Implementing logistic regression in Swift

The most important differences of this implementation from multiple linear regression are the following:

• Normalization is required only for feature matrix x, and not for the target vector y, because the output has range (0, 1)
• The hypothesis is different
• The cost function looks different, but the cost gradient remains the same

Again, we'll need some accelerate functions:

import Accelerate 

The logistic regression class definition looks similar to multiple linear regression:

public class LogisticRegression { 
public var weights: [Double]! 
 
public init(normalization: Bool) { 
    self.normalization = normalization 
} 
 
private(set) var normalization: Bool 
private(set) var xMeanVec = [Double]() 
private(set) var xStdVec = [Double]() 

The prediction part of logistic regression

This is the code that implements hypotheses for one sample input and for a matrix of inputs:

public func predict(xVec: [Double]) -> Double { 
    if normalization { 
        let input...