| #------------------------------------------------------------- |
| # |
| # 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. |
| # |
| #------------------------------------------------------------- |
| |
| msvm2 = function(Matrix[Double] X, Matrix[Double] Y, Boolean intercept = FALSE, |
| Double epsilon = 0.001, Double lambda = 1.0, Integer maxIterations = 100, Boolean verbose = FALSE) |
| return(Matrix[Double] model) |
| { |
| if(min(Y) < 0) |
| stop("MSVM: Invalid Y input, containing negative values") |
| |
| if(verbose) |
| print("Running Multiclass-SVM") |
| |
| num_rows_in_w = ncol(X) |
| if(intercept) { |
| num_rows_in_w = num_rows_in_w + 1 |
| } |
| |
| if(ncol(Y) > 1) |
| Y = rowMaxs(Y * t(seq(1,ncol(Y)))) |
| |
| # Assuming number of classes to be max contained in Y |
| w = matrix(0, rows=num_rows_in_w, cols=max(Y)) |
| |
| parfor(class in 1:max(Y), opt=CONSTRAINED, par=4, mode=REMOTE_SPARK) { |
| Y_local = 2 * (Y == class) - 1 |
| w[,class] = l2svm(X=X, Y=Y_local, intercept=intercept, |
| epsilon=epsilon, lambda=lambda, maxIterations=maxIterations, |
| verbose= verbose, columnId=class) |
| } |
| |
| model = w |
| } |
| |
| nclass = 10; |
| |
| X = rand(rows=$2, cols=$3, seed=1); |
| y = rand(rows=$2, cols=1, min=0, max=nclass, seed=2); |
| y = ceil(y); |
| |
| model = msvm2(X=X, Y=y, intercept=FALSE); |
| |
| write(model, $1); |