Weighting

Weights words based on the standard deviation of the per-word
confidences; closes #5

gptc/classifier.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 
-import gptc.tokenizer, gptc.compiler, gptc.exceptions
+import gptc.tokenizer, gptc.compiler, gptc.exceptions, gptc.weighting
 import warnings
 
 
@@ -63,7 +63,10 @@ class Classifier:
         probs = {}
         for word in text:
             try:
-                for category, value in enumerate(model[word]):
+                weight, weighted_numbers = gptc.weighting.weight(
+                    [i / 65535 for i in model[word]]
+                )
+                for category, value in enumerate(weighted_numbers):
                     try:
                         probs[category] += value
                     except KeyError:
@@ -71,8 +74,7 @@ class Classifier:
             except KeyError:
                 pass
         probs = {
-            model["__names__"][category]: value / 65535
-            for category, value in probs.items()
+            model["__names__"][category]: value for category, value in probs.items()
         }
         total = sum(probs.values())
         probs = {category: value / total for category, value in probs.items()}

gptc/weighting.py

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+
+import math
+
+
+def _mean(numbers):
+    """Calculate the mean of a group of numbers
+
+    Parameters
+    ----------
+    numbers : list of int or float
+        The numbers to calculate the mean of
+
+    Returns
+    -------
+    float
+        The mean of the numbers
+    """
+    return sum(numbers) / len(numbers)
+
+
+def _standard_deviation(numbers):
+    """Calculate the standard deviation of a group of numbers
+
+    Parameters
+    ----------
+    numbers : list of int or float
+        The numbers to calculate the mean of
+
+    Returns
+    -------
+    float
+        The standard deviation of the numbers
+
+    """
+    mean = _mean(numbers)
+    squared_deviations = [(mean - i) ** 2 for i in numbers]
+    return math.sqrt(_mean(squared_deviations))
+
+
+def weight(numbers):
+    standard_deviation = _standard_deviation(numbers)
+    weight = standard_deviation * 2
+    weighted_numbers = [i * weight for i in numbers]
+    return weight, weighted_numbers