[5.6, 3.0, 4.1, 1.3, 2], [4.7, 3.2, 1.6, 0.2, 1],
[6.6, 3.0, 4.4, 1.4, 2]]
from materials.decision_tree_learning import build_tree, classify, max_classification
if __name__ == '__main__':
x = input().split(', ')
test_case = list(map(float, x[:-1])) + [int(x[-1])]
thirty = int(len(dataset) * 0.3)
first_data = dataset[:thirty]
second_data = dataset[thirty:thirty * 2]
third_data = dataset[thirty * 2:]
first_tree = build_tree(first_data)
second_tree = build_tree(second_data)
third_tree = build_tree(third_data)
# what we want (dict):
# 0 -> 1; 1 -> 0; 2 -> 0
# 0 -> 1; 1 -> 1; 2 -> 0
# 0 -> 1; 1 -> 2; 2 -> 0
# prediction: 1 (with most votes)
# setting default values
# votes[0] = 0; votes[1] = 0; votes[2] = 0
votes = {0: 0, 1: 0, 2: 0}
# casting votes (the predictions)
first_pred = max_classification(classify(test_case, first_tree))
att3 = float(input())
att4 = float(input())
planttype = input()
testCase = [att1, att2, att3, att4, planttype]
# we use '//' for getting an integer as a result (we use integers as indices in slicing)
half = len(trainingData) // 2
# slice
# lista[startRange:endRange]
tr_data_1 = trainingData[:
half] # [:endRange] -> starts from the start of the list, in this case 'trainingData'
tr_data_2 = trainingData[
half:] # [startRange:] -> stops at the end of the list, in this case 'trainingData'
tree1 = build_tree(tr_data_1)
tree2 = build_tree(tr_data_2)
# pred_1 and pred_2 are dicts, so we need to send them to the max_classification() function to get the predicted class
pred_1 = classify(testCase, tree1)
pred_2 = classify(testCase, tree2)
pred_1_class = max_classification(pred_1)
pred_2_class = max_classification(pred_2)
if pred_1_class == pred_2_class:
# High == High
print(pred_1_class)
else:
# High != Low
print("KONTRADIKCIJA")
[600.0, 29.4, 32.0, 37.2, 41.5, 15.0, 'Bream'],
[145.0, 22.0, 24.0, 25.5, 25.0, 15.0, 'Perch'],
[1100.0, 40.1, 43.0, 45.5, 27.5, 16.3, 'Perch']]
if __name__ == "__main__":
test_case = input()
test_case = [float(x) for x in test_case.split(', ')[:-1]
] + [test_case.split(', ')[-1]]
roach_set = []
for row in data:
# list[-1] -> the last element of the list
if row[-1] == 'Roach':
roach_set.append(row)
roach_set = roach_set[:40]
# list comprehension -> the process of creating a list, from another list
# the syntax: new_list = [what-we-want-to-keep-in-the-new-list for item in otherList if some-condition]
# some-condition is optional
pike_set = [row for row in data if row[-1] == 'Pike'][:40]
# roach_set = [row for row in data if row[-1] == 'Roach'][:40]
# the '+' operator on lists combines the elements of the two lists into one list
train_data = roach_set + pike_set
tree = build_tree(train_data)
class_dict = classify(test_case, tree)
class_name = max_classification(class_dict)
print(class_name)
new_test.append(new_row)
# second way (one loop, one list comprehension)
"""
new_test = []
for row in test:
new_row = [row[col_index] for col_index in range(len(row)) if col_index != column_ind]
new_test.append(new_row)
"""
# third way (two nested list comprehensions)
"""
new_test = [[row[col_index] for col_index in range(len(row)) if col_index != column_ind] for row in test]
"""
tree1 = build_tree(train)
tree2 = build_tree(new_train)
num_correct_1 = 0
num_correct_2 = 0
# one way (with two separate loops)
for row in test:
tree_1_pred = max_classification(classify(row, tree1))
correct_class = row[-1]
if tree_1_pred == correct_class:
num_correct_1 = num_correct_1 + 1
for row in new_test: