))+1) <br/>
 <br/>
 The last class has probability<br/>
 <br/>
 1-(sum[j=1..(k-1)]Pj(Xi)) <br/>
        = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)<br/>
 <br/>
 The (negative) multinomial log-likelihood is thus: <br/>
 <br/>
 L = -sum[i=1..n]{<br/>
        sum[j=1..(k-1)](Yij * ln(Pj(Xi)))<br/>
        +(1 - (sum[j=1..(k-1)]Yij)) <br/>
        * ln(1 - sum[j=1..(k-1)]Pj(Xi))<br/>
        } + ridge * (B^2)<br/>
 <br/>
 In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search fo