from sklearn import model_selection
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from MNIST_Dataset_Loader.mnist_loader import MNIST
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
old_stdout = sys.stdout
log_file = open("summary.log", "w")
sys.stdout = log_file
print('\nLoading MNIST Data...')
# data = MNIST('./python-mnist/data/')
data = MNIST('./MNIST_Dataset_Loader/python-mnist/data/')
print('\nLoading Training Data...')
img_train, labels_train = data.load_training()
train_img = np.array(img_train)
train_labels = np.array(labels_train)
print('\nLoading Testing Data...')
img_test, labels_test = data.load_testing()
test_img = np.array(img_test)
test_labels = np.array(labels_test)
#Features
X = train_img
#Labels
from sklearn import model_selection
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from MNIST_Dataset_Loader.mnist_loader import MNIST
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
old_stdout = sys.stdout
log_file = open("summary.log", "w")
sys.stdout = log_file
print('\nLoading MNIST Data...')
# data = MNIST('./python-mnist/data/')
data = MNIST('./MNIST_Dataset_Loader/dataset/')
print('\nLoading Training Data...')
img_train, labels_train = data.load_training()
train_img = np.array(img_train)
train_labels = np.array(labels_train)
print('\nLoading Testing Data...')
img_test, labels_test = data.load_testing()
test_img = np.array(img_test)
test_labels = np.array(labels_test)
#Features
X = train_img
#Labels
from matplotlib import style
import os
from PIL import Image
import numpy as np
import PIL
np.set_printoptions(threshold=np.nan)
style.use('ggplot')
# Save all the Print Statements in a Log file.
old_stdout = sys.stdout
log_file = open("summary.log", "w")
#sys.stdout = log_file
# Load MNIST Data
print('\nLoading MNIST Data...')
data = MNIST('./MNIST_Dataset_Loader/')
features = []
labels = []
count = 1000
for file in os.listdir("A"):
try:
img = np.asarray(
Image.open("A/" + file).convert('L').resize(
(45, 45), Image.ANTIALIAS)).flatten()
features.append(img)
labels.append('A')
count = count - 1
if count == 0:
break
except Exception as e: