mirror of https://github.com/alibaba/MNN.git
70 lines
2.4 KiB
Markdown
70 lines
2.4 KiB
Markdown
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## linalg
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```python
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module linalg
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```
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linalg模块提供了[numpy.linalg](https://numpy.org/doc/stable/reference/routines.linalg.html)模块的部分函数，提供了对矩阵的求解和线性代数运算的函数
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---
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### `norm(a, ord=None, axis=None, keepdims=False)`
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作用等同与 `numpy` 中的 [`np.linalg.norm`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html) 函数，计算矩阵或向量的范数。
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参数：
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- `a:Var` 将要计算的变量
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- `ord:str` 范数的顺序，见下表
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- `axis:int` 计算的轴
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- `keepdims:bool` 是否保留计数维度
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| ord | 矩阵范数 | 数组范数 |
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|:---------|:---------------------|:---------------------|
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| `None` | Frobenius norm | 2-norm |
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| `fro` | Frobenius norm | - |
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| `nuc` | nuclear norm | - |
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| `inf` | max(sum(abs(x), axis=1)) | max(abs(x)) |
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| `-inf` | min(sum(abs(x), axis=1)) | min(abs(x)) |
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| `0` | - | sum(x != 0) |
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| `1` | max(sum(abs(x), axis=0)) | as below |
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| `-1` | min(sum(abs(x), axis=0)) | as below |
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| `2` | 2-norm | as below |
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| `-2` | smallest singular value | as below |
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| other | - | sum(abs(x)**ord)**(1./ord) |
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返回：计数得到的变量
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返回类型：`Var`
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示例
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```python
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>>> a = np.arange(9) - 4
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>>> np.linalg.norm(a)
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array(7.745967, dtype=float32)
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```
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---
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### `svd(a, |full_matrices, compute_uv, hermitian)`
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作用等同与 `numpy` 中的 [`np.linalg.svd`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html) 函数，奇异值分解
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*`full_matrices`, `compute_uv`, `hermitian`参数无效只能按照默认值执行*
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参数：
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- `a:Var` 输入矩阵
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- `full_matrices:bool` 是否计算全部的奇异值，numpy兼容参数
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- `compute_uv:bool` 是否计算奇异值，numpy兼容参数
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- `hermitian:bool` 是否计算对称矩阵的奇异值，numpy兼容参数
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返回：以`(u, w, vt)`的顺序返回奇异值分解结果
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返回类型：`tuple` of `Var`
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示例
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```python
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>>> x = np.arange(9.).reshape(3, 3)
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>>> np.linalg.svd(x)
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(array([[ 0.13511899, -0.90281564, 0.40824836],
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[ 0.49633518, -0.2949318 , -0.81649655],
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[ 0.8575514 , 0.31295216, 0.40824828]], dtype=float32), array([1.4226706e+01, 1.2652264e+00, 7.3003456e-08], dtype=float32), array([[ 0.46632814, 0.57099086, 0.67565346],
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[ 0.7847747 , 0.08545685, -0.6138614 ],
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[-0.40824845, 0.8164966 , -0.4082482 ]], dtype=float32))
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```
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