2440烧nboot.bin
时间: 2023-05-12 10:01:51 浏览: 90
2440是指三星公司的一种ARM处理器型号,nboot.bin则是代表Bootloader的二进制代码文件。因此,2440烧nboot.bin即是在将Bootloader的二进制代码烧录到三星2440处理器中以完成引导启动的过程。
烧录Bootloader时,我们需要一台烧录器、一根连接烧录器和2440的数据线以及nboot.bin的二进制代码文件。具体烧录步骤需要注意以下几点:
1.检查烧录器的电源和连接线路是否稳定,以免发生错误或烧毁设备。
2.将2440的开发板和烧录器连接,并进入烧录模式。
3.在电脑上运行烧录程序,并选择2440处理器的型号和nboot.bin文件。
4.按照提示操作,等待烧录程序运行完成。
5.完成烧录后,断开连接线路,拔掉烧录器。
总之,在2440烧nboot.bin这个过程中,我们需要特别注意安全性和准确性,以免产生不必要的损失和浪费。
相关问题
# Data from binomial(15, θ) for an unknown θ x = c(3, 5, 7, 9, 11, 13) binomSize = 15 # known size of binomial n = length(x) # sample size thetahat = mean(x)/binomSize # MLE for θ nboot = 5000 # number of bootstrap samples to use # nboot parametric samples of size n; organize in a matrix tmpdata = rbinom(n*nboot, binomSize, thetahat) bootstrapsample = matrix(tmpdata, nrow=n, ncol=nboot) # Compute bootstrap means thetahat* and differences delta* thetahatstar = colMeans(bootstrapsample)/binomSize deltastar = thetahatstar - thetahat # Find quantiles and make the bootstrap confidence interval d = quantile(deltastar, c(.1,.9)) ci = thetahat - c(d[2], d[1])
The code you provided demonstrates how to compute a bootstrap confidence interval for an unknown parameter θ in a binomial distribution.
Here's a breakdown of the code:
```R
# Data from binomial(15, θ) for an unknown θ
x = c(3, 5, 7, 9, 11, 13)
binomSize = 15 # known size of binomial
n = length(x) # sample size
# Maximum Likelihood Estimate (MLE) for θ
thetahat = mean(x)/binomSize
nboot = 5000 # number of bootstrap samples to use
# Generate nboot parametric samples of size n
tmpdata = rbinom(n*nboot, binomSize, thetahat)
bootstrapsample = matrix(tmpdata, nrow=n, ncol=nboot)
# Compute bootstrap means (thetahat*) and differences (delta*)
thetahatstar = colMeans(bootstrapsample)/binomSize
deltastar = thetahatstar - thetahat
# Find quantiles and make the bootstrap confidence interval
d = quantile(deltastar, c(.1,.9))
ci = thetahat - c(d[2], d[1])
```
In this code, `x` represents the observed data from a binomial distribution with an unknown parameter θ. The `binomSize` variable represents the known size of the binomial distribution. The MLE for θ is computed using the `mean` function.
The code then generates `nboot` parametric bootstrap samples of size `n` using the `rbinom` function. The bootstrap samples are organized in a matrix called `bootstrapsample`.
Next, the code computes the bootstrap means (thetahat*) by taking the column means of `bootstrapsample` and dividing them by `binomSize`. It also calculates the differences (delta*) between the bootstrap means and the MLE.
Finally, the code finds the 10th and 90th percentiles of `deltastar` using the `quantile` function and constructs the bootstrap confidence interval by subtracting these quantiles from the MLE.
Please let me know if you have any further questions!
samples=[36.34 103.48 170.72 242.14 303.85 414.85 640.49 1396.45 1726.12 ; 6.579 5.949 8.979 7.293 7.497 2.732 1.659 1.244 1.115 ]'; % 自助抽样1000次 nboot = 1000; samples_idx = bootstrp(nboot, @(y) randsample(length(y), length(y), true), samples); samples_x = samples(:, 1); samples_y = samples(:, 2); % 检测1000次抽样的均值变点及其对应X change_points = []; % 初始化为空矩阵 for i = 1:nboot mean_left = mean(samples_y(samples_idx(i, 1:5))); mean_right = mean(samples_y(samples_idx(i, 6:9))); idx = find(mean_right < mean_left, 1, 'first'); if isempty(idx) idx = length(samples_x); % 没有变点时,赋值为最大值 end change_points = [change_points; [idx, samples_x(idx)]]; end % 计算频次直方图和累积频率 [counts, edges] = histcounts(change_points(:,2), 'Normalization', 'count'); cdf = cumsum(counts); cdf = cdf / max(cdf); % 绘制双Y轴图 figure; yyaxis left; bar(edges(1:end-1), counts,0.3, 'histc'); xlim([-100 1800]); ylim([0 650]); ylabel('Count'); yyaxis right; plot(edges(1:end-1), cdf, 'LineWidth', 2); ylim([0 1.2]) ylabel('Cumulative Frequence'); xlabel('Distance'); legend('Frequence Count','Cumulative Frequence'); 未定义与 'double' 类型的输入参数相对应的函数 'histcounts'。
这个错误提示是因为你的 MATLAB 版本比较旧,没有包含 histcounts 函数。你可以使用 hist 函数代替,代码如下:
samples=[36.34 103.48 170.72 242.14 303.85 414.85 640.49 1396.45 1726.12 ; 6.579 5.949 8.979 7.293 7.497 2.732 1.659 1.244 1.115 ]'; % 自助抽样1000次 nboot = 1000; samples_idx = bootstrp(nboot, @(y) randsample(length(y), length(y), true), samples); samples_x = samples(:, 1); samples_y = samples(:, 2); % 检测1000次抽样的均值变点及其对应X change_points = []; % 初始化为空矩阵 for i = 1:nboot mean_left = mean(samples_y(samples_idx(i, 1:5))); mean_right = mean(samples_y(samples_idx(i, 6:9))); idx = find(mean_right < mean_left, 1, 'first'); if isempty(idx) idx = length(samples_x); % 没有变点时,赋值为最大值 end change_points = [change_points; [idx, samples_x(idx)]]; end % 计算频次直方图和累积频率 [counts, edges] = hist(change_points(:,2),10); cdf = cumsum(counts); cdf = cdf / max(cdf); % 绘制双Y轴图 figure; yyaxis left; bar(edges(1:end-1), counts,0.3, 'histc'); xlim([-100 1800]); ylim([0 650]); ylabel('Count'); yyaxis right; plot(edges(1:end-1), cdf, 'LineWidth', 2); ylim([0 1.2]) ylabel('Cumulative Frequence'); xlabel('Distance'); legend('Frequence Count','Cumulative Frequence');