56 lines
1.8 KiB
R
56 lines
1.8 KiB
R
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#!/usr/bin/env r
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#
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# Comparison benchmark -- using old and small Longley data set
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#
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# This shows how Armadillo improves on the previous version using GNU GSL,
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# and how both are doing better than lm.fit()
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#
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# Copyright (C) 2010 Dirk Eddelbuettel and Romain Francois
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#
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# This file is part of Rcpp.
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#
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# Rcpp is free software: you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 2 of the License, or
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# (at your option) any later version.
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#
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# Rcpp is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with Rcpp. If not, see <http://www.gnu.org/licenses/>.
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suppressMessages(library(utils))
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suppressMessages(library(Rcpp))
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suppressMessages(library(inline))
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suppressMessages(library(datasets))
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source("lmArmadillo.R")
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source("lmGSL.R")
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data(longley)
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longleydm <- data.matrix(data.frame(intcp=1, longley))
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X <- longleydm[,-8]
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y <- as.numeric(longleydm[,8])
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N <- 1000
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lmgsl <- lmGSL()
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lmarma <- lmArmadillo()
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tlm <- mean(replicate(N, system.time( lmfit <- lm(y ~ X - 1) )["elapsed"]), trim=0.05)
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tlmfit <- mean(replicate(N, system.time(lmfitfit <- lm.fit(X, y))["elapsed"]), trim=0.05)
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tlmgsl <- mean(replicate(N, system.time(lmgsl(y, X))["elapsed"]), trim=0.05)
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tlmarma <- mean(replicate(N, system.time(lmarma(y, X))["elapsed"]), trim=0.05)
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res <- c(tlm, tlmfit, tlmgsl, tlmarma)
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data <- data.frame(results=res, ratios=tlm/res)
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rownames(data) <- c("lm", "lm.fit", "lmGSL", "lmArma")
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cat("For Longley\n")
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print(t(data))
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print(t(1/data[,1,drop=FALSE])) # regressions per second
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