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# imports
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from polire import CustomInterpolator
import xgboost
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern
# sample data
X = [[0, 0], [0, 3], [3, 0], [3, 3]]
y = [0, 1.5, 1.5, 3]
X = np.array(X)
y = np.array(y)
for r in [
CustomInterpolator(xgboost.XGBRegressor()),
CustomInterpolator(RandomForestRegressor()),
CustomInterpolator(LinearRegression(normalize=True)),
CustomInterpolator(KNeighborsRegressor(n_neighbors=3, weights="distance")),
CustomInterpolator(
GaussianProcessRegressor(normalize_y=True, kernel=Matern())
),
]:
r.fit(X, y)
Z = r.predict_grid((0, 3), (0, 3)).reshape(100, 100)
sns.heatmap(Z)
plt.title(r)
plt.show()
plt.close()
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