/*
 * 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
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package org.apache.mahout.sparkbindings.blas

import org.apache.mahout.sparkbindings.drm.DrmRddInput
import org.apache.mahout.math.drm.logical.OpRowRange

object Slicing {

  def rowRange(op: OpRowRange, srcA: DrmRddInput[Int]): DrmRddInput[Int] = {
    val rowRange = op.rowRange
    val ncol = op.ncol
    val rdd = srcA.asRowWise()

        // Filter the rows in the range only
        .filter({
      case (key, vector) => rowRange.contains(key)
    })

        // Now we need to adjust the row index
        .map({
      case (key, vector) => (key - rowRange.head) -> vector
    })

    // TODO: we probably need to re-shuffle result or at least cut down the partitions of 0 size

    rdd
  }
}
