knn_aula_p4_scaling

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

X = np.random.random([1000,2])
Y = []
for x_value in X:
    y_value = 0
    if ( ((x_value[0] <= 0.5) and (x_value[1] > 0.5))  or ((x_value[1] <= 0.5) and (x_value[0] > 0.5 ))  ):   # XOR "logic"
        y_value = 1

    Y.append(y_value)


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

X[:,1] = 1*X[:,1]
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[:,0].min()
x_max = X[:,0].max()
y_min = X[:,1].min()
y_max = X[:,1].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()