kimi2.5 check

PCA/PCA.py

@@ -4,7 +4,7 @@
 import numpy as np
 from matplotlib import pyplot as plt
 from scipy import io as spio
-from sklearn.decomposition import pca
+from sklearn.decomposition import PCA as SklearnPCA
 
 '''
 主成分分析_2维数据降维1维演示函数

tests/conftest.py

@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+"""
+Pytest 共享 fixtures 配置文件
+为所有机器学习算法测试提供公共的数据加载和模型初始化功能
+"""
+import pytest
+import numpy as np
+import os
+import sys
+
+# 添加项目根目录到 Python 路径
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, PROJECT_ROOT)
+
+
+@pytest.fixture
+def sample_regression_data():
+    """生成小型回归数据集用于测试"""
+    np.random.seed(42)
+    m = 50
+    X = np.random.randn(m, 2)
+    true_theta = np.array([2.0, -1.5])
+    y = X[:, 0] * true_theta[0] + X[:, 1] * true_theta[1] + 0.1 * np.random.randn(m)
+    return X, y, true_theta
+
+
+@pytest.fixture
+def sample_classification_data():
+    """生成小型二分类数据集用于测试"""
+    np.random.seed(42)
+    m = 100
+    # 生成两类数据
+    X_pos = np.random.randn(m // 2, 2) + np.array([1.0, 1.0])
+    X_neg = np.random.randn(m // 2, 2) - np.array([1.0, 1.0])
+    X = np.vstack([X_pos, X_neg])
+    y = np.hstack([np.ones(m // 2), np.zeros(m // 2)])
+    return X, y
+
+
+@pytest.fixture
+def sample_multiclass_data():
+    """生成小型多分类数据集用于测试"""
+    np.random.seed(42)
+    m = 150
+    n_classes = 3
+    X_list = []
+    y_list = []
+    for i in range(n_classes):
+        center = np.array([np.cos(2 * np.pi * i / n_classes), 
+                          np.sin(2 * np.pi * i / n_classes)]) * 2
+        X_list.append(np.random.randn(m // n_classes, 2) + center)
+        y_list.append(np.ones(m // n_classes) * i)
+    X = np.vstack(X_list)
+    y = np.hstack(y_list)
+    return X, y.astype(int)
+
+
+@pytest.fixture
+def sample_clustering_data():
+    """生成小型聚类数据集用于测试"""
+    np.random.seed(42)
+    K = 3
+    m_per_cluster = 30
+    centroids = np.array([[0, 0], [5, 5], [10, 0]])
+    X_list = []
+    for i in range(K):
+        X_list.append(np.random.randn(m_per_cluster, 2) + centroids[i])
+    X = np.vstack(X_list)
+    return X, K, centroids
+
+
+@pytest.fixture
+def linear_regression_data_path():
+    """线性回归数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "LinearRegression", "data.txt")
+
+
+@pytest.fixture
+def logistic_regression_data_path():
+    """逻辑回归数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "LogisticRegression", "data2.txt")
+
+
+@pytest.fixture
+def kmeans_data_path():
+    """K-Means 数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "K-Means", "data.mat")
+
+
+@pytest.fixture
+def pca_data_path():
+    """PCA 数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "PCA", "data.mat")
+
+
+@pytest.fixture
+def anomaly_detection_data_path():
+    """异常检测数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "AnomalyDetection", "data1.mat")
+
+
+@pytest.fixture
+def neural_network_data_path():
+    """神经网络数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "NeuralNetwok", "data_digits.mat")
+
+
+@pytest.fixture
+def svm_data_path():
+    """SVM 数据文件路径"""
+    return os.path.join(PROJECT_ROOT, "SVM", "data1.mat")