def random_walk(adj_matrix, steps=100, baised=False, alpha=1.0):

	#D_I, D_J, D_K ----- D OF EVERYTHING-------- (DONE)

	d_term=[]

	for i in range(len(adj_matrix)):

		current=adj_matrix[i].count(1)

		d_term.append(current)

	d_sum=sum(d_term)

	#-----------Create C_I TERM FOR BAISED----------
	c_i_term=[]
	c_i_term_list=[]

	for i in range(len(adj_matrix)):

		for j in range(len(adj_matrix)):

			current_term= adj_matrix[i][j]*(adj_matrix[j].count(1)**alpha)

			c_i_term.append(current_term)

		#adding the sum of c_i to the list
		c_i_term_list.append(sum(c_i_term))

		#clear list
		c_i_term[:]=[]

	#---------------DENOMENATOR for T_i_J ----------
	current_term=[]
	current_term_list=[]
	denomenator_list=[]

	for i in range(len(adj_matrix)):

		for k in range(len(adj_matrix)):

			current_term=adj_matrix[i][k]*(adj_matrix[k].count(1)**alpha)

			current_term_list.append(current_term)

		denomenator_list.append(sum(current_term_list))


	#--------DENOMINATOR for P_I (BAISED)---------
	denomenator_term=[]

	for k in range(len(adj_matrix)-1):

		#denomenator for p_i
		d= c_i_term_list[k]*(adj_matrix[k].count(1)**alpha)

		denomenator_term.append(d)


	#------CREATING P_I LISTS -----------#

	if baised==False:

		# make p_i_list
		p_i_list=[]

		for i in range(len(adj_matrix)):

			p_i= adj_matrix[i].count(1)/d_sum

			p_i_list.append(p_i)

	else:

		#creating list of p_i terms
		p_i_list=[]

		for i in range(len(adj_matrix)):

			p_i= (c_i_term_list[i]*(adj_matrix[i].count(1**alpha))/sum(denomenator_term))

			p_i_list.append(p_i)

	#CREATING TRANSITION MATRIX ( DONE - BAISED==FALSE)
	trans_matrix = [ [0 for i in range(len(adj_matrix))] for j in range(len(adj_matrix)) ]


	for i in range(len(adj_matrix)):

		for j in range(len(adj_matrix)):

			#if baised=true
			if baised==False:

				adj_matrix[i][j] = adj_matrix[i][j]/adj_matrix[i].count(1)

			else:

				adj_matrix[i][j] = (adj_matrix[i][j]*adj_matrix[j].count(1))/denomenator_list[i]


	#setting up walk list with starting term

	current_vertex=choose_vertex(p_i_list)

	walk=[current_vertex]

	#adding to walk by iterating through the transition matrix
	for i in range(0, steps):

		new_vertex=choose_vertex(adj_matrix[current_vertex])

		walk.append(new_vertex)

		current_vertex=new_vertex


	print walk


random_walk(get_graph_from_series(), steps=200, baised=False, alpha=1)
print''

print''
random_walk(get_graph_from_series(), steps=200, baised=True, alpha=5.0)