RKHS inner product

docs/refs.bib

@@ -31,6 +31,14 @@ @article{baillo+grane_2009_local
   keywords = {62G08,62G30,Cross-validation,Fourier expansion,Functional data,Kernel regression,Local linear regression,Nonparametric smoothing}
 }
 
+@book{berlinet+thomas-agnan_2011_reproducing,
+  title={Reproducing kernel Hilbert spaces in probability and statistics},
+  author={Berlinet, Alain and Thomas-Agnan, Christine},
+  year={2011},
+  publisher={Springer Science \& Business Media}
+}
+
+
 @article{berrendero++_2016_mrmr,
   title = {The {{mRMR}} Variable Selection Method: A Comparative Study for Functional Data},
   shorttitle = {The {{mRMR}} Variable Selection Method},
@@ -293,6 +301,17 @@ @article{ghosh+chaudhuri_2005_maximum
   keywords = {Bayes risk,cross-validation,data depth,elliptic symmetry,kernel density estimation,location shift model,Mahalanobis distance,misclassification rate,Vapnik Chervonenkis dimension}
 }
 
+@article{gutierrez++_1992_numerical,
+  title={On the numerical expansion of a second order stochastic process},
+  author={Guti{\'e}rrez, Ram{\'o}n and Ruiz, Juan Carlos and Valderrama, Mariano J},
+  journal={Applied stochastic models and data analysis},
+  volume={8},
+  number={2},
+  pages={67--77},
+  year={1992},
+  publisher={Wiley Online Library}
+}
+
 @article{li++_2012_ddclassifier,
   title = {{{DD-Classifier}}: {{Nonparametric}} Classification Procedure Based on {{DD-Plot}}},
   shorttitle = {{{DD-Classifier}}},

