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))
