4. set.py

from tree import training_data, training_labels, testing_data, testing_labels, make_random_forest, make_single_tree, classify
import numpy as np
import random
np.random.seed(1)
random.seed(1)
from collections import Counter

tree = make_single_tree(training_data, training_labels)
single_tree_correct = 0

forest = make_random_forest(40, training_data, training_labels)
forest_correct = 0

for i in range(len(testing_data)):
  prediction = classify(testing_data[i], tree)
  if prediction == testing_labels[i]:
    single_tree_correct += 1
  predictions = []
  for forest_tree in forest:
    predictions.append(classify(testing_data[i], forest_tree))
  forest_prediction = max(predictions,key=predictions.count)
  if forest_prediction == testing_labels[i]:
    forest_correct += 1
    
print(single_tree_correct/len(testing_data))
print(forest_correct/len(testing_data))