sum.np.where

`np.where`是NumPy库中的一个函数，用于根据给定的条件返回数组中满足条件的元素的索引或值。它的语法如下：

np.where(condition, x, y)

其中，`condition`是一个布尔数组或条件表达式，`x`和`y`是两个数组或标量。当`condition`中的元素为True时，返回`x`中对应位置的元素；当`condition`中的元素为False时，返回`y`中对应位置的元素。

如果只传入`condition`参数，则返回满足条件的元素的索引。如果同时传入`x`和`y`参数，则返回满足条件的元素的值。

下面是一个示例：

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
condition = arr > 2

result = np.where(condition, arr, 0print(result)

输出结果为：`[0 0 3 4 5]`。在这个例子中，满足条件`arr > 2`的元素为3、4和5，所以返回的结果中对应位置的元素为3、4和5，不满足条件的元素为0。

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`.

def draw_line(zeroArray, C): notZeroRow = np.where(np.sum(zeroArray == 1, axis=1) == 0)[0] deletaZeroColumn = np.where(zeroArray[notZeroRow] == -1)[1] notZeroRow = np.concatenate((notZeroRow, np.where(np.isin(zeroArray[:, deletaZeroColumn], 1))[0])) '划线操作' lineListRow = np.setdiff1d(np.arange(zeroArray.shape[0]), notZeroRow) lineListColumn = deletaZeroColumn findMin = np.copy(C) findMin[lineListRow, :] = 100000 findMin[:, lineListColumn] = 100000 minIndex = np.unravel_index(np.argmin(findMin), findMin.shape) C[notZeroRow, :] -= findMin[minIndex[0], minIndex[1]] for i in notZeroRow: negatives = np.where(C[i, :] < 0)[0] if negatives.size > 0: C[:, negatives] += findMin[minIndex[0], minIndex[1]] return C 添加注释

以下是给上述代码添加注释的示例：

def draw_line(zeroArray, C):
    # 找出没有独立零元素的行
    notZeroRow = np.where(np.sum(zeroArray == 1, axis=1) == 0)[0]
    # 找出非独立零元素的列
    deletaZeroColumn = np.where(zeroArray[notZeroRow] == -1)[1]

    # 将具有独立零元素的行和非独立零元素的列合并
    notZeroRow = np.concatenate((notZeroRow, np.where(np.isin(zeroArray[:, deletaZeroColumn], 1))[0]))
    
    '划线操作'
    # 找出需要划线的行
    lineListRow = np.setdiff1d(np.arange(zeroArray.shape[0]), notZeroRow)
    # 找出需要划线的列
    lineListColumn = deletaZeroColumn
    
    # 创建C的副本并将需要划线的部分设为一个极大数
    findMin = np.copy(C)
    findMin[lineListRow, :] = 100000
    findMin[:, lineListColumn] = 100000
    
    # 找到需要减去的最小值的索引
    minIndex = np.unravel_index(np.argmin(findMin), findMin.shape)
    
    # 在具有独立零元素的行上减去最小值
    C[notZeroRow, :] -= findMin[minIndex[0], minIndex[1]]
    
    # 消除负数元素
    for i in notZeroRow:
        negatives = np.where(C[i, :] < 0)[0]
        if negatives.size > 0:
            C[:, negatives] += findMin[minIndex[0], minIndex[1]]
    
    return C

注释解释了每个步骤的目的和操作，使代码更易读和理解。这样其他开发人员在阅读或维护代码时，能够更清楚地了解代码的逻辑和功能。