def _label(self, x):
"""Label the data by comparison with the reference points."""
square_distances = (x * x).sum(1)[:, numx.newaxis] + (self.samples * self.samples).sum(1)
square_distances -= 2 * numx.dot(x, self.samples.T)
min_inds = square_distances.argsort()
win_inds = [numx.bincount(self.sample_label_indices[indices[0 : self.k]]).argmax(0) for indices in min_inds]
labels = [self.ordered_labels[i] for i in win_inds]
return labels
def _label(self, x):
"""Label the data by comparison with the reference points."""
square_distances = (x*x).sum(1)[:, numx.newaxis] \
+ (self.samples*self.samples).sum(1)
square_distances -= 2 * numx.dot(x, self.samples.T)
min_inds = square_distances.argsort()
win_inds = [
numx.bincount(
self.sample_label_indices[indices[0:self.k]]).argmax(0)
for indices in min_inds
]
labels = [self.ordered_labels[i] for i in win_inds]
return labels
def _inverse(self, x):
"""Take the mean of overlapping values."""
n_y_cons = numx.bincount(self.connections) # n. connections to y_i
y_cons = numx.argsort(self.connections) # x indices for y_i
y = numx.zeros((len(x), self.input_dim))
i_x_counter = 0 # counter for processed x indices
i_y = 0 # current y index
while True:
n_cons = n_y_cons[i_y]
if n_cons > 0:
y[:, i_y] = old_div(
numx.sum(x[:, y_cons[i_x_counter:i_x_counter + n_cons]],
axis=1), n_cons)
i_x_counter += n_cons
if i_x_counter >= self.output_dim:
break
i_y += 1
return y
def _inverse(self, x):
"""Take the mean of overlapping values."""
n_y_cons = numx.bincount(self.connections) # n. connections to y_i
y_cons = numx.argsort(self.connections) # x indices for y_i
y = numx.zeros((len(x), self.input_dim))
i_x_counter = 0 # counter for processed x indices
i_y = 0 # current y index
while True:
n_cons = n_y_cons[i_y]
if n_cons > 0:
y[:,i_y] = old_div(numx.sum(x[:,y_cons[i_x_counter:
i_x_counter + n_cons]],
axis=1), n_cons)
i_x_counter += n_cons
if i_x_counter >= self.output_dim:
break
i_y += 1
return y
