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object ClassifierInt

The ClassifierInt companion object provides methods to read in data matrices in a combined 'xy' format that can be later decomposed into 'x' the feature data matrix and 'y' the classification vector.

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  4. def analyze(model: ClassifierInt): Unit

    Analyze a dataset using the given model using ordinary training with the 'train' method.

    Analyze a dataset using the given model using ordinary training with the 'train' method.

    model

    the model to be used

  5. def apply(fname: String, m: Int, n: Int, skip: Int = 1, cc: Int = -1): MatrixI

    Read the data set (e.g., a CSV file) and return the 'xy' data matrix.

    Read the data set (e.g., a CSV file) and return the 'xy' data matrix. It will make sure the classification column 'cc' is last.

    fname

    the file-name (file should contain lines of data)

    m

    the number of data rows

    n

    the number of data columns/features (including the classification)

    skip

    the number of columns at the beginning the line to skip (e.g., id column)

    cc

    the classification column (the default (-1) => no position checking)

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  16. def pullResponse(xy: MatriI, col: Int = -1): (MatriI, VectoI)

    Pull out the designed response column from the combined matrix.

    Pull out the designed response column from the combined matrix. When 'col' is negative or the last column, slice out the last column.

    xy

    the combined data and response/classification matrix

    col

    the designated response column to be pulled out

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