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MAINT: fix more Sphinx warnings.

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1 parent e98c18e commit 8be148e86f2c65b2bf4c897dac6f2ed89c404d55 @rgommers rgommers committed Apr 22, 2012
@@ -129,13 +129,9 @@
* Mathematica is a registered trademark of The Wolfram Research, Inc.
-Copyright Notice
-----------------
-
-Copyright (C) Damian Eads, 2007-2008. New BSD License.
-
"""
+# Copyright (C) Damian Eads, 2007-2008. New BSD License.
# hierarchy.py (derived from cluster.py, http://scipy-cluster.googlecode.com)
#
@@ -171,13 +167,15 @@
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
import types
import warnings
import numpy as np
import _hierarchy_wrap
import scipy.spatial.distance as distance
+
_cpy_non_euclid_methods = {'single': 0, 'complete': 1, 'average': 2,
'weighted': 6}
_cpy_euclid_methods = {'centroid': 3, 'median': 4, 'ward': 5}
@@ -178,12 +178,12 @@ class LinearNDInterpolator(NDInterpolatorBase):
Notes
-----
The interpolant is constructed by triangulating the input data
- with Qhull [Qhull]_, and on each triangle performing linear
+ with Qhull [1]_, and on each triangle performing linear
barycentric interpolation.
References
----------
- .. [Qhull] http://www.qhull.org/
+ .. [1] http://www.qhull.org/
"""
@@ -509,9 +509,9 @@ cdef ${CDTYPE} _clough_tocher_2d_single_${DTYPE}(qhull.DelaunayInfo_t *d,
Parameters
----------
- d
+ d :
Delaunay info
- isimplex
+ isimplex : int
Triangle to evaluate on
b : shape (3,)
Barycentric coordinates of the point on the triangle
@@ -522,7 +522,7 @@ cdef ${CDTYPE} _clough_tocher_2d_single_${DTYPE}(qhull.DelaunayInfo_t *d,
Returns
-------
- w
+ w :
Value of the interpolant at the given point
References
@@ -758,7 +758,7 @@ class CloughTocher2DInterpolator(NDInterpolatorBase):
Notes
-----
The interpolant is constructed by triangulating the input data
- with Qhull [Qhull]_, and constructing a piecewise cubic
+ with Qhull [1]_, and constructing a piecewise cubic
interpolating Bezier polynomial on each triangle, using a
Clough-Tocher scheme [CT]_. The interpolant is guaranteed to be
continuously differentiable.
@@ -770,8 +770,7 @@ class CloughTocher2DInterpolator(NDInterpolatorBase):
References
----------
-
- .. [Qhull] http://www.qhull.org/
+ .. [1] http://www.qhull.org/
.. [CT] See, for example,
P. Alfeld,
@@ -22,7 +22,8 @@
def use_solver( **kwargs ):
"""
- Valid keyword arguments with defaults (other ignored):
+ Valid keyword arguments with defaults (other ignored)::
+
useUmfpack = True
assumeSortedIndices = False
@@ -31,8 +32,9 @@ def use_solver( **kwargs ):
based solver to be used.
Umfpack requires a CSR/CSC matrix to have sorted column/row indices. If
- sure that the matrix fulfills this, pass assumeSortedIndices=True
+ sure that the matrix fulfills this, pass ``assumeSortedIndices=True``
to gain some speed.
+
"""
if 'useUmfpack' in kwargs:
globals()['useUmfpack'] = kwargs['useUmfpack']
@@ -42,8 +44,7 @@ def use_solver( **kwargs ):
def spsolve(A, b, permc_spec=None, use_umfpack=True):
- """Solve the sparse linear system Ax=b
- """
+ """Solve the sparse linear system Ax=b """
if isspmatrix( b ):
b = b.toarray()
@@ -105,9 +106,8 @@ def splu(A, permc_spec=None, diag_pivot_thresh=None,
Parameters
----------
- A
+ A : sparse matrix
Sparse matrix to factorize. Should be in CSR or CSC format.
-
permc_spec : str, optional
How to permute the columns of the matrix for sparsity preservation.
(default: 'COLAMD')
@@ -218,12 +218,7 @@ def spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, permc_spec=None,
This function uses the SuperLU library.
- References
- ----------
- .. [SLU] SuperLU http://crd.lbl.gov/~xiaoye/SuperLU/
-
"""
-
if not isspmatrix_csc(A):
A = csc_matrix(A)
warn('splu requires CSC matrix format', SparseEfficiencyWarning)
@@ -1072,47 +1072,40 @@ def eigs(A, k=6, M=None, sigma=None, which='LM', v0=None,
Parameters
----------
- A : An N x N matrix, array, sparse matrix, or LinearOperator representing
- the operation A * x, where A is a real or complex square matrix.
- k : integer
+ A : ndarray, sparse matrix or LinearOperator
+ An array, sparse matrix, or LinearOperator representing
+ the operation ``A * x``, where A is a real or complex square matrix.
