knn_aula_p2

from sklearn import neighbors
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt

X = [ [2.0,1.5], [1.0,1.0], [1.0,3.0], [0.8,2.2], [3.0,1.0], [0.4,2.0], [1.2,1.2]  ]
Y = [ 0, 0, 1, 1, 0, 1, 1 ]

X = np.array(X)
Y = np.array(Y)

clf = neighbors.KNeighborsClassifier(n_neighbors=1)

clf = clf.fit(X, Y)


#****************
# Plot
# baseado em https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
#****************
step_size = 0.01
# encontrando os limites
x_min, x_max = X[:, 0].min(), X[:, 0].max()
y_min, y_max = X[:, 1].min(), X[:, 1].max()

x_min, x_max = x_min-abs(0.1*x_min), x_max+abs(0.1*x_max)
y_min, y_max = y_min-abs(0.1*y_min), y_max+abs(0.1*y_max)


xx, yy = np.meshgrid(np.arange(x_min, x_max, step_size), np.arange(y_min, y_max, step_size))

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

Z = Z.reshape(xx.shape)

plt.figure()
plt.pcolormesh(xx, yy, Z, cmap='Set3')

# plot training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolor='k', s=20, cmap='Set3')

plt.show()