[−][src]Type Definition opencv::types::PtrOfSVMSGD
type PtrOfSVMSGD = Ptr<dyn SVMSGD>;
Implementations
impl PtrOfSVMSGD
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pub fn as_raw_PtrOfSVMSGD(&self) -> *const c_void
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pub fn as_raw_mut_PtrOfSVMSGD(&mut self) -> *mut c_void
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Trait Implementations
impl AlgorithmTrait for PtrOfSVMSGD
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pub fn as_raw_Algorithm(&self) -> *const c_void
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pub fn as_raw_mut_Algorithm(&mut self) -> *mut c_void
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pub fn clear(&mut self) -> Result<()>
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pub fn write(&self, fs: &mut FileStorage) -> Result<()>
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pub fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>
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pub fn read(&mut self, fn_: &FileNode) -> Result<()>
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pub fn empty(&self) -> Result<bool>
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pub fn save(&self, filename: &str) -> Result<()>
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pub fn get_default_name(&self) -> Result<String>
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impl SVMSGD for PtrOfSVMSGD
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pub fn as_raw_SVMSGD(&self) -> *const c_void
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pub fn as_raw_mut_SVMSGD(&mut self) -> *mut c_void
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pub fn get_weights(&mut self) -> Result<Mat>
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pub fn get_shift(&mut self) -> Result<f32>
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pub fn set_optimal_parameters(
&mut self,
svmsgd_type: i32,
margin_type: i32
) -> Result<()>
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&mut self,
svmsgd_type: i32,
margin_type: i32
) -> Result<()>
pub fn get_svmsgd_type(&self) -> Result<i32>
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pub fn set_svmsgd_type(&mut self, svmsgd_type: i32) -> Result<()>
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pub fn get_margin_type(&self) -> Result<i32>
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pub fn set_margin_type(&mut self, margin_type: i32) -> Result<()>
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pub fn get_margin_regularization(&self) -> Result<f32>
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pub fn set_margin_regularization(
&mut self,
margin_regularization: f32
) -> Result<()>
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&mut self,
margin_regularization: f32
) -> Result<()>
pub fn get_initial_step_size(&self) -> Result<f32>
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pub fn set_initial_step_size(&mut self, initial_step_size: f32) -> Result<()>
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pub fn get_step_decreasing_power(&self) -> Result<f32>
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pub fn set_step_decreasing_power(
&mut self,
step_decreasing_power: f32
) -> Result<()>
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&mut self,
step_decreasing_power: f32
) -> Result<()>
pub fn get_term_criteria(&self) -> Result<TermCriteria>
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pub fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>
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impl StatModel for PtrOfSVMSGD
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pub fn as_raw_StatModel(&self) -> *const c_void
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pub fn as_raw_mut_StatModel(&mut self) -> *mut c_void
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pub fn get_var_count(&self) -> Result<i32>
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pub fn empty(&self) -> Result<bool>
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pub fn is_trained(&self) -> Result<bool>
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pub fn is_classifier(&self) -> Result<bool>
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pub fn train_with_data(
&mut self,
train_data: &Ptr<dyn TrainData>,
flags: i32
) -> Result<bool>
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&mut self,
train_data: &Ptr<dyn TrainData>,
flags: i32
) -> Result<bool>
pub fn train(
&mut self,
samples: &dyn ToInputArray,
layout: i32,
responses: &dyn ToInputArray
) -> Result<bool>
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&mut self,
samples: &dyn ToInputArray,
layout: i32,
responses: &dyn ToInputArray
) -> Result<bool>
pub fn calc_error(
&self,
data: &Ptr<dyn TrainData>,
test: bool,
resp: &mut dyn ToOutputArray
) -> Result<f32>
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&self,
data: &Ptr<dyn TrainData>,
test: bool,
resp: &mut dyn ToOutputArray
) -> Result<f32>
pub fn predict(
&self,
samples: &dyn ToInputArray,
results: &mut dyn ToOutputArray,
flags: i32
) -> Result<f32>
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&self,
samples: &dyn ToInputArray,
results: &mut dyn ToOutputArray,
flags: i32
) -> Result<f32>