[−][src]Trait opencv::ml::TrainData

pub trait TrainData {
    fn as_raw_TrainData(&self) -> *mut c_void;

    fn missing_value(&mut self) -> Result<f32> { ... }

    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: &dyn 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> { ... }

    fn get_train_responses(&self) -> Result<Mat> { ... }

    fn get_train_norm_cat_responses(&self) -> Result<Mat> { ... }

    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: &dyn ToInputArray, 
        values: &mut f32
    ) -> Result<()> { ... }

    fn get_norm_cat_values(
        &self, 
        vi: i32, 
        sidx: &dyn 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> { ... }

    fn get_cat_ofs(&self) -> Result<Mat> { ... }

    fn get_cat_map(&self) -> Result<Mat> { ... }

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

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

    fn shuffle_train_test(&mut self) -> Result<()> { ... }

    fn get_test_samples(&self) -> Result<Mat> { ... }

    fn get_names(&self, names: &mut VectorOfString) -> Result<()> { ... }
}

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

@ref ml_intro_data

Required methods

fn as_raw_TrainData(&self) -> *mut c_void

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Provided methods

fn missing_value(&mut self) -> Result<f32>

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: &dyn 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

Parameters

  • layout: The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
  • compressSamples: if true, the function returns only the training samples (specified by sampleIdx)
  • compressVars: if true, the function returns the shorter training samples, containing only the active variables.

In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).

C++ default parameters

  • layout: ROW_SAMPLE
  • compress_samples: true
  • compress_vars: true

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

Returns the vector of responses

The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.

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

Returns the vector of normalized categorical responses

The function returns vector of responses. Each response is integer from 0 to <number of classes>-1. The actual label value can be retrieved then from the class label vector, see TrainData::getClassLabels.

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: &dyn ToInputArray,
    values: &mut f32
) -> Result<()>

fn get_norm_cat_values(
    &self,
    vi: i32,
    sidx: &dyn 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

The function returns vector of unique labels occurred in the responses.

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

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

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

Splits the training data into the training and test parts

See also

TrainData::setTrainTestSplitRatio

C++ default parameters

  • shuffle: true

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

Splits the training data into the training and test parts

The function selects a subset of specified relative size and then returns it as the training set. If the function is not called, all the data is used for training. Please, note that for each of TrainData::getTrain* there is corresponding TrainData::getTest*, so that the test subset can be retrieved and processed as well.

See also

TrainData::setTrainTestSplit

C++ default parameters

  • shuffle: true

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

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

Returns matrix of test samples

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

Returns vector of symbolic names captured in loadFromCSV()

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Methods

impl<'_> dyn TrainData + '_[src]

pub fn get_sub_vector(vec: &Mat, idx: &Mat) -> Result<Mat>[src]

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: &Mat, idx: &Mat, layout: i32) -> Result<Mat>[src]

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: i8,
    missch: i8
) -> Result<PtrOfTrainData>[src]

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 create(
    samples: &dyn ToInputArray,
    layout: i32,
    responses: &dyn ToInputArray,
    var_idx: &dyn ToInputArray,
    sample_idx: &dyn ToInputArray,
    sample_weights: &dyn ToInputArray,
    var_type: &dyn ToInputArray
) -> Result<PtrOfTrainData>[src]

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()

Implementors

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