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import faiss
import numpy as np
class FaissNeighbors:
def __init__(self):
self.index = None
self.y = None
def fit(self, X, y):
self.index = faiss.IndexFlatL2(X.shape[1])
self.index.add(X.astype(np.float32))
self.y = y
def get_distances_and_indices(self, X, top_K=1000):
distances, indices = self.index.search(X.astype(np.float32), k=top_K)
return np.copy(distances), np.copy(indices), np.copy(self.y[indices])
def get_nearest_labels(self, X, top_K=1000):
distances, indices = self.index.search(X.astype(np.float32), k=top_K)
return np.copy(self.y[indices])
class FaissCosineNeighbors:
def __init__(self):
self.cindex = None
self.y = None
def fit(self, X, y):
self.cindex = faiss.index_factory(X.shape[1], "Flat", faiss.METRIC_INNER_PRODUCT)
X = np.copy(X)
X = X.astype(np.float32)
faiss.normalize_L2(X)
self.cindex.add(X)
self.y = y
def get_distances_and_indices(self, Q, topK):
Q = np.copy(Q)
faiss.normalize_L2(Q)
distances, indices = self.cindex.search(Q.astype(np.float32), k=topK)
return np.copy(distances), np.copy(indices), np.copy(self.y[indices])
def get_nearest_labels(self, Q, topK=1000):
Q = np.copy(Q)
faiss.normalize_L2(Q)
distances, indices = self.cindex.search(Q.astype(np.float32), k=topK)
return np.copy(self.y[indices]) |