# -*- coding: utf-8 -*-
from numpy import *

a = array([[1, 0.9, 0.8, 0.7],
           [0.9, 1, -0.2, -0.4],
           [0.8, -0.2, 1, 0.3],
           [0.7, -0.4, 0.3, 1]], dtype=float)  # dtype指定为float


def graphMove(a):  # 构造转移矩阵
    a = where(abs(a) >= 0.4, a, 0)
    print(a)
    b = transpose(a)  # b为a的转置矩阵
    c = zeros((a.shape), dtype=float)
    bcol = [b[j].sum() for j in range(a.shape[1])]

    for i in range(a.shape[0]):
        for j in range(a.shape[1]):
            c[i][j] = a[i][j] / bcol[j]  # 完成初始化分配
    # print c,"\n===================================================="
    return c


def firstPr(c):  # pr值得初始化
    pr = zeros((c.shape[0], 1), dtype=float)  # 构造一个存放pr值得矩阵
    for i in range(c.shape[0]):
        pr[i] = float(1) / c.shape[0]
        # print pr,"\n==================================================="
    return pr


def pageRank(p, m, v, iter=100):  # 计算pageRank值
    for i in range(iter):
        v = p * dot(m, v) + (1 - p) * v
        print('iteration {0}'.format(i))
    # ===========================================================================
    # while ((v == dot(m, v)).all() == False):  # 判断pr矩阵是否收敛,(v == p*dot(m,v) + (1-p)*v).all()判断前后的pr矩阵是否相等，若相等则停止循环
    #     # print v
    #     v = dot(m, v)
    #     #print((v == p*dot(m,v) + (1-p)*v).all())
    # ===========================================================================
    return v


if __name__ == "__main__":
    # a=where(abs(a) >= 0.4, a, 0)
    # print(a)
    M = graphMove(a)
    print(M)
    pr = firstPr(M)
    p = 0.85  # 引入浏览当前网页的概率为p,假设p=0.8
    print(pageRank(p, M, pr))  # 计算pr值
    pass