我想R中使用随机梯度下降,以建立自己的回归函数,但我现在所拥有的使权重成长过程中没有约束,因此从来没有停止:R中回归公式的执行
# Logistic regression
# Takes training example vector, output vector, learn rate scalar, and convergence delta limit scalar
my_logr <- function(training_examples,training_outputs,learn_rate,conv_lim) {
# Initialize gradient vector
gradient <- as.vector(rep(0,NCOL(training_examples)))
# Difference between weights
del_weights <- as.matrix(1)
# Weights
weights <- as.matrix(runif(NCOL(training_examples)))
weights_old <- as.matrix(rep(0,NCOL(training_examples)))
# Compute gradient
while(norm(del_weights) > conv_lim) {
for (k in 1:NROW(training_examples)) {
gradient <- gradient + 1/NROW(training_examples)*
((t(training_outputs[k]*training_examples[k,]
/(1+exp(training_outputs[k]*t(weights)%*%as.numeric(training_examples[k,]))))))
}
# Update weights
weights <- weights_old - learn_rate*gradient
del_weights <- as.matrix(weights_old - weights)
weights_old <- weights
print(weights)
}
return(weights)
}
的功能可以用下面的代码进行测试:
data(iris) # Iris data already present in R
# Dataset for part a (first 50 vs. last 100)
iris_a <- iris
iris_a$Species <- as.integer(iris_a$Species)
# Convert list to binary class
for (i in 1:NROW(iris_a$Species)) {if (iris_a$Species[i] != "1") {iris_a$Species[i] <- -1}}
random_sample <- sample(1:NROW(iris),50)
weights_a <- my_logr(iris_a[random_sample,1:4],iris_a$Species[random_sample],1,.1)
我双重检查我的针对Abu-Mostafa's算法,其如下:
- 初始化权重向量
- 对于每个时间段计算梯度:
gradient <- -1/N * sum_{1 to N} (training_answer_n * training_Vector_n/(1 + exp(training_answer_n * dot(weight,training_vector_n))))
weight_new <- weight - learn_rate*gradient
- 重复,直到体重增量足够小
我失去了一些东西在这里?
我是否缺少权重的标准化术语?这是一个交叉验证的问题,也许? – 2013-03-18 13:50:48
从数学的角度来看,权重向量的无约束幅度不会产生独特的解决方案。 - 权重/规范(权重)' ... '的权重< - weights_old - learn_rate * gradient' '权重 '权重<:当我加入这两行分类器函数,它在两个步骤会聚< - 权重/规范(权重)' – 2013-03-18 13:58:24
下面的答案有帮助吗? – 2013-03-18 16:43:21