def draw_graph():
dist = []
dist2 = []
win = []
win2 = []
data1 = []
data0 = []
tab = {}
k = 4
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1)
tab['data'] = X1
tab['target'] = Y1
knn = KNN(tab)
nearest = knn.findKNearest(np.zeros(2), k)
for i, e in enumerate(tab['data']):
dist.append([((e[0]**2 + e[1]**2) ** 0.5), tab['target'][i], i])
dist = sorted(dist, key=lambda x: x[0])
for i, e in enumerate(dist):
if e[1]:
data1.append(e[2])
else:
data0.append(e[2])
for i, e in enumerate(dist[:k]):
if e[1]:
win.append(e[2])
else:
win2.append(e[2])
dist2.append(e[0:2])
plt.plot(tab['data'][data0, 0], tab['data'][data0, 1], 'ro', mew=1.5, ms=1.5) #ms, mw length and width
plt.plot(tab['data'][data1, 0], tab['data'][data1, 1], 'go', mew=1.5, ms=1.5)
plt.plot(tab['data'][[win], 0], tab['data'][[win], 1], 'bo', mew=1.5, ms=1.5)
plt.plot(tab['data'][[win2], 0], tab['data'][[win2], 1], 'bo', mew=1.5, ms=1.5)
circle = plt.Circle((0, 0), dist[k-1][0], color='black', fill=False, linewidth=1)
ax = plt.gca()
ax.add_patch(circle)
plt.ylim(-3, 3) #hardcode y length
plt.xlim(-3, 3)
plt.show()
def test_find_nearest(self):
tab = {}
tab['data'] = np.array([[0.2, 0.31], [0.1, 0.4], [2, 3], [2, 2], [1, 4], [1.5, 2], [2, 1.4], [1.3, 3.1],
[-1, 2.3], [-1.4,-1.6], [-0.2, -0.3], [-0.9, 1.7], [2, -1.7], [0.5, -0.7], [1.2, -1.2]])
tab['target'] = np.array([0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1])
expectedNearestPoint = np.array([[-0.2, -0.3], [0.2, 0.31], [0.1, 0.4], [0.5, -0.7]])
expectedNearestDist = [[((-0.2)**2 + (-0.3)**2) ** 0.5, 0], [((0.2)**2 + (0.31)**2) ** 0.5, 0],
[((0.1)**2 + (0.4)**2) ** 0.5, 0], [((0.5)**2 + (-0.7)**2) ** 0.5, 1]]
k = 4
knn = KNN(tab)
nearest = knn.findKNearest(np.zeros(2), k)
self.assertListEqual(nearest, expectedNearestDist)