cost-based-optimizer

I am in the beginning stages of machine learning in R and I find it hard to believe that there are no packages to solving the cost function for different types of regression algorithms. For example, if I want to solve the cost function for a logistic regression, the manual way would be below: https://www.r-bloggers.com/logistic-regression-with-r-step-by-step-implementation-part-2/ # Implement Sigmoid function sigmoid <- function(z) { g <- 1/(1+exp(-z)) return(g) } #Cost Function cost <- function(theta) { m <- nrow(X) g <- sigmoid(X%*%theta) J <- (1/m)*sum((-Y*log(g)) - ((1-Y)*log(1-g))) return

I have a situation like this: create table a( a_id number(38) not null, constraint pk_a primary key (id) ); create table b( a_id number(38) not null ); create index b_a_id_index on b(a_id); Now b.a_id is in fact meant to be a foreign key referencing a.a_id , but it isn't formally declared as such. Obviously, it should be for integrity reasons. But does a foreign key constraint also improve join performance in general or in specific cases? If yes, for what types of query transformations? Is there any relevant documentation about this topic? I'm using Oracle 11g (11.2.0.2.0) Yes, having foreign

I'm dealing with a Postgres table (called "lives") that contains records with columns for time_stamp, usr_id, transaction_id, and lives_remaining. I need a query that will give me the most recent lives_remaining total for each usr_id There are multiple users (distinct usr_id's) time_stamp is not a unique identifier: sometimes user events (one by row in the table) will occur with the same time_stamp. trans_id is unique only for very small time ranges: over time it repeats remaining_lives (for a given user) can both increase and decrease over time example: time_stamp|lives_remaining|usr_id|trans