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fixing fluid.normalize json reloading issue #262

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Merged
tremblap merged 3 commits into from
Feb 23, 2024
Merged

fixing fluid.normalize json reloading issue #262

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e1abc9c
a simple line...
tremblap Feb 7, 2024
bf3b65f
proper initialisation
tremblap Feb 7, 2024
e1f5b5a
remove the change of scaling post fitting (as it was in the RT versio…
tremblap Feb 7, 2024
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3 changes: 3 additions & 0 deletions include/algorithms/public/Normalization.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,8 @@ class Normalization
using namespace _impl;
mMin = min;
mMax = max;
mRange = mMax - mMin;
handleZerosInScale(mRange);
mDataMin = asEigen<Array>(dataMin);
mDataMax = asEigen<Array>(dataMax);
mDataRange = mDataMax - mDataMin;
Expand Down Expand Up @@ -132,6 +134,7 @@ class Normalization
{
mMin = 0;
mMax = 1.0;
mRange = 1.0;
mDataMin.setZero();
mDataMax.setZero();
mDataRange.setZero();
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6 changes: 0 additions & 6 deletions include/clients/nrt/NormalizeClient.hpp
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Expand Up @@ -139,8 +139,6 @@ class NormalizeClient : public FluidBaseClient,
StringVector ids{srcDataSet.getIds()};
RealMatrix data(srcDataSet.size(), srcDataSet.pointSize());
if (!mAlgorithm.initialized()) return Error(NoDataFitted);
mAlgorithm.setMin(get<kMin>());
mAlgorithm.setMax(get<kMax>());
mAlgorithm.process(srcDataSet.getData(), data, invert);
FluidDataSet<string, double, 1> result(ids, data);
destPtr->setDataSet(result);
Expand All @@ -166,8 +164,6 @@ class NormalizeClient : public FluidBaseClient,
RealVector src(mAlgorithm.dims());
RealVector dest(mAlgorithm.dims());
src <<= BufferAdaptor::ReadAccess(in.get()).samps(0, mAlgorithm.dims(), 0);
mAlgorithm.setMin(get<kMin>());
mAlgorithm.setMax(get<kMax>());
mAlgorithm.processFrame(src, dest, invert);
outBuf.samps(0, mAlgorithm.dims(), 0) <<= dest;
return OK();
Expand Down Expand Up @@ -239,8 +235,6 @@ class NormalizeQuery : public FluidBaseClient, ControlIn, ControlOut
RealVector dest(algorithm.dims());
src <<= BufferAdaptor::ReadAccess(get<kInputBuffer>().get())
.samps(0, algorithm.dims(), 0);
// algorithm.setMin(get<kMin>());
// algorithm.setMax(get<kMax>());
algorithm.processFrame(src, dest, get<kInvert>() == 1);
outBuf.samps(0, algorithm.dims(), 0) <<= dest;
}
Expand Down