Implementing multiple linear regression in Swift

The MultipleLinearRegression class contains a vector of weights, and staff for data normalization:

class MultipleLinearRegression { 
public var weights: [Double]! 
public init() {} 
public var normalization = false 
public var xMeanVec = [Double]() 
public var xStdVec = [Double]() 
public var yMean = 0.0 
public var yStd = 0.0 
... 
} 

Hypothesis and prediction:

public func predict(xVec: [Double]) -> Double { 
if normalization { 
    let input = xVec 
    let differenceVec = vecSubtract([1.0]+input, xMeanVec) 
    let normalizedInputVec = vecDivide(differenceVec, xStdVec) 
     
    let h = hypothesis(xVec: normalizedInputVec) 
     
    return h * yStd + yMean 
} else { 
    return hypothesis(xVec: [1.0]+xVec) 
} 
} 
 
private func hypothesis(xVec: [Double]) -> Double { 
var result = 0.0 
vDSP_dotprD(xVec, 1, weights, 1, &result, vDSP_Length(xVec.count)) 
return result 
} 
 
public func predict(xMat: [[Double]]) -> [Double] { 
let...