2020-10-19 21:08:55

docs/da/060.md

@@ -23,7 +23,7 @@
 
 In [1]:
 
-```
+```py
 from scipy.sparse import *
 import numpy as np
 
@@ -33,14 +33,14 @@ import numpy as np
 
 In [2]:
 
-```
+```py
 coo_matrix((2,3))
 
 ```
 
 Out[2]:
 
-```
+```py
 <2x3 sparse matrix of type '<type 'numpy.float64'>'
 	with 0 stored elements in COOrdinate format>
 ```
@@ -49,13 +49,13 @@ Out[2]:
 
 In [3]:
 
-```
+```py
 A = coo_matrix([[1,2,0],[0,0,3],[4,0,5]])
 print A
 
 ```
 
-```
+```py
   (0, 0)	1
   (0, 1)	2
   (1, 2)	3
@@ -68,20 +68,20 @@ print A
 
 In [4]:
 
-```
+```py
 type(A)
 
 ```
 
 Out[4]:
 
-```
+```py
 scipy.sparse.coo.coo_matrix
 ```
 
 In [5]:
 
-```
+```py
 B = A.tocsr()
 type(B)
 
@@ -89,7 +89,7 @@ type(B)
 
 Out[5]:
 
-```
+```py
 scipy.sparse.csr.csr_matrix
 ```
 
@@ -97,7 +97,7 @@ scipy.sparse.csr.csr_matrix
 
 In [6]:
 
-```
+```py
 C = A.todense()
 C
 
@@ -105,7 +105,7 @@ C
 
 Out[6]:
 
-```
+```py
 matrix([[1, 2, 0],
         [0, 0, 3],
         [4, 0, 5]])
@@ -115,7 +115,7 @@ matrix([[1, 2, 0],
 
 In [7]:
 
-```
+```py
 v = np.array([1,0,-1])
 A.dot(v)
 
@@ -123,7 +123,7 @@ A.dot(v)
 
 Out[7]:
 
-```
+```py
 array([ 1, -3, -1])
 ```
 
@@ -131,7 +131,7 @@ array([ 1, -3, -1])
 
 In [8]:
 
-```
+```py
 I = np.array([0,3,1,0])
 J = np.array([0,3,1,2])
 V = np.array([4,5,7,9])
@@ -141,12 +141,12 @@ A = coo_matrix((V,(I,J)),shape=(4,4))
 
 In [9]:
 
-```
+```py
 print A
 
 ```
 
-```
+```py
   (0, 0)	4
   (3, 3)	5
   (1, 1)	7
@@ -158,7 +158,7 @@ COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到
 
 In [10]:
 
-```
+```py
 I = np.array([0,0,1,3,1,0,0])
 J = np.array([0,2,1,3,1,0,0])
 V = np.array([1,1,1,1,1,1,1])
@@ -167,7 +167,7 @@ print B
 
 ```
 
-```
+```py
   (0, 0)	1
   (0, 2)	1
   (1, 1)	1
@@ -182,13 +182,13 @@ print B
 
 In [11]:
 
-```
+```py
 C = B.tocsr()
 print C
 
 ```
 
-```
+```py
   (0, 0)	3
   (0, 2)	1
   (1, 1)	2
@@ -200,7 +200,7 @@ print C
 
 In [12]:
 
-```
+```py
 from scipy.sparse import lil_matrix
 from scipy.sparse.linalg import spsolve
 from numpy.linalg import solve, norm
@@ -212,7 +212,7 @@ from numpy.random import rand
 
 In [13]:
 
-```
+```py
 A = lil_matrix((1000, 1000))
 A[0, :100] = rand(100)
 A[1, 100:200] = A[0, :100]
@@ -224,7 +224,7 @@ A.setdiag(rand(1000))
 
 In [14]:
 
-```
+```py
 A = A.tocsr()
 b = rand(1000)
 x = spsolve(A, b)
@@ -235,7 +235,7 @@ x = spsolve(A, b)
 
 In [15]:
 
-```
+```py
 x_ = solve(A.toarray(), b)
 
 ```
@@ -244,7 +244,7 @@ x_ = solve(A.toarray(), b)
 
 In [16]:
 
-```
+```py
 err = norm(x-x_)
 err
 
@@ -252,7 +252,7 @@ err
 
 Out[16]:
 
-```
+```py
 6.4310987107687431e-13
 ```
 
@@ -262,7 +262,7 @@ Out[16]:
 
 In [17]:
 
-```
+```py
 from scipy import sparse
 
 row, col, val = sparse.find(C)
@@ -270,7 +270,7 @@ print row, col, val
 
 ```
 
-```
+```py
 [0 0 1 3] [0 2 1 3] [3 1 2 1]
 
 ```
@@ -281,14 +281,14 @@ print row, col, val
 
 In [18]:
 
-```
+```py
 sparse.issparse(B)
 
 ```
 
 Out[18]:
 
-```
+```py
 True
 ```
 
@@ -296,14 +296,14 @@ True
 
 In [19]:
 
-```
+```py
 sparse.isspmatrix(B.todense())
 
 ```
 
 Out[19]:
 
-```
+```py
 False
 ```
 
@@ -311,26 +311,26 @@ False
 
 In [20]:
 
-```
+```py
 sparse.isspmatrix_coo(B)
 
 ```
 
 Out[20]:
 
-```
+```py
 True
 ```
 
 In [21]:
 
-```
+```py
 sparse.isspmatrix_csr(B)
 
 ```
 
 Out[21]:
 
-```
+```py
 False
 ```