Scipy
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稀疏矩阵
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]；
#第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'])