NumPy的hstack函数详细教程

numpy.hstack(tup)

import numpy as np

# 一维数组示例
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print("a:", a)
print("b:", b)
print("a.shape:", a.shape)
print("b.shape:", b.shape)

result = np.hstack((a, b))
print("hstack result:", result)
print("result.shape:", result.shape)

# 二维数组示例
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

print("a:")
print(a)
print("b:")
print(b)
print("a.shape:", a.shape)
print("b.shape:", b.shape)

result = np.hstack((a, b))
print("hstack result:")
print(result)
print("result.shape:", result.shape)

# 三维数组示例
a = np.random.randn(2, 3, 4)
b = np.random.randn(2, 2, 4)

print("a.shape:", a.shape)
print("b.shape:", b.shape)

result = np.hstack((a, b))
print("result.shape:", result.shape)

   # 这会报错，因为行数不同
   a = np.array([[1, 2], [3, 4]])
   b = np.array([[5, 6, 7]])  # 形状不匹配
   # result = np.hstack((a, b))  # ValueError

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

print("hstack (水平):")
print(np.hstack((a, b)))

print("vstack (垂直):")
print(np.vstack((a, b)))

print("dstack (深度):")
print(np.dstack((a, b)))

# 合并特征矩阵
features1 = np.random.randn(100, 5)  # 100个样本，5个特征
features2 = np.random.randn(100, 3)  # 100个样本，3个特征

combined_features = np.hstack((features1, features2))
print("原始特征形状:", features1.shape, features2.shape)
print("合并后特征形状:", combined_features.shape)