np。linalg.lstsq

np.linalg.lstsq是numpy中的一个函数，用于求解线性最小二乘问题，即给定一个线性方程组Ax = b，求解x的值。其中A为m x n的矩阵，b为m维的向量。该函数返回一个元组，包含了以下四个元素：

1. x：求解得到的x值；
2. residuals：残差平方和；
3. rank：矩阵A的秩；
4. s：矩阵A的奇异值。

可以使用以下代码调用该函数：

import numpy as np
A = np.array([[1, 2], [3, 4], [5, 6]])
b = np.array([1, 2, 3])
x, residuals, rank, s = np.linalg.lstsq(A, b, rcond=None)

其中rcond参数用于设置奇异值的阈值，通常使用默认值即可。如果A矩阵不是满秩的，则无法求解出唯一的x值，此时可以使用numpy中的伪逆函数np.linalg.pinv来进行求解。

相关问题

np.linalg.lstsq

`np.linalg.lstsq` is a NumPy function that solves the least-squares problem, which is finding the best-fitting linear equation for a set of data points. It takes as input a matrix `A` representing the data points, a vector `b` representing the target values, and an optional parameter `rcond` that specifies the cutoff for small singular values. The function returns a tuple `(x, residuals, rank, s)` where `x` is the solution to the least-squares problem, `residuals` is the sum of squared residuals, `rank` is the rank of matrix `A`, and `s` is an array containing the singular values of `A`.

The solution `x` represents the coefficients of the linear equation that best fits the data. If `A` has more columns than rows, the solution is an over-determined system and the best-fitting solution is obtained by minimizing the sum of squared residuals. If `A` has more rows than columns, the solution is an under-determined system and the least-norm solution is obtained by minimizing the norm of `x`.

np.linalg.lstsq函数

np.linalg.lstsq函数是numpy库中的一个函数，用于求解线性最小二乘问题。它的参数包括系数矩阵a、目标值矩阵b和rcond参数。该函数返回一个包含最小二乘解、残差平方和、系数矩阵a的秩以及a的奇异值的元组。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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