def demo_k_means(d_x, d_y, k):
n = d_x.size()
# create a zipped vector for convenience
d_points = trtc.DVZipped([d_x, d_y], ['x','y'])
# operations
point_plus = trtc.Functor({ }, ['pos1', "pos2"],
'''
return decltype(pos1)({pos1.x + pos2.x, pos1.y + pos2.y});
''')
point_div = trtc.Functor({ }, ['pos', "count"],
'''
return decltype(pos)({pos.x/(float)count, pos.y/(float)count});
''')
# initialize centers
center_ids = [0] * k
d_min_dis = trtc.device_vector("float", n)
for i in range(1, k):
d_count = trtc.DVInt32(i)
d_center_ids = trtc.device_vector_from_list(center_ids[0:i], 'int32_t')
calc_min_dis = trtc.Functor({"points": d_points, "center_ids": d_center_ids, "count": d_count }, ['pos'],
'''
float minDis = FLT_MAX;
for (int i=0; i<count; i++)
{
int j = center_ids[i];
float dis = (pos.x - points[j].x)*(pos.x - points[j].x);
dis+= (pos.y - points[j].y)*(pos.y - points[j].y);
if (dis<minDis) minDis = dis;
}
return minDis;
''')
trtc.Transform(d_points, d_min_dis, calc_min_dis)
center_ids[i] = trtc.Max_Element(d_min_dis)
d_count = trtc.DVInt32(k)
d_center_ids = trtc.device_vector_from_list(center_ids, 'int32_t')
# initialize group-average values
d_group_aves_x = trtc.device_vector("float", k)
d_group_aves_y = trtc.device_vector("float", k)
d_group_aves = trtc.DVZipped([d_group_aves_x, d_group_aves_y], ['x','y'])
trtc.Gather(d_center_ids, d_points, d_group_aves)
# initialize labels
d_labels = trtc.device_vector("int32_t", n)
trtc.Fill(d_labels, trtc.DVInt32(-1))
# buffer for new-calculated lables
d_labels_new = trtc.device_vector("int32_t", n)
d_labels_sink = trtc.DVDiscard("int32_t", k)
d_group_sums = trtc.device_vector(d_points.name_elem_cls(), k)
d_group_cumulate_counts = trtc.device_vector("int32_t", k)
d_group_counts = trtc.device_vector("int32_t", k)
d_counter = trtc.DVCounter(trtc.DVInt32(0), k)
# iterations
while True:
# calculate new labels
calc_new_labels = trtc.Functor({"aves": d_group_aves, "count": d_count }, ['pos'],
'''
float minDis = FLT_MAX;
int label = -1;
for (int i=0; i<count; i++)
{
float dis = (pos.x - aves[i].x)*(pos.x - aves[i].x);
dis+= (pos.y - aves[i].y)*(pos.y - aves[i].y);
if (dis<minDis)
{
minDis = dis;
label = i;
}
}
return label;
''')
trtc.Transform(d_points, d_labels_new, calc_new_labels)
if trtc.Equal(d_labels, d_labels_new):
break
trtc.Copy(d_labels_new, d_labels)
# recalculate group-average values
trtc.Sort_By_Key(d_labels, d_points)
trtc.Reduce_By_Key(d_labels, d_points, d_labels_sink, d_group_sums, trtc.EqualTo(), point_plus)
trtc.Upper_Bound_V(d_labels, d_counter, d_group_cumulate_counts)
trtc.Adjacent_Difference(d_group_cumulate_counts, d_group_counts)
trtc.Transform_Binary(d_group_sums, d_group_counts, d_group_aves, point_div)
h_x = d_x.to_host()
h_y = d_y.to_host()
h_labels = d_labels.to_host()
h_group_aves_x = d_group_aves_x.to_host()
h_group_aves_y = d_group_aves_y.to_host()
h_group_counts = d_group_counts.to_host()
lines = []
for i in range(n):
label = h_labels[i]
lines.append([(h_x[i], h_y[i]), (h_group_aves_x[label], h_group_aves_y[label]) ] )
lc = mc.LineCollection(lines)
fig, ax = plt.subplots()
ax.set_xlim((0, 1000))
ax.set_ylim((0, 1000))
ax.add_collection(lc)
plt.show()
import ThrustRTC as trtc
compare_modulo_two = trtc.Functor({}, ['x', 'y'], '''
return (x % 2) == (y % 2);
''')
darr1 = trtc.device_vector_from_list([3, 1, 4, 1, 5, 9, 3], 'int32_t')
darr2 = trtc.device_vector_from_list([3, 1, 4, 2, 8, 5, 7], 'int32_t')
darr3 = trtc.device_vector_from_list([3, 1, 4, 1, 5, 9, 3], 'int32_t')
print(trtc.Equal(darr1, darr2))
print(trtc.Equal(darr1, darr3))
dx = trtc.device_vector_from_list([1, 2, 3, 4, 5, 6], 'int32_t')
dy = trtc.device_vector_from_list([7, 8, 9, 10, 11, 12], 'int32_t')
print(trtc.Equal(dx, dy, compare_modulo_two))