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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.tensor.functions;
import com.yahoo.tensor.evaluation.Name;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
/**
* @author bratseth
*/
public class L1Normalize<NAMETYPE extends Name> extends CompositeTensorFunction<NAMETYPE> {
private final TensorFunction<NAMETYPE> argument;
private final String dimension;
public L1Normalize(TensorFunction<NAMETYPE> argument, String dimension) {
this.argument = argument;
this.dimension = dimension;
}
@Override
public List<TensorFunction<NAMETYPE>> arguments() { return Collections.singletonList(argument); }
@Override
public TensorFunction<NAMETYPE> withArguments(List<TensorFunction<NAMETYPE>> arguments) {
if ( arguments.size() != 1)
throw new IllegalArgumentException("L1Normalize must have 1 argument, got " + arguments.size());
return new L1Normalize<>(arguments.get(0), dimension);
}
@Override
public PrimitiveTensorFunction<NAMETYPE> toPrimitive() {
TensorFunction<NAMETYPE> primitiveArgument = argument.toPrimitive();
// join(x, reduce(x, "avg", "dimension"), f(x,y) (x / y))
return new Join<>(primitiveArgument,
new Reduce<>(primitiveArgument, Reduce.Aggregator.sum, dimension),
ScalarFunctions.divide());
}
@Override
public String toString(ToStringContext<NAMETYPE> context) {
return "l1_normalize(" + argument.toString(context) + ", " + dimension + ")";
}
@Override
public int hashCode() { return Objects.hash("l1_normalize", argument, dimension); }
}
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