R/TODO.R

In avilesd/productConfig: Tools to analyze behavioral patterns from Product Configurators

#' TODO
#'
#' crisp and intervals (really later) with param EACH, calculate for each round
#' and the next the gains and losses, in decision matrix, an if at the end, if
#' TRUE then another result.
#'
#' (After Seminar) get_attr_ -> improve, do not assume that all users have the
#' same amount of attributes, consequences? (pending, after Seminar) prove if
#' alpha and beta are in [0,1] intervall with <=,>=.
#'
#' (pending, IMPORTANT) Consider removing gainm parameter in powerful_funciton,
#' see what diegopv function does.
#'
#' (pending, IMPORTANT) Test cost_ids and if refps should also be given with -.
#' Test with paper matrices current2.
#'
#' (pending, IMPORTANT) FIXED WEIGHTS AND WEIGHTS FUNCTION
#'
#' (pending) Alternatives 2c) a function that gives you all G & L matrices
#' between chosen alternatives and their next one. If rounds = c(x, x+1, x+2,
#' x+3) then give all following G & L: G&L(x, x+1) ; G&L(x+1, x+2) ; G&L(x+2,
#' x+3). Normalized?
#'
#' (pending, important) suppressWarning when giving rounds= vector not NULL and
#' not "all", try a way around.
#'
#' (pending, IMPORTANT) Change all 'data' args to dataset or other than x!
#'
#' (pending, IMPORTANT) Change ref_ps function when called, add attr parameter
#' and change function as well. attr already added powerful_f.
#'
#' (pending, middle important) Warning or try catch: Say if wrong attributes are
#' given.
#'
#' WORKS HEATMAP with the right data! multiply attr4 by 6.545 and then take 4 -
#' that!
#'
#' heatmap.2(tprueba,col=my_palette, symm=F,symkey=F,symbreaks=T, scale="none",
#' dendrogram="none", Colv= F, Rowv=F, trace="none", breaks =colbr,
#' margins(6,5))
#'
#' You can hack RStudio with Inspect!!!
#'
#' Invert prices for plots for example table with column 3 with normal prices:
#' 1) help <- newt[,3]*6.51 2) help <- 4-help 3)cbind(new, help)
#'
#' ggplot(data = new, aes(x = xt, y = yt, color = pt)) + geom_line(size=1.1) +
#' geom_point(size = 2)
#'
#' (FINAL, going to Poster) ggplot(data = t2last, aes(x = x2, y = y2, color =
#' p2last)) + geom_line(size=1.1) + geom_point(size = 2) +
#' scale_colour_gradient(low="red", high="green")
#'
#' ###### FIXED
#'
#' (FIXED) Error Catch: easy: by decision_matrix -- prove is provided userid is
#' in the userid of the data. (DONE, IMPORTANT) Add powerful_functions to the
#' powerful_function conditions (con7,8), think which ones. (DONE, NECESSARY
#' ASAP, last is implemented) allow for rounds= "first", "last".