import numpy as np
import math
import itertools

history_data_file = open("history.txt", "r")
queries_data_file = open("queries.txt", "r")

history_data = [[int(x) for x in line.split()] for line in history_data_file]
queries_data = [[int(x) for x in line.split()] for line in queries_data_file]
custNum = history_data[0][0]
itemNum = history_data[0][1]
transactions = history_data[0][2]


def calculate_vector(data, vectorList): #works as intended
	count = 0
	for x in data[1:]:
		if vectorList[x[1]-1][x[0]-1] != 1:
			vectorList[x[1]-1][x[0]-1] = 1
			count+=1
	return vectorList, count


def vector_angle(x, y):
	norm_x = np.linalg.norm(x)
	norm_y = np.linalg.norm(y)
	cos_theta = np.dot(x, y) / (norm_x * norm_y)
	theta = math.degrees(math.acos(cos_theta))
	return theta


def averageAngle(angles):
	total =0
	for x in angles:
		total+=x
	total = total /len(angles)
	return total


def calc_average_angle(list_vec):
	myList =[]
	myAverage=[]
	myAngles=[[0]*custNum for x in [0]*itemNum]

	for x in list_vec:
		for y in list_vec:
			if x!=y:
				myAngles[list_vec.index(x)][list_vec.index(y)] = vector_angle(np.array(x), np.array(y))

			if (x, y) not in myList:
				myList.append([x, y])
				if x!=y:
					myAverage.append(vector_angle(np.array(x), np.array(y)))
	return myAverage, myAngles


def recommendation(query, positive_entries, average_angle, all_angles):
	print("Positive entries: " + str(positive_entries))
	print("Average angle: " + str(round(average_angle, 2)))

	valid_nums =set(x for x in range(1, itemNum+1))

	for i in query:
		print("Shopping cart: " + str(i).strip('[]').replace(',',''))
		item = {'item': [], 'match': [], 'angle': []}
		for j in i:
			item['item'].append(j)
			min_angle =1000
			count =0
			for x in all_angles[j-1]:
				if x!=0 and x<min_angle:
					min_angle=x
					count = all_angles[j-1].index(x)
				if count in item['item']:
					min_angle = 1000

			item['match'].append(count+1)
			item['angle'].append(min_angle)

		print(item)
	"""item_angles = sorted(all_angles[j-1])
												item['angle'].append(item_angles[1])
												min_angle = item_angles[1]
												print(min_angle)
												print(item_angles)"""




#may need to rewrite vector list whenever i need it - changes when passed to
vector_list = [[0]*custNum for x in [0]*itemNum]

list_vec, positive_entries = calculate_vector(history_data, vector_list)

#print(np.mean(calc_average_angle(list_vec))) #calculates the mean of the angles in list
print(queries_data)
average_angle, all_angles = (calc_average_angle(list_vec))
average_angle = np.mean(average_angle)
print(positive_entries)
print(all_angles)
recommendation(queries_data, positive_entries, average_angle, all_angles)

#This is how you can calculate the average angle
"""def calc_average_angle(list_vec):
	myList =[]
	myArray=[]
	for x in list_vec:
		for y in list_vec:
			if (x, y) not in myList:
				myList.append([x, y])
				if x!=y:
					myArray.append(vector_angle(np.array(x), np.array(y)))
	return myArray"""