Machine Learning with Swift

Feature-wise standardization

Feature-wise standardization can be calculated as follows:

 // Calculates mean for every matrix column. func meanColumns(matrix: [Double], rows: Int, columns: Int) -> [Double] { assert(matrix.count == rows*columns) var resultVec = [Double](repeating: 0.0, count: columns) matrix.withUnsafeBufferPointer{ inputBuffer in resultVec.withUnsafeMutableBufferPointer{ outputBuffer in let inputPointer = inputBuffer.baseAddress! let outputPointer = outputBuffer.baseAddress! for i in 0 ..< columns { vDSP_meanvD(inputPointer.advanced(by: i), columns, outputPointer.advanced(by: i), vDSP_Length(rows)) } } } return resultVec } // Calculates standard deviation for every matrix column. func stdColumns(matrix: [Double], rows: Int, ...