Linter auto-fix.

Summary: `arc lint faiss/**/*`

Reviewed By: LowikC

Differential Revision: D22891305

fbshipit-source-id: 45bab7294ccccf70898b4967b03683894b6ae4c4

faiss/Clustering.cpp

@@ -209,7 +209,7 @@ void compute_centroids (size_t d, size_t k, size_t n,
 /** Handle empty clusters by splitting larger ones.
  *
  * It works by slightly changing the centroids to make 2 clusters from
- * a single one. Takes the same arguements as compute_centroids.
+ * a single one. Takes the same arguments as compute_centroids.
  *
  * @return           nb of spliting operations (larger is worse)
  */

faiss/IndexBinaryIVF.h

@@ -33,7 +33,7 @@ struct BinaryInvertedListScanner;
  * Otherwise the object is similar to the IndexIVF
  */
 struct IndexBinaryIVF : IndexBinary {
-    /// Acess to the actual data
+    /// Access to the actual data
     InvertedLists *invlists;
     bool own_invlists;
 

faiss/IndexIVF.h

@@ -97,7 +97,7 @@ struct InvertedListScanner;
  * the distance estimation from the query to databse vectors.
  */
 struct IndexIVF: Index, Level1Quantizer {
-    /// Acess to the actual data
+    /// Access to the actual data
     InvertedLists *invlists;
     bool own_invlists;
 

faiss/IndexPQ.cpp

@@ -835,7 +835,7 @@ struct MinSumK {
             x += ldx;
         }
 
-        { // intial result: take min for all elements
+        { // initial result: take min for all elements
             T sum = 0;
             terms[0] = 0;
             mark_seen (0);

faiss/gpu/impl/Distance.cu

@@ -59,7 +59,7 @@ void runDistance(bool computeL2,
 
   auto defaultStream = res->getDefaultStreamCurrentDevice();
 
-  // If we're quering against a 0 sized set, just return empty results
+  // If we're querying against a 0 sized set, just return empty results
   if (centroids.numElements() == 0) {
     thrust::fill(thrust::cuda::par.on(defaultStream),
                  outDistances.data(), outDistances.end(),

faiss/impl/PolysemousTraining.h

@@ -101,7 +101,7 @@ struct RandomGenerator;
                                  const SimulatedAnnealingParameters &p);
     RandomGenerator *rnd;
 
-    /// remember intial cost of optimization
+    /// remember initial cost of optimization
     double init_cost;
 
     // main entry point. Perform the optimization loop, starting from

tests/test_build_blocks.py

@@ -182,7 +182,7 @@ class TestClustering(unittest.TestCase):
         km2 = faiss.Kmeans(d, k, niter=4)
         km2.train(xt, init_centroids=km.centroids)
 
-        # check that the intial objective is better for km2 than km
+        # check that the initial objective is better for km2 than km
         self.assertGreater(km.obj[0], km2.obj[0] * 1.01)
 
     def test_stats(self):

tutorial/cpp/3-IVFPQ.cpp

@@ -39,7 +39,7 @@ int main() {
     int m = 8;                             // bytes per vector
     faiss::IndexFlatL2 quantizer(d);       // the other index
     faiss::IndexIVFPQ index(&quantizer, d, nlist, m, 8);
-    
+
     index.train(nb, xb);
     index.add(nb, xb);