这些信息,都可以通过NumPy提供的数组属性来获得。 ndarray.ndimthe number of axes (dimensions) of the array秩,数组轴的数量,或者维度的数量 ndarray.shapethe dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m ...
import numpy as np # 创建一个数组 my_array = np.array([[1, 2, 3], [4, 5, ...
Step3,进行n次迭代后,直到页面的影响力不再发生变化,也就是页面的影响力收敛=>最终的影响力 PageRank的代码推导:importnumpy as np a= np.array([[0, 1/2, 1, 0], [1/3, 0, 0, 1/2], [1/3, 0, 0, 1/2], [1/3, 1/2, 0, 0]]) b= np.array([1/4, 1/4, 1/4, 1/4]) ...
问在numpy中解释dim、shape、rank、dimension和axis之间的区别ENtorch.index_select(input, dim, index, ...
maxAbs(array) # [static] 获得数组array中绝对值最大的元素下标 train(self,maxIterationSize=100,threshold=0.0000001)import numpy as np; class PageRank: def __init__(self,pages=['A','B'],links=[(1,0),(0,1)],d=0.85): self.pages = pages; self.links = links; # 根据初始数据初始化...
import numpy as npimport matplotlib.pyplot as plt# 设置阻尼因子(跳转因子)防止Spider traps问题alpha = 0.85 # 跳转因子def draw(iter_list, pr_list):"""绘制收敛图:param iter_list: 迭代次数列表:param pr_list: 每一个向量的值:return:"""plt.plot(iter_list, pr_list)plt.show()def pagerank(...
Rank of each item of the said array: [2 3 5 4 1 0] Explanation: In the above exercise - array = numpy.array([24, 27, 30, 29, 18, 14]): It creates a 1-dimensional NumPy array array with the given elements. argsort_array = array.argsort(): It applies the argsort() function ...
array(A.sum(1))[:, 0] ri, ci = A.nonzero() A.data /= rsums[ri] # bool array of sink states sink = rsums == 0 # Compute pagerank r until we converge ro, r = np.zeros(n), np.ones(n) while np.sum(np.abs(r - ro)) > maxerr: ro = r.copy() # calculate each ...
import networkx as nx # 图数据挖掘 import numpy as np import pandas as pd import random import matplotlib.pyplot as plt import matplotlib as mpl %matplotlib inline plt.rcParams['font.sans-serif'] = ['Songti SC'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正...
#用NumPy库创建邻接矩阵图self.graph=np.array(graph)# 初始化PageRank值self.rank=np.ones(self.n)/self.n# 迭代计算new_rank=(1-self.alpha)/self.n+self.alpha*self.graph.T.dot(self.rank) 1. 2. 3. 4. 5. 6. 7. 8. 在性能优化过程中,我们可以使用思维导图来跟踪潜在的改进方法。