Merge branch 'main' of https://arnweb.nl/gitea/arne/EV5_Beeldherk_Bomen

src/experiments/algorithms/colour_FE.py

@@ -5,6 +5,7 @@ import os
 import numpy as np
 import csv
 from ...helpers.treenum import Tree
+from enum import Enum
 
 CSV_PATH = "src\\experiments\\algorithms\\data\\"
 IMG_PATH = "dataset\\accasia_1210048 (2023_09_28 12_19_26 UTC).JPG"

src/experiments/decision_tree/decision_tree.py

@@ -0,0 +1,53 @@
+from sklearn import tree
+# from ...helpers.treenum import Tree
+from enum import Enum
+import csv
+import random
+
+SIFT_PATH = "..\\algorithms\\data\\sift.csv"
+
+class Tree(Enum):
+    ACCASIA = 0
+    BERK = 1
+    EIK = 2
+    ELS = 3
+    ESDOORN = 4
+    ES = 5
+    LINDE = 6
+    PLATAAN = 7
+
+# [[tree1_data],[tree2_data]]
+# [tree1_label, tree2_label]
+
+labels = []
+dialect = csv.Dialect
+i = 0
+done = False
+test_index = random.randint(0, 102)
+print(test_index)
+
+with open(SIFT_PATH, 'r') as file:
+    reader = csv.reader(file, delimiter= ',')
+    matrix = list(reader)
+    data = [[] for x in range(len(matrix)-1)]
+    for row in matrix[1:]:
+        ## Remove test case
+        if i == test_index and done == False:
+            done = True
+            data.pop(i)
+            continue
+
+        ## append data to lists
+        labels.append(Tree[row[0].upper()].value)
+        for element in row[1:]:
+            data[i].append(element)
+        
+        ## iterator
+        i += 1
+
+clf = tree.DecisionTreeClassifier()
+clf = clf.fit(data, labels)
+# tree.plot_tree(clf)
+print(Tree[matrix[test_index][0].upper()])
+result = clf.predict([matrix[test_index][1:]])
+print(Tree(result[0]).name)