kriggingplswork.py

import pandas as pd
import numpy as np
from sklearn.datasets import make_friedman2
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel

df = pd.read_csv('./data.csv')[:200]

X = np.atleast_2d(pd.to_numeric(df['Ephemeris time']))
y = np.atleast_2d(pd.to_numeric(df['x1']))

#X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
print(X,y)

kernel = DotProduct()+WhiteKernel()

gpr = GaussianProcessRegressor(kernel=kernel, random_state=0).fit(y, X)
print(gpr.score(y,X))
print(gpr.predict(y[:2,:], return_std=True))