READY

src/all_feature_vectors.csv

@@ -1,5 +1,4 @@
 brightness;filename;concept;vector
-
 +1;X-10Y-10.png;Concept.LINKS_ABBIEGEN;[0, 58, 0, 40, 0, 2, 0, 18, 0, 79, 0, 3, 0, 64, 0, 33, 0, 3, 0, 79, 0, 18, 0, 3, 0, 21, 0, 76, 0, 3, 0, 69, 0, 28, 0, 3, 0, 39, 0, 60, 0, 1, 0, 68, 0, 31, 0, 1, 0, 77, 0, 20, 0, 3, 0, 79, 0, 18, 0, 3, 0, 100, 0, 0, 0, 0, 0, 62, 0, 37, 0, 1, 0, 73, 0, 23, 0, 4, 0, 20, 0, 76, 0, 4, 0, 77, 0, 20, 0, 3, 0, 62, 0, 34, 0, 4, 0, 15, 0, 82, 0, 3, 9, 33, 3]
 +1;X-10Y-20.png;Concept.LINKS_ABBIEGEN;[0, 58, 0, 40, 0, 2, 0, 17, 0, 81, 0, 2, 0, 64, 0, 33, 0, 3, 0, 80, 0, 17, 0, 3, 0, 22, 0, 75, 0, 3, 0, 65, 0, 32, 0, 3, 0, 41, 0, 57, 0, 2, 0, 71, 0, 28, 0, 1, 0, 77, 0, 20, 0, 3, 0, 81, 0, 16, 0, 3, 0, 100, 0, 0, 0, 0, 0, 61, 0, 38, 0, 1, 0, 70, 0, 26, 0, 4, 0, 23, 0, 73, 0, 4, 0, 78, 0, 18, 0, 4, 0, 59, 0, 37, 0, 4, 0, 12, 0, 85, 0, 3, 9, 32, 3]
 +1;X-10Y-30.png;Concept.LINKS_ABBIEGEN;[0, 57, 0, 40, 0, 3, 0, 15, 0, 83, 0, 2, 0, 63, 0, 34, 0, 3, 0, 81, 0, 16, 0, 3, 0, 23, 0, 73, 0, 4, 0, 61, 0, 36, 0, 3, 0, 43, 0, 55, 0, 2, 0, 75, 0, 24, 0, 1, 0, 76, 0, 20, 0, 4, 0, 83, 0, 14, 0, 3, 0, 99, 0, 0, 0, 1, 0, 61, 0, 38, 0, 1, 0, 66, 0, 30, 0, 4, 0, 27, 0, 70, 0, 3, 0, 78, 0, 18, 0, 4, 0, 57, 0, 39, 0, 4, 0, 9, 0, 88, 0, 3, 7, 31, 2]

src/classes/neural/network.py

@@ -33,11 +33,11 @@ class NeuralNetwork:
             return [interpritated, True]
     
     # takes feature vectors to train and generates weights
-    def learn(self, training_set: list[FeatureVector]):
+    def learn(self, training_set: list[FeatureVector], epochs = 10):
     
         correct_amount = 0
         false_amount = 0
-        for i in range(len(training_set)*100):
+        for i in range(len(training_set)*epochs):
             fv_to_train = training_set[i%len(training_set)] # random.choice(training_set)
             classified_concept, correct = self.classify(fv_to_train)
             

src/main.py

@@ -2,6 +2,7 @@ import os
 import ast
 import csv
 import numpy as np
+import random 
 import multiprocessing
 
 from classes.concept import Concept
@@ -59,56 +60,60 @@ def decision_tree(training = os.path.abspath(os.path.join(__file__, "..", "train
     # 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 neural_network(training = os.path.abspath(os.path.join(__file__, "..", "training.csv")), testing = os.path.abspath(os.path.join(__file__, "..", "testing.csv")), num_training_vectors = 100):
+def neural_network(training_set, testing_set, epochs = 10):
     from classes.neural.network import NeuralNetwork
     from classes.neural.neuron import Neuron
     
-    training_data = []
+    nn = NeuralNetwork(neurons=None)
-    with open(training, 'r') as csv_file:
+    nn.learn(training_set, epochs)
-        reader = csv.reader(csv_file, delimiter=";")
-        next(reader)
-        i = 0
-        for row in reader:
-            if i > num_training_vectors:
-                break
-            i += 1
 
