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Diffstat (limited to 'examples/src/main/r/linear_solver_mnist.R')
| -rw-r--r-- | examples/src/main/r/linear_solver_mnist.R | 107 |
1 files changed, 107 insertions, 0 deletions
diff --git a/examples/src/main/r/linear_solver_mnist.R b/examples/src/main/r/linear_solver_mnist.R new file mode 100644 index 0000000000..c864a4232d --- /dev/null +++ b/examples/src/main/r/linear_solver_mnist.R @@ -0,0 +1,107 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. +# + +# Instructions: https://github.com/amplab-extras/SparkR-pkg/wiki/SparkR-Example:-Digit-Recognition-on-EC2 + +library(SparkR) +library(Matrix) + +args <- commandArgs(trailing = TRUE) + +# number of random features; default to 1100 +D <- ifelse(length(args) > 0, as.integer(args[[1]]), 1100) +# number of partitions for training dataset +trainParts <- 12 +# dimension of digits +d <- 784 +# number of test examples +NTrain <- 60000 +# number of training examples +NTest <- 10000 +# scale of features +gamma <- 4e-4 + +sc <- sparkR.init(appName = "SparkR-LinearSolver") + +# You can also use HDFS path to speed things up: +# hdfs://<master>/train-mnist-dense-with-labels.data +file <- textFile(sc, "/data/train-mnist-dense-with-labels.data", trainParts) + +W <- gamma * matrix(nrow=D, ncol=d, data=rnorm(D*d)) +b <- 2 * pi * matrix(nrow=D, ncol=1, data=runif(D)) +broadcastW <- broadcast(sc, W) +broadcastB <- broadcast(sc, b) + +includePackage(sc, Matrix) +numericLines <- lapplyPartitionsWithIndex(file, + function(split, part) { + matList <- sapply(part, function(line) { + as.numeric(strsplit(line, ",", fixed=TRUE)[[1]]) + }, simplify=FALSE) + mat <- Matrix(ncol=d+1, data=unlist(matList, F, F), + sparse=T, byrow=T) + mat + }) + +featureLabels <- cache(lapplyPartition( + numericLines, + function(part) { + label <- part[,1] + mat <- part[,-1] + ones <- rep(1, nrow(mat)) + features <- cos( + mat %*% t(value(broadcastW)) + (matrix(ncol=1, data=ones) %*% t(value(broadcastB)))) + onesMat <- Matrix(ones) + featuresPlus <- cBind(features, onesMat) + labels <- matrix(nrow=nrow(mat), ncol=10, data=-1) + for (i in 1:nrow(mat)) { + labels[i, label[i]] <- 1 + } + list(label=labels, features=featuresPlus) + })) + +FTF <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$features + }), flatten=F)) + +FTY <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$label + }), flatten=F)) + +# solve for the coefficient matrix +C <- solve(FTF, FTY) + +test <- Matrix(as.matrix(read.csv("/data/test-mnist-dense-with-labels.data", + header=F), sparse=T)) +testData <- test[,-1] +testLabels <- matrix(ncol=1, test[,1]) + +err <- 0 + +# contstruct the feature maps for all examples from this digit +featuresTest <- cos(testData %*% t(value(broadcastW)) + + (matrix(ncol=1, data=rep(1, NTest)) %*% t(value(broadcastB)))) +featuresTest <- cBind(featuresTest, Matrix(rep(1, NTest))) + +# extract the one vs. all assignment +results <- featuresTest %*% C +labelsGot <- apply(results, 1, which.max) +err <- sum(testLabels != labelsGot) / nrow(testLabels) + +cat("\nFinished running. The error rate is: ", err, ".\n") |