Fix spelling (#1625)

docs/notebooks/FastText_Tutorial.ipynb

@@ -279,7 +279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The word vector lookup operation only works if atleast one of the component character ngrams is present in the training corpus. For example -"
+    "The word vector lookup operation only works if at least one of the component character ngrams is present in the training corpus. For example -"
    ]
   },
   {
@@ -346,7 +346,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarity operations work the same way as word2vec. **Out-of-vocabulary words can also be used, provided they have atleast one character ngram present in the training data.**"
+    "Similarity operations work the same way as word2vec. **Out-of-vocabulary words can also be used, provided they have at least one character ngram present in the training data.**"
    ]
   },
   {

docs/notebooks/Word2Vec_FastText_Comparison.ipynb

@@ -466,7 +466,7 @@
     "Both these subtractions would result in a very similar set of remaining ngrams.\n",
     "No surprise the fastText embeddings do extremely well on this.\n",
     "\n",
-    "Let's do a small test to validate this hypothesis - fastText differs from word2vec only in that it uses char n-gram embeddings as well as the actual word embedding in the scoring function to calculate scores and then likelihoods for each word, given a context word. In case char n-gram embeddings are not present, this reduces (atleast theoretically) to the original word2vec model. This can be implemented by setting 0 for the max length of char n-grams for fastText.\n"
+    "Let's do a small test to validate this hypothesis - fastText differs from word2vec only in that it uses char n-gram embeddings as well as the actual word embedding in the scoring function to calculate scores and then likelihoods for each word, given a context word. In case char n-gram embeddings are not present, this reduces (at least theoretically) to the original word2vec model. This can be implemented by setting 0 for the max length of char n-grams for fastText.\n"
    ]
   },
   {
@@ -1081,4 +1081,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

gensim/models/keyedvectors.py

@@ -633,7 +633,7 @@ def n_similarity(self, ws1, ws2):
 
         """
         if not(len(ws1) and len(ws2)):
-            raise ZeroDivisionError('Atleast one of the passed list is empty.')
+            raise ZeroDivisionError('At least one of the passed list is empty.')
         v1 = [self[word] for word in ws1]
         v2 = [self[word] for word in ws2]
         return dot(matutils.unitvec(array(v1).mean(axis=0)), matutils.unitvec(array(v2).mean(axis=0)))