def _nearest_neighbor(new, points): classe = None # Compute the nearest neighbor c = (points[0], dist(new[0], points[0][0], new[1], points[0][1])) for p in points: distance = dist(new[0], p[0], new[1], p[1]) if(distance < c[1]): c = (p, distance) points.remove(c[0]) # If the NN and the new point are the same, their class is the same if(new[0] == c[0][0] and new[1] == c[0][1]): classe = c[0][2] return c, points, classe
def main(argv):
points = [
[.2, .25, 'red', 0],
[.9, .25, 'red', 1],
[.25, .75, 'red', 2],
[.35, .5, 'blue', 3],
[.5, .25, 'blue', 4],
[.75, .8, 'blue', 5],
]
new = [.5, .5]
k = 5
print("----- POINTS -----\n")
for p in points:
print(p)
distances = []
for p in points:
distances.append([p[3], dist(p[0], new[0], p[1], new[1])])
print("\n\n----- DISTANCES BETWEEN POINTS -----\n")
for d in distances:
print(d)
distances.sort(key=lambda d: d[1])
print("\n\n----- SORTED DISTANCES -----\n")
for d in distances:
print(d)
print("\n\n----- K NEAREST POINTS (K="+ str(k) +") -----\n")
distances = distances[:k]
for d in distances:
print(d)
print("\n\n----- MOST REPRESENTED CLASS -----\n")
cl = [points[d[0]][2] for d in distances]
print(max(cl, key=lambda c: cl.count(c)))
print("\n\n----- ALGO RESULT -----\n")
print(knn.kmeans((.5, .5), points)[1])
# DISPLAY
# all pre-existing points
plt.scatter([p[0] for p in points],
[p[1] for p in points],
c=[p[2] for p in points])
# new point to classify
plt.scatter(new[0], new[1], c="#000000")
# nearest neighbors lines
for d in distances:
plt.plot([new[0], points[d[0]][0]], [new[1], points[d[0]][1]], c="#000000")
plt.show()
def _couples_creation(c, points): # dist(a, b) couples = [] for ida, a in enumerate(points): for idb in range(ida): couples.append([a, points[idb], dist(a[0], points[idb][0], a[1], points[idb][1])]) # dist(a, b) ~= dist(c, d) for couple in couples: couple[-1] = abs(couple[-1] - c[1]) couples.sort(key=lambda c: c[2]) couples.reverse() return couples
