提交代码

test.py

+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression  # 确保正确导入
+import torch
+
+def test_numpy():
+    print("Testing NumPy...")
+    a = np.array([1, 2, 3])
+    b = np.array([4, 5, 6])
+    c = a + b
+    print("NumPy array sum:", c)
+
+def test_pandas():
+    print("\nTesting Pandas...")
+    data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
+    df = pd.DataFrame(data)
+    print("Pandas DataFrame:")
+    print(df)
+
+def test_matplotlib():
+    print("\nTesting Matplotlib...")
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x)
+    plt.plot(x, y)
+    plt.title("Sine Wave")
+    plt.xlabel("x")
+    plt.ylabel("sin(x)")
+    plt.show()
+
+def test_sklearn():
+    print("\nTesting Scikit-learn (Linear Regression)...")
+    X = np.array([[1], [2], [3]])  # 特征变量
+    y = np.array([2, 4, 6])        # 目标变量
+    model = LinearRegression()
+    model.fit(X, y)
+    prediction = model.predict()  # 提供输入特征 
+    print("Prediction for x=4:", prediction[0])
+
+def test_torch():
+    print("\nTesting PyTorch...")
+    x = torch.tensor([1.0, 2.0, 3.0])
+    y = x * 2
+    print("Torch tensor result:", y)
+
+if __name__ == "__main__":
+    test_numpy()
+    test_pandas()
+    test_matplotlib()
+    # test_sklearn()
+    test_torch()
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