import idx2numpy import numpy from import_labs import import_labs from time import time import warnings from skimage.feature import hog from random import uniform with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) from sklearn.ensemble import RandomForestClassifier as RFC __author__ = 'vks' import_labs(["task4p1/", "task3/"]) from random_forest import RandomForest from kNN import Naive_kNN from CV import k_fold px_x = 2 px_y = 2 train_images = idx2numpy.convert_from_file("train-images.idx3-ubyte") train_images_hog = [hog(img, orientations=8, pixels_per_cell=(px_x, px_y), cells_per_block=(1, 1)) for img in train_images] train_labels = idx2numpy.convert_from_file("train-labels.idx1-ubyte") test_images = idx2numpy.convert_from_file("t10k-images.idx3-ubyte") test_images_hog = [hog(img, orientations=8, pixels_per_cell=(px_x, px_y), cells_per_block=(1, 1)) for img in test_images] test_labels = idx2numpy.convert_from_file("t10k-labels.idx1-ubyte")
from random_forest import RandomForest import pickle from import_labs import import_labs from time import time __author__ = 'vks' import_labs(["task3/"]) from CV import k_fold from kNN import Naive_kNN with open("iris.txt", "rb") as f: data, types = pickle.load(f, encoding="latin-1") forest = RandomForest(size=10, features=0.5) print(k_fold(10, data, types, forest)) knn = Naive_kNN() print(k_fold(10, data, types, knn))