-            float_list = ast.literal_eval(row[3])
-
-            fv = FeatureVector(concept = Concept.identify_by_str(row[2].split(".")[1]), features_list = [element * 0.01 for element in float_list], loaded = True)
-            training_data.append(fv)
-    
-    while len(training_data) < num_training_vectors:
-        training_data += training_data
-    training_data = training_data[:num_training_vectors+1]
-
-    nn = NeuralNetwork()
-    nn.learn(training_data)
-
-    cnt_data = 0
     cnt_correct = 0
-    with open(testing, 'r') as csv_file:
+    for testing_fv in testing_set:
-        reader = csv.reader(csv_file, delimiter=";")
+        classified_concept, _ = nn.classify(testing_fv)
-        next(reader)
+        cnt_correct += 1 if testing_fv.concept == classified_concept else 0
-        for row in reader:
+
-            cnt_data += 1
-            fv = FeatureVector(concept = Concept.identify_by_str(row[2].split(".")[1]), features_list = ast.literal_eval(row[3]), loaded = True)
-            classified_concept, _ = nn.classify(fv)
-            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)}%)")
-    return [cnt_correct, cnt_data - cnt_correct, cnt_data]  # richtig, falsch, amount of testing data
+    return [cnt_correct, len(testing_set) - cnt_correct, len(testing_set)]  # richtig, falsch, amount of testing data
 
-def repeat_n_times(n: int, num_training_vectors: int):
+def repeat_n_times(n: int, num_training_vectors: int, epochs: int):
-    for _ in range(n):
     cnt_correct = 0
     cnt_data = 0
-        result = neural_network(num_training_vectors=num_training_vectors)
+    for _ in range(n):
+        result = neural_network(num_training_vectors=num_training_vectors, epochs=epochs)
         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)}%)")
+    print(f"{epochs}: classified {cnt_correct}/{cnt_data} correctly  ({round(cnt_correct/cnt_data*100, 3)}%)")
+
+def load_fv(path_to_training_data = os.path.abspath(os.path.join(__file__, "..", "all_feature_vectors.csv"))):
+    training_data = []
+    with open(path_to_training_data, 'r') as csv_file:
+        reader = csv.reader(csv_file, delimiter=";")
+        next(reader)
+        for row in reader:
+            fv_float = ast.literal_eval(row[3])
+
+            fv = FeatureVector(concept = Concept.identify_by_str(row[2].split(".")[1]), features_list = [element * 0.01 for element in fv_float], loaded = True)
+            training_data.append(fv)
+    
+    return training_data
 
 if __name__ == "__main__":
-    for num_training_vectors in range(100, 1000, 100):
+    all_vectors = load_fv()
-        multiprocessing.Process(target=repeat_n_times, args=(100, num_training_vectors, )).start()
+
+    for input in range(40000, len(all_vectors), 400): 
+        inp_res = []
+        for epoch in range(10, 1000, 10):
+            l = []
+            for test in range(0, 1):
+                training_set = random.sample(all_vectors, input)
+                testing_set = list(set(all_vectors)-set(training_set))
+
+                # print(f"Start for {input} trainingsdata and test number {test} with epoch {epoch}")
+                correct, false, ammount = neural_network(training_set, testing_set, epoch)
+                print(f"Epoch {epoch}: correct_percentage={(correct/ammount)*100}%")
+                l.append(false/ammount)
+
+            inp_res.append(np.mean(l))
+        

