# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import numpy as np
import matplotlib.pyplot as mp
x = np.array([
    [3, 1],
    [2, 5],
    [1, 8],
    [6, 4],
    [5, 2],
    [3, 5],
    [4, 7],
    [4, -1]])
y = np.array([0, 1, 1, 0, 0, 1, 1, 0])
l, r, h = x[:, 0].min() - 1, x[:, 0].max() + 1, 0.005
b, t, v = x[:, 1].min() - 1, x[:, 1].max() + 1, 0.005
grid_x = np.meshgrid(np.arange(l, r, h),
                     np.arange(b, t, v))
flat_x = np.c_[grid_x[0].ravel(), grid_x[1].ravel()]
flat_y = np.zeros(len(flat_x), dtype=int)
flat_y[flat_x[:, 0] < flat_x[:, 1]] = 1
grid_y = flat_y.reshape(grid_x[0].shape)
mp.figure('Simple Classification',
          facecolor='lightgray')
mp.title('Simple Classification', fontsize=20)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
mp.pcolormesh(grid_x[0], grid_x[1], grid_y, cmap='gray')
mp.scatter(x[:, 0], x[:, 1], c=y, cmap='brg', s=60)
mp.show()

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import numpy as np
import matplotlib.pyplot as mp
x = np.array([
    [3, 1],
    [2, 5],
    [1, 8],
    [6, 4],
    [5, 2],
    [3, 5],
    [4, 7],
    [4, -1]])
y = np.array([0, 1, 1, 0, 0, 1, 1, 0])
l, r, h = x[:, 0].min() - 1, x[:, 0].max() + 1, 0.005
b, t, v = x[:, 1].min() - 1, x[:, 1].max() + 1, 0.005
grid_x = np.meshgrid(np.arange(l, r, h),
                     np.arange(b, t, v))
flat_x = np.c_[grid_x[0].ravel(), grid_x[1].ravel()]
flat_y = np.zeros(len(flat_x), dtype=int)
flat_y[flat_x[:, 0] < flat_x[:, 1]] = 1
grid_y = flat_y.reshape(grid_x[0].shape)
mp.figure('Simple Classification',
          facecolor='lightgray')
mp.title('Simple Classification', fontsize=20)
mp.xlabel('x', fontsize=14)
mp.ylabel('y', fontsize=14)
mp.tick_params(labelsize=10)
mp.pcolormesh(grid_x[0], grid_x[1], grid_y, cmap='gray')
mp.scatter(x[:, 0], x[:, 1], c=y, cmap='brg', s=60)
mp.show()

# -*- coding: utf-8 -*-