8 examples of 'python inverse matrix' in Python

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def Inverse(self):
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    r"""Inverse(DenseMatrix self) -> MatrixInverse"""
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    return _densemat.DenseMatrix_Inverse(self)

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def inv(self, matrix: Tensor) -> Tensor:
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  if len(matrix.shape) > 2:
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    raise ValueError(
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        "input to pytorch backend method `inv` has shape {}. Only matrices are supported."
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        .format(matrix.shape))
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  return matrix.inverse()

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def getInverse(self) -> "Matrix":
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    try:
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        return Matrix(numpy.linalg.inv(self._data))
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    except:
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        return Matrix(self._data)

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def test_inverse_simple(self):
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    T = np.array([
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        [2, 1, 1, 0],
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        [4, 3, 3, 1],
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        [8, 7, 9, 5],
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        [6, 7, 9, 8]
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    ])
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    actual = inverse(T)
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    expected = LA.inv(T)
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    self.assertTrue(np.allclose(actual, expected))

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def inverse(bmat):
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    '''
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    Inverse of a linear combination of Hs norms. We very strictly 
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    enforce the form of sum_j alpha_j H^{s_j}
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    '''
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    if isinstance(bmat, InterpolationMatrix):
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        return bmat**-1
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    if isinstance(bmat, block_utils.VectorizedOperator):
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        return block_utils.VectorizedOperator(inverse(bmat.bmat), bmat.W)
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    # Does it satisfy the definittion
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    assert is_well_defined(bmat)
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    # Try to see if sombody computes the eigenvalues
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    lmbda, U = extract_attributes(bmat, ('lmbda', 'U'))
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    # Do it your self
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    if U is None or lmbda is None:
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        A, M = extract_attributes(bmat, ('A', 'M'))
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        lmbda, U = eigh(A.array(), M.array())
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    diagonal = np.zeros_like(lmbda)
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    for alpha, s in collect(bmat):
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        diagonal[:] += alpha*(lmbda**s)
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    # Invert
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    diagonal[:] = 1./diagonal
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    array = U.dot(np.diag(diagonal).dot(U.T))
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    return numpy_to_petsc(array)

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@staticmethod
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@IMPLEMENT_NODE(returns=('Matrix44Pin', pyrr.Matrix44()), meta={'Category': 'Math|Matrix44', 'Keywords': ['create', 'matrix44']})
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def m44Inverse(m=('Matrix44Pin', pyrr.Matrix44())):
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    '''Returns the inverse of the matrix.\nThis is essentially a wrapper around numpy.linalg.inv.'''
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    return ~m

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def invert(self):
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    # TODO test
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    rst = []
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    for i in range(len(self)):
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        vi = []
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        for j in range(len(self)):
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            vi.append(self.minor(j, i).determinant())
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        vi = Vector(vi)
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        vi = vi * (1.0 / self.determinant())
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        rst.append(vi)
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    return Matrix(rst)

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def inv(x):
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    """
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    computes inverse of input matrix
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    Parameters
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    ----------
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    x : array_like (D, D)
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        square matrix
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    Returns
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    -------
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    Array
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        inverse of `x`
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    Raises
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    ------
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    ValueError
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        raises error if `x` is not square
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    """
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    if x.ndim != 2 or x.shape[0] != x.shape[1]:
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        raise ValueError
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    return _Inverse().forward(x)