numpy np.argmax 用法

例子1：

import numpy as np

# np.argmax(a) 返回的是a中元素最大值所对应的索引值
correct = np.array([[1, 3, 5, 7],[5, 7, 2, 2],[4, 6, 8, 1]])
print(correct, '\n')

# 行 最大值所对应的索引值
b = np.argmax(correct, axis=1)
print(b)

# 列 最大值所对应的索引值
c = np.argmax(correct, axis=0)
print(c)

[[1 3 5 7]
 [5 7 2 2]
 [4 6 8 1]] 

[3 1 2]
[1 1 2 0]

例子2

import numpy as np

correct = np.array([0, 1, 2, 3, 4, 5,  6])
test = np.array([0, 1, 2, 5, 4, 3, 6])
n_data = len(correct)
# -- 将 周一至周日 转换为独热编码格式 --
correct_data = np.zeros((n_data, 7))
test_data = np.zeros((n_data, 7))
#print(correct_data)
print("\n")

for i in range(n_data):
    correct_data[i, correct[i]] = 1
    test_data[i, test[i]] = 1
    
print(correct_data, '\n')

print(test_data, '\n')

b = np.argmax(correct_data, axis=1)
print(b)

c = np.argmax(test_data, axis=1)
print(c)

# 统计两个数组相等的情况
print(b == c)
# python中 true为1 false为0
count_test = np.sum(b == c)
print(count_test)