+ k : int, optional
The number of eigenvalues and eigenvectors desired.
`k` must be smaller than N. It is not possible to compute all
eigenvectors of a matrix.
-
- Returns
- -------
- w : array
- Array of k eigenvalues.
- v : array
- An array of `k` eigenvectors.
- ``v[:, i]`` is the eigenvector corresponding to the eigenvalue w[i].
-
- Other Parameters
- ----------------
- M : An N x N matrix, array, sparse matrix, or LinearOperator representing
+ M : ndarray, sparse matrix or LinearOperator, optional
+ An array, sparse matrix, or LinearOperator representing
the operation M*x for the generalized eigenvalue problem
- ``A * x = w * M * x``
+ ``A * x = w * M * x``.
M must represent a real, symmetric matrix if A is real, and must
represent a complex, hermitian matrix if A is complex. For best
results, the data type of M should be the same as that of A.
Additionally:
- * If sigma==None, M is positive definite
- * If sigma is specified, M is positive semi-definite
- If sigma==None, eigs requires an operator to compute the solution
- of the linear equation `M * x = b`. This is done internally via a
+
+ - If `sigma` is None, M is positive definite
+ - If sigma is specified, M is positive semi-definite
+
+ If sigma is None, eigs requires an operator to compute the solution
+ of the linear equation ``M * x = b``. This is done internally via a
(sparse) LU decomposition for an explicit matrix M, or via an
iterative solver for a general linear operator. Alternatively,
the user can supply the matrix or operator Minv, which gives
- x = Minv * b = M^-1 * b
- sigma : real or complex
+ ``x = Minv * b = M^-1 * b``.
+ sigma : real or complex, optional
Find eigenvalues near sigma using shift-invert mode. This requires
an operator to compute the solution of the linear system
- `[A - sigma * M] * x = b`, where M is the identity matrix if
+ ``[A - sigma * M] * x = b``, where M is the identity matrix if
unspecified. This is computed internally via a (sparse) LU
decomposition for explicit matrices A & M, or via an iterative
solver if either A or M is a general linear operator.
Alternatively, the user can supply the matrix or operator OPinv,
- which gives x = OPinv * b = [A - sigma * M]^-1 * b.
+ which gives ``x = OPinv * b = [A - sigma * M]^-1 * b``.
For a real matrix A, shift-invert can either be done in imaginary
mode or real mode, specified by the parameter OPpart ('r' or 'i').
Note that when sigma is specified, the keyword 'which' (below)
@@ -1124,12 +1117,12 @@ def eigs(A, k=6, M=None, sigma=None, which='LM', v0=None,
``w'[i] = 1/2i * [1/(w[i]-sigma) - 1/(w[i]-conj(sigma))]``.
- If A is complex, ``w'[i] = 1/(w[i]-sigma)``.
- v0 : ndarray
+ v0 : ndarray, optional
Starting vector for iteration.
- ncv : int
+ ncv : int, optional
The number of Lanczos vectors generated
`ncv` must be greater than `k`; it is recommended that ``ncv > 2*k``.
- which : str, ['LM' | 'SM' | 'LR' | 'SR' | 'LI' | 'SI']
+ which : str, ['LM' | 'SM' | 'LR' | 'SR' | 'LI' | 'SI'], optional
Which `k` eigenvectors and eigenvalues to find:
- 'LM' : largest magnitude
@@ -1143,20 +1136,28 @@ def eigs(A, k=6, M=None, sigma=None, which='LM', v0=None,
(see discussion in 'sigma', above). ARPACK is generally better
at finding large values than small values. If small eigenvalues are
desired, consider using shift-invert mode for better performance.
- maxiter : int
+ maxiter : int, optional
Maximum number of Arnoldi update iterations allowed
- tol : float
+ tol : float, optional
Relative accuracy for eigenvalues (stopping criterion)
The default value of 0 implies machine precision.
- return_eigenvectors : bool
+ return_eigenvectors : bool, optional
Return eigenvectors (True) in addition to eigenvalues
- Minv : N x N matrix, array, sparse matrix, or linear operator
+ Minv : ndarray, sparse matrix or LinearOperator, optional
See notes in M, above.
- OPinv : N x N matrix, array, sparse matrix, or linear operator
+ OPinv : ndarray, sparse matrix or LinearOperator, optional
See notes in sigma, above.
- OPpart : 'r' or 'i'.
+ OPpart : {'r' or 'i'}, optional
See notes in sigma, above
+ Returns
+ -------
+ w : ndarray
+ Array of k eigenvalues.
+ v : ndarray
+ An array of `k` eigenvectors.
+ ``v[:, i]`` is the eigenvector corresponding to the eigenvalue w[i].
+
Raises
------
ArpackNoConvergence
@@ -1319,15 +1320,17 @@ def eigsh(A, k=6, M=None, sigma=None, which='LM', v0=None,
represent a complex, hermitian matrix if A is complex. For best
results, the data type of M should be the same as that of A.
