Numpy Argmax

import numpy as np

arr = np.array([1, 3, 2, 7, 4])
index_of_max = np.argmax(arr)
print("Index of max value:", index_of_max)  # 输出结果应为3

import numpy as np

arr = np.array([[1, 3, 5], [4, 2, 6]])
index_of_max = np.argmax(arr)
print("Index of max value in flattened array:", index_of_max)  # 输出结果应为5

import numpy as np

arr = np.array([[1, 3, 5], [4, 2, 6]])
index_of_max = np.argmax(arr, axis=0)
print("Index of max values along axis 0:", index_of_max)  # 输出结果应为[1, 1, 1]

import numpy as np

arr = np.array([1, 3, 2, 7, 4])
filtered_indices = np.argmax(arr[arr > 3])
print("Index of max value greater than 3:", filtered_indices)  # 输出结果应为3

import numpy as np

dtype = [('name', 'S10'), ('height', float), ('age', int)]
values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ('Galahad', 1.85, 35)]
a = np.array(values, dtype=dtype)  # 创建结构化数组
index_of_tallest = np.argmax(a['height'])
print("Index of tallest person:", index_of_tallest)  # 输出结果应为1

import numpy as np

arr = np.random.rand(3, 3, 3)
index_of_max = np.argmax(arr, axis=2)
print("Index of max values along the last axis:", index_of_max)

import numpy as np

arr = np.random.rand(2, 2, 2, 2)
index_of_max = np.argmax(arr, axis=3)
print("Index of max values along the last axis:", index_of_max)

import numpy as np

arr = np.random.rand(1000, 1000)
indices = np.argmax(arr, axis=1)
max_values = np.take_along_axis(arr, indices[:, np.newaxis], axis=1)
print("Max values along axis 1:", max_values)

import numpy as np

arr = np.random.rand(10000)
max_index = np.argmax(arr)
max_value = arr[max_index]
print("Max value:", max_value)

import numpy as np

arr = np.array([])
try:
    print(np.argmax(arr))
except ValueError as e:
    print("Error:", e)