正如标题所述,我正在尝试使马尔可夫聚类算法在Python中工作,即Python 3.7
不幸的是,它没有做任何事情,它使我无所适从。
编辑:首先,我对主代码进行了调整,以使每一列的总和为100,即使它不是完美平衡。我将在最终答案中尝试说明这一点。
要明确的是,最大的问题是数字突然失控,变成了容易理解的数字5.56268465e-309,而我不知道如何将其转换为可以理解的数字。
这是到目前为止的代码:
import numpy as np
import math
## How far you'd like your random-walkers to go (bigger number -> more walking)
EXPANSION_POWER = 2
## How tightly clustered you'd like your final picture to be (bigger number -> more clusters)
INFLATION_POWER = 2
ITERATION_COUNT = 10
def normalize(matrix):
return matrix/np.sum(matrix, axis=0)
def expand(matrix, power):
return np.linalg.matrix_power(matrix, power)
def inflate(matrix, power):
for entry in np.nditer(transition_matrix, op_flags=['readwrite']):
entry[...] = math.pow(entry, power)
return matrix
def run(matrix):
#np.fill_diagonal(matrix, 1)
#print(matrix)
matrix = normalize(matrix)
print(matrix)
for _ in range(ITERATION_COUNT):
matrix = normalize(inflate(expand(matrix, EXPANSION_POWER), INFLATION_POWER))
return matrix
transition_matrix = np.array ([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0],
[0,0,0,0.33,0,0,0.5,0,0,0,0,0,0,0,0,0,0.125,1],
[0,0,0,0.33,0,0,0.5,1,1,0,0,0,0,0,0,0,0.125,0],
[0,0,0,0,0.166,0,0,0,0,0,0,0,0,0,0,0,0.125,0],
[0,0,0,0,0.166,0,0,0,0,0.2,0,0,0,0,0,0,0.125,0],
[0,0,0,0,0.167,0,0,0,0,0.2,0.25,0,0,0,0,0,0.125,0],
[0,0,0,0,0.167,0,0,0,0,0.2,0.25,0.5,0,0,0,0,0,0],
[0,0,0,0,0.167,0,0,0,0,0.2,0.25,0.5,0,1,0,0,0.125,0],
[0,0,0,0,0.167,0,0,0,0,0.2,0.25,0,1,0,1,0,0.125,0],
[0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0.5,0,0],
[0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0.5,0,0]])
run(transition_matrix)
print(transition_matrix)
这是uni作业的一部分-我需要对数组进行加权和不加权(尽管加权部分可以等到我血腥的东西都工作了)任何提示或建议?
答案 0 :(得分:3)
您的转换矩阵无效。
>>> transition_matrix.sum(axis=0)
>>> matrix([[1. , 1. , 0.99, 0.99, 0.96, 0.99, 1. , 1. , 0. , 1. ,
1. , 1. , 1. , 0. , 0. , 1. , 0.88, 1. ]])
不仅有些列的总和不为1,有些列的总和为0。
这意味着当您尝试对矩阵进行归一化时,由于要除以0,因此最终会得到nan
。
最后,您是否有理由使用Numpy矩阵而不是Numpy数组,这是此类数据的推荐容器?因为使用Numpy数组将简化某些操作,例如将每个项提升为幂。另外,Numpy矩阵和Numpy数组之间存在一些差异,可能会导致细微的错误。