protected void nextLambda(final GLMIterationTask glmt, GLMValidation val) {
currentLambdaIter = 0;
boolean improved = _model.setAndTestValidation(_lambdaIdx, val);
_model.clone().update(self());
boolean done = false; // _iter < max_iter && (improved || _runAllLambdas) && _lambdaIdx <
// (lambda.length-1);
if (_iter == max_iter) {
Log.info("GLM2 reached max #iterations.");
done = true;
} else if (!improved && !_runAllLambdas) {
Log.info("GLM2 converged as solution stopped improving with decreasing lambda.");
done = true;
} else if (_lambdaIdx == lambda.length - 1) {
Log.info("GLM2 done with all given lambdas.");
done = true;
} else if (_activeCols != null && _activeCols.length + 1 >= MAX_PREDICTORS) {
Log.info(
"GLM2 reached maximum allowed number of predictors at lambda = " + lambda[_lambdaIdx]);
done = true;
}
if (!done) { // continue with next lambda value?
++_lambdaIdx;
glmt._val = null;
if (glmt._gram == null) { // assume we had lambda search with strong rules
// we use strong rules so we can't really used this gram for the next lambda computation
// (different sets of coefficients)
// I expect that:
// 1) beta has been expanded to match current set of active cols
// 2) it is new GLMIteration ready to be launched
// caller (nextLambda(glmt,beta)) is expected to ensure this...
assert _activeCols == null || (glmt._beta.length == _activeCols.length + 1);
assert !glmt.isDone();
glmt.asyncExec(_activeData._adaptedFrame);
} else // we have the right gram, just solve with with next lambda
new Iteration().callback(glmt);
} else // nope, we're done
GLM2.this.complete(); // signal we're done to anyone waiting for the job
}
