np.where tensor
时间: 2023-08-20 21:08:13 浏览: 38
`np.where` 函数可以在 NumPy 中用来进行条件筛选和元素替换。你可以使用 `np.where` 来筛选出符合特定条件的元素或者替换满足条件的元素。下面是一个示例代码,用于在张量中使用 `np.where` 进行条件筛选:
```python
import numpy as np
tensor = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
target_number = 5
filtered_tensor = np.where(tensor == target_number)
print(filtered_tensor)
```
输出结果会返回一个包含满足条件的元素的索引的元组:
```
(array([1]), array([1]))
```
这里的输出表示满足条件的元素在张量中的位置,索引 `[1]` 代表行,索引 `[1]` 代表列。你可以根据自己的需求使用 `np.where` 进行条件筛选和替换操作。
相关问题
what's the difference between np.where and tf.where
np.where is a function in NumPy which returns an array of elements from x or y, depending on the conditions specified in the condition parameter. It can also be used to replace values in a NumPy array based on certain conditions.
tf.where is a similar function in TensorFlow which returns a new tensor containing elements either from x or y, depending on the conditions specified in the condition parameter. It can also be used to replace values in a tensor based on certain conditions.
The main difference between np.where and tf.where is that np.where operates on NumPy arrays, whereas tf.where operates on TensorFlow tensors. Additionally, tf.where can only be used with tensors of the same shape, while np.where can handle arrays of different shapes.
x, y = np.meshgrid(np.arange(range_x), np.arange(range_y)) # np.savetxt('reshape_data.txt', x, delimiter=' ', fmt="%i") x_o = x - range_x / 2 y_o = y - range_y / 2 x_i = x - dx y_i = y - dy z_critical = 50 R_o = 550 R_i = 200 def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical) data_crop = data_crop[:, :, 10:] C++ Eigen::Tensor实现 data_crop的数据类型为Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>
这段代码使用了numpy和Eigen::Tensor库实现了一个点云数据的裁剪过程。其中,np.meshgrid函数用于生成一个二维网格,x_o和y_o分别表示网格中每个点相对于中心点的偏移量,x_i和y_i分别表示网格中每个点相对于裁剪后的中心点的偏移量,z_critical表示裁剪的高度阈值,R_o和R_i表示外圆和内圆的半径,data_crop表示待裁剪的点云数据。crop_pointcloud函数实现了点云数据的裁剪操作,使用了np.where函数实现了条件判断和赋值操作。最终得到的data_crop是一个三维的张量,数据类型为uint8_t,存储方式为行优先。