View Source Evision.ML.KNearest (Evision v0.2.9)
Summary
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
empty
Finds the neighbors and predicts responses for input vectors.
Finds the neighbors and predicts responses for input vectors.
getAlgorithmType
getDefaultK
getDefaultName
getEmax
getIsClassifier
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 knearest 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
save
setAlgorithmType
setDefaultK
setEmax
setIsClassifier
Trains the statistical model
Trains the statistical model
Trains the statistical model
Stores algorithm parameters in a file storage
write
Types
@type t() :: %Evision.ML.KNearest{ref: reference()}
Type that represents an ML.KNearest
struct.
ref.
reference()
The underlying erlang resource variable.
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.KNearest.t()
data:
Evision.ML.TrainData.t()
.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.t()
.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.KNearest.t()
data:
Evision.ML.TrainData.t()
.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.t()
.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 clear(Keyword.t()) :: any() | {:error, String.t()}
@spec clear(t()) :: t() | {:error, String.t()}
Clears the algorithm state
Positional Arguments
- self:
Evision.ML.KNearest.t()
Python prototype (for reference only):
clear() -> None
Creates the empty model
Return
- retval:
Evision.ML.KNearest.t()
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
Python prototype (for reference only):
create() -> retval
@spec empty(Keyword.t()) :: any() | {:error, String.t()}
@spec empty(t()) :: boolean() | {:error, String.t()}
empty
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
@spec findNearest(t(), Evision.Mat.maybe_mat_in(), integer()) :: {number(), Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
Finds the neighbors and predicts responses for input vectors.
Positional Arguments
self:
Evision.ML.KNearest.t()
samples:
Evision.Mat
.Input samples stored by rows. It is a single-precision floating-point matrix of
<number_of_samples> * k
size.k:
integer()
.Number of used nearest neighbors. Should be greater than 1.
Return
retval:
float
results:
Evision.Mat.t()
.Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with
<number_of_samples>
elements.neighborResponses:
Evision.Mat.t()
.Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of
<number_of_samples> * k
size.dist:
Evision.Mat.t()
.Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of
<number_of_samples> * k
size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting. For each input vector, the neighbors are sorted by their distances to the vector. In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself. If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method. The function is parallelized with the TBB library.
Python prototype (for reference only):
findNearest(samples, k[, results[, neighborResponses[, dist]]]) -> retval, results, neighborResponses, dist
@spec findNearest( t(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: {number(), Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
Finds the neighbors and predicts responses for input vectors.
Positional Arguments
self:
Evision.ML.KNearest.t()
samples:
Evision.Mat
.Input samples stored by rows. It is a single-precision floating-point matrix of
<number_of_samples> * k
size.k:
integer()
.Number of used nearest neighbors. Should be greater than 1.
Return
retval:
float
results:
Evision.Mat.t()
.Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with
<number_of_samples>
elements.neighborResponses:
Evision.Mat.t()
.Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of
<number_of_samples> * k
size.dist:
Evision.Mat.t()
.Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of
<number_of_samples> * k
size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting. For each input vector, the neighbors are sorted by their distances to the vector. In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself. If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method. The function is parallelized with the TBB library.
