View Source Evision.ML.RTrees (Evision v0.1.7)
Link to this section Summary
cv
Clears the algorithm state
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Reads algorithm parameters from a file storage
Positional Arguments
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
simplified API for language bindings
simplified API for language bindings
cv.ml
Computes error on the training or test dataset
Computes error on the training or test dataset
Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
Python prototype (for reference):
@see setActiveVarCount
@see setCalculateVarImportance
@see setCVFolds
@see setMaxCategories
@see setMaxDepth
@see setMinSampleCount
Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
@see setPriors
@see setRegressionAccuracy
@see setTermCriteria
@see setTruncatePrunedTree
@see setUse1SERule
@see setUseSurrogates
Returns the number of variables in training samples
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
Positional Arguments
samples:
Evision.Mat
.
Positional Arguments
samples:
Evision.Mat
.
Returns true if the model is classifier
Returns true if the model is trained
Loads and creates a serialized RTree from a file
Loads and creates a serialized RTree from a file
Predicts response(s) for the provided sample(s)
Predicts response(s) for the provided sample(s)
Positional Arguments
- val:
int
@copybrief getActiveVarCount @see getActiveVarCount
Positional Arguments
- val:
bool
@copybrief getCalculateVarImportance @see getCalculateVarImportance
Positional Arguments
- val:
int
@copybrief getCVFolds @see getCVFolds
Positional Arguments
- val:
int
@copybrief getMaxCategories @see getMaxCategories
Positional Arguments
- val:
int
@copybrief getMaxDepth @see getMaxDepth
Positional Arguments
- val:
int
@copybrief getMinSampleCount @see getMinSampleCount
Positional Arguments
- val:
float
@copybrief getRegressionAccuracy @see getRegressionAccuracy
Positional Arguments
- val:
TermCriteria
@copybrief getTermCriteria @see getTermCriteria
Positional Arguments
- val:
bool
@copybrief getTruncatePrunedTree @see getTruncatePrunedTree
Positional Arguments
- val:
bool
@copybrief getUse1SERule @see getUse1SERule
Positional Arguments
- val:
bool
@copybrief getUseSurrogates @see getUseSurrogates
Trains the statistical model
Trains the statistical model
Trains the statistical model
Functions
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Link to this section cv
Clears the algorithm state
Python prototype (for reference):
clear() -> None
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Python prototype (for reference):
getDefaultName() -> retval
Reads algorithm parameters from a file storage
Positional Arguments
- fn_:
FileNode
Python prototype (for reference):
read(fn_) -> None
Positional Arguments
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Python prototype (for reference):
save(filename) -> None
simplified API for language bindings
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None
simplified API for language bindings
Positional Arguments
- fs:
Ptr<FileStorage>
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None
Link to this section cv.ml
Computes error on the training or test dataset
Positional Arguments
data:
Ptr<TrainData>
.the training data
test:
bool
.if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
Return
resp:
Evision.Mat
.the optional output responses.
The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
Python prototype (for reference):
calcError(data, test[, resp]) -> retval, resp
Computes error on the training or test dataset
Positional Arguments
data:
Ptr<TrainData>
.the training data
test:
bool
.if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
Return
resp:
Evision.Mat
.the optional output responses.
The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
Python prototype (for reference):
calcError(data, test[, resp]) -> retval, resp
Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
Python prototype (for reference):
create() -> retval
Python prototype (for reference):
empty() -> retval
@see setActiveVarCount
Python prototype (for reference):
getActiveVarCount() -> retval
@see setCalculateVarImportance
Python prototype (for reference):
getCalculateVarImportance() -> retval
@see setCVFolds
Python prototype (for reference):
getCVFolds() -> retval
@see setMaxCategories
Python prototype (for reference):
getMaxCategories() -> retval
@see setMaxDepth
Python prototype (for reference):
getMaxDepth() -> retval
@see setMinSampleCount
Python prototype (for reference):
getMinSampleCount() -> retval
Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
Python prototype (for reference):
getOOBError() -> retval
@see setPriors
Python prototype (for reference):
getPriors() -> retval
@see setRegressionAccuracy
Python prototype (for reference):
getRegressionAccuracy() -> retval
@see setTermCriteria
Python prototype (for reference):
getTermCriteria() -> retval
@see setTruncatePrunedTree
Python prototype (for reference):
getTruncatePrunedTree() -> retval
@see setUse1SERule
Python prototype (for reference):
getUse1SERule() -> retval
@see setUseSurrogates
Python prototype (for reference):
getUseSurrogates() -> retval
Returns the number of variables in training samples
Python prototype (for reference):
getVarCount() -> retval
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
Python prototype (for reference):
getVarImportance() -> retval
Positional Arguments
samples:
Evision.Mat
.Array containing the samples for which votes will be calculated.
flags:
int
.Flags for defining the type of RTrees.
