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Removing unumpy.py core dependency on matrices #120

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Removing unumpy.py core dependency on matrices #120

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acd8a92
Removing unumpy.py core dependency on matrices
AdityaSavara Feb 11, 2020
3e44594
Adding .conj() for non-real numbers.
AdityaSavara Feb 11, 2020
7fbe471
Changing some of the comments to help save time
AdityaSavara Feb 11, 2020
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30 changes: 15 additions & 15 deletions uncertainties/unumpy/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -462,12 +462,12 @@ def inv_with_derivatives(arr, input_type, derivatives):
yield inverse

# It is mathematically convenient to work with matrices:
inverse_mat = numpy.asmatrix(inverse)
inverse_mat = numpy.asarray(inverse)

# Successive derivatives of the inverse:
for derivative in derivatives:
derivative_mat = numpy.asmatrix(derivative)
yield -inverse_mat * derivative_mat * inverse_mat
derivative_mat = numpy.asarray(derivative)
yield -inverse_mat @ derivative_mat @ inverse_mat

inv = func_with_deriv_to_uncert_func(inv_with_derivatives)
inv.__doc__ = """\
Expand Down Expand Up @@ -503,8 +503,8 @@ def pinv_with_derivatives(arr, input_type, derivatives, rcond):
inverse = inverse.view(numpy.matrix)
yield inverse

# It is mathematically convenient to work with matrices:
inverse_mat = numpy.asmatrix(inverse)
# Numpy arrays will allow matrix multiplication with the @ operator:
inverse_mat = numpy.asarray(inverse)

# Formula (4.12) from The Differentiation of Pseudo-Inverses and
# Nonlinear Least Squares Problems Whose Variables
Expand All @@ -515,21 +515,21 @@ def pinv_with_derivatives(arr, input_type, derivatives, rcond):
# See also
# http://mathoverflow.net/questions/25778/analytical-formula-for-numerical-derivative-of-the-matrix-pseudo-inverse

# Shortcuts. All the following factors should be numpy.matrix objects:
PA = arr*inverse_mat
AP = inverse_mat*arr
factor21 = inverse_mat*inverse_mat.H
# Shortcuts. The @ operator is used for matrix multiplication of the factors:
PA = arr@inverse_mat
AP = inverse_mat@arr
factor21 = inverse_mat@numpy.transpose(inverse_mat.conj())
factor22 = numpy.eye(arr.shape[0])-PA
factor31 = numpy.eye(arr.shape[1])-AP
factor32 = inverse_mat.H*inverse_mat
factor32 = numpy.transpose(inverse_mat.conj())@inverse_mat

# Successive derivatives of the inverse:
for derivative in derivatives:
derivative_mat = numpy.asmatrix(derivative)
term1 = -inverse_mat*derivative_mat*inverse_mat
derivative_mat_H = derivative_mat.H
term2 = factor21*derivative_mat_H*factor22
term3 = factor31*derivative_mat_H*factor32
derivative_mat = numpy.asarray(derivative)
term1 = -inverse_mat@derivative_mat@inverse_mat
derivative_mat_H = numpy.transpose(derivative_mat.conj())
term2 = factor21@derivative_mat_H@factor22
term3 = factor31@derivative_mat_H@factor32
yield term1+term2+term3

# Default rcond argument for the generalization of numpy.linalg.pinv:
Expand Down