pub struct StatModel { /* private fields */ }
Expand description
Base class for statistical models in OpenCV ML.
Trait Implementations§
source§impl AlgorithmTrait for StatModel
impl AlgorithmTrait for StatModel
source§impl AlgorithmTraitConst for StatModel
impl AlgorithmTraitConst for StatModel
fn as_raw_Algorithm(&self) -> *const c_void
source§fn write(&self, fs: &mut FileStorage) -> Result<()>
fn write(&self, fs: &mut FileStorage) -> Result<()>
Stores algorithm parameters in a file storage
source§fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>
fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>
Stores algorithm parameters in a file storage Read more
source§fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>
fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>
@deprecated Read more
source§fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>
fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>
👎Deprecated:
Note
Deprecated: ## Note
This alternative version of [write_with_name] function uses the following default values for its arguments: Read more
source§fn empty(&self) -> Result<bool>
fn empty(&self) -> Result<bool>
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
source§fn save(&self, filename: &str) -> Result<()>
fn save(&self, filename: &str) -> Result<()>
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
source§fn get_default_name(&self) -> Result<String>
fn get_default_name(&self) -> Result<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.
source§impl Boxed for StatModel
impl Boxed for StatModel
source§impl From<LogisticRegression> for StatModel
impl From<LogisticRegression> for StatModel
source§fn from(s: LogisticRegression) -> Self
fn from(s: LogisticRegression) -> Self
Converts to this type from the input type.
source§impl From<NormalBayesClassifier> for StatModel
impl From<NormalBayesClassifier> for StatModel
source§fn from(s: NormalBayesClassifier) -> Self
fn from(s: NormalBayesClassifier) -> Self
Converts to this type from the input type.
source§impl StatModelTrait for StatModel
impl StatModelTrait for StatModel
fn as_raw_mut_StatModel(&mut self) -> *mut c_void
source§fn train_with_data(
&mut self,
train_data: &Ptr<TrainData>,
flags: i32
) -> Result<bool>
fn train_with_data( &mut self, train_data: &Ptr<TrainData>, flags: i32 ) -> Result<bool>
Trains the statistical model Read more
source§fn train_with_data_def(&mut self, train_data: &Ptr<TrainData>) -> Result<bool>
fn train_with_data_def(&mut self, train_data: &Ptr<TrainData>) -> Result<bool>
Trains the statistical model Read more
source§fn train(
&mut self,
samples: &impl ToInputArray,
layout: i32,
responses: &impl ToInputArray
) -> Result<bool>
fn train( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray ) -> Result<bool>
Trains the statistical model Read more
source§impl StatModelTraitConst for StatModel
impl StatModelTraitConst for StatModel
fn as_raw_StatModel(&self) -> *const c_void
source§fn get_var_count(&self) -> Result<i32>
fn get_var_count(&self) -> Result<i32>
Returns the number of variables in training samples
fn empty(&self) -> Result<bool>
source§fn is_trained(&self) -> Result<bool>
fn is_trained(&self) -> Result<bool>
Returns true if the model is trained
source§fn is_classifier(&self) -> Result<bool>
fn is_classifier(&self) -> Result<bool>
Returns true if the model is classifier
source§fn calc_error(
&self,
data: &Ptr<TrainData>,
test: bool,
resp: &mut impl ToOutputArray
) -> Result<f32>
fn calc_error( &self, data: &Ptr<TrainData>, test: bool, resp: &mut impl ToOutputArray ) -> Result<f32>
Computes error on the training or test dataset Read more
source§fn predict(
&self,
samples: &impl ToInputArray,
results: &mut impl ToOutputArray,
flags: i32
) -> Result<f32>
fn predict( &self, samples: &impl ToInputArray, results: &mut impl ToOutputArray, flags: i32 ) -> Result<f32>
Predicts response(s) for the provided sample(s) Read more
source§fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>
fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>
Predicts response(s) for the provided sample(s) Read more
source§impl TryFrom<StatModel> for LogisticRegression
impl TryFrom<StatModel> for LogisticRegression
source§impl TryFrom<StatModel> for NormalBayesClassifier
impl TryFrom<StatModel> for NormalBayesClassifier
impl Send for StatModel
Auto Trait Implementations§
impl RefUnwindSafe for StatModel
impl !Sync for StatModel
impl Unpin for StatModel
impl UnwindSafe for StatModel
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more