#@ s0: TimeoutDelay=180
# DAKOTA INPUT FILE - dakota_herbie_gpais.in
# This file tests the Gaussian Process adaptive importance sampling (GPAIS)
# capabilities.  The Herbie function is used because it is multi-modal. 
# The "true" CDF probability of the Herbie function for a response level 
# of -1.065 is approximately 1.5%.  The "true" CDF probability 
# of the Herbie function for a response level of -1.0985 is 
# approximately 0.25%.  The main controls here are build_samples (number of initial 
# LHS samples to build the initial GP), samples_on_emulator (number of samples
# used to query the emulator: preferably, this should be at least 1000), and 
# the max_iterations (number of additional iterations where a "true" 
# simulation point is added to the GP at each iteration). The results 
# are more accurate with the default settings of  samples_on_emulator = 10000 
# and max_iterations = 150.  However, for the purposes of testing cost, 
# I have lowered these numbers. 
	
environment, 
        tabular_data

method,
#	importance_sampling 
#	  import
#	output debug
        gpais 
        samples_on_emulator = 100
        max_iterations = 5 
#	response_levels = -1.0985
        response_levels = -1.065 
        compute probabilities
        build_samples = 100
#	seed6 = 3847 
        seed = 4326
#	seed8 = 19993 
#	seed = 25632

variables,
	uniform_uncertain = 2 
        lower_bounds = -2. -2. 
        upper_bounds =  2. 2.
        descriptors = 'x1' 'x2'

interface,
         direct
	  analysis_driver = 'herbie'

responses,
	response_functions = 1 
	no_gradients
	no_hessians