def predict_proba1(self, X):
near_locs, near_dists = af.vision.nearest_neighbour(X, self._data, self._dim, \
self._num_nearest, self._match_type)
weights = self._get_neighbor_weights(near_dists)
top_labels = af.moddims(self._labels[near_locs], \
get_dims(near_locs)[0], get_dims(near_locs)[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
probs, _ = af.imax(accum_weights, dim=0)
return probs.T
def _predict_proba(self, query, train_feats, train_labels, k, dist_type,
weight_by_dist):
near_locs, near_dists = af.vision.nearest_neighbour(query, train_feats, 1, \
k, dist_type)
weights = self._get_neighbor_weights(near_dists, weight_by_dist, k)
top_labels = af.moddims(train_labels[near_locs], \
near_locs.dims()[0], near_locs.dims()[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
probs, _ = af.imax(accum_weights, dim=0)
return probs.T
def predict(self, X):
near_locs, near_dists = af.vision.nearest_neighbour(X, self._data, self._dim, \
self._num_nearest, self._match_type)
weights = self._get_neighbor_weights(near_dists)
top_labels = af.moddims(self._labels[near_locs], \
get_dims(near_locs)[0], get_dims(near_locs)[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
_, max_weight_locs = af.imax(accum_weights, dim=0)
pred_idxs = af.range(get_dims(accum_weights)[1]) * get_dims(
accum_weights)[0] + max_weight_locs.T
top_labels_flat = af.flat(top_labels)
pred_classes = top_labels_flat[pred_idxs]
return pred_classes
def _predict(self, query, train_feats, train_labels, k, dist_type,
weight_by_dist):
near_locs, near_dists = af.vision.nearest_neighbour(query, train_feats, 1, \
k, dist_type)
weights = self._get_neighbor_weights(near_dists, weight_by_dist, k)
top_labels = af.moddims(train_labels[near_locs], \
near_locs.dims()[0], near_locs.dims()[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
_, max_weight_locs = af.imax(accum_weights, dim=0)
pred_idxs = af.range(accum_weights.dims()
[1]) * accum_weights.dims()[0] + max_weight_locs.T
top_labels_flat = af.flat(top_labels)
pred_classes = top_labels_flat[pred_idxs]
return pred_classes
def histogram_deposition(current_indices_flat, currents_flat, grid_elements):
'''
function: histogram_deposition(current_indices_flat, currents_flat, grid)
inputs: current_indices_flat, currents_flat, grid_elements
current_indices_flat, currents_flat: They denote the indices and the currents
to be deposited on the flattened current vector.
grid_elements: The number of elements present the matrix/vector representing the
currents.
'''
# setting default indices and current for histogram deposition
indices_fix = af.data.range(grid_elements + 1, dtype=af.Dtype.s64)
currents_fix = 0 * af.data.range(grid_elements + 1, dtype=af.Dtype.f64)
# Concatenating the indices and currents in a single vector
combined_indices_flat = af.join(0, indices_fix, current_indices_flat)
combined_currents_flat = af.join(0, currents_fix, currents_flat)
# Sort by key operation
indices, currents = af.sort_by_key(combined_indices_flat,
combined_currents_flat,
dim=0)
# scan by key operation with default binary addition operation which sums up currents
# for the respective indices
Histogram_scan = af.scan_by_key(indices, currents)
# diff1 operation to determine the uniques indices in the current
diff1_op = af.diff1(indices, dim=0)
# Determining the uniques indices for current deposition
indices_unique = af.where(diff1_op > 0)
# Determining the current vector
J_flat = Histogram_scan[indices_unique]
af.eval(J_flat)
return J_flat
def simple_algorithm(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
k = af.constant(1, 3, 3, dtype=af.Dtype.u32)
af.eval(k)
