56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
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from sympy.core import Basic, Expr
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from sympy.core.sympify import _sympify
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from sympy.matrices.expressions.transpose import transpose
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class DotProduct(Expr):
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"""
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Dot product of vector matrices
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The input should be two 1 x n or n x 1 matrices. The output represents the
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scalar dotproduct.
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This is similar to using MatrixElement and MatMul, except DotProduct does
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not require that one vector to be a row vector and the other vector to be
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a column vector.
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>>> from sympy import MatrixSymbol, DotProduct
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>>> A = MatrixSymbol('A', 1, 3)
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>>> B = MatrixSymbol('B', 1, 3)
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>>> DotProduct(A, B)
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DotProduct(A, B)
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>>> DotProduct(A, B).doit()
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A[0, 0]*B[0, 0] + A[0, 1]*B[0, 1] + A[0, 2]*B[0, 2]
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"""
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def __new__(cls, arg1, arg2):
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arg1, arg2 = _sympify((arg1, arg2))
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if not arg1.is_Matrix:
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raise TypeError("Argument 1 of DotProduct is not a matrix")
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if not arg2.is_Matrix:
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raise TypeError("Argument 2 of DotProduct is not a matrix")
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if not (1 in arg1.shape):
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raise TypeError("Argument 1 of DotProduct is not a vector")
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if not (1 in arg2.shape):
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raise TypeError("Argument 2 of DotProduct is not a vector")
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if set(arg1.shape) != set(arg2.shape):
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raise TypeError("DotProduct arguments are not the same length")
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return Basic.__new__(cls, arg1, arg2)
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def doit(self, expand=False, **hints):
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if self.args[0].shape == self.args[1].shape:
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if self.args[0].shape[0] == 1:
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mul = self.args[0]*transpose(self.args[1])
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else:
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mul = transpose(self.args[0])*self.args[1]
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else:
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if self.args[0].shape[0] == 1:
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mul = self.args[0]*self.args[1]
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else:
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mul = transpose(self.args[0])*transpose(self.args[1])
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return mul[0]
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