A collection of interpolation methods.
Project description
Polire
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.
Examples
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))
More info
Contributors: S Deepak Narayanan, Zeel B Patel, Apoorv Agnihotri, and Nipun Batra.
This project is a part of Sustainability Lab at IIT Gandhinagar.
Acknowledgement to sklearn template for helping to package into a PiPy package.
Project details
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