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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.accum;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Calculate the mean of the vector
*
* @author Adam Gibson
*/
public class Mean extends Sum {
public Mean(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, keepDims, dimensions);
}
public Mean() {
}
public Mean(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
}
public Mean(INDArray x, INDArray y, long n) {
super(x, y, n);
}
public Mean(INDArray x) {
super(x);
}
public Mean(INDArray x, INDArray y) {
super(x, y);
}
public Mean(INDArray x, INDArray y, INDArray z) {
super(x, y, z, x.lengthLong());
}
public Mean(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int[] dimensions) {
super(x, y, z, newFormat, keepDims, dimensions);
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "reduce_mean";
}
@Override
public List doDiff(List i_v1) {
if(!newFormat)
throw new IllegalStateException("Cannot doDiff with newFormat == false");
//If out = mean(in), then dL/dIn = 1/N * dL/dOut (broadcast to appropriate shape)
//Note that N differs for "along dimension" vs. "whole array" reduce cases
return Collections.singletonList(f().meanBp(arg(), i_v1.get(0), keepDims, dimensions));
}
@Override
public String onnxName() {
return "ReduceMean";
}
@Override
public String tensorflowName() {
return "Mean";
}
}