#-------------------------------------------------------------
#
# 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
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#
#-------------------------------------------------------------

# This function imputes values with exponential moving average (single, double or triple).
#
# INPUT:
# ------------------------------------------------------------------------------------------
# X       Frame that contains time series data that needs to be imputed
#         search_iterations	Integer	--	Budget iterations for parameter optimization,
#         used if parameters weren't set
# mode    Type of EMA method. Either "single", "double" or "triple"
# freq    Seasonality when using triple EMA.
# alpha   alpha- value for EMA
# beta    beta- value for EMA
# gamma   gamma- value for EMA
# ------------------------------------------------------------------------------------------
#
# OUTPUT:
# -----------------------------------------------------------------------------------------------
# M     Frame with EMA results
# -----------------------------------------------------------------------------------------------

# TODO: convert to DML builtin using cumsumprod(data, alpha) 

f_ema = function(Frame[Double] X, Integer search_iterations, String mode, Integer freq, 
  Double alpha, Double beta, Double gamma) return (Frame[Double] Y) {
  M = map(X, "UtilFunctions.exponentialMovingAverageImputation(" + search_iterations + ";"
    + mode + ";" + freq + ";" + alpha + ";" + beta + ";" + gamma + ")")
  Y = as.frame(as.matrix(M))
}

