src/experiments/algorithms/colour_FE.py

@@ -5,7 +5,6 @@ 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

@@ -1,53 +0,0 @@
-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)