使用优化(最好是使用梯度)优化R中的多重输出函数 [英] Optimizing a multiple output function in R using optim, preferably with gradient
问题描述
我最近从matlab切换到R,我想运行一个优化方案.
I recently switched to R from matlab and I want to run an optimization scenario.
在matlab中,我能够:
In matlab I was able to:
options = optimset('GradObj', 'on', 'MaxIter', 400);
[theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
这里相当于costFunctionReg(在这里我称之为logisticRegressionCost)
Here is the equivalent of costFunctionReg (here I call it logisticRegressionCost)
logisticRegressionCost <- function(theta, X, y) {
J = 0;
theta = as.matrix(theta);
X = as.matrix(X);
y = as.matrix(y);
rows = dim(theta)[2];
cols = dim(theta)[1];
grad = matrix(0, rows, cols);
predicted = sigmoid(X %*% theta);
J = (-y) * log(predicted) - (1 - y) * log(1 - predicted);
J = sum(J) / dim(y)[1];
grad = t(predicted - y);
grad = grad %*% X;
grad = grad / dim(y)[1];
return(list(J = J, grad = t(grad)));
}
但是,当我尝试对其进行优化时:
However when I try to run an optimization on it like:
o = optim(theta <- matrix(0, dim(X)[2]), fn = logisticRegressionCost, X = X, y = y, method="Nelder-Mead")
由于列表返回,我得到了一个错误. (当我只返回J时,它可以工作)
I get an error due of the list return. (When I just only return J it works)
错误:
(list)对象不能被强制键入"double"
(list) object cannot be coerced to type 'double'
Q1:有没有一种方法可以指定乐观者应使用哪种收益最小化? (如fn $ J)
Q1: Is there a way to specify which return should the optim use for the minimization? (like fn$J)
Q2:是否有解决方案可以使用在logisticRegressionCost中计算出的梯度?
Q2: Is there a solution where I can use the gradient I calculate in the logisticRegressionCost?
推荐答案
我不认为您可以这样做,因为optim
的文档说fn
应该返回标量结果.
I don't think you can do this because the documentation for optim
says fn
should return a scalar result.
也许您只需要编写一个辅助函数即可进行优化.像这样:
Perhaps you could just write a helper function to optimize. Something like:
logisticRegressionCost.helper <- function(theta, X, y) {
logisticRegressionCost(theta, X, y)$J
}
此外,您不需要使用半冒号来抑制R中的输出.当我也从MatLab切换时,我也有同样的习惯:)
Also, you don't need the semi colons to suppress output in R. I had that same habit when I switched from MatLab too :)
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