Struct TrainData

pub struct TrainData { /* private fields */ }

Class encapsulating training data.

Please note that the class only specifies the interface of training data, but not implementation. All the statistical model classes in ml module accepts Ptr<TrainData> as parameter. In other words, you can create your own class derived from TrainData and pass smart pointer to the instance of this class into StatModel::train.

See also

[ml_intro_data]

Implementations

impl TrainData

pub fn missing_value() -> Result<f32>

pub fn get_sub_vector( vec: &impl MatTraitConst, idx: &impl MatTraitConst, ) -> Result<Mat>

Extract from 1D vector elements specified by passed indexes.

Parameters

• vec: input vector (supported types: CV_32S, CV_32F, CV_64F)
• idx: 1D index vector

pub fn get_sub_matrix( matrix: &impl MatTraitConst, idx: &impl MatTraitConst, layout: i32, ) -> Result<Mat>

Extract from matrix rows/cols specified by passed indexes.

Parameters

• matrix: input matrix (supported types: CV_32S, CV_32F, CV_64F)
• idx: 1D index vector
• layout: specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)

pub fn load_from_csv( filename: &str, header_line_count: i32, response_start_idx: i32, response_end_idx: i32, var_type_spec: &str, delimiter: char, missch: char, ) -> Result<Ptr<TrainData>>

Reads the dataset from a .csv file and returns the ready-to-use training data.

Parameters

• filename: The input file name
• headerLineCount: The number of lines in the beginning to skip; besides the header, the function also skips empty lines and lines staring with #
• responseStartIdx: Index of the first output variable. If -1, the function considers the last variable as the response
• responseEndIdx: Index of the last output variable + 1. If -1, then there is single response variable at responseStartIdx.
• varTypeSpec: The optional text string that specifies the variables’ types. It has the format ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]. That is, variables from n1 to n2 (inclusive range), n3, n4 to n5 … are considered ordered and n6, n7 to n8 … are considered as categorical. The range [n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8] should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules:
  • all input variables are considered ordered by default. If some column contains has non- numerical values, e.g. ‘apple’, ‘pear’, ‘apple’, ‘apple’, ‘mango’, the corresponding variable is considered categorical.
  • if there are several output variables, they are all considered as ordered. Error is reported when non-numerical values are used.
  • if there is a single output variable, then if its values are non-numerical or are all integers, then it’s considered categorical. Otherwise, it’s considered ordered.
• delimiter: The character used to separate values in each line.
• missch: The character used to specify missing measurements. It should not be a digit. Although it’s a non-numerical value, it surely does not affect the decision of whether the variable ordered or categorical.

Note: If the dataset only contains input variables and no responses, use responseStartIdx = -2 and responseEndIdx = 0. The output variables vector will just contain zeros.

C++ default parameters

• response_start_idx: -1
• response_end_idx: -1
• var_type_spec: String()
• delimiter: ‘,’
• missch: ‘?’

pub fn load_from_csv_def( filename: &str, header_line_count: i32, ) -> Result<Ptr<TrainData>>

Reads the dataset from a .csv file and returns the ready-to-use training data.

Parameters

• filename: The input file name
• headerLineCount: The number of lines in the beginning to skip; besides the header, the function also skips empty lines and lines staring with #
• responseStartIdx: Index of the first output variable. If -1, the function considers the last variable as the response
• responseEndIdx: Index of the last output variable + 1. If -1, then there is single response variable at responseStartIdx.
• varTypeSpec: The optional text string that specifies the variables’ types. It has the format ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]. That is, variables from n1 to n2 (inclusive range), n3, n4 to n5 … are considered ordered and n6, n7 to n8 … are considered as categorical. The range [n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8] should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules:
  • all input variables are considered ordered by default. If some column contains has non- numerical values, e.g. ‘apple’, ‘pear’, ‘apple’, ‘apple’, ‘mango’, the corresponding variable is considered categorical.
  • if there are several output variables, they are all considered as ordered. Error is reported when non-numerical values are used.
  • if there is a single output variable, then if its values are non-numerical or are all integers, then it’s considered categorical. Otherwise, it’s considered ordered.
• delimiter: The character used to separate values in each line.
• missch: The character used to specify missing measurements. It should not be a digit. Although it’s a non-numerical value, it surely does not affect the decision of whether the variable ordered or categorical.

Note: If the dataset only contains input variables and no responses, use responseStartIdx = -2 and responseEndIdx = 0. The output variables vector will just contain zeros.

Note

This alternative version of TrainData::load_from_csv function uses the following default values for its arguments:

• response_start_idx: -1
• response_end_idx: -1
• var_type_spec: String()
• delimiter: ‘,’
• missch: ‘?’

pub fn create( samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray, var_idx: &impl ToInputArray, sample_idx: &impl ToInputArray, sample_weights: &impl ToInputArray, var_type: &impl ToInputArray, ) -> Result<Ptr<TrainData>>

Creates training data from in-memory arrays.

