A collection of interpolation methods.
The word "interpolation" has Latin origin and is composed of two words - Inter meaning between and Polire meaning to polish.
This repository is a collection of several spatial interpolation algorithms.
Minimal example of interpolation
import numpy as np from polire import Kriging # Data X = np.random.rand(10, 2) # Spatial 2D points y = np.random.rand(10) # Observations X_new = np.random.rand(100, 2) # New spatial points # Fit model = Kriging() model.fit(X, y) # Predict y_new = model.predict(X_new)
Supported Interpolation Methods
from polire import ( Kriging, # Best spatial unbiased predictor GP, # Gaussian process interpolator from GPy IDW, # Inverse distance weighting SpatialAverage, Spline, Trend, Random, # Predict uniformly within the observation range, a reasonable baseline NaturalNeighbor, CustomInterpolator # Supports any regressor from Scikit-learn )
Use GP kernels from GPy and regressors from sklearn
from sklearn.linear_model import LinearRegression # or any Scikit-learn regressor from GPy.kern import Matern32 # or any other GPy kernel from polire import GP, CustomInterpolator # GP model model = GP(Matern32(input_dim=2)) # Sklearn model model = CustomInterpolator(LinearRegression(normalize = True))
This project is a part of Sustainability Lab at IIT Gandhinagar.
Acknowledgement to sklearn template for helping to package into a PiPy package.
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