import numpy as np
import random

class RankPageCalculator(object):
	def __init__(self, connections):
		self._connections = connections
		self._dim = len(connections)
		self._hits = np.arange(0, self._dim, 1)
		self._current = random.randint(0, self._dim - 1)

	def run(self, n, alpha):
		for i in range(n):
			if(np.random.uniform(0, 1) < alpha):
				self._current = random.randint(0, self._dim - 1)
				self._hits[self._current] += 1
				continue
			uniform = np.random.uniform(0, 1, self._dim)
			uniform[1 != self._connections[self._current]] = 0
			self._current = uniform.argmax()
			self._hits[self._current] += 1

		return self._hits


adj = np.array([[0, 1, 1, 0, 0, 0, 0, 0, 0]
		, [0, 0, 1, 0, 1, 0, 0, 0, 0]
		, [1, 1, 0, 1, 0, 0, 0, 0, 0]
		, [0, 0, 1, 0, 1, 0, 0, 0, 0]
		, [0, 1, 0, 1, 0, 0, 0, 0, 0]
		, [0, 0, 0, 0, 1, 0, 1, 0, 1]
		, [0, 0, 0, 0, 0, 1, 0, 0, 0]
		, [0, 0, 0, 0, 0, 0, 1, 0, 0]
		, [0, 0, 0, 0, 0, 1, 1, 1, 0]], dtype=np.int16)
	

print(RankPageCalculator(adj).run(100000, 0.05))
print(RankPageCalculator(adj).run(100000, 0.3))

adj[4, 5] = 1
adj[5, 4] = 1
print(RankPageCalculator(adj).run(100000, 0.05))
print(RankPageCalculator(adj).run(100000, 0.3))