nav.lam(i,j)=lama(i,frq(j));

As an AI language model, I can see that this is a line of code written in MATLAB that assigns the value of the function lama evaluated at the ith row and jth column of the matrix frq to the ith row and jth column of the matrix nav.lam.

This code is essentially populating the matrix nav.lam with the values obtained by evaluating the function lama at the corresponding frequencies stored in the matrix frq.

Without knowing the exact values of lama and frq, it is difficult to provide a more specific explanation.

相关问题

SELECT A.ORG_RRN , A.DOC_ID FROM WIP_WO A INNER JOIN WIP_WO B ON B.ORG_RRN = A.ORG_RRN AND B.PARENT_ID = A.DOC_ID INNER JOIN MM_BOM D ON D.ORG_RRN = A.ORG_RRN AND D.NAME = A.RESERVED3 WHERE D.RESERVED8 IN ('LAM') 怎么优化

对于这个查询语句，可以考虑以下优化：

1. 确保表 WIP_WO 和 MM_BOM 上有适当的索引，以便加快连接操作和筛选操作的速度。具体可以根据查询语句中使用的列建立索引，例如在 WIP_WO 上可以建立 (ORG_RRN, DOC_ID, RESERVED3) 的复合索引，在 MM_BOM 上可以建立 (ORG_RRN, NAME, RESERVED8) 的复合索引。

2. 尝试使用 EXISTS 或者 IN 子查询来代替 INNER JOIN，这样可以避免产生笛卡尔积，提高查询效率。

3. 如果数据量较大，可以考虑分批次查询，使用 LIMIT 和 OFFSET 控制每次查询的数据量，避免一次查询过多数据造成性能问题。

下面是使用 EXISTS 来替换 INNER JOIN 的优化示例：

SELECT A.ORG_RRN, A.DOC_ID 
FROM WIP_WO A 
WHERE EXISTS (
    SELECT 1 
    FROM WIP_WO B, MM_BOM D 
    WHERE B.ORG_RRN = A.ORG_RRN 
        AND B.PARENT_ID = A.DOC_ID 
        AND D.ORG_RRN = A.ORG_RRN 
        AND D.NAME = A.RESERVED3 
        AND D.RESERVED8 IN ('LAM')
);

这样可以避免 INNER JOIN 产生的笛卡尔积，提高查询效率。

set.seed(0) n = 50 p = 30 x = matrix(rnorm(n*p),nrow=n) bstar = c(runif(30,0.5,1)) mu = as.numeric(x%*%bstar) par(mar=c(4.5,4.5,0.5,0.5)) hist(bstar,breaks=30,col="gray",main="", xlab="True coefficients") library(MASS) set.seed(1) R = 100 nlam = 60 lam = seq(0,25,length=nlam) fit.ls = matrix(0,R,n) fit.rid = array(0,dim=c(R,nlam,n)) err.ls = numeric(R) err.rid = matrix(0,R,nlam) for (i in 1:R) { cat(c(i,", ")) y = mu + rnorm(n) ynew = mu + rnorm(n) a = lm(y~x+0) bls = coef(a) fit.ls[i,] = x%*%bls err.ls[i] = mean((ynew-fit.ls[i,])^2) aa = lm.ridge(y~x+0,lambda=lam) brid = coef(aa) fit.rid[i,,] = brid%*%t(x) err.rid[i,] = rowMeans(scale(fit.rid[i,,],center=ynew,scale=F)^2) } aveerr.ls = mean(err.ls) aveerr.rid = colMeans(err.rid) bias.ls = sum((colMeans(fit.ls)-mu)^2)/n var.ls = sum(apply(fit.ls,2,var))/n bias.rid = rowSums(scale(apply(fit.rid,2:3,mean),center=mu,scale=F)^2)/n var.rid = rowSums(apply(fit.rid,2:3,var))/n mse.ls = bias.ls + var.ls mse.rid = bias.rid + var.rid prederr.ls = mse.ls + 1 prederr.rid = mse.rid + 1 bias.ls var.ls p/n prederr.ls aveerr.ls cbind(prederr.rid,aveerr.rid) par(mar=c(4.5,4.5,0.5,0.5)) plot(lam,prederr.rid,type="l", xlab="Amount of shrinkage",ylab="Prediction error") abline(h=prederr.ls,lty=2) text(c(1,24),c(1.48,1.48),c("Low","High")) legend("topleft",lty=c(2,1), legend=c("Linear regression","Ridge regression")) par(mar=c(4.5,4.5,0.5,0.5)) plot(lam,mse.rid,type="l",ylim=c(0,max(mse.rid)), xlab=expression(paste(lambda)),ylab="") lines(lam,bias.rid,col="red") lines(lam,var.rid,col="blue") abline(h=mse.ls,lty=2) legend("bottomright",lty=c(2,1,1,1), legend=c("Linear MSE","Ridge MSE","Ridge Bias^2","Ridge Var"), col=c("black","black","red","blue")) 为每句代码加上注释解释

