refactor: Fehlerrate überarbeitet

aufgaben/p6/error_rate.py

+class ErrorRate:
+    false_positives = 0
+    false_negatives = 0
+    true_positives = 0
+    true_negatives = 0
+
+    def evaluate(self, expected_class: float, actual_class: float):
+        if expected_class == -1:
+            if actual_class == -1:
+                self.true_negatives += 1
+            else:
+                self.false_positives += 1
+
+        if expected_class == 1:
+            if actual_class == 1:
+                self.true_positives += 1
+            else:
+                self.false_negatives += 1
+
+    def error_rate(self):
+        false_values = self.false_positives + self.false_negatives
+        correct_values = self.true_positives + self.true_negatives
+        return false_values / (false_values + correct_values)

aufgaben/p6/evaluate.py

-import math
-
-from algorithm.pla.perceptron import Perceptron
-
-
-def evaluate(iterations: int, amount_of_values: int, correct_data: list, incorrect_data: list, perceptron: Perceptron):
-    cnt_true_pos = 0
-    cnt_true_neg = 0
-    cnt_false_pos = 0
-    cnt_false_neg = 0
-    unclassified = 0
-    for i in range(500):
-        corr = []
-        incorr = []
-        for j in range(amount_of_values):
-            corr.append(correct_data[j][i])
-            incorr.append(incorrect_data[j][i])
-        jog_res = perceptron.classify(corr)
-        knie_res = perceptron.classify(incorr)
-        if math.isclose(1, jog_res, abs_tol=0.1):
-            cnt_true_pos += 1
-        elif math.isclose(0, jog_res, abs_tol=0.1):
-            cnt_false_neg += 1
-        else:
-            unclassified += 1
-        if math.isclose(0, knie_res, abs_tol=0.1):
-            cnt_true_neg += 1
-        elif math.isclose(1, knie_res, abs_tol=0.1):
-            cnt_false_pos += 1
-        else:
-            unclassified += 1
-    print(f"""
-    Anzahl Prüfungen: {iterations*2}
-    True Positive: {cnt_true_pos}
-    False Positive: {cnt_false_pos}
-    True Negative: {cnt_true_neg}
-    False Negative: {cnt_false_neg}
-    Unclassifiable: {unclassified}
-    Fehlerrate: {(cnt_false_neg+cnt_false_pos) / 10}% bzw. {(cnt_false_neg+cnt_false_pos+unclassified) / 10}%
-    """)
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