Hello everybody,
Could somebody give me a hint how to introduce new operator mapping for
sympy to theano code generation?
What are limitations of this mapping? For example, «We could map only
subclass of expression to subclass of something else.»
I have got the following simplified test snippet:
from sympy import MatrixSymbol, Matrix, pprint, HadamardProduct
from sympy.abc import x, y
M = 3
A = 4
Nu = MatrixSymbol('Nu', M, A)
p = MatrixSymbol('p', M, 2)
G = Matrix([[1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 1]])
fs = [x*1+10,
x*2+10,
x*3+10,
x*4+10,
x*5+10,
x*6+10]
F = Matrix(M, len(fs), lambda i, j: fs[j].subs({x: x-p[i, 0], y: y-p[i, 1
]}))
P = Matrix([[Matrix(1, M, lambda i, j: 0 if j == k else 1)*(HadamardProduct(
Nu*G, F))
for k in xrange(0, M)]])
pprint(P, num_columns=500)
inputs = [x, y, p]
outputs = [P]
dtypes = {inp: 'float64' for inp in inputs}
from sympy.printing.theanocode import theano_function
f = theano_function(inputs, outputs)
Unfortunately, an attempt of code generation failed, since there is no
mapping exists for MatrixElement.
File "/Users/aloschil/workspace/sympy/sympy/printing/theanocode.py", line
102, in _print_Basic
op = mapping[type(expr)]
KeyError: <class 'sympy.matrices.expressions.matexpr.MatrixElement'>
I have made an assumption that I should add the following mapping:
MatrixElement -> Subtensor{int64, int64}.
I am missing something. I have got no clear vision of underlying ideas,
that is why it is a little bit tricky for me to go ahead.
I did not dive into details and tried the following:
sympy.matrices.expressions.matexpr.MatrixElement: tt.var.TensorVariable
Unfortunately, this straightforward approach failed.
I would like to read documentation that describes core ideas, which lies
behind operation building either in SymPy and Theano.
best regards,
Alexander.
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