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#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------
# Implements binary-class SVM with squared slack variables
#
#
# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME TYPE DEFAULT MEANING
# ---------------------------------------------------------------------------------------------
# X String --- Location to read the matrix X of feature vectors
# Y String --- Location to read response matrix Y
# icpt Int 0 Intercept presence
# 0 = no intercept
# 1 = add intercept;
# tol Double 0.001 Tolerance (epsilon);
# reg Double 1.0 Regularization parameter (lambda) for L2 regularization
# maxiter Int 100 Maximum number of conjugate gradient iterations
# model String --- Location to write model
# fmt String "text" The output format of the output, such as "text" or "csv"
# ---------------------------------------------------------------------------------------------
# Example Execution:
# systemds -f $SYSTEMDS_ROOT/scripts/algorithms/l2-svm.dml \
# -nvargs X=$INPUT_DIR/X Y=$INPUT_DIR/Y \
# icpt=FALSE tol=0.001 reg=1 maxiter=100 \
# model=$OUPUT_DIR/w fmt="text"
# Note about inputs:
# Assumes that labels (entries in Y) are set to either -1 or +1 or non-negative integers
fmt = ifdef($fmt, "text")
intercept = ifdef($icpt, FALSE)
epsilon = ifdef($tol, 0.001)
lambda = ifdef($reg, 1.0)
maxIterations = ifdef($maxiter, 100)
verbose = ifdef($verbose, FALSE)
X = read($X)
Y = read($Y)
positive_label = max(Y)
negative_label = min(Y)
dimensions = ncol(X)
w = l2svm(X=X, Y=Y, intercept=intercept,
epsilon=epsilon, lambda=lambda,
maxIterations=maxIterations,
verbose=verbose)
extra_model_params = matrix(0, 4, 1)
extra_model_params[1,1] = positive_label
extra_model_params[2,1] = negative_label
extra_model_params[3,1] = as.double(intercept)
extra_model_params[4,1] = dimensions
w = rbind(w, extra_model_params)
write(w, $model, format=fmt)