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#
# Licensed to the Apache Software Foundation (ASF) under one
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# 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,
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
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#-------------------------------------------------------------

e1 = "element1"
modelList = list(e1)
X = matrix(1, rows=200, cols=30)
Y = matrix(2, rows=200, cols=1)
X_val = matrix(3, rows=200, cols=30)
Y_val = matrix(4, rows=200, cols=1)

gradients = function(matrix[double] features,
                     matrix[double] labels,
                     list[unknown] hyperparams,
                     list[unknown] model)
            return (list[unknown] gradients) {
  gradients = model
  print(toString(as.matrix(gradients["worker_err"])))
}

aggregation = function(list[unknown] model,
                       list[unknown] gradients,
                       list[unknown] hyperparams)
              return (list[unknown] modelResult) {
  modelResult = model
}

e2 = "element2"
params = list(e2)

modelList = list("model")

# Use paramserv function
modelList2 = paramserv(model=modelList, features=X, labels=Y, val_features=X_val, val_labels=Y_val, upd="gradients", agg="aggregation", mode="LOCAL", utype="BSP", epochs=10, hyperparams=params, k=1)

print(toString(as.matrix(modelList2[1])))