experimental

src/classes/neural/network.py

@@ -37,8 +37,8 @@ class NeuralNetwork:
     
         correct_amount = 0
         false_amount = 0
-        for i in range(len(training_set)):
-            fv_to_train = training_set # random.choice(training_set)
+        for i in range(len(training_set)*100):
+            fv_to_train = training_set[i%len(training_set)] # random.choice(training_set)
             classified_concept, correct = self.classify(fv_to_train)
             
             if not correct:
@@ -47,7 +47,7 @@ class NeuralNetwork:
             else:
                 correct_amount += 1
 
-        print(f"{i}:  {(correct_amount/(correct_amount + false_amount)) * 100}%")
+        #print(f"{i}:  {(correct_amount/(correct_amount + false_amount)) * 100}%")
         pass
 
     # for a sequence of 3 neurons interpretate their meaning

src/main.py

@@ -2,6 +2,7 @@ import os
 import ast
 import csv
 import numpy as np
+import multiprocessing
 
 from classes.concept import Concept
 from classes.feature_vector import FeatureVector
@@ -96,10 +97,18 @@ def neural_network(training = os.path.abspath(os.path.join(__file__, "..", "trai
             cnt_correct += 1 if fv.concept == classified_concept else 0
             # print(f"{fv.concept} was classified as {classified_concept}")
 
-    print(f"classified {cnt_correct}/{cnt_data} correctly  ({round(cnt_correct/cnt_data*100, 3)}%)")
+    #print(f"classified {cnt_correct}/{cnt_data} correctly  ({round(cnt_correct/cnt_data*100, 3)}%)")
     return [cnt_correct, cnt_data - cnt_correct, cnt_data]  # richtig, falsch, amount of testing data
 
+def repeat_n_times(n: int, num_training_vectors: int):
+    for _ in range(n):
+        cnt_correct = 0
+        cnt_data = 0
+        result = neural_network(num_training_vectors=num_training_vectors)
+        cnt_correct += result[0]
+        cnt_data += result[2]
+    print(f"{num_training_vectors}: classified {cnt_correct}/{cnt_data} correctly  ({round(cnt_correct/cnt_data*100, 3)}%)")
+
 if __name__ == "__main__":
-    for i in range(100, 10000, 100):
-        print(f"{i} training vectors: ", end="")
-        neural_network()
+    for num_training_vectors in range(100, 1000, 100):
+        multiprocessing.Process(target=repeat_n_times, args=(100, num_training_vectors, )).start()