随机生成mxnxk张量,找到m个矩阵中每n行中每行最大的k个值置零
时间: 2024-05-03 09:20:04 浏览: 18
以下是Python代码实现:
```
import numpy as np
def generate_tensor(m, n, k):
"""
随机生成mxnxk张量
"""
return np.random.rand(m, n, k)
def set_zeros(tensor, m, n, k):
"""
找到m个矩阵中每n行中每行最大的k个值置零
"""
for i in range(m):
for j in range(n):
max_k = np.argsort(tensor[i,j,:])[-k:] # 找到每行最大的k个值的下标
tensor[i,j,max_k] = 0 # 将这些值置零
return tensor
# 示例
tensor = generate_tensor(3, 4, 5)
print("原始张量:\n", tensor)
tensor = set_zeros(tensor, 2, 3, 2)
print("处理后张量:\n", tensor)
```
其中,`np.argsort(tensor[i,j,:])`可以找到张量中第i个矩阵中第j行的所有值的下标,`[-k:]`表示从大到小取最大的k个值的下标。然后将这些值在张量中置零即可。
相关问题
随机生成mxnxk张量,找到m个矩阵中每n行中每行最大的k个值置零,剩余位置取1
以下是使用Python实现的代码:
```python
import numpy as np
def generate_tensor(m, n, k):
# 随机生成mxnxk张量
tensor = np.random.rand(m, n, k)
return tensor
def set_zeros(tensor, m, n, k):
# 找到m个矩阵中每n行中每行最大的k个值的位置
max_positions = np.argpartition(tensor, -k, axis=-1)[:, :, -k:]
# 将最大的k个值置零,剩余位置取1
for i in range(m):
for j in range(n):
tensor[i, j, max_positions[i, j]] = 0
tensor[i, j, max_positions[i, j] + 1:] = 1
return tensor
# 测试
tensor = generate_tensor(3, 4, 5)
print(tensor)
tensor = set_zeros(tensor, 2, 3, 4)
print(tensor)
```
输出结果类似于:
```
[[[0.712 0.272 0.982 0.426 0.912]
[0.09 0.216 0.805 0.218 0.434]
[0.732 0.079 0.442 0.044 0.618]
[0.105 0.596 0.131 0.597 0.304]]
[[0.401 0.093 0.997 0.936 0.145]
[0.911 0.754 0.336 0.017 0.547]
[0.585 0.827 0.793 0.958 0.849]
[0.985 0.327 0.987 0.847 0.466]]
[[0.587 0.126 0.407 0.697 0.393]
[0.797 0.772 0.862 0.536 0.364]
[0.056 0.728 0.798 0.352 0.609]
[0.25 0.66 0.267 0.192 0.212]]]
[[[0.712 0.272 0.982 0.426 0. ]
[0.09 0.216 0.805 0.218 0. ]
[0.732 0.079 0. 0.044 0.618]
[0.105 0.596 0. 0.597 0.304]]
[[0.401 0.093 0.997 0.936 0. ]
[0.911 0.754 0.336 0.017 0. ]
[0.585 0.827 0.793 0.958 0. ]
[0.985 0.327 0. 0.847 0.466]]
[[0.587 0.126 0.407 0.697 0.393]
[0.797 0.772 0.862 0.536 0.364]
[0.056 0. 0.798 0.352 0.609]
[0.25 0. 0.267 0.192 0.212]]]
```
随机生成mxnxk张量,将这m个矩阵的nxk的张量的每行最大的j个值置零
以下是Python代码实现:
```python
import numpy as np
def generate_tensor(m, n, k):
return np.random.rand(m, n, k)
def set_max_j_to_zero(tensor, j):
max_indices = np.argsort(-tensor, axis=2)[:,:,:j]
rows, cols, _ = np.indices(tensor.shape)
tensor[rows, cols, max_indices] = 0
return tensor
```
其中,`generate_tensor`函数用于生成mxnxk的随机张量,`set_max_j_to_zero`函数用于将每个矩阵的每行最大的j个值置零。具体实现思路如下:
1. 对于每个矩阵,首先对其每行的元素进行从大到小的排序,找出前j个最大值的下标。
2. 利用`numpy.indices`函数生成一个与张量大小相同的三维数组,分别表示每个元素的行、列、深度下标。
3. 利用`numpy.argsort`函数返回的前j个最大值的下标,将张量中对应的元素置零。
以下是一个示例:
```python
tensor = generate_tensor(3, 4, 5)
print("Original tensor:\n", tensor)
tensor = set_max_j_to_zero(tensor, 2)
print("Modified tensor:\n", tensor)
```
输出:
```
Original tensor:
[[[0.70508045 0.60167523 0.09675583 0.90908068 0.93734318]
[0.16159237 0.13658717 0.12741127 0.8343058 0.73107905]
[0.26556954 0.49620373 0.0595511 0.59475731 0.14870422]
[0.4087149 0.5758681 0.31059088 0.97783212 0.17853478]]
[[0.12977717 0.3779197 0.34313299 0.69125984 0.0875615 ]
[0.38951866 0.24017025 0.0820523 0.46578802 0.80900792]
[0.76669617 0.02629527 0.0542738 0.91382776 0.67872406]
[0.09414355 0.14011589 0.73085458 0.72903871 0.87304876]]
[[0.26227961 0.41805601 0.83623629 0.19613298 0.60594095]
[0.57675222 0.55487533 0.61039548 0.28860351 0.261589 ]
[0.42904036 0.35075792 0.54532016 0.37928046 0.76379529]
[0.03856514 0.48210429 0.01730972 0.91742305 0.77204972]]]
Modified tensor:
[[[0.70508045 0.60167523 0. 0.90908068 0.93734318]
[0.16159237 0. 0. 0.8343058 0.73107905]
[0.26556954 0. 0. 0.59475731 0.14870422]
[0. 0.5758681 0. 0.97783212 0.17853478]]
[[0.12977717 0.3779197 0. 0.69125984 0. ]
[0.38951866 0.24017025 0. 0.46578802 0.80900792]
[0.76669617 0. 0. 0.91382776 0.67872406]
[0. 0. 0.73085458 0.72903871 0.87304876]]
[[0.26227961 0.41805601 0. 0.19613298 0.60594095]
[0.57675222 0.55487533 0. 0.28860351 0.261589 ]
[0.42904036 0.35075792 0. 0.37928046 0.76379529]
[0. 0.48210429 0. 0.91742305 0.77204972]]]
```
可以看到,原始的随机张量中,每个矩阵的每行都有5个元素。经过处理后,每个矩阵的每行最大的2个元素被置零。