numpy的矩阵运算

>>> x1=np.arange(0,5)  # array([0, 1, 2, 3, 4])
>>> x2=np.arange(1,6)  # array([1, 2, 3, 4, 5])
>>> x3=np.linspace(0,5,6) # array([ 0.,  1.,  2.,  3.,  4.,  5.])

>>> transpose(x2.reshape(1,5))  #如果不变维的话，返回值仍是array([1, 2, 3, 4, 5])
array([[1],
       [2],
       [3],
       [4],
       [5]])

#[0, 1, 2, 3, 4] x [1, 2, 3, 4, 5]‘
>>> dot(x1.reshape(1,5),transpose(x2.reshape(1,5)))
array([[40]])

>>> linalg.inv([[1,2],[3,4]])
array([[-2. ,  1. ],
       [ 1.5, -0.5]])

>>> a = np.arange(0,12)
>>> a.shape = 3,4
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a.tofile("a.bin")
>>> b = np.fromfile("a.bin", dtype=np.float) # 按照float类型读入数据
>>> b # 读入的数据是错误的
array([  2.12199579e-314,   6.36598737e-314,   1.06099790e-313,
         1.48539705e-313,   1.90979621e-313,   2.33419537e-313])
>>> a.dtype # 查看a的dtype
dtype(‘int32‘)
>>> b = np.fromfile("a.bin", dtype=np.int32) # 按照int32类型读入数据
>>> b # 数据是一维的
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> b.shape = 3, 4 # 按照a的shape修改b的shape
>>> b # 这次终于正确了
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])