结合numpy和sympy [英] Combining numpy with sympy
问题描述
我有以下代码:
p = classp();
for i in range(1,10):
x = numpy.array([[2],[4],[5]])
print p.update(x)
class classp:
def __init__(self):
self.mymodel = array([2*x[1]], [3*x[0]], [x[2]]);
def update(self, x):
return self.mymodel #replace x(0)...x(1) with the given parameter
我的问题与上面的代码有关,我想尽可能使用sympy定义模型,然后在update函数中用x值替换sympy变量.是否有可能?我该怎么办?
My question is related the code above, I would like to define a model using sympy if it's possible, afterwards in the update function replace the sympy variables with the x values. Is it possible? How can I do that?
推荐答案
我可以为您提出两个解决方案.
I can propose you two solutions.
首先,有一个DeferedVector
是为与lambdify
一起使用而创建的:
Firstly, there is DeferedVector
that was created for use with lambdify
:
In [1]: from sympy.matrices import DeferredVector
In [2]: v = DeferredVector('v')
In [3]: func = lambdify(v, Matrix([v[1], 2*v[2]]))
In [4]: func(np.array([10,20,30]))
Out[4]:
[[20]
[60]]
但是lambdify不能满足我的口味.
However lambdify does too much magic for my taste.
另一种选择是使用.subs
方法:
Another option is to use the .subs
method:
In [11]: x1, x2, x3 = symbols('x1:4')
In [12]: m = Matrix([x2,2*x1,x3/2])
In [13]: m.subs({x1:10, x2:20, x3:30})
Out[13]:
⎡20⎤
⎢ ⎥
⎢20⎥
⎢ ⎥
⎣15⎦
您可以像这样创建用于替换的字典:
You can create the dictionary for the substitution like that:
dict(zip(symbols('x1:4'), your_value_array))
.
不要忘记所有返回对象都是sympy矩阵.要将它们转换为numpy数组,只需使用np.array(the_matrix_in_question)
且不要忘记指定dtype
,否则它将默认为dtype=object
.
Do not forget that all the return objects are sympy matrices. To convert them to numpy arrays just use np.array(the_matrix_in_question)
and do not forget to specify the dtype
, otherwise it will default to dtype=object
.
这篇关于结合numpy和sympy的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!