temp2a(j,:)=[min_x+(max_x-min_x)*rand,min_y+(max_y-min_y)*rand];
时间: 2024-01-31 22:03:13 浏览: 35
这段代码是在生成一个二维随机向量,其中 `min_x` 和 `max_x` 是 x 轴的最小值和最大值,`min_y` 和 `max_y` 是 y 轴的最小值和最大值。`rand` 函数生成一个 0 到 1 之间的随机数,将其乘以对应轴的范围再加上最小值,就得到了一个随机的二维坐标点。这个坐标点被添加到 `temp2a` 的第 `j` 行。通常这段代码是在循环中执行,生成多个随机坐标点。
相关问题
for (const auto &det : Z) { Eigen::VectorXd a = x_ + K * (det - z_pre_); x_filter = x_filter + beta(i) * a; ++i; } x_filter = last_beta * x_ + x_filter; Eigen::MatrixXd P_temp = Eigen::MatrixXd(n_x_, n_x_); P_temp.fill(0.); for (int i = 0; i < Z.size() + 1; ++i) { Eigen::VectorXd a = Eigen::VectorXd(n_x_); a.setZero(); if (i == Z.size()) { a = x_; } else { a = x_ + K * (Z.at(i) - z_pre_); } P_temp = P_temp + beta(i) * (a * a.transpose() - x_filter * x_filter.transpose()); } x_ = x_filter; P_ -= (1 - last_beta) * K * S_ * K.transpose(); P_ += P_temp;
这段代码是一个卡尔曼滤波器的更新过程,其中更新了卡尔曼滤波器的状态向量x_和协方差矩阵P_。具体的更新过程如下:
1. 对于Z中的每个观测值det,计算卡尔曼增益K和状态向量a,然后更新x_filter(滤波后的状态向量)。
2. 计算P_temp(一个临时的协方差矩阵),其中beta是一个权重向量,用于加权每个状态向量的影响。对于每个状态向量a,计算出其对应的协方差矩阵,然后使用beta权重加权求和,得到P_temp。
3. 根据最终的滤波后状态向量x_filter和上一时刻的状态向量x_,以及最后一个观测值对应的beta值last_beta,计算出最终的状态向量x_。
4. 根据卡尔曼增益K和残差协方差矩阵S_,更新协方差矩阵P_。
总的来说,这段代码是用于对一个向量序列进行卡尔曼滤波的,其中涉及到了矩阵和向量的计算。
import collections import math coordinate_X = [3, 8, 2, 6, 8] coordinate_Y = [8, 2, 5, 4, 8] Rate = [5, 5, 7.5, 7.5, 7.5] Volumn = [20, 30, 25, 10, 15] length = len(coordinate_X) temp_x = [] temp_y = [] temp_z = [] # 第一次坐标值计算 for i in range(length): temp_x.append(Rate[i] * Volumn[i] * coordinate_X[i]) temp_y.append(Rate[i] * Volumn[i] * coordinate_Y[i]) temp_z.append(Rate[i] * Volumn[i]) sigma_x = 0 sigma_y = 0 sigma_z = 0 for i in range(length): sigma_x += temp_x[i] sigma_y += temp_y[i] sigma_z += temp_z[i] before_x = sigma_x / sigma_z before_y = sigma_y / sigma_z print("第一次的坐标值", before_x, before_y) # 继续优化坐标值 flag = 1 while flag != 0: before_dis = [] for i in range(length): before_dis.append( math.sqrt((before_x - coordinate_X[i]) ** 2 + (before_y - coordinate_Y[i]) ** 2)) temp_xm = [] temp_ym = [] temp_zm = [] for i in range(length): temp_xm.append(Rate[i] * Volumn[i] * coordinate_X[i] / before_dis[i]) temp_ym.append(Rate[i] * Volumn[i] * coordinate_Y[i] / before_dis[i]) temp_zm.append(Rate[i] * Volumn[i] / before_dis[i]) sigma_xm = 0 sigma_ym = 0 sigma_zm = 0 for i in range(length): sigma_xm += temp_xm[i] sigma_ym += temp_ym[i] sigma_zm += temp_zm[i] after_x = sigma_xm / sigma_zm after_y = sigma_ym / sigma_zm