# 设置随机数种子
set.seed(0)

# 设定样本量和自变量个数
n = 50
p = 30

# 生成 n 行 p 列的随机数矩阵 x
x = matrix(rnorm(n*p),nrow=n)

# 生成真实系数向量 bstar
bstar = c(runif(30,0.5,1))

# 通过矩阵乘法得到因变量 mu
mu = as.numeric(x%*%bstar)

# 绘制真实系数的直方图
par(mar=c(4.5,4.5,0.5,0.5))
hist(bstar,breaks=30,col="gray",main="", xlab="True coefficients")

# 导入 MASS 包
library(MASS)

# 设置随机数种子和 lambda 序列
set.seed(1)
R = 100
nlam = 60
lam = seq(0,25,length=nlam)

# 初始化存储结果的对象
fit.ls = matrix(0,R,n)
fit.rid = array(0,dim=c(R,nlam,n))
err.ls = numeric(R)
err.rid = matrix(0,R,nlam)

# 循环 R 次进行线性回归和岭回归
for (i in 1:R) {
cat(c(i,", "))
y = mu + rnorm(n)
ynew = mu + rnorm(n)

# 线性回归
a = lm(y~x+0)
bls = coef(a)
fit.ls[i,] = x%*%bls
err.ls[i] = mean((ynew-fit.ls[i,])^2)

# 岭回归
aa = lm.ridge(y~x+0,lambda=lam)
brid = coef(aa)
fit.rid[i,,] = brid%*%t(x)
err.rid[i,] = rowMeans(scale(fit.rid[i,,],center=ynew,scale=F)^2)
}

# 计算预测误差和平均误差
aveerr.ls = mean(err.ls)
aveerr.rid = colMeans(err.rid)

# 计算偏差和方差
bias.ls = sum((colMeans(fit.ls)-mu)^2)/n
var.ls = sum(apply(fit.ls,2,var))/n

bias.rid = rowSums(scale(apply(fit.rid,2:3,mean),center=mu,scale=F)^2)/n
var.rid = rowSums(apply(fit.rid,2:3,var))/n

# 计算均方误差和预测误差
mse.ls = bias.ls + var.ls
mse.rid = bias.rid + var.rid
prederr.ls = mse.ls + 1
prederr.rid = mse.rid + 1

# 输出偏差、方差和自由度调整的预测误差
bias.ls
var.ls
p/n
prederr.ls
aveerr.ls
cbind(prederr.rid,aveerr.rid)

# 绘制岭回归预测误差和线性回归预测误差的比较图
par(mar=c(4.5,4.5,0.5,0.5))
plot(lam,prederr.rid,type="l", xlab="Amount of shrinkage",ylab="Prediction error")
abline(h=prederr.ls,lty=2)
text(c(1,24),c(1.48,1.48),c("Low","High"))
legend("topleft",lty=c(2,1), legend=c("Linear regression","Ridge regression"))

# 绘制岭回归的偏差、方差和均方误差分解图
par(mar=c(4.5,4.5,0.5,0.5))
plot(lam,mse.rid,type="l",ylim=c(0,max(mse.rid)), xlab=expression(paste(lambda)),ylab="")
lines(lam,bias.rid,col="red")
lines(lam,var.rid,col="blue")
abline(h=mse.ls,lty=2)
legend("bottomright",lty=c(2,1,1,1), legend=c("Linear MSE","Ridge MSE","Ridge Bias^2","Ridge Var"), col=c("black","black","red","blue"))