print_func(af.sum(a), af.product(a), af.min(a), af.max(a),
af.count(a), af.any_true(a), af.all_true(a))
display_func(af.sum(a, 0))
display_func(af.sum(a, 1))
display_func(af.product(a, 0))
display_func(af.product(a, 1))
display_func(af.min(a, 0))
display_func(af.min(a, 1))
display_func(af.max(a, 0))
display_func(af.max(a, 1))
display_func(af.count(a, 0))
display_func(af.count(a, 1))
display_func(af.any_true(a, 0))
display_func(af.any_true(a, 1))
display_func(af.all_true(a, 0))
display_func(af.all_true(a, 1))
display_func(af.accum(a, 0))
display_func(af.accum(a, 1))
display_func(af.scan(a, 0, af.BINARYOP.ADD))
display_func(af.scan(a, 1, af.BINARYOP.MAX))
display_func(af.scan_by_key(k, a, 0, af.BINARYOP.ADD))
display_func(af.scan_by_key(k, a, 1, af.BINARYOP.MAX))
display_func(af.sort(a, is_ascending=True))
display_func(af.sort(a, is_ascending=False))
b = (a > 0.1) * a
c = (a > 0.4) * a
d = b / c
print_func(af.sum(d));
print_func(af.sum(d, nan_val=0.0));
display_func(af.sum(d, dim=0, nan_val=0.0));
val,idx = af.sort_index(a, is_ascending=True)
display_func(val)
display_func(idx)
val,idx = af.sort_index(a, is_ascending=False)
display_func(val)
display_func(idx)
b = af.randu(3,3)
keys,vals = af.sort_by_key(a, b, is_ascending=True)
display_func(keys)
display_func(vals)
keys,vals = af.sort_by_key(a, b, is_ascending=False)
display_func(keys)
display_func(vals)
c = af.randu(5,1)
d = af.randu(5,1)
cc = af.set_unique(c, is_sorted=False)
dd = af.set_unique(af.sort(d), is_sorted=True)
display_func(cc)
display_func(dd)
display_func(af.set_union(cc, dd, is_unique=True))
display_func(af.set_union(cc, dd, is_unique=False))
display_func(af.set_intersect(cc, cc, is_unique=True))
display_func(af.set_intersect(cc, cc, is_unique=False))
def simple_algorithm(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(3, 3)
k = af.constant(1, 3, 3, dtype=af.Dtype.u32)
af.eval(k)
print_func(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a),
af.any_true(a), af.all_true(a))
display_func(af.sum(a, 0))
display_func(af.sum(a, 1))
display_func(af.product(a, 0))
display_func(af.product(a, 1))
display_func(af.min(a, 0))
display_func(af.min(a, 1))
display_func(af.max(a, 0))
display_func(af.max(a, 1))
display_func(af.count(a, 0))
display_func(af.count(a, 1))
display_func(af.any_true(a, 0))
display_func(af.any_true(a, 1))
display_func(af.all_true(a, 0))
display_func(af.all_true(a, 1))
display_func(af.accum(a, 0))
display_func(af.accum(a, 1))
display_func(af.scan(a, 0, af.BINARYOP.ADD))
display_func(af.scan(a, 1, af.BINARYOP.MAX))
display_func(af.scan_by_key(k, a, 0, af.BINARYOP.ADD))
display_func(af.scan_by_key(k, a, 1, af.BINARYOP.MAX))
display_func(af.sort(a, is_ascending=True))
display_func(af.sort(a, is_ascending=False))
b = (a > 0.1) * a
c = (a > 0.4) * a
d = b / c
print_func(af.sum(d))
print_func(af.sum(d, nan_val=0.0))
display_func(af.sum(d, dim=0, nan_val=0.0))
val, idx = af.sort_index(a, is_ascending=True)
display_func(val)
display_func(idx)
val, idx = af.sort_index(a, is_ascending=False)
display_func(val)
display_func(idx)
b = af.randu(3, 3)
keys, vals = af.sort_by_key(a, b, is_ascending=True)
display_func(keys)
display_func(vals)
keys, vals = af.sort_by_key(a, b, is_ascending=False)
display_func(keys)
display_func(vals)
c = af.randu(5, 1)
d = af.randu(5, 1)
cc = af.set_unique(c, is_sorted=False)
dd = af.set_unique(af.sort(d), is_sorted=True)
display_func(cc)
display_func(dd)
display_func(af.set_union(cc, dd, is_unique=True))
display_func(af.set_union(cc, dd, is_unique=False))
display_func(af.set_intersect(cc, cc, is_unique=True))
display_func(af.set_intersect(cc, cc, is_unique=False))