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1、线性代数
矩阵乘法 dot 函数
x= np.array([[1,2,3],[4,5,6]]) y=np.array([[6,23],[-1,7],[8,9]]) x Out[16]: array([[1, 2, 3], [4, 5, 6]]) y Out[17]: array([[ 6, 23], [-1, 7], [ 8, 9]]) x.dot(y) Out[18]: array([[ 28, 64], [ 67, 181]])
一个二维数组跟一个大小合适的一维数组的矩阵点积运算之后将会得到一个一维数组。
np.dot(x,np.ones(3))
Out[19]: array([ 6., 15.])
numpy.linalg
from numpy.linalg import inv,qr x = np.random.randn(5,5) mat = x.T.dot(x) inv(mat) Out[24]: array([[ 183.76974989, -623.36361091, -583.49826184, -235.16948917, -181.68152874], [ -623.36361091, 2121.59301898, 1985.26883645, 799.39704159, 619.72162247], [ -583.49826184, 1985.26883645, 1858.87861876, 747.67011221, 578.69498867], [ -235.16948917, 799.39704159, 747.67011221, 301.90295918, 233.89701649], [ -181.68152874, 619.72162247, 578.69498867, 233.89701649, 182.77441114]])
2、随机数生成
numpy.random模块对python的内置函数random进行了补充
如 ： normal函数 可以生成 4*4的样本数组:
samples = np.random.normal(size = (4,4)) samples Out[11]: array([[-1.22102285, 2.08688133, 1.15874399, 0.14342708], [-0.29772372, 0.36137871, 0.60243437, -0.09287792], [-0.49263459, 0.69445334, 1.02035894, -1.18263174], [-0.07184985, -1.11834445, 0.89547984, 0.0585053 ]])
3、范例
随机漫步1000：
nsteps = 1000 draws = np.random.randint(0,2,size = nsteps) steps = np.where(draws>0,1,-1) walk = steps.cumsum() walk.min()
一次模拟多个多个随机漫步。
nwalk = 5000 nsteps =1000 nwalks =5000 draws = np.random.randint(0,2,size = (nwalks,nsteps)) steps = np.where(draws > 0 ,1,-1) walks = steps.cumsum(1) walks Out[28]: array([[ 1, 2, 1, ..., 16, 15, 16], [ -1, 0, -1, ..., 22, 21, 22], [ -1, 0, 1, ..., -36, -35, -36], ..., [ -1, 0, 1, ..., -16, -17, -18], [ 1, 0, 1, ..., 12, 11, 10], [ -1, 0, -1, ..., -8, -9, -8]], dtype=int32)
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原文地址：http://www.cnblogs.com/groupe/p/4915062.html
