# DAKOTA INPUT FILE - dakota_textbook_lhs_approx.in

environment,
	  method_pointer = 'UQ'

method,
	id_method = 'UQ'
	model_pointer = 'UQ_M'
	sampling
	  samples = 5000 seed = 5
	  sample_type lhs
	  response_levels = 2.e-5 3.e-5 4.e-5		#s0,#s1,#s2,#s3,#s6,#s7

model,
	id_model = 'UQ_M'
	surrogate global
	  dace_method_pointer = 'DACE'
	  polynomial quadratic		#s0
# 	  neural_network		#s1
#	  gaussian_process surfpack	#s2,#s4,#s5,#s6,#s7
#           export_model                #s2
#           filename_prefix = 'exported_GP'  formats = algebraic_file binary_archive #s2
#	  mars				#s3

# Test 2 with a samples file, free-form or annotated
#	samples_file = 'dakota_uq_textbook_lhs_approx.annotated' #s6
#	samples_file = 'dakota_uq_textbook_lhs_approx.freeform'	 #s7
#	  freeform     						 #s7

variables,
	lognormal_uncertain =  2		#s0,#s1,#s2,#s3,#s4,#s6,#s7
	  means             =  1.0  1.0		#s0,#s1,#s2,#s3,#s4,#s6,#s7
#	  std_deviations    =  0.5  0.5		# Alternative to err factors
	  error_factors     =  1.1  1.1		#s0,#s1,#s2,#s3,#s4,#s6,#s7
	  descriptors       =  'TF1ln' 'TF2ln'	#s0,#s1,#s2,#s3,#s4,#s6,#s7
#	discrete_uncertain_set integer = 1				#s4
#	  set_probs  = .1 .2 .4 .2 .1					#s4
#	  set_values =  1  2  3  4  5					#s4
#	  descriptors = 'ModelForm'					#s4
#	histogram_point_uncertain real = 2				#s5
#	  num_pairs 7 7		  					#s5
#	  abscissas 0.200 2.166 4.133 6.100 8.067 10.033 12.000		#s5
#	            0.200 2.166 4.133 6.100 8.067 10.033 12.000		#s5
#	  counts    1     1     1     1     1     1      1		#s5
#	            1     1     1     1     1     1      1		#s5

responses,
	response_functions = 1
	no_gradients
	no_hessians

#########################################
# interface truth model and dace method #
#########################################

method,
	id_method = 'DACE'
	model_pointer = 'DACE_M'
	sampling
	  samples = 2			#s0
#	  samples = 20			#s3,#s4,#s5
	  seed = 50
	  sample_type lhs

model,
	id_model = 'DACE_M'
	single
	  interface_pointer = 'I1'

interface,
	id_interface = 'I1'
	system asynchronous
	  analysis_driver = 'text_book'