View Source Evision.ML.LogisticRegression (Evision v0.1.20)
Link to this section Summary
Types
Type that represents an Evision.ML.LogisticRegression
struct.
Functions
Computes error on the training or test dataset
Computes error on the training or test dataset
Clears the algorithm state
Creates empty model.
empty
This function returns the trained parameters arranged across rows.
getDefaultName
getIterations
getLearningRate
getMiniBatchSize
getRegularization
getTermCriteria
getTrainMethod
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 LogisticRegression from a file
Loads and creates a serialized LogisticRegression from a file
Predicts responses for input samples and returns a float type.
Predicts responses for input samples and returns a float type.
Reads algorithm parameters from a file storage
save
setIterations
setLearningRate
setMiniBatchSize
setRegularization
setTermCriteria
setTrainMethod
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.LogisticRegression{ref: reference()}
Type that represents an Evision.ML.LogisticRegression
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
self:
Evision.ML.LogisticRegression.t()
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 only):
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
self:
Evision.ML.LogisticRegression.t()
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 only):
calcError(data, test[, resp]) -> retval, resp
Clears the algorithm state
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Python prototype (for reference only):
clear() -> None
Creates empty model.
Return
- retval:
Evision.ML.LogisticRegression
Creates Logistic Regression model with parameters given.
Python prototype (for reference only):
create() -> retval
empty
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
@spec get_learnt_thetas(t()) :: Evision.Mat.t() | {:error, String.t()}
This function returns the trained parameters arranged across rows.
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
Evision.Mat
For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.
Python prototype (for reference only):
get_learnt_thetas() -> retval
getDefaultName
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
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 only):
getDefaultName() -> retval
getIterations
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
int
@see setIterations/2
Python prototype (for reference only):
getIterations() -> retval
getLearningRate
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
double
@see setLearningRate/2
Python prototype (for reference only):
getLearningRate() -> retval
getMiniBatchSize
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
int
@see setMiniBatchSize/2
Python prototype (for reference only):
getMiniBatchSize() -> retval
getRegularization
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
int
@see setRegularization/2
Python prototype (for reference only):
getRegularization() -> retval
getTermCriteria
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
TermCriteria
@see setTermCriteria/2
Python prototype (for reference only):
getTermCriteria() -> retval
getTrainMethod
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
int
@see setTrainMethod/2
Python prototype (for reference only):
getTrainMethod() -> retval
Returns the number of variables in training samples
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
int
Python prototype (for reference only):
getVarCount() -> retval
Returns true if the model is classifier
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
bool
Python prototype (for reference only):
isClassifier() -> retval
Returns true if the model is trained
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
Return
- retval:
bool
Python prototype (for reference only):
isTrained() -> retval
Loads and creates a serialized LogisticRegression from a file
Positional Arguments
filepath:
String
.path to serialized LogisticRegression
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Return
- retval:
Evision.ML.LogisticRegression
Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 only):
load(filepath[, nodeName]) -> retval
Loads and creates a serialized LogisticRegression from a file
Positional Arguments
filepath:
String
.path to serialized LogisticRegression
Keyword Arguments
nodeName:
String
.name of node containing the classifier
Return
- retval:
Evision.ML.LogisticRegression
Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 only):
load(filepath[, nodeName]) -> retval
@spec predict(t(), Evision.Mat.maybe_mat_in()) :: {number(), Evision.Mat.t()} | {:error, String.t()}
Predicts responses for input samples and returns a float type.
Positional Arguments
self:
Evision.ML.LogisticRegression.t()
samples:
Evision.Mat
.The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
Keyword Arguments
flags:
int
.Not used.
Return
retval:
float
results:
Evision.Mat
.Predicted labels as a column matrix of type CV_32S.
Python prototype (for reference only):
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 responses for input samples and returns a float type.
Positional Arguments
self:
Evision.ML.LogisticRegression.t()
samples:
Evision.Mat
.The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
Keyword Arguments
flags:
int
.Not used.
Return
retval:
float
results:
Evision.Mat
.Predicted labels as a column matrix of type CV_32S.
Python prototype (for reference only):
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
- self:
Evision.ML.LogisticRegression.t()
- fn_:
Evision.FileNode
Python prototype (for reference only):
read(fn_) -> None
save
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- 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 only):
save(filename) -> None
setIterations
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
int
@see getIterations/1
Python prototype (for reference only):
setIterations(val) -> None
setLearningRate
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
double
@see getLearningRate/1
Python prototype (for reference only):
setLearningRate(val) -> None
setMiniBatchSize
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
int
@see getMiniBatchSize/1
Python prototype (for reference only):
setMiniBatchSize(val) -> None
setRegularization
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
int
@see getRegularization/1
Python prototype (for reference only):
setRegularization(val) -> None
setTermCriteria
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
TermCriteria
@see getTermCriteria/1
Python prototype (for reference only):
setTermCriteria(val) -> None
setTrainMethod
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- val:
int
@see getTrainMethod/1
Python prototype (for reference only):
setTrainMethod(val) -> None
@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
self:
Evision.ML.LogisticRegression.t()
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 only):
train(trainData[, flags]) -> retval
@spec train(t(), Evision.ML.TrainData.t(), [{atom(), term()}, ...] | nil) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
self:
Evision.ML.LogisticRegression.t()
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 only):
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
self:
Evision.ML.LogisticRegression.t()
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 only):
train(samples, layout, responses) -> retval
@spec write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}
simplified API for language bindings
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- fs:
Evision.FileStorage
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference only):
write(fs[, name]) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) :: :ok | {:error, String.t()}
simplified API for language bindings
Positional Arguments
- self:
Evision.ML.LogisticRegression.t()
- fs:
Evision.FileStorage
Keyword Arguments
- name:
String
.
Has overloading in C++
Python prototype (for reference only):
write(fs[, name]) -> None