scripts/datagen/genRandData4Multinomial.dml

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num_records = $1;
num_features = $2;

p = $3; #sparsity
num_categories = $4; #num classes
is_intercept = $5==1; 

stdevLT = 1.0;
beta_range = 3.0 * stdevLT / sqrt (num_features * p);

if (is_intercept) {
    intercept = Rand (rows = 1, cols = num_categories - 1, min = -1.0, max = 1.0);
}

X = Rand( rows = num_records, 
          cols = num_features, 
          min = 1, 
          max = 5, 
          pdf = "uniform", 
          sparsity = p );

B = Rand (rows = num_features, 
          cols = num_categories - 1, 
          min = -1.0, 
          max = 1.0, 
          pdf = "uniform", 
          sparsity = 1.0) * beta_range;

LT = X %*% B;
if (is_intercept) {
    LT = LT + matrix (1, rows = num_records, cols = 1) %*% intercept;
}

Prob = exp (LT);
Prob = Prob / (1.0 + rowSums(Prob));
Prob = t(cumsum (t(Prob)));

r = Rand (rows = num_records, cols = 1, min = 0, max = 1, pdf = "uniform");
Y = 1 + rowSums (Prob < r);

# ensure all classes are represented
Y[(num_records-num_categories+1):num_records,1] = seq(1,num_categories);

write(X, $6, format=$8)
write(Y, $7, format=$8);