def mapPointsToNearestLine(inputPoints, inputLineArray):
linemapping = {}
for p in inputPoints:
mindist = 99999999999999999999999999999999999999999999999
nearestline = None
dpl = []
for l in inputLineArray:
x1, y1, x2, y2 = l
xm, ym = (x1 + x2) / 2.0, (y1 + y2) / 2.0
dx = x1 - xm
dy = y1 - ym
dx, dy = -dy, dx
ort_x1 = xm + dx
ort_y1 = ym + dy
d = distance((x1, y1), (x2, y2), p) + distance((xm, ym), (ort_x1, ort_y1), p)
dpl.append(d)
if d < mindist:
mindist = d
nearestline = l
if str(nearestline) not in linemapping:
linemapping[str(nearestline)] = []
linemapping[str(nearestline)].append(p)
return linemapping
lines.append(line)
for rounds in range(100):
assignment = KMeans.mapPointsToNearestLine(points, lines)
cols = itertools.cycle(["ro", "yo", "go", "mo", "bo", "co"])
new_lines = []
new_centers = []
for key, values in assignment.items():
xm = np.array([e[0] for e in values]).mean()
ym = np.array([e[1] for e in values]).mean()
new_possible_lines = [generate_line((xm, ym), 2 * math.pi * (angle / 360.0)) for angle in range(0, 361, 2)]
lines_with_error = [
(sum([distance((_line[0], _line[1]), (_line[2], _line[3]), p) for p in values]), _line)
for _line in new_possible_lines
]
lines_with_error_sorted = sorted(lines_with_error, key=lambda fg: fg[0])
new_lines.append(lines_with_error_sorted[0][1])
new_centers.append((xm, ym))
lines = new_lines
centers = new_centers
# Calculate the center of the assignment
# Calculate for each of those centers all lines through that center and recalculate the error
# Choose the line for the new stuff which minimizes the error - do that for all
# Repeat
plt.figure(rounds)
