Description Usage Arguments Details Value Communication References See Also Examples
Linear regression (Gaussian GLM), logistic regression, and poisson regression model fitters.
1 2 3 4 5 | reg.fit(x, y, maxiter = 100)
logistic.fit(x, y, maxiter = 100)
poisson.fit(x, y, maxiter = 100)
|
x, y |
The input data |
maxiter |
The maximum number of iterations. |
Each function is implemented with gradient descent using the conjugate
gradients method ("CG") of the optim()
function.
Both of x
and y
must be distributed in an identical fashion.
This means that the number of rows owned by each MPI rank should match, and
the data rows x
and response rows y
should be aligned.
Additionally, each MPI rank should own at least one row. Ideally they should
be load balanced, so that each MPI rank owns roughly the same amount of data.
The return is the output of an optim()
call.
The communication consists of an allreduce of 1 double (the local cost/objective function value) at each iteration of the optimization.
McCullagh, P. and Nelder, J.A., 1989. Generalized Linear Models, no. 37 in Monograph on Statistics and Applied Probability.
Duda, R.O., Hart, P.E. and Stork, D.G., 1973. Pattern classification (pp. 526-528). Wiley, New York.
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