FastPrepImpute Missing Values and Normalize Columns

Impute Missing Values and Normalize Columns

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Problem statement

In each column of dataset, treat zero as missing and replace it with the mean of that column's nonzero values. Then z-score normalize the imputed column using its population mean and population standard deviation.

Function

imputeAndNormalize(dataset: double[][]) → double[][]

Examples

Example 1

dataset = [[1,2,0],[0,1,1],[5,6,5]]return = [[-1.224744871391589,-0.4629100498862757,0.0],[0.0,-0.9258200997725514,-1.224744871391589],[1.224744871391589,1.3887301496588271,1.224744871391589]]

Zeros become the nonzero column means before population z-score normalization.

Constraints

  • 2 <= dataset.length <= 50.
  • 1 <= dataset[i].length <= 50 and all rows have equal length.
  • Every column has at least two different nonzero values.
  • Results use absolute tolerance 1e-5.

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public double[][] imputeAndNormalize(double[][] dataset) {
    // Impute zero entries, then z-score every column.
}
dataset[[1,2,0],[0,1,1],[5,6,5]]
expected[[-1.224744871391589,-0.4629100498862757,0.0],[0.0,-0.9258200997725514,-1.224744871391589],[1.224744871391589,1.3887301496588271,1.224744871391589]]
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