Return
results:
Evision.Mat
.Array where the result of the calculation will be written.
Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
Python prototype (for reference):
getVotes(samples, flags[, results]) -> results
Positional Arguments
samples:
Evision.Mat
.Array containing the samples for which votes will be calculated.
flags:
int
.Flags for defining the type of RTrees.
Return
results:
Evision.Mat
.Array where the result of the calculation will be written.
Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
Python prototype (for reference):
getVotes(samples, flags[, results]) -> results
Returns true if the model is classifier
Python prototype (for reference):
isClassifier() -> retval
Returns true if the model is trained
Python prototype (for reference):
isTrained() -> retval
Loads and creates a serialized RTree from a file
Positional Arguments
filepath:
String
.path to serialized RTree
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
Python prototype (for reference):
load(filepath[, nodeName]) -> retval
Loads and creates a serialized RTree from a file
Positional Arguments
filepath:
String
.path to serialized RTree
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
Python prototype (for reference):
load(filepath[, nodeName]) -> retval
Predicts response(s) for the provided sample(s)
Positional Arguments
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
int
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
Predicts response(s) for the provided sample(s)
Positional Arguments
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
int
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
Positional Arguments
- val:
int
@copybrief getActiveVarCount @see getActiveVarCount
Python prototype (for reference):
setActiveVarCount(val) -> None
Positional Arguments
- val:
bool
@copybrief getCalculateVarImportance @see getCalculateVarImportance
Python prototype (for reference):
setCalculateVarImportance(val) -> None
Positional Arguments
- val:
int
@copybrief getCVFolds @see getCVFolds
Python prototype (for reference):
setCVFolds(val) -> None
Positional Arguments
- val:
int
@copybrief getMaxCategories @see getMaxCategories
Python prototype (for reference):
setMaxCategories(val) -> None
Positional Arguments
- val:
int
@copybrief getMaxDepth @see getMaxDepth
Python prototype (for reference):
setMaxDepth(val) -> None
Positional Arguments
- val:
int
@copybrief getMinSampleCount @see getMinSampleCount
Python prototype (for reference):
setMinSampleCount(val) -> None
Positional Arguments
- val:
Evision.Mat
@copybrief getPriors @see getPriors
Python prototype (for reference):
setPriors(val) -> None
Positional Arguments
- val:
float
@copybrief getRegressionAccuracy @see getRegressionAccuracy
Python prototype (for reference):
setRegressionAccuracy(val) -> None
Positional Arguments
- val:
TermCriteria
@copybrief getTermCriteria @see getTermCriteria
Python prototype (for reference):
setTermCriteria(val) -> None
Positional Arguments
- val:
bool
@copybrief getTruncatePrunedTree @see getTruncatePrunedTree
Python prototype (for reference):
setTruncatePrunedTree(val) -> None
Positional Arguments
- val:
bool
@copybrief getUse1SERule @see getUse1SERule
Python prototype (for reference):
setUse1SERule(val) -> None
Positional Arguments
- val:
bool
@copybrief getUseSurrogates @see getUseSurrogates
Python prototype (for reference):
setUseSurrogates(val) -> None
Trains the statistical model
Positional Arguments
trainData:
Ptr<TrainData>
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
int
.optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
Python prototype (for reference):
train(trainData[, flags]) -> retval
Trains the statistical model
Positional Arguments
trainData:
Ptr<TrainData>
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
int
.optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
Python prototype (for reference):
train(trainData[, flags]) -> retval
Trains the statistical model
Positional Arguments
samples:
Evision.Mat
.training samples
layout:
int
.See ml::SampleTypes.
responses:
Evision.Mat
.vector of responses associated with the training samples.
Python prototype (for reference):
train(samples, layout, responses) -> retval
Link to this section Functions
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