Parameters

• samples: matrix of samples. It should have CV_32F type.
• layout: see ml::SampleTypes.
• responses: matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
• varIdx: vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
• sampleIdx: vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
• sampleWeights: optional vector with weights for each sample. It should have CV_32F type.
• varType: optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

C++ default parameters

• var_idx: noArray()
• sample_idx: noArray()
• sample_weights: noArray()
• var_type: noArray()

pub fn create_def( samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray, ) -> Result<Ptr<TrainData>>

Creates training data from in-memory arrays.

Parameters

• samples: matrix of samples. It should have CV_32F type.
• layout: see ml::SampleTypes.
• responses: matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
• varIdx: vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
• sampleIdx: vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
• sampleWeights: optional vector with weights for each sample. It should have CV_32F type.
• varType: optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

Note

This alternative version of TrainData::create function uses the following default values for its arguments:

• var_idx: noArray()
• sample_idx: noArray()
• sample_weights: noArray()
• var_type: noArray()

Trait Implementations

impl Boxed for TrainData

unsafe fn from_raw(ptr: <TrainData as OpenCVFromExtern>::ExternReceive) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> <TrainData as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <TrainData as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut( &mut self, ) -> <TrainData as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl Debug for TrainData

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for TrainData

fn drop(&mut self)

Executes the destructor for this type.

impl TrainDataTrait for TrainData

fn as_raw_mut_TrainData(&mut self) -> *mut c_void

fn set_train_test_split(&mut self, count: i32, shuffle: bool) -> Result<()>

Splits the training data into the training and test parts

fn set_train_test_split_def(&mut self, count: i32) -> Result<()>

Splits the training data into the training and test parts

fn set_train_test_split_ratio( &mut self, ratio: f64, shuffle: bool, ) -> Result<()>

Splits the training data into the training and test parts

fn set_train_test_split_ratio_def(&mut self, ratio: f64) -> Result<()>

Splits the training data into the training and test parts

fn shuffle_train_test(&mut self) -> Result<()>

impl TrainDataTraitConst for TrainData

fn as_raw_TrainData(&self) -> *const c_void

fn get_layout(&self) -> Result<i32>

fn get_n_train_samples(&self) -> Result<i32>

fn get_n_test_samples(&self) -> Result<i32>

fn get_n_samples(&self) -> Result<i32>

fn get_n_vars(&self) -> Result<i32>

fn get_n_all_vars(&self) -> Result<i32>

fn get_sample( &self, var_idx: &impl ToInputArray, sidx: i32, buf: &mut f32, ) -> Result<()>

fn get_samples(&self) -> Result<Mat>

fn get_missing(&self) -> Result<Mat>

fn get_train_samples( &self, layout: i32, compress_samples: bool, compress_vars: bool, ) -> Result<Mat>

Returns matrix of train samples

fn get_train_samples_def(&self) -> Result<Mat>

Returns matrix of train samples

fn get_train_responses(&self) -> Result<Mat>

Returns the vector of responses

fn get_train_norm_cat_responses(&self) -> Result<Mat>

Returns the vector of normalized categorical responses

fn get_test_responses(&self) -> Result<Mat>

fn get_test_norm_cat_responses(&self) -> Result<Mat>

fn get_responses(&self) -> Result<Mat>

fn get_norm_cat_responses(&self) -> Result<Mat>

fn get_sample_weights(&self) -> Result<Mat>

fn get_train_sample_weights(&self) -> Result<Mat>

fn get_test_sample_weights(&self) -> Result<Mat>

fn get_var_idx(&self) -> Result<Mat>

fn get_var_type(&self) -> Result<Mat>

fn get_var_symbol_flags(&self) -> Result<Mat>

fn get_response_type(&self) -> Result<i32>

fn get_train_sample_idx(&self) -> Result<Mat>

fn get_test_sample_idx(&self) -> Result<Mat>

fn get_values( &self, vi: i32, sidx: &impl ToInputArray, values: &mut f32, ) -> Result<()>

fn get_norm_cat_values( &self, vi: i32, sidx: &impl ToInputArray, values: &mut i32, ) -> Result<()>

fn get_default_subst_values(&self) -> Result<Mat>

fn get_cat_count(&self, vi: i32) -> Result<i32>

fn get_class_labels(&self) -> Result<Mat>

Returns the vector of class labels

fn get_cat_ofs(&self) -> Result<Mat>

fn get_cat_map(&self) -> Result<Mat>

fn get_test_samples(&self) -> Result<Mat>

Returns matrix of test samples

fn get_names(&self, names: &mut Vector<String>) -> Result<()>

Returns vector of symbolic names captured in loadFromCSV()

impl Send for TrainData