Merge pull request #35 from wwhenxuan/master

Update the Version of PySDKit 0.4.20

Author: wwhenxuan

docs/source/API/pysdkit.tsa.rst

@@ -32,7 +32,7 @@ tsa.knn
 ---------------------------
 
 .. automodule:: pysdkit.tsa._knn
-.. autoclass:: pysdkit.tsa._knn.KnnDtw
+.. autoclass:: pysdkit.tsa._knn.KNN
     :members:
     :undoc-members:
     :private-members:

example/data_generator.ipynb

@@ -114,7 +114,7 @@
    },
    "cell_type": "code",
    "source": [
-    "# Here we switch the number of the test samples and visualize their frequency domain characteristics.\n",
+    "# Here we switch the number of the test X and visualize their frequency domain characteristics.\n",
     "time, signal = test_univariate_signal(case=2)\n",
     "fig = plot_signal(time, signal, spectrum=True)"
    ],
@@ -190,7 +190,7 @@
    },
    "cell_type": "code",
    "source": [
-    "# Switch the number of test samples\n",
+    "# Switch the number of test X\n",
     "time, signal = test_multivariate_signal(case=2)\n",
     "fig = plot_signal(time, signal, spectrum=True)"
    ],

example/demo.ipynb

@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:49.953258Z",
-     "start_time": "2025-07-17T03:21:49.216362Z"
+     "end_time": "2025-07-22T13:58:02.559853Z",
+     "start_time": "2025-07-22T13:58:01.838243Z"
     }
    },
    "source": [
@@ -30,8 +30,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:50.206086Z",
-     "start_time": "2025-07-17T03:21:50.111514Z"
+     "end_time": "2025-07-22T13:58:02.654908Z",
+     "start_time": "2025-07-22T13:58:02.563875Z"
     }
    },
    "id": "1af048fdecdd07b5",
@@ -60,8 +60,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:50.428697Z",
-     "start_time": "2025-07-17T03:21:50.210156Z"
+     "end_time": "2025-07-22T13:58:03.032538Z",
+     "start_time": "2025-07-22T13:58:02.796522Z"
     }
    },
    "id": "65e9ea5b377df1a4",
@@ -82,8 +82,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:50.664629Z",
-     "start_time": "2025-07-17T03:21:50.446632Z"
+     "end_time": "2025-07-22T13:58:03.301349Z",
+     "start_time": "2025-07-22T13:58:03.049584Z"
     }
    },
    "cell_type": "code",
@@ -127,8 +127,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:51.257821Z",
-     "start_time": "2025-07-17T03:21:50.678659Z"
+     "end_time": "2025-07-22T13:58:04.006736Z",
+     "start_time": "2025-07-22T13:58:03.318341Z"
     }
    },
    "id": "36cd37054ec6ba22",
@@ -164,8 +164,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:51.657130Z",
-     "start_time": "2025-07-17T03:21:51.340061Z"
+     "end_time": "2025-07-22T13:58:04.337209Z",
+     "start_time": "2025-07-22T13:58:04.027737Z"
     }
    },
    "id": "d066e800d5dd681a",
@@ -193,26 +193,26 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-17T03:21:51.687147Z",
-     "start_time": "2025-07-17T03:21:51.671130Z"
+     "end_time": "2025-07-22T13:58:04.368264Z",
+     "start_time": "2025-07-22T13:58:04.355190Z"
     }
    },
    "cell_type": "code",
    "source": "",
-   "id": "ed3ea5b6e4975fcc",
+   "id": "90b80872d654572e",
    "outputs": [],
    "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-07-17T01:18:57.758434Z",
-     "start_time": "2025-07-17T01:18:57.742430Z"
+     "end_time": "2025-07-22T13:58:04.399956Z",
+     "start_time": "2025-07-22T13:58:04.384984Z"
     }
    },
    "cell_type": "code",
    "source": "",
-   "id": "cde2d45ca5b42b5e",
+   "id": "3413958f0630f43a",
    "outputs": [],
    "execution_count": null
   },
@@ -222,7 +222,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "90b80872d654572e"
+   "id": "8c71740dde571d35"
   }
  ],
  "metadata": {

example/example_zh/models/PCA.ipynb

@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

example/example_zh/models/knn.ipynb

@@ -2,7 +2,7 @@
 A Python library for signal decomposition algorithms.
 """
 
