berechnet Vektorbetrag und nutzt diesen als Feature

.gitignore

@@ -0,0 +1 @@
+.venv

main.py

@@ -34,6 +34,12 @@ def calc_f1_score(y_true, y_pred):
     fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
     fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
     
+    '''print(tp)
+    print(fp)
+
+    precision = calc_precision(tp, fp)
+    recall = calc_recall(tp, fn)'''
+
     if tp != 0 and fp != 0:
         precision = calc_precision(tp, fp)
     else:
@@ -58,9 +64,9 @@ def calc_recall(tp, fn):
 
 def get_score_from_cli():
     try:
-        culmen_depth = float(input("x: "))
-        culmen_length = float(input("y: "))
-        return np.array([culmen_depth, culmen_length]).reshape(1, -1)
+        x = float(input("x: "))
+        y = float(input("y: "))
+        return np.array([x, y]).reshape(1, -1)
     except ValueError:
         print("Invalid input. Please enter numeric values.")
         return None
@@ -78,9 +84,8 @@ def main():
     sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
     plt.show()'''
 
-    features = ["x", "y"]
-
-    X = df[features]
+    df["radius"] = np.sqrt(df["x"]**2 + df["y"]**2)
+    X = df[["radius"]]
     
     y = pd.get_dummies(df['points'])