“public PearsonCorrelationSimilarity(DataModel dataModel, Weighting weighting) throws Exception { this.dataModel = dataModel; this.cachedNumItems = dataModel.getNumItems(); this.cachedNumUsers = dataModel.getNumUsers(); this.weighted = weighting == Weighting.WEIGHTED; }” 解释代码
时间: 2023-12-06 16:40:50 浏览: 30
这段代码是一个Java类的构造函数,该类的名称是“PearsonCorrelationSimilarity”。构造函数接受两个参数:一个是“DataModel”类型的数据模型,另一个是“Weighting”类型的加权方式。该类的目的是计算基于皮尔逊相关系数的相似度矩阵。
在构造函数中,首先将传入的数据模型保存到成员变量“dataModel”中。然后使用数据模型中的“getNumItems()”和“getNumUsers()”方法获取物品数和用户数,并将它们缓存到成员变量“cachedNumItems”和“cachedNumUsers”中。最后,根据传入的加权方式设置成员变量“weighted”的值,如果加权方式是“WEIGHTED”,则设置为true,否则设置为false。
相关问题
“public PearsonCorrelationSimilarity(DataModel dataModel) throws Exception { this(dataModel, Weighting.UNWEIGHTED); } ” 解释到吗
这是一个 Java 方法的构造函数,该函数接受一个 DataModel 对象作为参数,并且可能会抛出异常。这个方法使用 Weighting.UNWEIGHTED 构造了一个 PearsonCorrelationSimilarity 对象,并将其赋值给当前对象。 PearsonCorrelationSimilarity 是用于计算两个用户之间的相似度的类,这里使用了无权重的 Pearson 相关系数计算方法。
how to estimate the ordered probit model by using MLE, please give the code
To estimate the ordered probit model using Maximum Likelihood Estimation (MLE), you can use the "VGAM" package in R. Here is an example code:
```R
# Load the "VGAM" package
library(VGAM)
# Load the data (in this example, we use the "Prestige" dataset)
data(Prestige)
# Define the dependent and independent variables
y <- Prestige$prestige
x1 <- Prestige$education
x2 <- Prestige$income
# Define the log-likelihood function for the ordered probit model
loglik <- function(theta, y, X) {
mu <- X %*% theta
p <- pnorm(c(-Inf, mu[-length(mu)], Inf))
lp <- diff(p)
lp[y == length(p)] <- p[y == length(p)]
ll <- sum(log(lp))
return(-ll)
}
# Estimate the ordered probit model using MLE
op <- vglm(y ~ x1 + x2, family = cumulative(parallel = TRUE),
weights = rep(1, length(y)),
control = list(maxit = 1000, trace = TRUE),
initdata = data.frame("estimate" = c(0, 0, 0)),
coef = TRUE,
method = "BFGS",
crit = 1e-6,
loglik = loglik,
X = cbind(1, x1, x2))
# Print the summary of the model
summary(op)
```
In this example, we define the log-likelihood function for the ordered probit model, which takes the parameter vector `theta`, the dependent variable `y`, and the independent variables `X` as input and returns the negative log-likelihood. We then use the `vglm` function to estimate the model using MLE. The `family` argument specifies the cumulative link function, and the `weights` argument specifies the weighting scheme. The `initdata` argument specifies the initial values for the optimization procedure. The `loglik` argument specifies the log-likelihood function, and the `X` argument specifies the design matrix. Finally, we print the summary of the model using the `summary` function.
Note that the exact code may vary depending on the dataset and software package used. Also, it is important to note that MLE can be more computationally intensive than other methods for estimating the ordered probit model.