after_dis = [] for i in range(length): after_dis.append(math.sqrt((after_x - coordinate_X[i]) ** 2 + (after_y - coordinate_Y[i]) ** 2)) before_TC = 0 after_TC = 0 for i in range(length): before_TC += (Rate[i] * Volumn[i] * before_dis[i]) after_TC += (Rate[i] * Volumn[i] * after_dis[i]) if before_TC > after_TC and before_TC-after_TC<0.002: print("第", flag, "次坐标值是", after_x, after_y) flag += 1 before_x = after_x before_y = after_y else: flag = 0这个代码如何在迭代结果=0.002时停止并输出
可以在代码中加入一个判断条件,在 before_TC 和 after_TC 差值小于等于 0.002 时停止迭代并输出最终结果。以下是修改后的代码:
```python
import collections
import math
coordinate_X = [3, 8, 2, 6, 8]
coordinate_Y = [8, 2, 5, 4, 8]
Rate = [5, 5, 7.5, 7.5, 7.5]
Volumn = [20, 30, 25, 10, 15]
length = len(coordinate_X)
temp_x = []
temp_y = []
temp_z = []
# 第一次坐标值计算
for i in range(length):
temp_x.append(Rate[i] * Volumn[i] * coordinate_X[i])
temp_y.append(Rate[i] * Volumn[i] * coordinate_Y[i])
temp_z.append(Rate[i] * Volumn[i])
sigma_x = 0
sigma_y = 0
sigma_z = 0
for i in range(length):
sigma_x += temp_x[i]
sigma_y += temp_y[i]
sigma_z += temp_z[i]
before_x = sigma_x / sigma_z
before_y = sigma_y / sigma_z
print("第一次的坐标值", before_x, before_y)
# 继续优化坐标值
flag = 1
while flag != 0:
before_dis = []
for i in range(length):
before_dis.append(
math.sqrt((before_x - coordinate_X[i]) ** 2 + (before_y - coordinate_Y[i]) ** 2))
temp_xm = []
temp_ym = []
temp_zm = []
for i in range(length):
temp_xm.append(Rate[i] * Volumn[i] * coordinate_X[i] / before_dis[i])
temp_ym.append(Rate[i] * Volumn[i] * coordinate_Y[i] / before_dis[i])
temp_zm.append(Rate[i] * Volumn[i] / before_dis[i])
sigma_xm = 0
sigma_ym = 0
sigma_zm = 0
for i in range(length):
sigma_xm += temp_xm[i]
sigma_ym += temp_ym[i]
sigma_zm += temp_zm[i]
after_x = sigma_xm / sigma_zm
after_y = sigma_ym / sigma_zm
after_dis = []
for i in range(length):
after_dis.append(math.sqrt((after_x - coordinate_X[i]) ** 2 + (after_y - coordinate_Y[i]) ** 2))
before_TC = 0
after_TC = 0
for i in range(length):
before_TC += (Rate[i] * Volumn[i] * before_dis[i])
after_TC += (Rate[i] * Volumn[i] * after_dis[i])
if before_TC - after_TC <= 0.002:
print("第", flag, "次坐标值是", after_x, after_y)
flag = 0
else:
print("第", flag, "次坐标值是", after_x, after_y)
flag += 1
before_x = after_x
before_y = after_y
```
在修改后的代码中,增加了一个判断条件 `if before_TC - after_TC <= 0.002:`,当 before_TC 和 after_TC 差值小于等于 0.002 时,停止迭代并输出最终结果。同时,在迭代过程中,增加了输出每次迭代结果的代码,方便查看。
相关推荐
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)