scripts/perftest/slab/mlAlgorithms/distributed/slabNonNegativeMatrixFactorizationDistr.dml

#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------

source("../../slabUtils.dml") as utils

dataPath = $1
X = read(dataPath, format="csv")
rvect = rand(rows=1, cols=1)
y = rvect > 0.80
p = sum( X )
q = sum( y )
print(p)
print(q)


for(ix in 1:5){
    tmp = gnmf(X, 10, 10)
    print(tmp)
}

gnmf = function(matrix[double] X, Integer r, Integer iterations)
    return (integer iteration) {

    W = rand(rows = nrow(X), cols = r, pdf = 'uniform')
    H = rand(rows = r, cols = ncol(X), pdf = 'uniform')

    for (i in 1:3) {
        W = W * ((X %*% t(H)) / (W %*% (H %*% t(H))))
        H = H * ((t(W) %*% X) / ((t(W) %*% W) %*% H))
    }
    if ((as.scalar(W[1,1]) > 0) & (as.scalar(H[1,1]) > 0)) {
        print(as.scalar(H[1,1]))
        print(as.scalar(W[1,1]))
    }

    iteration = 0
}