org.opencv.ml

## Class TrainData

- java.lang.Object
- - org.opencv.ml.TrainData

-     public class TrainData
      extends Object

  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: REF: ml_intro_data

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `protected long` | `nativeObj`

  - ### Constructor Summary

    Constructors
    Modifier | Constructor and Description
    --- | ---
    `protected` | `TrainData(long addr)`

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `static TrainData` | `__fromPtr__(long addr)`
    `static TrainData` | `create(Mat samples, int layout, Mat responses)` Creates training data from in-memory arrays.
    `static TrainData` | `create(Mat samples, int layout, Mat responses, Mat varIdx)` Creates training data from in-memory arrays.
    `static TrainData` | `create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx)` Creates training data from in-memory arrays.
    `static TrainData` | `create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights)` Creates training data from in-memory arrays.
    `static TrainData` | `create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType)` Creates training data from in-memory arrays.
    `protected void` | `finalize()`
    `int` | `getCatCount(int vi)`
    `Mat` | `getCatMap()`
    `Mat` | `getCatOfs()`
    `Mat` | `getClassLabels()` Returns the vector of class labels The function returns vector of unique labels occurred in the responses.
    `Mat` | `getDefaultSubstValues()`
    `int` | `getLayout()`
    `Mat` | `getMissing()`
    `int` | `getNAllVars()`
    `void` | `getNames(List<String> names)` Returns vector of symbolic names captured in loadFromCSV()
    `long` | `getNativeObjAddr()`
    `Mat` | `getNormCatResponses()`
    `int` | `getNSamples()`
    `int` | `getNTestSamples()`
    `int` | `getNTrainSamples()`
    `int` | `getNVars()`
    `Mat` | `getResponses()`
    `int` | `getResponseType()`
    `void` | `getSample(Mat varIdx, int sidx, float buf)`
    `Mat` | `getSamples()`
    `Mat` | `getSampleWeights()`
    `static Mat` | `getSubMatrix(Mat matrix, Mat idx, int layout)` Extract from matrix rows/cols specified by passed indexes.
    `static Mat` | `getSubVector(Mat vec, Mat idx)` Extract from 1D vector elements specified by passed indexes.
    `Mat` | `getTestNormCatResponses()`
    `Mat` | `getTestResponses()`
    `Mat` | `getTestSampleIdx()`
    `Mat` | `getTestSamples()` Returns matrix of test samples
    `Mat` | `getTestSampleWeights()`
    `Mat` | `getTrainNormCatResponses()` Returns the vector of normalized categorical responses The function returns vector of responses.
    `Mat` | `getTrainResponses()` Returns the vector of responses The function returns ordered or the original categorical responses.
    `Mat` | `getTrainSampleIdx()`
    `Mat` | `getTrainSamples()` Returns matrix of train samples transposed.
    `Mat` | `getTrainSamples(int layout)` Returns matrix of train samples
    `Mat` | `getTrainSamples(int layout, boolean compressSamples)` Returns matrix of train samples
    `Mat` | `getTrainSamples(int layout, boolean compressSamples, boolean compressVars)` Returns matrix of train samples
    `Mat` | `getTrainSampleWeights()`
    `void` | `getValues(int vi, Mat sidx, float values)`
    `Mat` | `getVarIdx()`
    `Mat` | `getVarSymbolFlags()`
    `Mat` | `getVarType()`
    `void` | `setTrainTestSplit(int count)` Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
    `void` | `setTrainTestSplit(int count, boolean shuffle)` Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
    `void` | `setTrainTestSplitRatio(double ratio)` 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.
    `void` | `setTrainTestSplitRatio(double ratio, boolean shuffle)` 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.
    `void` | `shuffleTrainTest()`

    - ### Methods inherited from class java.lang.Object

      `clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### nativeObj

          protected final long nativeObj

  - ### Constructor Detail

    - #### TrainData

          protected TrainData(long addr)

  - ### Method Detail

    - #### getNativeObjAddr

          public long getNativeObjAddr()

    - #### __fromPtr__

          public static TrainData __fromPtr__(long addr)

    - #### getCatMap

          public Mat getCatMap()

    - #### getCatOfs

          public Mat getCatOfs()

    - #### getClassLabels

          public Mat getClassLabels()

      Returns the vector of class labels The function returns vector of unique labels occurred in the responses.
      Returns:
        automatically generated

    - #### getDefaultSubstValues

          public Mat getDefaultSubstValues()

    - #### getMissing

          public Mat getMissing()

    - #### getNormCatResponses

          public Mat getNormCatResponses()

    - #### getResponses

          public Mat getResponses()

    - #### getSampleWeights

          public Mat getSampleWeights()

    - #### getSamples

          public Mat getSamples()

    - #### getSubMatrix

          public static Mat getSubMatrix(Mat matrix,
                                         Mat idx,
                                         int layout)