examples/plot_rkhs_inner_product.py

@@ -0,0 +1,210 @@
+r"""
+Reproducing Kernel Hilbert Space Inner Product for the Brownian Bridge
+=======================================================================
+
+This example shows how to compute the inner product of two functions in the
+reproducing kernel Hilbert space (RKHS) of the Brownian Bridge.
+"""
+
+# Author: Martín Sánchez Signorini
+# License: MIT
+
+import numpy as np
+import pandas as pd
+
+from skfda.misc.rkhs_product import rkhs_inner_product
+from skfda.representation import FDataGrid
+from skfda.typing._numpy import NDArrayFloat
+
+###############################################################################
+# The kernel corresponding to a Brownian Bridge process
+# :cite:p:`gutierrez++_1992_numerical` in the interval :math:`[0, 1]` is
+#
+# .. math::
+#     k(s, t) = \min(s, t) - st
+#
+# The RKHS inner product method
+# :func:`~skfda.misc.rkhs_product.rkhs_inner_product` requires the kernel to
+# be defined as a function of two vector arguments that returns the matrix of
+# values of the kernel in the corresponding grid.
+# The following function defines the kernel as such a function.
+
+
+def brownian_bridge_covariance(
+    t: NDArrayFloat,
+    s: NDArrayFloat,
+) -> NDArrayFloat:
+    """Covariance function of the Brownian Bridge process.
+
+    This function must receive two vectors of points while returning the
+    matrix of values of the covariance function in the corresponding grid.
+    """
+    # Compute the matrix of minimum values between the points of the vectors
+    min_values = np.amin(
+        np.transpose(
+            np.meshgrid(t, s),
+            (2, 1, 0),
+        ),
+        axis=2,
+    )
+    return (  # type: ignore[no-any-return]
+        min_values - np.outer(t, s)
+    )
+
+
+###############################################################################
+# The RKHS of this kernel :cite:p:`berlinet+thomas-agnan_2011_reproducing`
+# is the set of functions
+#
+# .. math::
+#     f: [0, 1] \to \mathbb{R} \quad \text{ such that }
+#     f \text{ is absolutely continuous, }
+#     f(0) = f(1) = 0 \text{ and }
+#     f' \in L^2([0, 1]).
+#
+# For this example we will be using the following functions in this RKHS:
+#
+# .. math::
+#     \begin{align}
+#     f(t) &= 1 - (2t - 1)^2 \\
+#     g(t) &= \sin(\pi t)
+#     \end{align}
+#
+# The following code defines a method to compute the inner product of these
+# two functions in the RKHS of the Brownian Bridge, using a variable number of
+# points of discretization of the functions.
+
+def brownian_bridge_rkhs_inner_product(
+    num_points: int,
+) -> float:
+    """Inner product of two functions in the RKHS of the Brownian Bridge."""
+    # Define the functions
+    # Remove first and last points to avoid a singular covariance matrix
+    grid_points = np.linspace(0, 1, num_points + 2)[1:-1]
+    f = FDataGrid(
+        [1 - (2 * grid_points - 1)**2],
+        grid_points,
+    )
+    g = FDataGrid(
+        [np.sin(np.pi * grid_points)],
+        grid_points,
+    )
+
+    # Compute the inner product
+    return rkhs_inner_product(  # type: ignore[no-any-return]
+        fdata1=f,
+        fdata2=g,
+        cov_function=brownian_bridge_covariance,
+    )[0]
+
+
+# Plot the functions f and g in the same figure
+FDataGrid(
+    np.concatenate(
+        [
+            1 - (2 * np.linspace(0, 1, 100) - 1)**2,
+            np.sin(np.pi * np.linspace(0, 1, 100)),
+        ],
+        axis=0,
+    ).reshape(2, 100),
+    np.linspace(0, 1, 100),
+).plot()
+
+
+###############################################################################
+# The inner product of two functions :math:`f, g` in this RKHS
+# :cite:p:`berlinet+thomas-agnan_2011_reproducing` is
+#
+# .. math::
+#     \langle f, g \rangle = \int_0^1 f'(t) g'(t) dt.
+#
+# Therefore, the exact value of the product of these two functions in the RKHS
+# of the Brownian Bridge can be explicitly calculated.
+# First, we have that their derivatives are
+#
+# .. math::
+#     \begin{align}
+#     f'(t) &= 4(1 - 2t) \\
+#     g'(t) &= \pi \cos(\pi t)
+#     \end{align}
+#
+# Then, the inner product in :math:`L^2([0, 1])` of these derivatives is
+#
+# .. math::
+#     \begin{align}
+#     \langle f', g' \rangle &= \int_0^1 f'(t) g'(t) dt \\
+#                            &= \int_0^1 4(1 - 2t) \pi \cos(\pi t) dt \\
+#                            &= \frac{16}{\pi}.
+#     \end{align}
+#
+# Which is the exact value of their inner product in the RKHS of the Brownian
+# Bridge.
+# Thus, we measure the difference between the exact value and the value
+# computed by the method :func:`~skfda.misc.rkhs_product.rkhs_inner_product`
+# for increasing numbers of discretization points.
+# In particular, we will be using from 500 to 10000 points with a step of 500.
+#
+# The following code computes the inner product for each number of points and
+# plots the difference between the exact value and the computed value.
+
+num_points_list = np.arange(
+    start=500,
+    stop=10001,
+    step=500,
+)
+expected_value = 16 / np.pi
+
+errors_df = pd.DataFrame(
+    columns=["Number of points of discretization", "Absolute error"],
+)
+
+for num_points in num_points_list:
+    computed_value = brownian_bridge_rkhs_inner_product(num_points)
+    error = np.abs(computed_value - expected_value)
+
+    # Add new row to the dataframe
+    errors_df = pd.concat(
+        [
+            errors_df,
+            pd.DataFrame(
+                [[num_points, error]],
+                columns=[
+                    "Number of points of discretization",
+                    "Absolute error",
+                ],
+            ),
+        ],
+        ignore_index=True,
+    )
+
+# Plot the errors
+errors_df.plot(
+    x="Number of points of discretization",
+    y="Absolute error",
+    title="Absolute error of the inner product",
+    xlabel="Number of points of discretization",
+    ylabel="Absolute error",
+)
+
+
+###############################################################################
+# The following code plots the errors using a logarithmic scale in the y-axis.
+
+errors_df.plot(
+    x="Number of points of discretization",
+    y="Absolute error",
+    title="Absolute error of the inner product",
+    xlabel="Number of points of discretization",
+    ylabel="Absolute error",
+    logy=True,
+)
+
+###############################################################################
+# This example shows the convergence of the method
+# :func:`~skfda.misc.rkhs_product.rkhs_inner_product` for the Brownian Bridge
+# kernel, while also showing how to apply this method using a custom covariance
+# function.
+
+###############################################################################
+# **References:**
+#     .. bibliography::

skfda/misc/covariances.py

@@ -804,6 +804,8 @@ def __call__(self, x: ArrayLike, y: ArrayLike) -> NDArrayFloat:
         Returns:
             Covariance function evaluated at the grid formed by x and y.
         """
+        x = _transform_to_2d(x)
+        y = _transform_to_2d(y)
         return self.cov_fdata([x, y], grid=True)[0, ..., 0]