fix: alpha

Author: cosmic-cortex

docs/source/content/query_strategies/ranked_batch_mode.ipynb

@@ -18,7 +18,7 @@
     "\n",
     "$$score = \\alpha(1 - \\Phi(x, X_{labeled})) + (1 - \\alpha) U(x),$$\n",
     "\n",
-    "where $\\alpha = \\frac{|X_{labeled}|}{|X_{unlabeled}| + |X_{labeled}|}$, $X_{labeled}$ is the labeled dataset, $U(x)$ is the uncertainty of predictions for $x$, and $\\Phi$ is a so-called similarity function, for instance [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity). This latter function measures how well the feature space is explored near $x$. (The lower the better.)\n",
+    "where $\\alpha = \\frac{|X_{unlabeled}|}{|X_{unlabeled}| + |X_{labeled}|}$, $X_{labeled}$ is the labeled dataset, $U(x)$ is the uncertainty of predictions for $x$, and $\\Phi$ is a so-called similarity function, for instance [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity). This latter function measures how well the feature space is explored near $x$. (The lower the better.)\n",
     "\n",
     "After scoring, the highest scored instance is put at the top of a list. The instance is removed from the pool and the score is recalculated until the desired amount of instances are selected."
    ]
@@ -130,7 +130,7 @@
     "distance_scores = pairwise_distances(X_pool, X_training, metric='euclidean').min(axis=1)\n",
     "similarity_scores = 1 / (1 + distance_scores)\n",
     "\n",
-    "alpha = len(X_training)/len(X_raw)\n",
+    "alpha = len(X_pool)/len(X_raw)\n",
     "\n",
     "scores = alpha * (1 - similarity_scores) + (1 - alpha) * uncertainty"
    ]