## sample_student.py
	import numpy as np
	#It's kk to import whatever you want from the local util module if you would like:
	#from util.X import ...
	def classify(im):
	'''
	Example submission for coding challenge.
	Args: im (nxmx3) unsigned 8-bit color image
	Returns: One of three strings: 'brick', 'ball', or 'cylinder'
	'''
	#Let's guess randomly! Maybe we'll get lucky.
	labels = ['brick', 'ball', 'cylinder']
	random_integer = np.random.randint(low = 0, high = 3)
	return labels[random_integer]

## ------------------------- ##
	##
	## evaluate.py
	## Basic image processing utilties.
	##
	##
	## ------------------------- ##
	import numpy as np
	import glob
	from easydict import EasyDict
	from matplotlib.pyplot import imread
	#Import student's method:
	from sample_student import classify
	dataset_names = ['easy', 'medium_1', 'medium_2', 'hard']
	weights = [0.5, 0.2, 0.2, 0.1]
	classes = ['ball', 'brick', 'cylinder']
	data_path = '../data' #Assuming data is stored in current directory.
	dataset_weights = {}
	for i in range(len(dataset_names)):
	dataset_weights[dataset_names[i]] = weights[i]
	#Store data performance data in a nested easydict:
	performance = EasyDict()
	#Iterate through images:
	for dataset_name in dataset_names:
	performance[dataset_name] = EasyDict()
	performance[dataset_name]['overall'] = np.array([0, 0]) #overall Correct/incorrect
	for image_class in classes:
	performance[dataset_name][image_class] = np.array([0, 0])
	image_paths = glob.glob(data_path + '/'
	+ dataset_name + '/'
	+ image_class + '/*.jpg')
	for image_path in image_paths:
	im = imread(image_path)
	prediction = classify(im)
	if image_class == prediction:
	performance[dataset_name][image_class][0] += 1
	else:
	performance[dataset_name][image_class][1] += 1
	#Tally overall performance for class
	performance[dataset_name]['overall'] = performance[dataset_name]['overall'] \
	+ performance[dataset_name][image_class]
	#Print out errors:
	print('Fraction of Correct Predictions: ')
	overall_accuracy = 0
	for dataset_name in dataset_names:
	total_correct, total_incorrect = performance[dataset_name]['overall']
	total = total_correct + total_incorrect
	accuracy = np.round(float(total_correct)/total, 4)
	overall_accuracy += accuracy * dataset_weights[dataset_name]
	performance[dataset_name]['accuracy'] = accuracy
	print(dataset_name + ': ' + \
	'(' + str(total_correct) + '/' + str(total) + ' = ' + \
	str(accuracy) + ')')
	for image_class in classes:
	print(' ' + image_class + ': '
	+ str(performance[dataset_name][image_class][0])
	+ '/'
	+ str(np.sum(performance[dataset_name][image_class])))
	overall_accuracy = np.round(overall_accuracy, 2)
	score = 0
	if overall_accuracy >= 0.6:
	score = 10
	elif overall_accuracy >= 0.55:
	score = 9
	elif overall_accuracy >= 0.5:
	score = 8
	elif overall_accuracy >= 0.45:
	score = 7
	elif overall_accuracy >= 0.4:
	score = 6
	elif overall_accuracy >= 0.35:
	score = 5
	elif overall_accuracy >= 0:
	score = 4
	print(" Overall Accuracy = ", overall_accuracy)
	print("Score = ", score)