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"""
This is a module for GP Interpolation
"""
import numpy as np
from ..base import Base
from GPy.models import GPRegression
from GPy.kern import RBF
class GP(Base):
"""A class that is declared for performing GP interpolation.
GP interpolation (usually) works on the principle of finding the
best unbiased predictor.
Parameters
----------
type : str, optional
This parameter defines the type of Kriging under consideration. This
implementation uses PyKrige package (https://github.com/bsmurphy/PyKrige).
The user needs to choose between "Ordinary" and "Universal".
"""
def __init__(
self,
kernel=RBF(2, ARD=True),
):
super().__init__()
self.kernel = kernel
def _fit(self, X, y, n_restarts=5, verbose=False, random_state=None):
"""Fit method for GP Interpolation
This function shouldn't be called directly.
"""
np.random.seed(random_state)
if len(y.shape) == 1:
y = y.reshape(-1, 1)
self.model = GPRegression(X, y, self.kernel)
self.model.optimize_restarts(n_restarts, verbose=verbose)
return self
def _predict_grid(self, x1lim, x2lim):
"""The function that is called to return the interpolated data in Kriging Interpolation
in a grid. This method shouldn't be called directly"""
lims = (*x1lim, *x2lim)
x1min, x1max, x2min, x2max = lims
x1 = np.linspace(x1min, x1max, self.resolution)
x2 = np.linspace(x2min, x2max, self.resolution)
X1, X2 = np.meshgrid(x1, x2)
X = np.array([(i, j) for i, j in zip(X1.ravel(), X2.ravel())])
predictions = self.model.predict(X)[0].reshape(len(x1), len(x2))
return predictions.ravel()
def _predict(self, X, return_variance=False):
"""This function should be called to return the interpolated data in kriging
in a pointwise manner. This method shouldn't be called directly."""
predictions, variance = self.model.predict(X)
if return_variance:
return predictions.ravel(), variance
else:
return predictions.ravel()
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