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idmtools_calibra.algorithms package
idmtools_calibra.algorithms.pbnb package
idmtools_calibra.algorithms.fisher_inf_matrix module
idmtools_calibra.algorithms.generic_iterative_next_point module
idmtools_calibra.algorithms.gpc module
idmtools_calibra.algorithms.imis module
idmtools_calibra.algorithms.next_point_algorithm module
idmtools_calibra.algorithms.optim_tool module
idmtools_calibra.algorithms.optim_tools_pspo module
idmtools_calibra.algorithms.optim_tools_spsa module
idmtools_calibra.algorithms.separatrix_bhm module
idmtools_calibra.analyzers package
idmtools_calibra.cli package
idmtools_calibra.output package
idmtools_calibra.plotters package
idmtools_calibra.resamplers package
idmtools_calibra.utilities package
idmtools_calibra.calib_base_app module
idmtools_calibra.calib_manager module
idmtools_calibra.calib_site module
idmtools_calibra.iteration_state module
idmtools_calibra.process_state module
idmtools_calibra.resample_manager module
idmtools_calibra.rmse_site module
idmtools_calibra.singularity_json_python_task module
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idmtools_calibra.algorithms.gpc module
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idmtools_calibra.algorithms.gpc module
¶
class
idmtools_calibra.algorithms.gpc.
GPC
(
x_cols
,
y_col
,
training_data
,
param_info
,
kernel_mode
=
'RBF'
,
kernel_params
=
None
,
verbose
=
False
,
debug
=
False
,
**
kwargs
)
[source]
¶
Bases:
object
classmethod
from_config
(
config_fn
)
[source]
¶
classmethod
from_dict
(
config
)
[source]
¶
set_training_data
(
new_training_data
)
[source]
¶
save
(
save_to
=
None
)
[source]
¶
define_kernel
(
params
)
[source]
¶
kernel_xx
(
x
,
theta
)
[source]
¶
static
kernel_xp
(
x
,
p
,
theta
)
[source]
¶
kxx_gpu_wrapper
(
x
,
theta
,
deriv
=
-1
)
[source]
¶
kxp_gpu_wrapper
(
x
,
p
,
theta
)
[source]
¶
assign_rep
(
sample
)
[source]
¶
expectation_propagation
(
theta
)
[source]
¶
find_posterior_mode
(
theta
,
f_guess
=
None
,
tol_grad
=
1e-06
,
max_iter
=
100
)
[source]
¶
negative_log_marginal_likelihood
(
theta
)
[source]
¶
negative_log_marginal_likelihood_and_gradient
(
theta
,
f_guess
=
None
)
[source]
¶
static
func_wrapper
(
f
,
cache_size
=
100
)
[source]
¶
laplace_predict
(
theta
,
f_hat
,
p
)
[source]
¶
ep_predict
(
theta
,
p
)
[source]
¶
optimize_hyperparameters
(
x0
,
bounds
=
()
,
k
=
-1
,
eps
=
0.01
,
disp
=
True
,
maxiter
=
15000
)
[source]
¶
evaluate
(
data
)
[source]
¶
plot_data
(
samples_to_circle
=
None
)
[source]
¶
plot_histogram
(
)
[source]
¶
plot
(
x_center
,
res
=
10
)
[source]
¶
plot_errors
(
train
,
test
,
mean_col
,
var_predictive_col
,
truth_col
=
None
,
figsize
=
(16,
10)
)
[source]
¶
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