def adjust(G, X, Y, pos, hci, alpha, selection, tmp):
vector = G[pos]
# vector = vector + alpha * (vector - sel)
ufunc.subtract(vector, selection, out=tmp)
ufunc.multiply(tmp, alpha, out=tmp)
ufunc.add(vector, tmp, out=vector)
G[pos] = vector
def adjust(G, X, Y, pos, hci, alpha, selection, tmp):
vector = G[pos]
# vector = vector + alpha * (vector - sel)
ufunc.subtract(vector, selection, out=tmp)
ufunc.multiply(tmp, alpha, out=tmp)
ufunc.add(vector, tmp, out=vector)
G[pos] = vector
def bmu(grid, x, y, sel, tmp):
""" Entry in the grid that has the smallest euclidean distance. """
min_pos = 0
min_dist = float(10000)
for i in range(x*y):
entry = grid[i]
ufunc.subtract(sel, entry, out=tmp)
# euclidean dist
ufunc.multiply(tmp, tmp, out=tmp)
d = ufunc.sqrt(tmp.sum())
if d < min_dist:
min_dist = d
min_pos = i
return min_pos
def bmu(grid, x, y, sel, tmp):
""" Entry in the grid that has the smallest euclidean distance. """
min_pos = 0
min_dist = float(10000)
for i in range(x * y):
entry = grid[i]
ufunc.subtract(sel, entry, out=tmp)
# euclidean dist
ufunc.multiply(tmp, tmp, out=tmp)
d = ufunc.sqrt(tmp.sum())
if d < min_dist:
min_dist = d
min_pos = i
return min_pos
