查看“Scipy”的源代码

http://www.scipy-lectures.org

==稀疏矩阵==
*[https://blog.csdn.net/Sherry_Yue/article/details/102652829]
 from scipy.sparse import csr_matrix
 import numpy as np
 indptr = np.array([0,2,3,6])  #游标指针 数据 = n+1，前面n个分别是各行数据的起点，最后一个是非零数据的总数
  第0组（第0行）在 indices / data 中的起始下标[0]；1组（第1行）在 indices / data 中的起始下标[2]；第2组（第2行）在 indices / data 中的起始下标[3]。
 indices = np.array([0,2,2,0,1,2]) #非零数据所在的列
 data = np.array([1,2,3,4,5,6]) #非零数据的数值
 csr_matrix_0 = csr_matrix((data,indices,indptr),shape=(3,3))
 print(csr_matrix_0.toarray())

==求函数最小值==
*Methods based on conjugate gradient are named with ‘cg’ in scipy. The simple conjugate gradient method to minimize a function is scipy.optimize.fmin_cg():
*n scipy, scipy.optimize.fmin() implements the Nelder-Mead approach: (不太依赖于倒数)
*Brute force: a grid search
:scipy.optimize.brute() evaluates the function on a given grid of parameters and returns the parameters corresponding to the minimum value.The parameters are specified with ranges given to numpy.mgrid. By default, 20 steps are taken in each direction:
*Non-linear least squares: Levenberg–Marquardt algorithm implemented in scipy.optimize.leastsq().
*If the function is linear, this is a linear-algebra problem, and should be solved with scipy.linalg.lstsq().

==文件输出== 
 # Some test data
 x = np.arange(200).reshape((4,5,10))
 # Specify the filename of the .mat file
 matfile = 'test_mat.mat'
 # Write the array to the mat file. For this to work, the array must be the value
 # corresponding to a key name of your choice in a dictionary
 scipy.io.savemat(matfile, mdict={'out': x}, oned_as='row')

 # For the above line, I specified the kwarg oned_as since python (2.7 with 
 # numpy 1.6.1) throws a FutureWarning.  Here, this isn't really necessary 
 # since oned_as is a kwarg for dealing with 1-D arrays.
 
 # Now load in the data from the .mat that was just saved
 matdata = scipy.io.loadmat(matfile)
 # And just to check if the data is the same:
 assert np.all(x == matdata['out'])