def draw_data(points=[], lines=[]):
#points = shift_points( points )
line_print(lines)
lines = shift_lines(lines, min_max_points(points)[:2])
points = shift_points(points)
line_print(lines)
im = svgwrite.drawing.Drawing()
for point in points:
im.add( im.circle(center = point,\
r = 5,\
fill = 'blue'))
#im.add( im.circle(center = find_right( points ),\
# r = 5,\
# fill = 'red'))
#
# #im.add( im.circle(center = points[ smallest_angle( find_right(points), points) ],\
# # r = 5,\
# fill = 'purple'))
for line in lines:
#print line
im.add( im.line(start = line[:2],\
end = line[2:],\
stroke = 'green'))
im.saveas('img/data.svg')
return
def draw_data(points=[], lines=[]):
# points = shift_points( points )
line_print(lines)
lines = shift_lines(lines, min_max_points(points)[:2])
points = shift_points(points)
line_print(lines)
im = svgwrite.drawing.Drawing()
for point in points:
im.add(im.circle(center=point, r=5, fill="blue"))
# im.add( im.circle(center = find_right( points ),\
# r = 5,\
# fill = 'red'))
#
# #im.add( im.circle(center = points[ smallest_angle( find_right(points), points) ],\
# # r = 5,\
# fill = 'purple'))
for line in lines:
# print line
im.add(im.line(start=line[:2], end=line[2:], stroke="green"))
im.saveas("img/data.svg")
return
def drawIrregularPolygon(points, filename=''):
a, b, size_x, size_y = min_max_points(points)
size_x += a
size_y += b
im = Image.new("RGB", (size_x, size_y), (255,255,255))
pixels = irregularPolygon( points, size_x, size_y)
for pixel in pixels:
im.putpixel( pixel, (0,0,0) )
if filename:
im.save('img/'+ filename +'.png')
else:
im.show()
return
def drawIrregularPolygon(points, filename=''):
a, b, size_x, size_y = min_max_points(points)
size_x += a
size_y += b
im = Image.new("RGB", (size_x, size_y), (255, 255, 255))
pixels = irregularPolygon(points, size_x, size_y)
for pixel in pixels:
im.putpixel(pixel, (0, 0, 0))
if filename:
im.save('img/' + filename + '.png')
else:
im.show()
return
def k_means(data, k, sorted_centers=False):
data = lists_to_pairs(data)
if sorted_centers:
data = [[x[0], x[1], dist([0, 0], [x[0], x[1]])] for x in data]
data.sort(key=operator.itemgetter(2))
n = len(data) - 1
tmp_data_A = data[::]
centers = []
for i in xrange(k):
tmp_data_B = tmp_data_A[:n / k]
centers.append(random.choice(tmp_data_B))
tmp_data_A = tmp_data_A[n / k:]
else:
anchor = find_center(data)
dif = max(min_max_points(data)[2:]) / k**2
centers = []
for i in xrange(k):
centers.append(
list(anchor + array([uniform(-dif, dif),
uniform(-dif, dif)])))
classes = [[] for i in xrange(k)]
tmp_class = []
all_centers = list(centers)
iteration = 0
while tmp_class != classes:
tmp_class = list(classes)
classes = [[] for i in xrange(k)]
for point in data:
classes[nearest_center(centers, point)].append(point)
for i, center in enumerate(centers):
centers[i] = find_center(classes[i], center)
iteration += 1
all_centers.extend(centers)
return classes, all_centers
def k_means(data, k, sorted_centers=False):
data = lists_to_pairs( data )
if sorted_centers:
data = [[x[0], x[1], dist([0,0], [x[0],x[1]])] for x in data]
data.sort(key=operator.itemgetter(2))
n = len( data ) - 1
tmp_data_A = data[::]
centers = []
for i in xrange(k):
tmp_data_B = tmp_data_A[:n / k]
centers.append( random.choice( tmp_data_B ) )
tmp_data_A = tmp_data_A[n / k:]
else:
anchor = find_center( data )
dif = max( min_max_points( data )[2:]) / k**2
centers = []
for i in xrange(k):
centers.append(list(anchor + array([uniform(-dif, dif), uniform(-dif, dif)])))
classes = [ [] for i in xrange(k) ]
tmp_class = []
all_centers = list(centers)
iteration = 0
while tmp_class != classes:
tmp_class = list(classes)
classes = [ [] for i in xrange(k) ]
for point in data:
classes[ nearest_center(centers, point) ].append( point )
for i, center in enumerate(centers):
centers[ i ] = find_center( classes[ i ], center )
iteration += 1
all_centers.extend( centers )
return classes, all_centers
