From 7f18a1ccdfc55fb0a48f2fd0a7d8522abe9111cf Mon Sep 17 00:00:00 2001
From: Theo Steininger <theo.steininger@ultimanet.de>
Date: Wed, 21 Jun 2017 10:01:09 +0200
Subject: [PATCH] Removed trace_log and determinat methods from diagonal
operator.
---
nifty/minimization/conjugate_gradient.py | 2 +-
.../diagonal_operator/diagonal_operator.py | 121 ------------------
.../response_operator/response_operator.py | 2 +-
test/test_operators/test_diagonal_operator.py | 58 ---------
4 files changed, 2 insertions(+), 181 deletions(-)
diff --git a/nifty/minimization/conjugate_gradient.py b/nifty/minimization/conjugate_gradient.py
index 00a12efa9..d7caccdd0 100644
--- a/nifty/minimization/conjugate_gradient.py
+++ b/nifty/minimization/conjugate_gradient.py
@@ -70,7 +70,7 @@ class ConjugateGradient(Loggable, object):
References
----------
- Thomas V. Mikosch et al., "Numerical Optimization", Second Edition,
+ Jorge Nocedal & Stephen Wright, "Numerical Optimization", Second Edition,
2006, Springer-Verlag New York
"""
diff --git a/nifty/operators/diagonal_operator/diagonal_operator.py b/nifty/operators/diagonal_operator/diagonal_operator.py
index 77f843fc2..f45cac9cb 100644
--- a/nifty/operators/diagonal_operator/diagonal_operator.py
+++ b/nifty/operators/diagonal_operator/diagonal_operator.py
@@ -132,127 +132,6 @@ class DiagonalOperator(EndomorphicOperator):
return self._times_helper(x, spaces,
operation=lambda z: z.adjoint().__rdiv__)
- def diagonal(self, bare=False, copy=True):
- """ Returns the diagonal of the Operator.
-
- Parameters
- ----------
- bare : boolean
- Whether the returned Field values should be bare or not.
- copy : boolean
- Whether the returned Field should be copied or not.
-
- Returns
- -------
- out : Field
- The diagonal of the Operator.
-
- """
- if bare:
- diagonal = self._diagonal.weight(power=-1)
- elif copy:
- diagonal = self._diagonal.copy()
- else:
- diagonal = self._diagonal
- return diagonal
-
- def inverse_diagonal(self, bare=False):
- """ Returns the inverse-diagonal of the operator.
-
- Parameters
- ----------
- bare : boolean
- Whether the returned Field values should be bare or not.
-
- Returns
- -------
- out : Field
- The inverse of the diagonal of the Operator.
-
- """
- return 1./self.diagonal(bare=bare, copy=False)
-
- def trace(self, bare=False):
- """ Returns the trace the operator.
-
- Parameters
- ----------
- bare : boolean
- Whether the returned Field values should be bare or not.
-
- Returns
- -------
- out : scalar
- The trace of the Operator.
-
- """
- return self.diagonal(bare=bare, copy=False).sum()
-
- def inverse_trace(self, bare=False):
- """ Returns the inverse-trace of the operator.
-
- Parameters
- ----------
- bare : boolean
- Whether the returned Field values should be bare or not.
-
- Returns
- -------
- out : scalar
- The inverse of the trace of the Operator.
-
- """
- return self.inverse_diagonal(bare=bare).sum()
-
- def trace_log(self):
- """ Returns the trave-log of the operator.
-
- Returns
- -------
- out : scalar
- the trace of the logarithm of the Operator.
-
- """
- log_diagonal = nifty_log(self.diagonal(copy=False))
- return log_diagonal.sum()
-
- def determinant(self):
- """ Returns the determinant of the operator.
-
- Returns
- -------
- out : scalar
- out : scalar
- the determinant of the Operator
-
- """
-
- return self.diagonal(copy=False).val.prod()
-
- def inverse_determinant(self):
- """ Returns the inverse-determinant of the operator.
-
- Returns
- -------
- out : scalar
- the inverse-determinant of the Operator
-
- """
-
- return 1/self.determinant()
-
- def log_determinant(self):
- """ Returns the log-eterminant of the operator.
-
- Returns
- -------
- out : scalar
- the log-determinant of the Operator
-
- """
-
- return np.log(self.determinant())
-
# ---Mandatory properties and methods---
@property
diff --git a/nifty/operators/response_operator/response_operator.py b/nifty/operators/response_operator/response_operator.py
index 0e25e4282..75361469d 100644
--- a/nifty/operators/response_operator/response_operator.py
+++ b/nifty/operators/response_operator/response_operator.py
@@ -85,7 +85,7 @@ class ResponseOperator(LinearOperator):
kernel_smoothing = len(self._domain)*[None]
kernel_exposure = len(self._domain)*[None]
- if len(sigma)!= len(exposure):
+ if len(sigma) != len(exposure):
raise ValueError("Length of smoothing kernel and length of"
"exposure do not match")
diff --git a/test/test_operators/test_diagonal_operator.py b/test/test_operators/test_diagonal_operator.py
index ef953fa06..9b8bf86ce 100644
--- a/test/test_operators/test_diagonal_operator.py
+++ b/test/test_operators/test_diagonal_operator.py
@@ -76,61 +76,3 @@ class DiagonalOperator_Tests(unittest.TestCase):
D = DiagonalOperator(space, diagonal=diag, bare=bare, copy=copy)
tt = D.adjoint_inverse_times(rand1)
assert_equal(tt.domain[0], space)
-
- @expand(product(spaces, [True, False]))
- def test_diagonal(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- diag_op = D.diagonal()
- assert_allclose(diag.val.get_full_data(), diag_op.val.get_full_data())
-
- @expand(product(spaces, [True, False]))
- def test_inverse(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- diag_op = D.inverse_diagonal()
- assert_allclose(1./diag.val.get_full_data(), diag_op.val.get_full_data())
-
- @expand(product(spaces, [True, False]))
- def test_trace(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- trace_op = D.trace()
- assert_allclose(trace_op, np.sum(diag.val.get_full_data()))
-
- @expand(product(spaces, [True, False]))
- def test_inverse_trace(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- trace_op = D.inverse_trace()
- assert_allclose(trace_op, np.sum(1./diag.val.get_full_data()))
-
- @expand(product(spaces, [True, False]))
- #MR FIXME: what if any diagonal element <=0?
- def test_trace_log(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- trace_log = D.trace_log()
- assert_allclose(trace_log, np.sum(np.log(diag.val.get_full_data())))
-
- @expand(product(spaces, [True, False]))
- def test_determinant(self, space, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, copy=copy)
- det = D.determinant()
- assert_allclose(det, np.prod(diag.val.get_full_data()))
-
- @expand(product(spaces, [True, False], [True, False]))
- def test_inverse_determinant(self, space, bare, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, bare=bare, copy=copy)
- inv_det = D.inverse_determinant()
- assert_allclose(inv_det, 1./D.determinant())
-
- @expand(product(spaces, [True, False], [True, False]))
- #MR FIXME: what if determinant <=0?
- def test_log_determinant(self, space, bare, copy):
- diag = Field.from_random('normal', domain=space)
- D = DiagonalOperator(space, diagonal=diag, bare=bare, copy=copy)
- log_det = D.log_determinant()
- assert_allclose(log_det, np.log(D.determinant()))
--
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