def preview_nearest(ref_idx, codes, filenames, n=5):
if not isinstance(codes, list):
codes = [codes]
neighbors = Neighbors(k=n).fit(codes)
for i, code_set in enumerate(codes):
distances, nearest = neighbors.kneighbors(code_set[ref_idx])
for j, f in enumerate([filenames[k] for k, _ in nearest]):
columns = len(nearest) + 1
rows = len(codes)
sp = plt.subplot(rows, columns, (i * columns) + j + 1)
sp.imshow(np.flipud(mpimg.imread(f)))
def local_match(data, code, query_size=10, ratio=1.0, seed=0):
"""Estimate the average accuracy of knn queries."""
n_samples = data.shape[0]
n_subset = int(ratio * n_samples)
accuracies = np.zeros(n_subset)
data_neighbors = Neighbors(k=query_size + 1).fit(data)
code_neighbors = Neighbors(k=query_size + 1).fit(code)
rng = Random(seed)
for i, j in enumerate(rng.sample(xrange(n_samples), n_subset)):
_, knn_data = data_neighbors.kneighbors(data[j])
knn_data = knn_data[1:]
_, knn_code = code_neighbors.kneighbors(code[j])
knn_code = knn_code[1:]
accuracies[i] = float(len(set(knn_data) & set(knn_code))) / query_size
return accuracies.mean()
