View Source Evision.ML.DTrees (Evision v0.1.13)
Link to this section Summary
Types
Type that represents an Evision.ML.DTrees
struct.
Functions
Computes error on the training or test dataset
Computes error on the training or test dataset
Clears the algorithm state
Creates the empty model
Return
- retval:
bool
Python prototype (for reference):
Return
- retval:
String
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.
Returns the number of variables in training samples
Returns true if the model is classifier
Returns true if the model is trained
Loads and creates a serialized DTrees from a file
Loads and creates a serialized DTrees from a file
Predicts response(s) for the provided sample(s)
Predicts response(s) for the provided sample(s)
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).
Trains the statistical model
Trains the statistical model
Trains the statistical model
simplified API for language bindings
simplified API for language bindings
Link to this section Types
@type t() :: %Evision.ML.DTrees{ref: reference()}
Type that represents an Evision.ML.DTrees
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
@spec calcError(t(), Evision.ML.TrainData.t(), boolean()) :: {number(), Evision.Mat.t()} | {:error, String.t()}
Computes error on the training or test dataset
Positional Arguments
data:
Evision.ML.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
retval:
float
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
@spec calcError( t(), Evision.ML.TrainData.t(), boolean(), [{atom(), term()}, ...] | nil ) :: {number(), Evision.Mat.t()} | {:error, String.t()}
Computes error on the training or test dataset
Positional Arguments
data:
Evision.ML.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
retval:
float
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
Clears the algorithm state
Python prototype (for reference):
clear() -> None
Creates the empty model
Return
- retval:
Evision.ML.DTrees
The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load\<DTrees>(filename).
Python prototype (for reference):
create() -> retval
Return
- retval:
bool
Python prototype (for reference):
empty() -> retval
Return
- retval:
int
@see setCVFolds/2
Python prototype (for reference):
getCVFolds() -> retval
Return
- retval:
String
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
Return
- retval:
int
@see setMaxCategories/2
Python prototype (for reference):
getMaxCategories() -> retval
Return
- retval:
int
@see setMaxDepth/2
Python prototype (for reference):
getMaxDepth() -> retval
Return
- retval:
int
@see setMinSampleCount/2
Python prototype (for reference):
getMinSampleCount() -> retval
@spec getPriors(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
cv::Mat
@see setPriors/2
Python prototype (for reference):
getPriors() -> retval
Return
- retval:
float
Python prototype (for reference):
getRegressionAccuracy() -> retval
Return
- retval:
bool
Python prototype (for reference):
getTruncatePrunedTree() -> retval
Return
- retval:
bool
@see setUse1SERule/2
Python prototype (for reference):
getUse1SERule() -> retval
Return
- retval:
bool
@see setUseSurrogates/2
Python prototype (for reference):
getUseSurrogates() -> retval
Returns the number of variables in training samples
Return
- retval:
int
Python prototype (for reference):
getVarCount() -> retval
Returns true if the model is classifier
Return
- retval:
bool
Python prototype (for reference):
isClassifier() -> retval
Returns true if the model is trained
Return
- retval:
bool
Python prototype (for reference):
isTrained() -> retval
Loads and creates a serialized DTrees from a file
Positional Arguments
filepath:
String
.path to serialized DTree
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Return
- retval:
Evision.ML.DTrees
Use DTree::save to serialize and store an DTree to disk. Load the DTree 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 DTrees from a file
Positional Arguments
filepath:
String
.path to serialized DTree
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Return
- retval:
Evision.ML.DTrees
Use DTree::save to serialize and store an DTree to disk. Load the DTree 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
@spec predict(t(), Evision.Mat.maybe_mat_in()) :: {number(), Evision.Mat.t()} | {:error, String.t()}
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
retval:
float
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
@spec predict(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number(), Evision.Mat.t()} | {:error, String.t()}
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
retval:
float
results:
Evision.Mat
.The optional output matrix of results.
Python prototype (for reference):
predict(samples[, results[, flags]]) -> retval, results
@spec read(t(), Evision.FileNode.t()) :: :ok | {:error, String.t()}
Reads algorithm parameters from a file storage
Positional Arguments
- fn_:
Evision.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
Positional Arguments
- val:
int
@see getCVFolds/1
Python prototype (for reference):
setCVFolds(val) -> None
Positional Arguments
- val:
int
@see getMaxCategories/1
Python prototype (for reference):
setMaxCategories(val) -> None
Positional Arguments
- val:
int
@see getMaxDepth/1
Python prototype (for reference):
setMaxDepth(val) -> None
Positional Arguments
- val:
int
@see getMinSampleCount/1
Python prototype (for reference):
setMinSampleCount(val) -> None
@spec setPriors(t(), Evision.Mat.maybe_mat_in()) :: :ok | {:error, String.t()}
Positional Arguments
- val:
Evision.Mat
@see getPriors/1
Python prototype (for reference):
setPriors(val) -> None
Positional Arguments
- val:
float
Python prototype (for reference):
setRegressionAccuracy(val) -> None
Positional Arguments
- val:
bool
Python prototype (for reference):
setTruncatePrunedTree(val) -> None
Positional Arguments
- val:
bool
@see getUse1SERule/1
Python prototype (for reference):
setUse1SERule(val) -> None
Positional Arguments
- val:
bool
@see getUseSurrogates/1
Python prototype (for reference):
setUseSurrogates(val) -> None
@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
trainData:
Evision.ML.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).
Return
- retval:
bool
Python prototype (for reference):
train(trainData[, flags]) -> retval
@spec train(t(), Evision.ML.TrainData.t(), [{atom(), term()}, ...] | nil) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
trainData:
Evision.ML.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).
Return
- retval:
bool
Python prototype (for reference):
train(trainData[, flags]) -> retval
@spec train(t(), Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in()) :: boolean() | {:error, String.t()}
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.
Return
- retval:
bool
Python prototype (for reference):
train(samples, layout, responses) -> retval
@spec write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}
simplified API for language bindings
Positional Arguments
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) :: :ok | {:error, String.t()}
simplified API for language bindings
Positional Arguments
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference):
write(fs[, name]) -> None