      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)
      Returns:
        automatically generated

    - #### getSubVector

          public static Mat getSubVector(Mat vec,
                                         Mat idx)

      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
      Returns:
        automatically generated

    - #### getTestNormCatResponses

          public Mat getTestNormCatResponses()

    - #### getTestResponses

          public Mat getTestResponses()

    - #### getTestSampleIdx

          public Mat getTestSampleIdx()

    - #### getTestSampleWeights

          public Mat getTestSampleWeights()

    - #### getTestSamples

          public Mat getTestSamples()

      Returns matrix of test samples
      Returns:
        automatically generated

    - #### getTrainNormCatResponses

          public Mat getTrainNormCatResponses()

      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.
      Returns:
        automatically generated

    - #### getTrainResponses

          public Mat getTrainResponses()

      Returns the vector of responses The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.
      Returns:
        automatically generated

    - #### getTrainSampleIdx

          public Mat getTrainSampleIdx()

    - #### getTrainSampleWeights

          public Mat getTrainSampleWeights()

    - #### getTrainSamples

          public Mat getTrainSamples(int layout,
                                     boolean compressSamples,
                                     boolean compressVars)

      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).
      Returns:
        automatically generated

    - #### getTrainSamples

          public Mat getTrainSamples(int layout,
                                     boolean compressSamples)

      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) 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).
      Returns:
        automatically generated

    - #### getTrainSamples

          public Mat getTrainSamples(int layout)

      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. sampleIdx) 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).
      Returns:
        automatically generated

    - #### getTrainSamples

          public Mat getTrainSamples()

      Returns matrix of train samples transposed. See ml::SampleTypes. sampleIdx) 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).
      Returns:
        automatically generated

    - #### getVarIdx

          public Mat getVarIdx()

    - #### getVarSymbolFlags

          public Mat getVarSymbolFlags()

    - #### getVarType

          public Mat getVarType()

    - #### create

          public static TrainData create(Mat samples,
                                         int layout,
                                         Mat responses,
                                         Mat varIdx,
                                         Mat sampleIdx,
                                         Mat sampleWeights,
                                         Mat varType)

      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.
      Returns:
        automatically generated

    - #### create

          public static TrainData create(Mat samples,
                                         int layout,
                                         Mat responses,
                                         Mat varIdx,
                                         Mat sampleIdx,
                                         Mat sampleWeights)

      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. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.
      Returns:
        automatically generated

    - #### create

          public static TrainData create(Mat samples,
                                         int layout,
                                         Mat responses,
                                         Mat varIdx,
                                         Mat sampleIdx)

      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. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.
      Returns:
        automatically generated

    - #### create

          public static TrainData create(Mat samples,
                                         int layout,
                                         Mat responses,
                                         Mat varIdx)

      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. vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.
      Returns:
        automatically generated

    - #### create

          public static TrainData create(Mat samples,
                                         int layout,
                                         Mat responses)

      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) (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables. vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.
      Returns:
        automatically generated

    - #### getCatCount

          public int getCatCount(int vi)

    - #### getLayout

          public int getLayout()

    - #### getNAllVars

          public int getNAllVars()

    - #### getNSamples

          public int getNSamples()

    - #### getNTestSamples

          public int getNTestSamples()

    - #### getNTrainSamples

          public int getNTrainSamples()

    - #### getNVars

          public int getNVars()

    - #### getResponseType

          public int getResponseType()

    - #### getNames

          public void getNames(List<String> names)

      Returns vector of symbolic names captured in loadFromCSV()
      Parameters:
        `names` - automatically generated

    - #### getSample

          public void getSample(Mat varIdx,
                                int sidx,
                                float buf)

    - #### getValues

          public void getValues(int vi,
                                Mat sidx,
                                float values)

    - #### setTrainTestSplit

          public void setTrainTestSplit(int count,
                                        boolean shuffle)

      Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
      Parameters:
        `count` - automatically generated
        `shuffle` - automatically generated

    - #### setTrainTestSplit

          public void setTrainTestSplit(int count)

      Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
      Parameters:
        `count` - automatically generated

    - #### setTrainTestSplitRatio

          public void setTrainTestSplitRatio(double ratio,
                                             boolean shuffle)

      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: TrainData::setTrainTestSplit
      Parameters:
        `ratio` - automatically generated
        `shuffle` - automatically generated

    - #### setTrainTestSplitRatio

          public void setTrainTestSplitRatio(double ratio)

      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: TrainData::setTrainTestSplit
      Parameters:
        `ratio` - automatically generated

    - #### shuffleTrainTest

          public void shuffleTrainTest()

    - #### finalize

          protected void finalize()
                           throws Throwable

      Overrides:
        `finalize` in class `Object`
      Throws:
        `Throwable`

Generated on Thu Jul 25 2019 21:39:44 UTC / OpenCV 3.4.7