"""
============================
Nearest Neighbors regression
============================

Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.
"""

# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Generate sample data
# --------------------
# Here we generate a few data points to use to train the model. We also generate
# data in the whole range of the training data to visualize how the model would
# react in that whole region.
import matplotlib.pyplot as plt
import numpy as np

from xlearn import neighbors

rng = np.random.RandomState(0)
X_train = np.sort(5 * rng.rand(40, 1), axis=0)
X_test = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X_train).ravel()

# Add noise to targets
y[::5] += 1 * (0.5 - np.random.rand(8))

# %%
# Fit regression model
# --------------------
# Here we train a model and visualize how `uniform` and `distance`
# weights in prediction effect predicted values.
n_neighbors = 5

for i, weights in enumerate(["uniform", "distance"]):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_ = knn.fit(X_train, y).predict(X_test)

    plt.subplot(2, 1, i + 1)
    plt.scatter(X_train, y, color="darkorange", label="data")
    plt.plot(X_test, y_, color="navy", label="prediction")
    plt.axis("tight")
    plt.legend()
    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))

plt.tight_layout()
plt.show()
