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RSS订阅原创 使用\begin{aligned} 出现 Environment aligned undefined.解决办法
使用\begin{aligned} 出现 Environment aligned undefined.解决办法
2022-11-05 19:26:12
3125
原创 conda install
具体解决方法是：1、使用以下命令查找我们想要安装的包anaconda search -t conda folium目前folium最新的版本是0.12.1，这里我们找到了一个接近这个版本的包，包名为 conda-forge/folium。2、使用show指令来查看该包的详细情况anaconda show conda-forge/folium终端会显示这个包的具体channel。3、按照终端显示的channel，输入命令等待安装conda install --channel https://c
2021-12-08 15:47:19
2174
原创 10-C为AB对应位置最大元组成的矩阵
#C中的元素为A,B对应位置的最大元素import numpy as npfrom scipy.sparse import csr_matrixA=csr_matrix(np.array([[1,0,0],[1,2,3],[2,2,2]]))B=csr_matrix(np.array([[2,0,0],[2,2,0],[3,3,3]]))print("A=")print(A.toarray())print("B=")print(B.toarray())C=A.maximum(B)pri
2021-11-15 15:51:33
1153
原创 9-csr_matrix.multiply(B)-对应元素相乘
import numpy as npfrom scipy.sparse import csr_matrixA=csr_matrix(np.array([[1,0,0],[1,2,3],[2,2,2]]))B=csr_matrix(np.array([[2,0,0],[2,2,0],[3,3,3]]))C=A.multiply(B)print(C.toarray())[[2 0 0][2 4 0][6 6 6]]
2021-11-15 15:44:14
551
原创 8-scipy.sparse与np.mat转换
```pythonimport numpy as npfrom scipy.sparse import csr_matrixB=np.mat([[0,1,0,0,0],[0,0,0,1,1],[1,1,1,0,0],[0,0,0,0,0],[1,0,0,0,0]])print("B=")print(B)sparseB=csr_matrix(B)print("sparseB=")print(sparseB)`B=[[0 1 0 0 0][0 0 0 1 1][1 1 1 0 0].
2021-11-15 15:27:25
1010
原创 7-csr_matrix.astype
import numpy as npfrom scipy.sparse import csr_matrix#1-创建网络row = np.array([0, 0, 1, 2, 2, 2, 3])col = np.array([0, 2, 2, 0, 1, 2, 2])data = np.array([1, 1, 2.4, 1, 1, 1.6,1 ])A=csr_matrix((data, (row, col)), shape=(4, 3))print("A=")print(A)B = A.
2021-11-15 15:03:52
778
原创 6-csr_matrix的shape
from scipy.sparse import csr_matrix#1-创建网络row = np.array([0, 0, 1, 2, 2, 2, 3])col = np.array([0, 2, 2, 0, 1, 2, 2])data = np.array([1, 1, 1, 1, 1, 1,1 ])A=csr_matrix((data, (row, col)), shape=(4, 3))print("A=")print(A)arr_1=A.toarray()print("arr_
2021-11-15 11:16:31
2664
原创 random.rand()生成[0,1)的随机小数
#生成一个[0,1)之间的随机浮点数或N维浮点数组。#type(u)= <class 'numpy.ndarray'> import numpy as npn=6k=2np.random.seed(0)u = np.random.rand(n, k)np.random.seed(0)v = np.random.rand(n, k)np.random.seed(0)w = np.random.rand(k, k)print("type(u)=",type(u))print
2021-11-15 10:22:58
3257
原创 sparseA @ sparseB
import numpy as npfrom scipy.sparse import csr_matrixrow = np.array([0, 0, 1, 1, 2, 2])col = np.array([0, 2, 1, 2, 0, 1])data = np.array([1, 1, 1, 1, 1, 1])sparseA=csr_matrix((data, (row, col)), shape=(3, 3))print("sparseA=")print(sparseA)arrA=spa
2021-11-14 15:11:42
370
原创 sp.sparse.diags()方法
import scipy as spd=np.array([5,1,11])print(d)B1=sp.sparse.diags(1 / d)print(“B1=”)print(B1)print(type(B1))arrB1=B1.toarray()print(“arrB1=”)print(arrB1)print(type(arrB1))运行结果：[ 5 1 11]B1=(0, 0) 0.2(1, 1) 1.0(2, 2) 0.09090909090909091<c
2021-11-14 14:45:37
1076
原创 ndarray1维数组为0的地方改为1
import numpy as npfrom scipy.sparse import csr_matrixrow = np.array([0, 0, 1, 2, 2])col = np.array([0, 2, 2, 0, 2])data = np.array([1, 2, 3, 4, 6])sparseA=csr_matrix((data, (row, col)), shape=(3, 3))print(“sparseA=”)print(sparseA)arrA=sparseA.toarr
2021-11-14 14:31:56
919
原创 np.matrix.A1
#知识点：关于A1x = np.matrix(np.arange(12).reshape((3,4)))print(“x=”)print(x)M=x.A1print(“M=”)print(M)print(type(M))结果x=[[ 0 1 2 3][ 4 5 6 7][ 8 9 10 11]]M=[ 0 1 2 3 4 5 6 7 8 9 10 11]<class ‘numpy.ndarray’>...
2021-11-14 14:14:27
272
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