Merge pull request #39 from wwhenxuan/master

Update PySDKit 0.4.21 for STL

Author: wwhenxuan

.gitignore

@@ -1,41 +1,26 @@
+# Byte-compiled / optimized / DLL files
 __pycache__/
-*workspace
 *.py[cod]
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-htmlcov/
-.tox/
-.nox/
-.hypothesis/
-.pytest_cache/
+*$py.class
+
+# C developer
+*.so
+
+# File for PyPI
 *.egg-info/
-.vscode/
-.*.swp
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-junit*.xml
-*.cover
-MANIFEST
-.installed.cfg
-*.egg
-*.manifest
-*.spec
-*.log
-*.con
-*.out
-env.sh
-*-env.sh
+dist/
+build/
+
+# IDE config
+.idea/
+.spyderproject
+.ropeproject
+
+# Python env
+.venv/
+venv/
+.env
+.python-version
+
+# save the test data
+/data/

README.md

@@ -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/demo.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/tsa/stl.ipynb

@@ -2,7 +2,7 @@
 A Python library for signal decomposition algorithms.
 """
 
-__version__ = "0.4.20"
+__version__ = "0.4.21"
 
 # Empirical Mode Decomposition
 from ._emd import EMD
@@ -79,6 +79,9 @@
 # Moving Average Decomposition
 from .tsa import Moving_Decomp
 
+# Seasonal-Trend decomposition using LOESS (STL)
+from .tsa import STL
+
 # Hilbert-Huang Transform
 from ._emd import HHT
 
@@ -130,6 +133,7 @@ def print_functions():
 Empirical Wavelet Transform                     |    EWT
 Jump Plus AM-FM Mode Decomposition              |    JMD
 Moving Average Decomposition                    |    Moving
+Seasonal-Trend decomposition using LOESS        |    STL
 Hilbert-Huang Transform                         |    HHT
 _______________________________________________________________
 """
@@ -164,6 +168,7 @@ def print_functions():
     "EWT",
     "JMD",
     "Moving_Decomp",
+    "STL",
     "models",
     "data",
     "entropy",

example/tsa/stl.ipynb

@@ -90,4 +90,4 @@
 from ._cube import test_multivariate_cube
 
 # Test case for Principal Component Analysis
-from ._models import test_pca
+from ._models import test_pca

pysdkit/__init__.py

@@ -9,7 +9,9 @@
 from typing import Optional, Tuple
 
 
-def test_pca(number: int = 200, dim: int = 2, random_state: Optional[int] = 42) -> np.ndarray:
+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)
@@ -30,4 +32,4 @@ def test_pca(number: int = 200, dim: int = 2, random_state: Optional[int] = 42)
     # Generate a random rotation matrix
     rot = rng.rand(dim, dim)
 
-    return np.dot(rot, rng.randn(dim, number)).T
+    return np.dot(rot, rng.randn(dim, number)).T

pysdkit/data/__init__.py

@@ -208,6 +208,7 @@ def variance_(self) -> np.ndarray | List[float]:
         else:
             raise ValueError("Please run the PCA algorithm first!")
 
+
 # if __name__ == "__main__":
 #     # 创建示例数据（4个样本，3个特征）
 #     X = np.array([[2.5, 2.4, 3.1], [0.5, 0.7, 1.2], [2.2, 2.9, 2.5], [1.9, 2.2, 1.8]])

pysdkit/data/_models.py

@@ -27,14 +27,22 @@ def test_create_pca(self) -> None:
                 n_components,
                 msg="创建的PCA对象中的属性参数与传入的超参数不一致",
             )
+
     def test_none(self) -> None:
         """测试在未执行`fit_transform`方法前PCA中的初始化数据属性是否为None"""
         # 创建K近邻分类器实例对象
         pca = PCA(n_components=2)
         # 检验初始的拟合数据是否为None
-        self.assertIsNone(pca.X_reduced, msg="创建的PCA对象在未拟合数据时数据对象为非None")
-        self.assertIsNone(pca._components, msg="创建的PCA对象在未拟合数据时数据对象为非None")
-        self.assertIsNone(pca._explained_variance_ratio, msg="创建的PCA对象在未拟合数据时数据对象为非None")
+        self.assertIsNone(
+            pca.X_reduced, msg="创建的PCA对象在未拟合数据时数据对象为非None"
+        )
+        self.assertIsNone(
+            pca._components, msg="创建的PCA对象在未拟合数据时数据对象为非None"
+        )
+        self.assertIsNone(
+            pca._explained_variance_ratio,
+            msg="创建的PCA对象在未拟合数据时数据对象为非None",
+        )
 
     def test_wrong_shape_inputs(self) -> None:
         """测试PCA算法的非法的形状输入"""
@@ -60,4 +68,4 @@ def test_wrong_type_inputs(self) -> None:
 
     def test_fit_transform(self) -> None:
         """测试PCA算法能否正确执行"""
-        X = ...
+        X = ...