-__version__ = "0.4.19"
+__version__ = "0.4.20"
 
 # Empirical Mode Decomposition
 from ._emd import EMD
@@ -164,6 +164,7 @@ def print_functions():
     "EWT",
     "JMD",
     "Moving_Decomp",
+    "models",
     "data",
     "entropy",
     "HHT",

example/tsa/knn.ipynb

@@ -4,6 +4,33 @@
 @author: Whenxuan Wang
 @email: wwhenxuan@gmail.com
 """
+__all__ = [
+    "generate_sin_signal",
+    "generate_cos_signal",
+    "generate_time_series",
+    "generate_square_wave",
+    "generate_triangle_wave",
+    "generate_sawtooth_wave",
+    "generate_am_signal",
+    "generate_exponential_signal",
+    "test_emd",
+    "test_hht",
+    "add_noise",
+    "test_univariate_signal",
+    "test_univariate_nonlinear_chip",
+    "test_univariate_gaussamp_quadfm",
+    "test_univariate_duffing",
+    "test_univariate_logistic_am",
+    "test_univariate_cubic_quad",
+    "test_multivariate_signal",
+    "get_meshgrid_2D",
+    "test_grayscale",
+    "test_univariate_image",
+    "test_multivariate_image",
+    "test_univariate_cube",
+    "test_multivariate_cube",
+    "test_pca",
+]
 
 # A series of functions for generating 1D NumPy signals
 from ._generator import generate_sin_signal, generate_cos_signal
@@ -24,12 +51,26 @@
 # Generate the test function for HHT
 from ._generator import test_hht
 
-# Generator for 1D multivariate
-from ._generator import test_multivariate_signal
-
 # add Gaussian noise
 from ._add_noise import add_noise
 
+# Generator for 1D univariate
+from ._test_univariate import test_univariate_signal
+
+# Generator for 1D univariate nonlinear chip signal
+from ._test_univariate import test_univariate_nonlinear_chip
+
+# Generator for other univariate with different characterization
+from ._test_univariate import (
+    test_univariate_gaussamp_quadfm,
+    test_univariate_duffing,
+    test_univariate_logistic_am,
+    test_univariate_cubic_quad,
+)
+
+# Generator for 1D multivariate
+from ._generator import test_multivariate_signal
+
 # Generate a grid matrix given an input and output range
 from ._image import get_meshgrid_2D
 
@@ -48,16 +89,5 @@
 # Test case for multivariate 3D cube
 from ._cube import test_multivariate_cube
 
-# Generator for 1D univariate
-from ._test_univariate import test_univariate_signal
-
-# Generator for 1D univariate nonlinear chip signal
-from ._test_univariate import test_univariate_nonlinear_chip
-
-# Generator for other univariate with different characterization
-from ._test_univariate import (
-    test_univariate_gaussamp_quadfm,
-    test_univariate_duffing,
-    test_univariate_logistic_am,
-    test_univariate_cubic_quad,
-)
+# Test case for Principal Component Analysis
+from ._models import test_pca

pysdkit/__init__.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+"""
+Created on 2025/07/22 17:46:28
+@author: Whenxuan Wang
+@email: wwhenxuan@gmail.com
+"""
+import numpy as np
+
+from typing import Optional, Tuple
+
+
+def test_pca(number: int = 200, dim: int = 2, random_state: Optional[int] = 42) -> np.ndarray:
+    """
+    Generate synthetic data for testing Principal Component Analysis (PCA).
+    The dataset is created by multiplying a set of coordinate points (default 2-D)
+    by a random rotation matrix.
+
+    :param number: int, optional
+        Number of data points to generate, by default 200.
+    :param dim: int, optional
+        Dimensionality of the generated data, by default 2.
+    :param random_state: int, optional
+        Random seed for reproducibility, by default 42.
+    :return: X : np.ndarray
+        Generated test data of shape [n_samples, n_features].
+    """
+    # Initialize random number generator
+    rng = np.random.RandomState(random_state)
+
+    # Generate a random rotation matrix
+    rot = rng.rand(dim, dim)
+
+    return np.dot(rot, rng.randn(dim, number)).T

pysdkit/data/__init__.py

@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+"""
+Created on 2025/07/21 15:26:00
+@author: Whenxuan Wang
+@email: wwhenxuan@gmail.com
+"""
+
+from pysdkit.models._knn import KNN
+
+from pysdkit.models._pca import pca, PCA