def _partition_estimators(n_estimators, n_jobs):
 n_jobs = min(_get_n_jobs(n_jobs), n_estimators)
 n_estimators_per_job = ((n_estimators // n_jobs) * np.ones(n_jobs, dtype=np.int))
 n_estimators_per_job[:(n_estimators % n_jobs)] += 1
 starts = np.cumsum(n_estimators_per_job)
 return (n_jobs, n_estimators_per_job.tolist(), ([0] + starts.tolist()))

def _partition_estimators ( n_estimators , n_jobs ) : n_jobs = min ( _get_n_jobs ( n_jobs ) , n_estimators ) n_estimators_per_job = ( ( n_estimators // n_jobs ) * np . ones ( n_jobs , dtype = np . int ) ) n_estimators_per_job [ : ( n_estimators % n_jobs ) ] += 1 starts = np . cumsum ( n_estimators_per_job ) return ( n_jobs , n_estimators_per_job . tolist ( ) , ( [ 0 ] + starts . tolist ( ) ) )
private function used to partition estimators between jobs .