"""
Gaussian Process Regression: multiple input dimensions
======================================================
"""

# %%
# In this example we are going to create an approximation of a model response using a GP model.
# We consider a bidimensional function with Gaussian inputs.
# Then we create a GP metamodel with a constant basis and a :class:`~openturns.SquaredExponential` covariance.
#
# We consider the function
#
# .. math::
#    g(\vect{x}) = \cos(x_1 + x_2)
#
#
# for any :math:`\vect{x} \in \Rset^2`.
# We assume that :math:`X_1` and :math:`X_2` have a Gaussian distribution:
#
# .. math::
#    X_1 \sim \mathcal{N}(0,1) \textrm{ and } X_2 \sim \mathcal{N}(0,1).
#

# %%
import openturns as ot
import openturns.experimental as otexp
import openturns.viewer as otv


# %%
# We define the model.

# %%
dimension = 2
input_names = ["x1", "x2"]
formulas = ["cos(x1 + x2)"]
model = ot.SymbolicFunction(input_names, formulas)

# %%
# We generate a simple Monte-Carlo input sample and evaluate the corresponding output sample.

# %%
distribution = ot.Normal(dimension)
samplesize = 15
x = distribution.getSample(samplesize)
y = model(x)

# %%
# Then we create a GP metamodel, using a constant trend and a squared exponential covariance model.
basis = ot.ConstantBasisFactory(dimension).build()
covarianceModel = ot.SquaredExponential([0.1] * dimension, [1.0])
fitter = otexp.GaussianProcessFitter(x, y, covarianceModel, basis)
fitter.run()
fitter_result = fitter.getResult()
algo = otexp.GaussianProcessRegression(fitter_result)
algo.run()
result = algo.getResult()
metamodel = result.getMetaModel()

# %%
# It is not so easy to visualize a bidimensional function. In order to simplify the graphics, we consider the value of the function at the input :math:`x_{1,ref}=0.5`.
# This amounts to create a :class:`~openturns.ParametricFunction` where the first variable :math:`x_1` (at input index 0) is set to :math:`0.5`.

# %%
x1ref = 0.5
metamodelAtXref = ot.ParametricFunction(metamodel, [0], [x1ref])
modelAtXref = ot.ParametricFunction(model, [0], [x1ref])

# %%
# For this given value of :math:`x_1`, we plot the model and the metamodel with :math:`x_2` from its 1% up to its 99% quantile.
# We configure the X title to "X2" because the default setting would state that this axis is the first value of the parametric function, which default name is "X0".

# %%
x2min = ot.Normal().computeQuantile(0.01)[0]
x2max = ot.Normal().computeQuantile(0.99)[0]
graph = metamodelAtXref.draw(x2min, x2max)
graph.setLegends(["Kriging"])
curve = modelAtXref.draw(x2min, x2max)
curve.setLegends(["Model"])
curve.setColors(["red"])
graph.add(curve)
graph.setLegendPosition("upper right")
graph.setTitle("Sample size = %d" % (samplesize))
graph.setXTitle("X2")
view = otv.View(graph)

# %%
view.ShowAll()

# %%
# As we can see, the metamodel is quite accurate in this case.
# The metamodel is very close to the model in the center of the domain, where the density of the input distribution is highest.
