线性插值，克里金插值，最近和以及其他机器学习模型插值代码邻插值，方法的代码

线性插值：

import numpy as np

def linear_interpolation(x, y, x_new):
    y_new = np.interp(x_new, x, y)
    return y_new

克里金插值：

from pykrige import OrdinaryKriging

def kriging_interpolation(x, y, z, x_new, y_new, variogram_model='linear'):
    OK = OrdinaryKriging(x, y, z, variogram_model=variogram_model, verbose=False, enable_plotting=False)
    z_new, error = OK.execute(style='points', xpoints=x_new, ypoints=y_new)
    return z_new

最近邻插值：

from scipy.interpolate import NearestNDInterpolator

def nearest_neighbor_interpolation(x, y, z, x_new, y_new):
    points = np.column_stack((x, y))
    interp = NearestNDInterpolator(points, z)
    z_new = interp(x_new, y_new)
    return z_new

其他机器学习模型插值：

from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPRegressor
from sklearn.svm import SVR

def ml_interpolation(x, y, z, x_new, y_new, model='knn', **kwargs):
    if model == 'knn':
        regressor = KNeighborsRegressor(**kwargs)
    elif model == 'linear':
        regressor = LinearRegression(**kwargs)
    elif model == 'mlp':
        regressor = MLPRegressor(**kwargs)
    elif model == 'svm':
        regressor = SVR(**kwargs)
    else:
        raise ValueError('Invalid model name')
    regressor.fit(np.column_stack((x, y)), z)
    z_new = regressor.predict(np.column_stack((x_new, y_new)))
    return z_new

其中，`**kwargs` 可以传递特定机器学习模型的参数。例如，对于 KNN 模型，可以传递 `n_neighbors` 参数来指定邻居数量。