Additionally:
- * If sigma == None, M is symmetric positive definite
- * If sigma is specified, M is symmetric positive semi-definite
- * In buckling mode, M is symmetric indefinite.
- If sigma == None, eigsh requires an operator to compute the solution
- of the linear equation `M * x = b`. This is done internally via a
+
+ - If sigma is None, M is symmetric positive definite
+ - If sigma is specified, M is symmetric positive semi-definite
+ - In buckling mode, M is symmetric indefinite.
+
+ If sigma is None, eigsh requires an operator to compute the solution
+ of the linear equation ``M * x = b``. This is done internally via a
(sparse) LU decomposition for an explicit matrix M, or via an
iterative solver for a general linear operator. Alternatively,
the user can supply the matrix or operator Minv, which gives
- x = Minv * b = M^-1 * b
+ ``x = Minv * b = M^-1 * b``.
sigma : real
Find eigenvalues near sigma using shift-invert mode. This requires
an operator to compute the solution of the linear system
@@ -1336,9 +1339,9 @@ def eigsh(A, k=6, M=None, sigma=None, which='LM', v0=None,
decomposition for explicit matrices A & M, or via an iterative
solver if either A or M is a general linear operator.
Alternatively, the user can supply the matrix or operator OPinv,
- which gives x = OPinv * b = [A - sigma * M]^-1 * b.
+ which gives ``x = OPinv * b = [A - sigma * M]^-1 * b``.
Note that when sigma is specified, the keyword 'which' refers to
- the shifted eigenvalues w'[i] where:
+ the shifted eigenvalues ``w'[i]`` where:
- if mode == 'normal', ``w'[i] = 1 / (w[i] - sigma)``.
- if mode == 'cayley', ``w'[i] = (w[i] + sigma) / (w[i] - sigma)``.
View
@@ -59,56 +59,10 @@
sqeuclidean -- the squared Euclidean distance.
yule -- the Yule dissimilarity (boolean).
+"""
-References
-----------
-
-.. [Sta07] "Statistics toolbox." API Reference Documentation. The MathWorks.
- http://www.mathworks.com/access/helpdesk/help/toolbox/stats/.
- Accessed October 1, 2007.
-
-.. [Mti07] "Hierarchical clustering." API Reference Documentation.
- The Wolfram Research, Inc.
- http://reference.wolfram.com/mathematica/HierarchicalClustering/tutorial/HierarchicalClustering.html.
- Accessed October 1, 2007.
-
-.. [Gow69] Gower, JC and Ross, GJS. "Minimum Spanning Trees and Single Linkage
- Cluster Analysis." Applied Statistics. 18(1): pp. 54--64. 1969.
-
-.. [War63] Ward Jr, JH. "Hierarchical grouping to optimize an objective
- function." Journal of the American Statistical Association. 58(301):
- pp. 236--44. 1963.
-
-.. [Joh66] Johnson, SC. "Hierarchical clustering schemes." Psychometrika.
- 32(2): pp. 241--54. 1966.
-
-.. [Sne62] Sneath, PH and Sokal, RR. "Numerical taxonomy." Nature. 193: pp.
- 855--60. 1962.
-
-.. [Bat95] Batagelj, V. "Comparing resemblance measures." Journal of
- Classification. 12: pp. 73--90. 1995.
-
-.. [Sok58] Sokal, RR and Michener, CD. "A statistical method for evaluating
- systematic relationships." Scientific Bulletins. 38(22):
- pp. 1409--38. 1958.
-
-.. [Ede79] Edelbrock, C. "Mixture model tests of hierarchical clustering
- algorithms: the problem of classifying everybody." Multivariate
- Behavioral Research. 14: pp. 367--84. 1979.
-
-.. [Jai88] Jain, A., and Dubes, R., "Algorithms for Clustering Data."
- Prentice-Hall. Englewood Cliffs, NJ. 1988.
-
-.. [Fis36] Fisher, RA "The use of multiple measurements in taxonomic
- problems." Annals of Eugenics, 7(2): 179-188. 1936
-
-
-Copyright Notice
-----------------
-
-Copyright (C) Damian Eads, 2007-2008. New BSD License.
+# Copyright (C) Damian Eads, 2007-2008. New BSD License.
-"""
import warnings
import numpy as np
View
@@ -616,14 +616,14 @@ def query_pairs(self, r, p=2., eps=0):
``1 <= p <= infinity``.
eps : float, optional
Approximate search. Branches of the tree are not explored
- if their nearest points are further than r/(1+eps), and branches
- are added in bulk if their furthest points are nearer than
- ``r * (1+eps)``. `eps` has to be non-negative.
+ if their nearest points are further than ``r/(1+eps)``, and
+ branches are added in bulk if their furthest points are nearer
+ than ``r * (1+eps)``. `eps` has to be non-negative.
Returns
-------
results : set
- Set of pairs ``(i,j)``, with ``i<j`, for which the corresponding
+ Set of pairs ``(i,j)``, with ``i < j`, for which the corresponding
positions are close.
"""
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