def test_fail_vec_mat_mult():
dims = {'2': (2, 5),
'3': (3, 7),
'4': (4, 13),
'6': (13, 19),
'X': (6, 4)}
for k, v in vectors.items():
m = e.MatrixXd(*dims[k])
assert_raises(TypeError, lambda x,y: x*y, v, m)
def test_vec_mat_mult():
cols = {'2': 5,
'3': 7,
'4': 13,
'6': 19,
'X': 4}
for k, v in vectors.items():
m = e.MatrixXd(1, cols[k])
res = v*m
assert(isinstance(res, e.MatrixXd))
assert(res.rows() == v.rows())
assert(res.cols() == cols[k])
assert((abs(np.array(res) - np.array(v).dot(np.array(m))) < precision).all())
def __init__(self):
self.nrvar = 6
self.nreq = 3
self.nrineq = 2
self.Q = eigen.MatrixXd.Identity(self.nrvar, self.nrvar)
self.Aeq = eigen.MatrixXd([ [1., -1., 1., 0., 3., 1.],
[-1., 0., -3., -4., 5., 6.],
[2., 5., 3., 0., 1., 0.] ])
self.Beq = eigen.VectorXd([1., 2., 3.])
self.Aineq = eigen.MatrixXd([ [0., 1., 0., 1., 2., -1.],
[-1., 0., 2., 1., 1., 0.] ])
self.Bineq = eigen.VectorXd([-1., 2.5])
self.XL = eigen.VectorXd([ -1000., -10000., 0., -1000., -1000.,-1000. ])
self.XU = eigen.VectorXd([ 10000., 100., 1.5, 100., 100., 1000. ])
self.C = eigen.VectorXd([ 1., 2., 3., 4., 5., 6. ])
self.X = eigen.VectorXd([ 1.7975426, -0.3381487, 0.1633880, -4.9884023, 0.6054943, -3.1155623 ])
print dir(y) x1 = x() print x1 y1 = y() print y1 print dir(x1) print dir(y1) x2 = x(10) print x2[0] print dir(x2[0]) print dir(eigen) s = eigen.MatrixXd(3, 4) print s r = eigen.VectorXd(10) print r print dir(r) r.__setitem__(0, 20) print r print type(s) print type(r)
# -*- encoding=utf-8 -*- import numpy as np import sys import eigen from ctypes import * # 查看python模块默认安装路径 print sys.path A = np.random.random((2000, 50)) B = eigen.MatrixXd(A) n = np.linalg.norm(B) # Implicit conversion to numpy object vector_1D = eigen.VectorXd(3) vector_1D.coeff(1, 2) print type(vector_1D) print vector_1D matrix_1D = eigen.MatrixXd(3, 1) matrix_1D.setZero() print matrix_1D