src/training.csv

@@ -2167,7 +2167,6 @@ brightness;filename;concept;vector
 +1;X-10Y-50.png;Concept.VORFAHRT_STRASSE;[0, 0, 15, 77, 0, 8, 0, 0, 0, 99, 0, 1, 0, 0, 3, 86, 0, 11, 0, 0, 1, 85, 0, 14, 0, 0, 0, 99, 0, 1, 0, 0, 11, 79, 0, 10, 0, 0, 85, 12, 0, 3, 0, 0, 35, 60, 0, 5, 0, 0, 1, 82, 0, 17, 0, 1, 10, 78, 0, 11, 0, 0, 79, 17, 0, 4, 0, 0, 16, 74, 0, 10, 0, 0, 1, 88, 0, 11, 0, 0, 0, 92, 0, 8, 0, 0, 1, 86, 0, 13, 0, 0, 0, 94, 0, 6, 0, 0, 0, 100, 0, 0, 10, 38, 1]
 +2;X50Y50.png;Concept.STOP;[43, 0, 0, 0, 0, 57, 0, 0, 0, 0, 0, 100, 8, 0, 0, 0, 0, 92, 65, 0, 0, 0, 0, 35, 73, 0, 0, 0, 0, 27, 11, 0, 0, 0, 0, 89, 65, 0, 0, 0, 0, 35, 70, 0, 0, 0, 0, 30, 64, 0, 0, 0, 0, 36, 48, 0, 0, 0, 0, 52, 59, 0, 0, 0, 0, 41, 57, 0, 0, 0, 0, 43, 10, 0, 0, 0, 0, 90, 72, 0, 0, 0, 0, 28, 74, 0, 0, 0, 0, 26, 15, 0, 0, 0, 0, 85, 0, 0, 0, 0, 0, 100, 9, 16, 5]
 -1;X-80Y10.png;Concept.STOP;[49, 0, 0, 49, 0, 2, 22, 0, 0, 76, 0, 2, 71, 0, 0, 27, 0, 2, 54, 0, 0, 44, 0, 2, 6, 0, 0, 93, 0, 1, 48, 0, 0, 49, 0, 3, 64, 0, 0, 34, 0, 2, 47, 0, 0, 50, 0, 3, 44, 0, 0, 54, 0, 2, 44, 0, 0, 54, 0, 2, 98, 0, 0, 1, 0, 1, 98, 0, 0, 1, 0, 1, 62, 0, 0, 36, 0, 2, 0, 0, 0, 98, 0, 2, 49, 0, 0, 48, 0, 3, 55, 0, 0, 42, 0, 3, 17, 0, 0, 80, 0, 3, 19, 13, 56]
-
 +1;X60Y-80.png;Concept.VORFAHRT_STRASSE;[0, 0, 3, 94, 0, 3, 0, 0, 0, 93, 0, 7, 0, 0, 0, 98, 0, 2, 0, 0, 0, 100, 0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 99, 0, 1, 0, 0, 21, 72, 0, 7, 0, 0, 2, 94, 0, 4, 0, 0, 0, 100, 0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 1, 97, 0, 2, 0, 0, 28, 66, 0, 6, 0, 0, 0, 97, 0, 3, 0, 0, 0, 100, 0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 99, 0, 1, 0, 0, 1, 92, 0, 7, 16, 19, 3]
 -2;X10Y60.png;Concept.VORFAHRT_GEWAEHREN;[24, 0, 0, 74, 0, 2, 59, 0, 0, 35, 1, 5, 43, 0, 0, 53, 0, 4, 34, 0, 0, 63, 0, 3, 11, 0, 0, 87, 0, 2, 38, 0, 0, 58, 0, 4, 16, 0, 0, 83, 0, 1, 3, 0, 0, 95, 0, 2, 40, 0, 0, 58, 0, 2, 4, 0, 0, 93, 0, 3, 45, 0, 0, 53, 0, 2, 28, 0, 0, 70, 0, 2, 3, 0, 0, 95, 0, 2, 0, 0, 0, 99, 0, 1, 43, 0, 0, 52, 0, 5, 11, 0, 0, 87, 0, 2, 0, 0, 0, 99, 0, 1, 8, 23, 5]
 0;X-40Y10.png;Concept.VORFAHRT_STRASSE;[0, 0, 15, 77, 0, 8, 0, 0, 0, 97, 0, 3, 0, 0, 7, 80, 0, 13, 0, 0, 9, 81, 0, 10, 0, 0, 0, 98, 0, 2, 0, 0, 0, 85, 0, 15, 0, 0, 75, 20, 0, 5, 0, 0, 87, 10, 0, 3, 0, 0, 4, 83, 0, 13, 0, 0, 0, 91, 0, 9, 0, 0, 16, 74, 0, 10, 0, 0, 41, 53, 0, 6, 0, 0, 0, 86, 0, 14, 0, 0, 0, 100, 0, 0, 0, 0, 0, 90, 0, 10, 0, 0, 0, 80, 6, 14, 0, 0, 0, 99, 0, 1, 8, 43, 1]