blob: 1c02e8b8ef260f2987384b51a4f5fb9358c99573 [file] [log] [blame]
#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------
sum = function(matrix[double] X) return (double total)
{
total = 0
for (i in 1:nrow(X))
{
for (j in 1:ncol(X))
{
total = total + as.scalar(X[i,j])
}
}
print("Override sum is " + total)
}
min = function(matrix[double] X) return (double minimum)
{
MinRow = rowMins(X)
MinCol = colMins(MinRow)
minimum = as.scalar(MinCol[1,1])
print("Minimum is " + minimum)
}
minMax = function(matrix[double] M) return (double minVal, double maxVal)
{
# Access local overrides (defined before or after) instead of built-ins
minVal = min(M)
maxVal = max(M)
}
max = function(matrix[double] X) return (double maximum)
{
MaxRow = rowMaxs(X)
MaxCol = colMaxs(MaxRow)
maximum = as.scalar(MaxCol[1,1])
print("Maximum is " + maximum)
}