization is now needed to maintain the same mean and variance as the original sequence. if self.revin: self.simulated_series = simulated_series * self.std + self.mean else: self.simulated_series = simulated_series - + return self.simulated_series - + def invoke(self, white_noise: np.ndarray) -> np.ndarray: """ Generate directly by calling the filter (without generating random noise). - + :param white_noise: The input white noise sequence to excite the filter, with shape 1D [seq_len + filter_order, ]. - + :return: The generated time series with shape 1D [seq_len, ]. """ if self._coeffs is None: - raise ValueError("The filter coefficients have not been calculated yet; please call the `fit` method first.") - - # Implemented using linear filtering: + raise ValueError( + "The filter coefficients have not been calculated yet; please call the `fit` method first." + ) + + # Implemented using linear filtering: # New sequence = White noise * Wiener filter (convolution) simulated_series = signal.lfilter(a=self._coeffs, b=1.0, x=white_noise) - + # Remove edge effects after filtering (first filter_order points) - return simulated_series[self.filter_order:] + return simulated_series[self.filter_order :] - def acf(self, time_series: np.ndarray, lag_max: Optional[int] = None, fft: Optional[bool] = True) -> np.ndarray: + def acf( + self, + time_series: np.ndarray, + lag_max: Optional[int] = None, + fft: Optional[bool] = True, + ) -> np.ndarray: """ Calculate the autocorrelation function (ACF) of the input time series. - + :param time_series: The input time series for which to calculate the ACF, with shape 1D [seq_len, ]. :param lag_max: The maximum lag for which to calculate the ACF. If None, it defaults to self.lag_max (which is filter_order * 2). :param fft: Whether to use FFT to speed up the ACF calculation. Default is True. - + :return: The autocorrelation function values for lags from 0 to lag_max, with shape 1D [lag_max + 1, ]. """ - return acf(time_series, nlags=self.lag_max if lag_max is None else lag_max, fft=fft) # Use FFT to speed up the calculation + return acf( + time_series, nlags=self.lag_max if lag_max is None else lag_max, fft=fft + ) # Use FFT to speed up the calculation def check_inputs(self, time_series: np.ndarray) -> np.ndarray: + """ + Check the format and length of the input time series. + + :param time_series: The input time series to check, with shape 1D [seq_len, ] or 2D [num_samples, seq_len]. + + :return: The checked and possibly reshaped time series, with shape 1D [seq_len, ]. + """ + + # Check if the input is a NumPy array if not isinstance(time_series, np.ndarray): - raise ValueError("输入时间序列必须是numpy数组") + raise ValueError("The input time series must be a NumPy array.") + # Check the shape of the input time series. It should be either 1D [seq_len, ] or 2D [num_samples, seq_len]. if len(time_series.shape) > 2: raise ValueError( "The input time series must be 1D with [seq_len, ] or 2D with [num_samples, seq_len], but got shape: {}".format( @@ -199,35 +244,21 @@ def check_inputs(self, time_series: np.ndarray) -> np.ndarray: ) if len(time_series.shape) == 2: - time_series = time_series.flatten() # 将2D数组展平为1D + # Flatten a 2D array into a 1D array + time_series = time_series.flatten() if len(time_series) < 2 * self.filter_order: raise ValueError( - f"输入时间序列长度必须至少为{2 * self.filter_order}以确保ACF估计的准确性" + f"Input time series length must be at least {2 * self.filter_order} to ensure accurate ACF estimation." ) return time_series - + @property def coeffs(self) -> np.ndarray: """Get the Wiener filter coefficients after fitting the model.""" if self._coeffs is None: - raise ValueError("The filter coefficients have not been calculated yet; please call the `fit` method first.") + raise ValueError( + "The filter coefficients have not been calculated yet; please call the `fit` method first." + ) return self._coeffs - - -if __name__ == "__main__": - from datasets import Dataset - - ds = Dataset.load_from_disk(r"data\train\spectrum\Madrid") - ds.set_format(type="numpy") - target = ds[0]["target"] - - simulator = WienerFilterSimulator(filter_order=6, revin=True, random_state=42) - simulator.fit(target) - simulated_data = simulator.transform(num_samples=5, seq_len=target.shape[0]) - - fig = plot_simulator_statistics( - original_series=target, - generated_series=simulated_data[2, :], - residuals=simulator.residuals,) \ No newline at end of file diff --git a/s2generator/utils/_tools.py b/s2generator/utils/_tools.py index 37468ac..a3ce6a2 100644 --- a/s2generator/utils/_tools.py +++ b/s2generator/utils/_tools.py @@ -533,7 +533,7 @@ def eacf_rlike( def yule_walker(A: np.ndarray) -> Tuple[np.ndarray, Union[float, np.ndarray]]: """ Solving the Yule-Walker equations yields the parameters of the AR model. - + The Yule-Walker equations can be expressed in matrix form as:: r(0)*1 + r(1)*a[1] + r(2)*a[2] + ... + r(n)*a[n] = σ² @@ -542,45 +542,48 @@ def yule_walker(A: np.ndarray) -> Tuple[np.ndarray, Union[float, np.ndarray]]: ... r(n-1)*1 + r(n-2)*a[1] + r(n-3)*a[2] + ... + r(1)*a[n] = 0 r(n)*1 + r(n-1)*a[1] + r(n-2)*a[2] + ... + r(0)*a[n] = 0 - - Where r(k) is the value of the autocorrelation function at lag k, + + Where r(k) is the value of the autocorrelation function at lag k, a[i] are the coefficients of the AR model, and σ² is the variance of the noise. - This equation can be derived based on the orthogonality principle, + This equation can be derived based on the orthogonality principle, representing the relationship between the parameters of the AR model and the autocorrelation function of the sequence. - + :param A: The matrix representing the Yule-Walker equations, where the first row corresponds to the equation for σ² and the subsequent rows correspond to the equations for a1~an. - + :return: A tuple containing the vector of AR coefficients (a1~an) and the variance of the noise (σ²). """ # Check if A is a ndarray and has the correct shape if not isinstance(A, np.ndarray): raise ValueError("Input A must be a numpy array.") if A.shape[0] < 2 or A.shape[1] < 2: - raise ValueError("Input A must have at least 2 rows and 2 columns to solve for a1~an and σ².") - + raise ValueError( + "Input A must have at least 2 rows and 2 columns to solve for a1~an and σ²." + ) + # Check if the matrix A is square if A.shape[0] != A.shape[1]: raise ValueError("Input A must be a square matrix.") - + # Solve for the coefficients using the equations from the second line onwards. B = A[1:, 1:] # Extracting the coefficient matrix c = -A[1:, 0] # Extract the constant term and take its negative. - + try: # Solve for a1~an a = np.linalg.solve(B, c) - + except np.linalg.LinAlgError as e: print(f"Solution failed: {e}") - print("Reason: Matrix B is not invertible, and therefore cannot uniquely determine a1~an.") - + print( + "Reason: Matrix B is not invertible, and therefore cannot uniquely determine a1~an." + ) + # If B is not invertible, we can use least squares to find an approximate solution. a = np.linalg.lstsq(B, c, rcond=None)[0] - - + # Solve for σ² using the first line of the equations - x = np.hstack([1, a]) # Construct the x vector + x = np.hstack([1, a]) # Construct the x vector sigma_sq = np.dot(A[0], x) - - return x, sigma_sq \ No newline at end of file + + return x, sigma_sq From b1f17806d3370be3ca8257f5363c363717cea3d1 Mon Sep 17 00:00:00 2001 From: wwhenxuan Date: Wed, 18 Feb 2026 14:28:56 +0800 Subject: [PATCH 5/5] whenxuan: update the init for simulator --- s2generator/__init__.py | 4 ++++ s2generator/simulator/__init__.py | 3 +++ s2generator/simulator/wiener_filter.py | 10 +++++++++- 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/s2generator/__init__.py b/s2generator/__init__.py index fa36c02..fecc83d 100644 --- a/s2generator/__init__.py +++ b/s2generator/__init__.py @@ -16,6 +16,7 @@ "print_ascii", "print_hello", "excitation", + "simulator", "utils", "params", ] @@ -35,6 +36,9 @@ # Generic interface for generating stimulus time series data from .excitation import Excitation +# # The simulators for generating time series data +# from .simulator import ARIMASimulator, WienerFilterSimulator + # Visualize the generated S2 object from .utils.visualization import plot_series diff --git a/s2generator/simulator/__init__.py b/s2generator/simulator/__init__.py index 20df1d7..e2e287f 100644 --- a/s2generator/simulator/__init__.py +++ b/s2generator/simulator/__init__.py @@ -3,6 +3,9 @@ Created on 2026/02/13 13:04:42 @author: Whenxuan Wang @email: wwhenxuan@gmail.com +@url: https://github.com/wwhenxuan/S2Generator """ from .arima import ARIMASimulator + +from .wiener_filter import WienerFilterSimulator diff --git a/s2generator/simulator/wiener_filter.py b/s2generator/simulator/wiener_filter.py index 0c7887a..fd2b50e 100644 --- a/s2generator/simulator/wiener_filter.py +++ b/s2generator/simulator/wiener_filter.py @@ -1,4 +1,12 @@ -from typing import Optional, Union, Tuple +# -*- coding: utf-8 -*- +""" +Created on 2026/02/18 14:28:15 +@author: Whenxuan Wang +@email: wwhenxuan@gmail.com +@url: https://github.com/wwhenxuan/S2Generator +""" + +from typing import Optional import numpy as np from scipy import signal