#! /usr/bin/env python3
#
def knn_demo ( ):

  from matplotlib.colors import ListedColormap
  from sklearn import datasets
  from sklearn import neighbors
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns

  n_neighbors = 15
#
# import data
#
  iris = datasets.load_iris()
#
# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
#
  X = iris.data[:, :2]
  y = iris.target
#
#  mesh stepsize
#
  h = 0.02
#
# Create color maps
#
  cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
  cmap_bold = ["darkorange", "c", "darkblue"]

  for weights in ["uniform", "distance"]:
#
#  Create an instance of Neighbours Classifier and fit the data.
#
    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
    clf.fit(X, y)
#
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
#
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
#
# Put the result into a color plot
#
    Z = Z.reshape(xx.shape)
    plt.figure(figsize=(8, 6))
    plt.contourf(xx, yy, Z, cmap=cmap_light)
#
# Plot also the training points
#
    sns.scatterplot(
      x=X[:, 0],
      y=X[:, 1],
      hue=iris.target_names[y],
      palette=cmap_bold,
      alpha=1.0,
      edgecolor="black",
    )
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title(
        "3-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights)
    )
    plt.xlabel(iris.feature_names[0])
    plt.ylabel(iris.feature_names[1])

  filename = 'knn_demo_' + weights + '.png'
  plt.savefig ( filename )
  plt.show()
  plt.close ( )

if ( __name__ == "__main__" ):
  knn_demo ( )