Python prototype (for reference only):
findNearest(samples, k[, results[, neighborResponses[, dist]]]) -> retval, results, neighborResponses, dist
@spec getAlgorithmType(Keyword.t()) :: any() | {:error, String.t()}
@spec getAlgorithmType(t()) :: integer() | {:error, String.t()}
getAlgorithmType
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
integer()
@see setAlgorithmType/2
Python prototype (for reference only):
getAlgorithmType() -> retval
@spec getDefaultK(Keyword.t()) :: any() | {:error, String.t()}
@spec getDefaultK(t()) :: integer() | {:error, String.t()}
getDefaultK
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
integer()
@see setDefaultK/2
Python prototype (for reference only):
getDefaultK() -> retval
@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}
@spec getDefaultName(t()) :: binary() | {:error, String.t()}
getDefaultName
Positional Arguments
- self:
Evision.ML.KNearest.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
@spec getEmax(Keyword.t()) :: any() | {:error, String.t()}
@spec getEmax(t()) :: integer() | {:error, String.t()}
getEmax
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
integer()
@see setEmax/2
Python prototype (for reference only):
getEmax() -> retval
@spec getIsClassifier(Keyword.t()) :: any() | {:error, String.t()}
@spec getIsClassifier(t()) :: boolean() | {:error, String.t()}
getIsClassifier
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
bool
@see setIsClassifier/2
Python prototype (for reference only):
getIsClassifier() -> retval
@spec getVarCount(Keyword.t()) :: any() | {:error, String.t()}
@spec getVarCount(t()) :: integer() | {:error, String.t()}
Returns the number of variables in training samples
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
integer()
Python prototype (for reference only):
getVarCount() -> retval
@spec isClassifier(Keyword.t()) :: any() | {:error, String.t()}
@spec isClassifier(t()) :: boolean() | {:error, String.t()}
Returns true if the model is classifier
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
bool
Python prototype (for reference only):
isClassifier() -> retval
@spec isTrained(Keyword.t()) :: any() | {:error, String.t()}
@spec isTrained(t()) :: boolean() | {:error, String.t()}
Returns true if the model is trained
Positional Arguments
- self:
Evision.ML.KNearest.t()
Return
- retval:
bool
Python prototype (for reference only):
isTrained() -> retval
@spec load(Keyword.t()) :: any() | {:error, String.t()}
@spec load(binary()) :: t() | {:error, String.t()}
Loads and creates a serialized knearest from a file
Positional Arguments
filepath:
String
.path to serialized KNearest
Return
- retval:
Evision.ML.KNearest.t()
Use KNearest::save to serialize and store an KNearest to disk. Load the KNearest from this file again, by calling this function with the path to the file.
Python prototype (for reference only):
load(filepath) -> 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
self:
Evision.ML.KNearest.t()
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
integer()
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
retval:
float
results:
Evision.Mat.t()
.The optional output matrix of results.
Python prototype (for reference only):
predict(samples[, results[, flags]]) -> retval, results
@spec predict(t(), Evision.Mat.maybe_mat_in(), [{:flags, term()}] | nil) :: {number(), Evision.Mat.t()} | {:error, String.t()}
Predicts response(s) for the provided sample(s)
Positional Arguments
self:
Evision.ML.KNearest.t()
samples:
Evision.Mat
.The input samples, floating-point matrix
Keyword Arguments
flags:
integer()
.The optional flags, model-dependent. See cv::ml::StatModel::Flags.
Return
retval:
float
results:
Evision.Mat.t()
.The optional output matrix of results.
Python prototype (for reference only):
predict(samples[, results[, flags]]) -> retval, results
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}
Reads algorithm parameters from a file storage
Positional Arguments
- self:
Evision.ML.KNearest.t()
- func:
Evision.FileNode
Python prototype (for reference only):
read(fn) -> None
save
Positional Arguments
- self:
Evision.ML.KNearest.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
setAlgorithmType
Positional Arguments
- self:
Evision.ML.KNearest.t()
- val:
integer()
@see getAlgorithmType/1
Python prototype (for reference only):
setAlgorithmType(val) -> None
setDefaultK
Positional Arguments
- self:
Evision.ML.KNearest.t()
- val:
integer()
@see getDefaultK/1
Python prototype (for reference only):
setDefaultK(val) -> None
setEmax
Positional Arguments
- self:
Evision.ML.KNearest.t()
- val:
integer()
@see getEmax/1
Python prototype (for reference only):
setEmax(val) -> None
setIsClassifier
Positional Arguments
- self:
Evision.ML.KNearest.t()
- val:
bool
@see getIsClassifier/1
Python prototype (for reference only):
setIsClassifier(val) -> None
@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
self:
Evision.ML.KNearest.t()
trainData:
Evision.ML.TrainData.t()
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
integer()
.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(), [{:flags, term()}] | nil) :: boolean() | {:error, String.t()}
Trains the statistical model
Positional Arguments
self:
Evision.ML.KNearest.t()
trainData:
Evision.ML.TrainData.t()
.training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
Keyword Arguments
flags:
integer()
.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.KNearest.t()
samples:
Evision.Mat
.training samples
layout:
integer()
.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()) :: t() | {:error, String.t()}
Stores algorithm parameters in a file storage
Positional Arguments
- self:
Evision.ML.KNearest.t()
- fs:
Evision.FileStorage
Python prototype (for reference only):
write(fs) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}
write
Positional Arguments
- self:
Evision.ML.KNearest.t()
- fs:
Evision.FileStorage
- name:
String
Has overloading in C++
Python prototype (for reference only):
write(fs, name) -> None