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  • Manuel Grizonnet's avatar
    ENH: rename files with txx extension to hxx · 3a1fd1fc
    Manuel Grizonnet authored 
    OTB followed since the beginning the ITK convention and use .txx extension for all
template classes. Nevertheless, some development tools do not recognize .txx
file extension. Other tool like GitHub can't do in-browser syntax highlighting for
txx files I think.

The root problem is the use of the txx which should be changed to hxx (or hpp).

In 2011, after an in-depth discussion near April 20, 2011 on the
Insight-Developers mailing list, ITK rename all txx files to hxx (and event
prevent the push of .txx files with a pre-commit hook). It happens is major release v4.

You can find some arguments in the discussion about the change and also in other
projects related to ITK which applied the same modification, see for instance VXL:

https://github.com/vxl/vxl/issues/209

This commit apply now the same modification for OTB.

I understand that it will change some habit for developers and don't bring new
features but I think that in general it is better to stay align w...
    3a1fd1fc
otbTrainRandomForests.hxx 5.95 KiB
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/*
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 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
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 *
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 * This file is part of Orfeo Toolbox
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 *
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 *     https://www.orfeo-toolbox.org/
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 *
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 * Licensed under the Apache License, Version 2.0 (the "License");
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 * you may not use this file except in compliance with the License.
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 * You may obtain a copy of the License at
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 *
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 *     http://www.apache.org/licenses/LICENSE-2.0
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 *
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 * Unless required by applicable law or agreed to in writing, software
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 * See the License for the specific language governing permissions and
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 * limitations under the License.
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 */
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#ifndef otbTrainRandomForests_txx
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#define otbTrainRandomForests_txx
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#include "otbLearningApplicationBase.h"
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#include "otbRandomForestsMachineLearningModel.h"
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namespace otb
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{
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namespace Wrapper
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{
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template <class TInputValue, class TOutputValue>
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void
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LearningApplicationBase<TInputValue,TOutputValue>
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::InitRandomForestsParams()
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{
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  AddChoice("classifier.rf", "Random forests classifier");
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  SetParameterDescription("classifier.rf",
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                          "This group of parameters allows setting Random Forests classifier parameters. "
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                          "See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/random_trees.html}.");
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  //MaxDepth
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  AddParameter(ParameterType_Int, "classifier.rf.max", "Maximum depth of the tree");
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  SetParameterInt("classifier.rf.max",5);
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  SetParameterDescription(
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      "classifier.rf.max",
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      "The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. "
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      "The optimal value can be obtained using cross validation or other suitable methods.");
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  //MinSampleCount
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  AddParameter(ParameterType_Int, "classifier.rf.min", "Minimum number of samples in each node");
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  SetParameterInt("classifier.rf.min",10);
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  SetParameterDescription(
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      "classifier.rf.min", "If the number of samples in a node is smaller than this parameter, "
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      "then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.");
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  //RegressionAccuracy
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  AddParameter(ParameterType_Float, "classifier.rf.ra", "Termination Criteria for regression tree");
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  SetParameterFloat("classifier.rf.ra",0.);
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  SetParameterDescription("classifier.rf.ra", "If all absolute differences between an estimated value in a node "
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                          "and the values of the train samples in this node are smaller than this regression accuracy parameter, "
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                          "then the node will not be split.");
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  //UseSurrogates : don't need to be exposed !
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  //AddParameter(ParameterType_Empty, "classifier.rf.sur", "Surrogate splits will be built");
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  //SetParameterDescription("classifier.rf.sur","These splits allow working with missing data and compute variable importance correctly.");
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  //MaxNumberOfCategories
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  AddParameter(ParameterType_Int, "classifier.rf.cat",
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               "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split");
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  SetParameterInt("classifier.rf.cat",10);
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  SetParameterDescription(
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"classifier.rf.cat", "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split."); //Priors are not exposed. //CalculateVariableImportance not exposed //MaxNumberOfVariables AddParameter(ParameterType_Int, "classifier.rf.var", "Size of the randomly selected subset of features at each tree node"); SetParameterInt("classifier.rf.var",0); SetParameterDescription( "classifier.rf.var", "The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). " "If you set it to 0, then the size will be set to the square root of the total number of features."); //MaxNumberOfTrees AddParameter(ParameterType_Int, "classifier.rf.nbtrees", "Maximum number of trees in the forest"); SetParameterInt("classifier.rf.nbtrees",100); SetParameterDescription( "classifier.rf.nbtrees", "The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. " "However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. " "Also to keep in mind, increasing the number of trees increases the prediction time linearly."); //ForestAccuracy AddParameter(ParameterType_Float, "classifier.rf.acc", "Sufficient accuracy (OOB error)"); SetParameterFloat("classifier.rf.acc",0.01); SetParameterDescription("classifier.rf.acc","Sufficient accuracy (OOB error)."); //TerminationCriteria not exposed } template <class TInputValue, class TOutputValue> void LearningApplicationBase<TInputValue,TOutputValue> ::TrainRandomForests(typename ListSampleType::Pointer trainingListSample, typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath) { typedef otb::RandomForestsMachineLearningModel<InputValueType, OutputValueType> RandomForestType; typename RandomForestType::Pointer classifier = RandomForestType::New(); classifier->SetRegressionMode(this->m_RegressionFlag); classifier->SetInputListSample(trainingListSample); classifier->SetTargetListSample(trainingLabeledListSample); classifier->SetMaxDepth(GetParameterInt("classifier.rf.max")); classifier->SetMinSampleCount(GetParameterInt("classifier.rf.min")); classifier->SetRegressionAccuracy(GetParameterFloat("classifier.rf.ra")); classifier->SetMaxNumberOfCategories(GetParameterInt("classifier.rf.cat")); classifier->SetMaxNumberOfVariables(GetParameterInt("classifier.rf.var")); classifier->SetMaxNumberOfTrees(GetParameterInt("classifier.rf.nbtrees")); classifier->SetForestAccuracy(GetParameterFloat("classifier.rf.acc")); classifier->Train(); classifier->Save(modelPath); } } //end namespace wrapper } //end namespace otb #endif