极客笔记如何使用Numpy中的argmax函数来处理二维数组
参考：numpy argmax two dimensions
在数据分析和机器学习中，经常需要处理和分析多维数组。Numpy是Python中一个强大的库，它提供了大量的函数来处理多维数组。本文将详细介绍如何使用Numpy中的argmax函数来处理二维数组，包括如何找到数组中最大值的位置。
1. 理解argmax函数
numpy.argmax()函数返回的是数组中最大值的索引。在处理多维数组时，可以指定axis参数来决定沿着哪个维度进行最大值的查找。
示例代码1：基本使用
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
result = np.argmax(array)
print(result) # 输出结果不显示
Output:
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
result = np.argmax(array, axis=0)
print(result) # 输出结果不显示
Output:
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
row_max_indices = np.argmax(array, axis=1)
print(row_max_indices) # 输出结果不显示
Output:
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
col_max_indices = np.argmax(array, axis=0)
print(col_max_indices) # 输出结果不显示
Output:
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
max_indices = np.argmax(array, axis=1)
max_values = np.take_along_axis(array, np.expand_dims(max_indices, axis=1), axis=1)
print(max_values) # 输出结果不显示
Output:
import numpy as np
array = np.array([[1, 3, 5], [4, 2, 6]])
max_val_index = np.argmax(array)
max_val = array.flat[max_val_index]
result = np.where(array == max_val)
print(result) # 输出结果不显示
Output:
import numpy as np
array = np.array([[6, 3, 6], [4, 6, 6]])
max_indices = np.argmax(array, axis=1)
print(max_indices) # 输出结果不显示
Output:
import numpy as np
try:
array = np.array([[1, 3, 5], [4, 2, 6]])
result = np.argmax(array, axis=2)
except np.AxisError as e:
print(e) # 输出结果不显示
Output:
import numpy as np
import time
array = np.random.rand(1000, 1000)
start_time = time.time()
result = np.argmax(array, axis=1)
end_time = time.time()
print(f"argmax duration: {end_time - start_time}") # 输出结果不显示
start_time = time.time()
manual_max_indices = [np.argmax(row) for row in array]
end_time = time.time()
print(f"manual duration: {end_time - start_time}") # 输出结果不显示
Output:
