Added structure Authors (chapter, section) to old inbook references

Changes suggested by vnmabus

+

Added structure Authors (chapter, section) to old inbook references

Author: aleexarias

examples/plot_pairwise_alignment.py

@@ -35,8 +35,8 @@
 # In the case of elastic registration it is taken as energy function the
 # Fisher-Rao distance with a penalisation term, due to the property of
 # invariance to reparameterizations of warpings functions,
-# as detailed in Srivastava and Klassen's chapter, *"Functional Data and 
-# Elastic Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`.
+# as detailed in Srivastava and Klassen (chapter 4)\ 
+# :footcite:p:`srivastava+klassen_2016`.
 #
 # .. math::
 #   E[f \circ \gamma, g] = d_{FR} (f \circ \gamma, g)

examples/plot_phonemes_classification.py

@@ -38,8 +38,7 @@
 # We will first load the (binary) Phoneme dataset and plot the first 20
 # functions.
 # We restrict the data to the first 150 variables, as done in Ferraty and 
-# Vieu's chapter, *"Computational Issues"*, pp. 99.-108\
-# :footcite:ps:`ferraty+vieu_2006`, because most of the
+# Vieu (chapter 7)\ :footcite:ps:`ferraty+vieu_2006`, because most of the
 # useful information is in the lower frequencies.
 X, y = fetch_phoneme(return_X_y=True)
 

examples/plot_tecator_regression.py

@@ -48,8 +48,7 @@
 
 # %%
 # For spectrometric data, the relevant information of the curves can often
-# be found in the derivatives, as discussed in Ferraty and Vieu's chapter, 
-# *"Computational Issues"*, pp. 99–108\ 
+# be found in the derivatives, as discussed in Ferraty and Vieu (chapter 7)\
 # :footcite:`ferraty+vieu_2006`.
 # Thus, we compute numerically the second derivative and plot it.
 X_der = X.derivative(order=2)

skfda/datasets/_real_datasets.py

@@ -1234,8 +1234,8 @@ def fetch_gait(
 
 if fetch_gait.__doc__ is not None:  # docstrings can be stripped off
     fetch_gait.__doc__ += _gait_template.format(
-        cite="Ramsay and Silverman, *'Introduction'*, pp. 1-18 "
-             ":footcite:p:`ramsay+silverman_2005`.",
+        cite="Ramsay and Silverman (chapter 1) "
+             ":footcite:p:`ramsay+silverman_2005`",
         bibliography=".. footbibliography::",
     ) + _param_descr
 
@@ -1345,9 +1345,8 @@ def fetch_handwriting(
 
 if fetch_handwriting.__doc__ is not None:  # docstrings can be stripped off
     fetch_handwriting.__doc__ += _handwriting_template.format(
-        cite="Ramsay and Silverman,"
-        "*'From Functional Data to Smooth Functions'*, pp. 37-58"
-        ":footcite:p:`ramsay+silverman_2005`.",
+        cite="Ramsay and Silverman (chapter 3) "
+            ":footcite:p:`ramsay+silverman_2005`",
         bibliography=".. footbibliography::",
     ) + _param_descr
 

skfda/exploratory/stats/_fisher_rao.py

@@ -73,9 +73,8 @@ def _fisher_rao_warping_mean(
 
     The karcher mean :math:`\bar \gamma` is defined as the warping that
     minimises locally the sum of Fisher-Rao squared distances.
-    See Srivastava and Klassen's chapter, *"Statistical Modeling of Functional
-    Data"*, pp. 269-303\ :footcite:p:`srivastava+klassen_2016`
-    for a detailed explanation.
+    See Srivastava and Klassen (chapter 8)\ 
+    :footcite:p:`srivastava+klassen_2016` for a detailed explanation.
 
     .. math::
         \bar \gamma = argmin_{\gamma \in \Gamma} \sum_{i=1}^{n}
@@ -213,8 +212,8 @@ def fisher_rao_karcher_mean(
     equivalence class which makes the mean of the warpings employed be the
     identity.
 
-    See Srivastava and Klassen's chapter, *"Statistical Modeling of Functional
-    Data"*, pp. 269-303\ :footcite:p:`srivastava+klassen_2016` and 
+    See Srivastava and Klassen (chapter 8)\ 
+    :footcite:p:`srivastava+klassen_2016` and 
     :footcite:`srivastava++_2011_registration`.
 
     Args:

skfda/misc/hat_matrix.py

@@ -150,14 +150,13 @@ class NadarayaWatsonHatMatrix(HatMatrix):
     For smoothing, :math:`\{x_1, ..., x_n\}` are the points with known value
     and :math:`\{x_1', ..., x_m'\}` are the points for which it is desired to
     estimate the smoothed value. The distance :math:`d` is the absolute value
-    function, as detailed in Wasserman's chapter *"Nonparametric Regression"*,
-    pp. 61-123\ :footcite:`wasserman_2006`.
+    function, as detailed in Wasserman (chapter 5)\ 
+    :footcite:`wasserman_2006`.
 
     For regression, :math:`\{x_1, ..., x_n\}` is the functional data and
     :math:`\{x_1', ..., x_m'\}` are the functions for which it is desired to
     estimate the scalar value. Here, :math:`d` is some functional distance.
-    For more information, see Ferraty and Vieu's chapter, *"Functional 
-    Nonparametric Prediction Methodologies"*, pp. 49-59\
+    For more information, see Ferraty and Vieu (chapter 5)\
     :footcite:`ferraty+vieu_2006`.
 
     In both cases :math:`K(\cdot)` is a kernel function and :math:`h` is the
@@ -228,8 +227,8 @@ class LocalLinearRegressionHatMatrix(HatMatrix):
     where :math:`\{t_1, t_2, ..., t_n\}` are points with known value and
     :math:`\{t_1', t_2', ..., t_m'\}` are the points for which it is
     desired to estimate the smoothed value.
-    For a more detailed explanation, see Wasserman's chapter *"Nonparametric
-    Regression"*, pp. 61-123\ :footcite:`wasserman_2006`.
+    For a more detailed explanation, see Wasserman (chapter 5)\ 
+    :footcite:`wasserman_2006`.
 
     For **kernel regression** algorithm:
 
@@ -451,8 +450,8 @@ class KNeighborsHatMatrix(HatMatrix):
     In both cases, :math:`K(\cdot)` is a kernel function and
     :math:`h_{i}` is calculated as the distance from :math:`x_i'` to its
     ``n_neighbors``-th nearest neighbor in :math:`\{x_1, ..., x_n\}`, as
-    detailed in Ferraty and Vieu's chapter, *"Computational Issues"*,
-    pp. 99.-108\ :footcite:ps:`ferraty+vieu_2006`.
+    detailed in Ferraty and Vieu (chapter 7)\ 
+    :footcite:ps:`ferraty+vieu_2006`.
 
     Used with the uniform kernel, it takes the average of the closest
     ``n_neighbors`` points to a given point.

skfda/misc/metrics/_fisher_rao.py

@@ -166,9 +166,8 @@ def fisher_rao_amplitude_distance(
     .. math::
         \mathcal{R}(\gamma) = \|\sqrt{\gamma'}- 1 \|_{\mathbb{L}^2}^2
 
-    See Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-    Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`
-    for a detailed explanation.
+    See Srivastava and Klassen (chapter 4)\ 
+    :footcite:p:`srivastava+klassen_2016` for a detailed explanation.
 
     If the observations are defined in a :term:`domain` different than (0,1)
     their domains are normalized to this interval with an affine
@@ -265,9 +264,8 @@ def fisher_rao_phase_distance(
 
     where :math:`\gamma_{id}` is the identity warping.
 
-    See Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-    Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`
-    for a detailed explanation.
+    See Srivastava and Klassen (chapter 4)\ 
+    :footcite:p:`srivastava+klassen_2016` for a detailed explanation.
 
     If the observations are defined in a :term:`domain` different than (0,1)
     their domains are normalized to this interval with an affine
@@ -350,9 +348,8 @@ def _fisher_rao_warping_distance(
         d_{\Gamma}(\gamma_i, \gamma_j) = cos^{-1} \left ( \int_0^1
         \sqrt{\gamma_i'(t)\gamma_j'(t)}dt \right )
 
-    See Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-    Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`
-    for a detailed explanation.
+    See Srivastava and Klassen (chapter 4)\ 
+    :footcite:p:`srivastava+klassen_2016` for a detailed explanation.
 
     If the warpings are not defined in [0,1], an affine transformation is made
     to change the :term:`domain`.

skfda/misc/operators/_srvf.py

@@ -29,8 +29,8 @@ class SRSF(
     This representation is used to compute the extended non-parametric
     Fisher-Rao distance between functions, wich under the SRSF representation
     becomes the usual :math:`\mathbb{L}^2` distance between functions.
-    See Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-    Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`.
+    See Srivastava and Klassen (chapter 4)\ 
+    :footcite:p:`srivastava+klassen_2016`.
 
     The inverse SRSF transform is defined as
 
@@ -134,8 +134,8 @@ def transform(self, X: FDataGrid, y: object = None) -> FDataGrid:
 
         Let :math:`f : [a,b] \rightarrow \mathbb{R}` be an absolutely
         continuous function, the SRSF transform is defined as in
-        Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-        Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`
+        Srivastava and Klassen (chapter 4)\ 
+        :footcite:p:`srivastava+klassen_2016`
 
         .. math::
 
@@ -186,8 +186,7 @@ def inverse_transform(self, X: FDataGrid, y: None = None) -> FDataGrid:
         Compute the inverse SRSF transform.
 
         Given the srsf and the initial value the original function can be
-        obtained as detailed in Srivastava and Klassen's chapter, *"Functional
-        Data and Elastic Registration"*, pp. 73-123\ 
+        obtained as detailed in Srivastava and Klassen (chapter 4)\ 
         :footcite:p:`srivastava+klassen_2016`:
 
         .. math::

skfda/preprocessing/dim_reduction/_fpca.py

@@ -32,9 +32,8 @@ class FPCA(  # noqa: WPS230 (too many public attributes)
     when fitting a FDataBasis or FDataGrid, except for ``components_basis``.
 
     For more information about the implementation of the computation of the
-    first principal components see Ramsay and Silverman's chapter, 
-    *"Basis function expansion of the functions"*, pp. 161-164\ 
-    :footcite:`ramsay+silverman_2005`.
+    first principal components see Ramsay and Silverman (chapter 8, section 
+    4)\ :footcite:`ramsay+silverman_2005`.
 
     Parameters:
         n_components: Number of principal components to keep from

skfda/preprocessing/registration/_fisher_rao.py

@@ -61,8 +61,8 @@ class FisherRaoElasticRegistration(
     distances. See :func:`~elastic_mean`.
 
     The algorithms employed and the SRSF framework are described extensively
-    in Srivastava and Klassen's chapter, *"Functional Data and Elastic 
-    Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`. 
+    in Srivastava and Klassen (chapter 4)\ 
+    :footcite:p:`srivastava+klassen_2016`. 
 
     Args:
         template: Template to

skfda/preprocessing/registration/_landmark_registration.py

@@ -203,8 +203,7 @@ def landmark_elastic_registration_warping(
         :math:`h_i(t^*_j)=t_{ij}`.
         The registered samples can be obtained as :math:`x^*_i(t)=x_i(h_i(t))`.
 
-        See Ramsay and Silverman's chapter, *"The Registration and Display of 
-        Functional Data"*, pp. 127-145\ :footcite:`ramsay+silverman_2005`
+        See Ramsay and Silverman (chapter 7)\ :footcite:`ramsay+silverman_2005`
         for a detailed explanation.
 
     Args:
@@ -345,8 +344,7 @@ def landmark_elastic_registration(
     The registered samples will have their features aligned, i.e.,
     :math:`x^*_i(t^*_j)=x_i(t_{ij})`.
     
-    See Ramsay and Silverman's chapter, *"The Registration and Display of 
-    Functional Data"*, pp. 127-145\ :footcite:`ramsay+silverman_2005`
+    See Ramsay and Silverman (chapter7)\ :footcite:`ramsay+silverman_2005`
     for a detailed explanation.
 
     Args:

skfda/preprocessing/registration/_lstsq_shift_registration.py

@@ -28,8 +28,7 @@ class LeastSquaresShiftRegistration(
     r"""Register data using shift alignment by least squares criterion.
 
     Realizes the registration of a set of curves using a shift aligment,
-    as detailed in Ramsay and Silverman's chapter, *"The Registration 
-    and Display of Functional Data"*, pp. 127-145\ 
+    as detailed in Ramsay and Silverman (chapter 7)\ 
     :footcite:`ramsay+silverman_2005`.
     Let :math:`\{x_i(t)\}_{i=1}^{N}` be a functional dataset, calculates
     :math:`\delta_{i}` for each sample such that :math:`x_i(t + \delta_{i})`
@@ -42,8 +41,7 @@ class LeastSquaresShiftRegistration(
     Estimates each shift parameter :math:`\delta_i` iteratively by
     using a modified Newton-Raphson algorithm, updating the template
     :math:`\mu` in each iteration as is described in detail in
-    Ramsay and Silverman's chapter, *"The Registration and Display 
-    of Functional Data"*, pp. 127-145\ :footcite:`ramsay+silverman_2005`.
+    Ramsay and Silverman (chapter 7)\ :footcite:`ramsay+silverman_2005`.
 
     Method only implemented for univariate functional data.
 
@@ -64,20 +62,17 @@ class LeastSquaresShiftRegistration(
             template to the registration, if it is a callable or "mean" the
             template is computed iteratively constructing a temporal template
             in each iteration.
-            In Ramsay and Silverman's chapter, *"The Registration 
-            and Display of Functional Data"*, pp. 127-145\ 
-            :footcite:`ramsay+silverman_2005`
-            this procedure is described in detail. Defaults to "mean".
+            In Ramsay and Silverman (chapter 7)\ 
+            :footcite:`ramsay+silverman_2005` this procedure is described in 
+            detail. Defaults to "mean".
         extrapolation: Controls the
             extrapolation mode for points outside the :term:`domain` range.
             By default uses the method defined in the data to be transformed.
             See the `extrapolation` documentation to obtain more information.
         step_size: Parameter to adjust the rate of
             convergence in the Newton-Raphson algorithm, see
-            Ramsay and Silverman's chapter, *"The Registration 
-            and Display of Functional Data"*, pp. 127-145\ 
-            :footcite:`ramsay+silverman_2005`.
-            Defaults to 1.
+            Ramsay and Silverman (chapter 7)\ 
+            :footcite:`ramsay+silverman_2005`. Defaults to 1.
         restrict_domain: If True restricts the :term:`domain`
             to avoid the need of using extrapolation, in which
             case only the fit_transform method will be available, as training

skfda/representation/basis/_bspline_basis.py

@@ -31,8 +31,7 @@ class BSplineBasis(Basis):
 
     Implementation details: In order to allow a discontinuous behaviour at
     the boundaries of the domain it is necessary to place m knots at the
-    boundaries, as done in Ramsay and Silverman's chapter, *"From 
-    Functional Data to Smooth Functions"*, pp. 37-58\
+    boundaries, as done in Ramsay and Silverman (chapter 3)\
     :footcite:p:`ramsay+silverman_2005`. This is
     automatically done so that the user only has to specify a single knot
     at the boundaries.
@@ -389,8 +388,7 @@ class BSpline(BSplineBasis):
 
     Implementation details: In order to allow a discontinuous behaviour at
     the boundaries of the domain it is necessary to place m knots at the
-    boundaries, as done in Ramsay and Silverman's chapter, *"From 
-    Functional Data to Smooth Functions"*, pp. 37-58\
+    boundaries, as done in Ramsay and Silverman (chapter 3)\
     :footcite:p:`ramsay+silverman_2005`. This is
     automatically done so that the user only has to specify a single knot
     at the boundaries.