diff options
Diffstat (limited to 'analysis/R/alternative.R')
| -rwxr-xr-x | analysis/R/alternative.R | 83 |
1 files changed, 83 insertions, 0 deletions
diff --git a/analysis/R/alternative.R b/analysis/R/alternative.R new file mode 100755 index 0000000..3f0e66d --- /dev/null +++ b/analysis/R/alternative.R @@ -0,0 +1,83 @@ +# Copyright 2014 Google Inc. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +library(limSolve) +library(Matrix) + +# The next two functions create a matrix (G) and a vector (H) encoding +# linear inequality constraints that a solution vector (x) must satisfy: +# G * x >= H + +# Currently represent three sets of constraints on the solution vector: +# - all solution coefficients are nonnegative +# - the sum total of all solution coefficients is no more than 1 +# - in each of the coordinates of the target vector (estimated Bloom filter) +# we don't overshoot by more than three standard deviations. +MakeG <- function(n, X) { + d <- Diagonal(n) + last <- rep(-1, n) + rbind2(rbind2(d, last), -X) +} + +MakeH <- function(n, Y, stds) { + # set the floor at 0.01 to avoid degenerate cases + YY <- apply(Y + 3 * stds, # in each bin don't overshoot by more than 3 stds + 1:2, + function(x) min(1, max(0.01, x))) # clamp the bound to [0.01,1] + + c(rep(0, n), # non-negativity condition + -1, # coefficients sum up to no more than 1 + -as.vector(t(YY)) # t is important! + ) +} + +MakeLseiModel <- function(X, Y, stds) { + m <- dim(X)[1] + n <- dim(X)[2] + +# no slack variables for now +# slack <- Matrix(FALSE, nrow = m, ncol = m, sparse = TRUE) +# colnames(slack) <- 1:m +# diag(slack) <- TRUE +# +# G <- MakeG(n + m) +# H <- MakeH(n + m) +# +# G[n+m+1,n:(n+m)] <- -0.1 +# A = cbind2(X, slack) + + w <- as.vector(t(1 / stds)) + w_median <- median(w[!is.infinite(w)]) + if(is.na(w_median)) # all w are infinite + w_median <- 1 + w[w > w_median * 2] <- w_median * 2 + w <- w / mean(w) + + list(# coerce sparse Boolean matrix X to sparse numeric matrix + A = Diagonal(x = w) %*% (X + 0), + B = as.vector(t(Y)) * w, # transform to vector in the row-first order + G = MakeG(n, X), + H = MakeH(n, Y, stds), + type = 2) # Since there are no equality constraints, lsei defaults to + # solve.QP anyway, but outputs a warning unless type == 2. +} + +# CustomLM(X, Y) +ConstrainedLinModel <- function(X,Y) { + model <- MakeLseiModel(X, Y$estimates, Y$stds) + coefs <- do.call(lsei, model)$X + names(coefs) <- colnames(X) + + coefs +}
\ No newline at end of file |